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Academic Editor: Marco A.
Velasco-Velázquez
Received: 31 October 2024
Revised: 3 January 2025
Accepted: 8 January 2025
Published: 10 January 2025
Citation: Sabit, H.; Arneth, B.;
Pawlik, T.M.; Abdel-Ghany, S.; Ghazy,
A.; Abdelazeem, R.M.; Alqosaibi, A.;
Al-Dhuayan, I.S.; Almulhim, J.;
Alrabiah, N.A.; et al. Leveraging
Single-Cell Multi-Omics to Decode
Tumor Microenvironment Diversity
and Therapeutic Resistance.
Pharmaceuticals 2025,18, 75.
https://doi.org/10.3390/
ph18010075
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Review
Leveraging Single-Cell Multi-Omics to Decode Tumor
Microenvironment Diversity and Therapeutic Resistance
Hussein Sabit 1, *,† , Borros Arneth 2 ,*, † , Timothy M. Pawlik 3, Saimaa Abdel-Ghany 4, Aysha Ghazy 5,
Rawan M. Abdelazeem 1, Amany Alqosaibi 6, Ibtesam S. Al-Dhuayan 6, Jawaher Almulhim 7,
Noof A. Alrabiah 7and Ahmed Hashash 8
1
Department of Medical Biotechnology, College of Biotechnology, Misr University for Science and Technology,
P.O. Box 77, Giza 3237101, Egypt
2
Institute of Laboratory Medicine and Pathobiochemistry, Molecular Diagnostics, Hospital of the Universities
of Giessen and Marburg (UKGM), Philipps University Marburg, Baldingerstr. 1, 35043 Marburg, Germany
3Department of Surgery, The Ohio State University, Wexner Medical Center, Columbus, OH 43210, USA
4Department of Environmental Biotechnology, College of Biotechnology, Misr University for Science and
Technology, P.O. Box 77, Giza 3237101, Egypt
5Department of Agricultural Biotechnology, College of Biotechnology, Misr University for Science and
Technology, P.O. Box 77, Giza 3237101, Egypt
6Department of Biology, College of Science, Imam Abdulrahman bin Faisal University,
P.O. Box 1982, Dammam 31441, Saudi Arabia
7Department of Biological Sciences, King Faisal University, Alahsa 31982, Saudi Arabia
8Department of Biomedicine, Texas A&M University, College Station, TX 77843, USA
*Correspondence: hussein.sabit@must.edu.eg (H.S.); borros.arneth@staff.uni-marburg.de (B.A.)
†These authors contributed equally to this work.
Abstract: Recent developments in single-cell multi-omics technologies have provided the
ability to identify diverse cell types and decipher key components of the tumor microenvi-
ronment (TME), leading to important advancements toward a much deeper understanding
of how tumor microenvironment heterogeneity contributes to cancer progression and ther-
apeutic resistance. These technologies are able to integrate data from molecular genomic,
transcriptomic, proteomics, and metabolomics studies of cells at a single-cell resolution
scale that give rise to the full cellular and molecular complexity in the TME. Understanding
the complex and sometimes reciprocal relationships among cancer cells, CAFs, immune
cells, and ECs has led to novel insights into their immense heterogeneity in functions, which
can have important consequences on tumor behavior. In-depth studies have uncovered
immune evasion mechanisms, including the exhaustion of T cells and metabolic repro-
gramming in response to hypoxia from cancer cells. Single-cell multi-omics also revealed
resistance mechanisms, such as stromal cell-secreted factors and physical barriers in the
extracellular matrix. Future studies examining specific metabolic pathways and targeting
approaches to reduce the heterogeneity in the TME will likely lead to better outcomes with
immunotherapies, drug delivery, etc., for cancer treatments. Future studies will incorporate
multi-omics data, spatial relationships in tumor micro-environments, and their translation
into personalized cancer therapies. This review emphasizes how single-cell multi-omics
can provide insights into the cellular and molecular heterogeneity of the TME, reveal-
ing immune evasion mechanisms, metabolic reprogramming, and stromal cell influences.
These insights aim to guide the development of personalized and targeted cancer therapies,
highlighting the role of TME diversity in shaping tumor behavior and treatment outcomes.
Keywords: single-cell multi-omics; tumor microenvironment (TME); cancer therapeutic
resistance; immune evasion; metabolic reprogramming; personalized cancer therapy
Pharmaceuticals 2025,18, 75 https://doi.org/10.3390/ph18010075
Pharmaceuticals 2025,18, 75 2 of 38
1. Introduction
Cancer represents a disease marked by uncontrolled cell growth, in which these
transformed cells evolve under natural selection pressures [
1
]. The progression of cancer
and resistance to treatment are strongly influenced by the tumor microenvironment (TME),
a dynamic and intricate network comprising cancer cells, stromal cells, immune cells, and
the extracellular matrix (ECM), all working together to support tumor development and
metastasis [
2
]. The complexity and diversity of the TME represent unique challenges and
opportunities to treat cancer. Breakthroughs in single-cell multi-omics technologies have
allowed researchers to gain unprecedented insights into the TME, demonstrating its critical
role in cancer growth and resistance to treatments.
A major component of the TME is the cancer-associated fibroblasts (CAFs). These
fibroblasts are key players in remodeling the ECM, secreting growth factors, and modu-
lating the immune system, significantly affecting tumor expansion, spread, and treatment
response [
3
]. For example, CAFs release cytokines such as TGF-
β
, which drives the
epithelial–mesenchymal transition (EMT) in cancer cells, thereby boosting their ability to
migrate and invade [4].
Immune cells within the TME, such as T cells, macrophages, and neutrophils, also
have critical roles. Tumor-associated macrophages (TAMs) can foster tumor growth by
encouraging blood vessel formation, tissue remodeling, and immune suppression. On the
other hand, cytotoxic T lymphocytes (CTLs) and natural killer (NK) cells can attack and
destroy cancer cells, although their activity is often dampened in the TME due to various
immune evasion mechanisms deployed by tumors [5,6].
The introduction of single-cell multi-omics has dramatically enhanced our understand-
ing of the TME by allowing the simultaneous examination of several molecular layers
at a single-cell level. Methods like single-cell RNA sequencing (scRNA-seq), single-cell
ATAC-seq (Assay for Transposase-Accessible Chromatin), and single-cell proteomics have
exposed the TME’s vast heterogeneity [
7
]. These techniques deliver detailed maps of
gene expression, chromatin accessibility, and protein levels in individual cells, offering new
perspectives on the cellular interactions and states that propel cancer growth
and resistance
.
For instance, scRNA-seq has uncovered various immune cell types within tumors,
including exhausted T cells marked by an elevated expression of inhibitory receptors like
PD-1 and CTLA-4 [
8
]. These discoveries have major implications for immunotherapy,
as they identify potential targets for immune checkpoint inhibitors. Additionally, single-
cell analyses have pinpointed distinct metabolic profiles in both cancer and stromal cells,
illuminating how altered metabolism fuels tumor growth in low-oxygen conditions [9].
The knowledge obtained from single-cell multi-omics studies of the TME is set to
revolutionize cancer treatment. Mapping tumor and microenvironment diversity enables
the design of more targeted and effective therapies. For instance, focusing on specific
CAF subsets or their secretions could boost the effectiveness of existing treatments by
bypassing resistance mechanisms [
10
]. Likewise, modifying the immune landscape within
the TME can enhance immunotherapies by reactivating exhausted T cells and mitigating
immunosuppression [11].
Moreover, single-cell multi-omics provides a foundation for personalized medicine
by identifying specific molecular changes driving cancer in each patient. This precision
approach allows for the selection of therapies that are more likely to succeed, reducing
resistance risks and improving outcomes [
6
]. Understanding the metabolic interactions
in the TME also opens doors to metabolic targeting, which can cut off energy supplies to
cancer cells and amplify the impact of chemotherapy and radiotherapy [12].
Future research should prioritize integrating single-cell multi-omics data to achieve a
complete understanding of the TME. Long-term studies tracking the TME’s changes over
Pharmaceuticals 2025,18, 75 3 of 38
time and in response to treatment could uncover early resistance biomarkers and guide
adaptive therapy strategies [
13
]. Spatial transcriptomics, which merges single-cell sequenc-
ing with spatial context, can chart the cellular layout within the TME, demonstrating how
physical location affects tumor behavior [14].
Ultimately, single-cell multi-omics technologies have unlocked new opportunities in
cancer research, offering deep insights into the TME’s influence on cancer progression and
resistance. Leveraging these insights allows researchers to create innovative, personalized
treatment strategies, leading to better outcomes for patients. As these technologies advance,
their potential to transform cancer therapy continues to grow, marking the dawn of a new
era in oncology.
This review highlights the potential of single-cell multi-omics to uncover the cellular
and molecular heterogeneity within the tumor microenvironment (TME). By elucidating
mechanisms such as immune evasion, metabolic reprogramming, and the influence of
stromal cells, these approaches provide a deeper understanding of TME complexity. Such
insights are instrumental in shaping the development of personalized and targeted cancer
therapies, emphasizing the critical role of TME diversity in determining tumor behavior
and influencing therapeutic outcomes.
2. Tumor Microenvironment (TME)
The tumor microenvironment (TME) is a highly dynamic and complex ecosystem sur-
rounding tumor cells, playing an essential role in cancer growth and progression (Figure 1).
The TME includes various cellular and non-cellular elements that constantly interact with
cancer cells, impacting their behavior and response to treatments [2].
Pharmaceuticals 2025,18, x FOR PEER REVIEW 4of 39
Figure 1. The TME components and their interactions. The TME comprises CAFs, which modify
the ECM and secrete growth factors; immune-suppressive cells like myeloid-derived suppressor
cells (MDSCs) and regulatory T cells (Tregs); and anti-tumor immune cells such as CD4/CD8 T cells
and NK cells, whose activity is often hindered. Additionally, endothelial cells, pericytes, and blood
vessels support angiogenesis and metastasis, while lymphatic vessels assist in metastasis and im-
mune regulation. Adipocytes and mesenchymal stromal cells contribute metabolic support, pro-
moting tumor growth, while neurons emphasize the growing role of neuro-immune interactions.
This complex network demonstrates how the TME shapes tumor behavior and resistance to ther-
apy.
2.1. The Role of TME in Cancer Biology
The TME significantly influences cancer biology, affecting everything from tumor
initiation to metastasis. Understanding the interactions between cancer cells and the
various TME components is vital for developing effective treatments.
The interactions between cancer cells and the tumor microenvironment (TME) are
critical in driving tumor initiation, growth, and progression (Figure 2). Cancer-associated
fibroblasts (CAFs) within the TME secrete growth factors like TGF-β, which induce an
epithelial–mesenchymal transition (EMT), enhancing the migratory and invasive poten-
tial of cancer cells [4]. Chronic inflammation, often fueled by immune cells within the
TME, can further promote genetic mutations and tumor growth [18]. For tumors to grow
beyond a certain size, angiogenesis is essential, and the TME drives this process through
the release of pro-angiogenic factors such as VEGF from cancer cells, CAFs, and tu-
mor-associated macrophages (TAMs) [19]. This newly formed vasculature not only
nourishes the tumor but also provides pathways for metastasis. Additionally, the TME
plays a central role in immune evasion by creating an immunosuppressive environment
that suppresses the activity of cytotoxic T lymphocytes (CTLs) and natural killer (NK)
cells. TAMs, for instance, secrete IL-10 and TGF-β, which inhibit anti-tumor immune re-
Figure 1. The TME components and their interactions. The TME comprises CAFs, which modify
the ECM and secrete growth factors; immune-suppressive cells like myeloid-derived suppressor
cells (MDSCs) and regulatory T cells (Tregs); and anti-tumor immune cells such as CD4/CD8 T
cells and NK cells, whose activity is often hindered. Additionally, endothelial cells, pericytes, and
blood vessels support angiogenesis and metastasis, while lymphatic vessels assist in metastasis
and immune regulation. Adipocytes and mesenchymal stromal cells contribute metabolic support,
promoting tumor growth, while neurons emphasize the growing role of neuro-immune interactions.
This complex network demonstrates how the TME shapes tumor behavior and resistance to therapy.
Pharmaceuticals 2025,18, 75 4 of 38
At the core of the tumor microenvironment (TME) are cancer cells, which are char-
acterized by unchecked growth and an ability to bypass normal regulatory mechanisms.
These cells undergo genetic and epigenetic changes that drive their proliferation, survival,
and metastatic potential [
15
]. Surrounding these cancer cells are stromal cells, including
fibroblasts, endothelial cells, and pericytes, which provide structural support within the
TME. Cancer-associated fibroblasts (CAFs) play a crucial role by secreting growth factors,
cytokines, and extracellular matrix (ECM) components that create a favorable environment
for tumor growth. Endothelial cells further contribute to this environment by promoting
angiogenesis, which is essential for tumor growth and metastasis [3].
The TME also hosts a diverse array of immune cells such as macrophages, lymphocytes,
dendritic cells, and neutrophils. Tumor-associated macrophages (TAMs) can have pro-
tumorigenic effects, such as promoting angiogenesis, tissue remodeling, and suppressing
the immune response [
5
]. On the other hand, cytotoxic T lymphocytes (CTLs) and natural
killer cells, which are capable of destroying cancer cells, are often rendered ineffective
within the TME [
16
]. The ECM, composed of proteins like collagen, elastin, and fibronectin,
provides a structural scaffold for tissues. In the TME, the ECM undergoes significant
remodeling through the action of matrix metalloproteinases (MMPs), which are produced
by both cancer and stromal cells, facilitating cancer cell invasion and metastasis [17].
2.1. The Role of TME in Cancer Biology
The TME significantly influences cancer biology, affecting everything from tumor
initiation to metastasis. Understanding the interactions between cancer cells and the
various TME components is vital for developing effective treatments.
The interactions between cancer cells and the tumor microenvironment (TME) are
critical in driving tumor initiation, growth, and progression (Figure 2). Cancer-associated
fibroblasts (CAFs) within the TME secrete growth factors like TGF-
β
, which induce an
epithelial–mesenchymal transition (EMT), enhancing the migratory and invasive potential
of cancer cells [
4
]. Chronic inflammation, often fueled by immune cells within the TME,
can further promote genetic mutations and tumor growth [
18
]. For tumors to grow beyond
a certain size, angiogenesis is essential, and the TME drives this process through the release
of pro-angiogenic factors such as VEGF from cancer cells, CAFs, and tumor-associated
macrophages (TAMs) [
19
]. This newly formed vasculature not only nourishes the tumor
but also provides pathways for metastasis. Additionally, the TME plays a central role
in immune evasion by creating an immunosuppressive environment that suppresses the
activity of cytotoxic T lymphocytes (CTLs) and natural killer (NK) cells. TAMs, for in-
stance, secrete IL-10 and TGF-
β
, which inhibit anti-tumor immune responses [
20
], and the
expression of immune checkpoint proteins like PD-L1 on cancer cells further dampens T
cell function [11].
The TME also facilitates metastasis through mechanisms such as ECM remodeling
and EMT, allowing cancer cells to detach from the primary tumor, invade nearby tissues,
and establish secondary tumors in distant locations [
21
]. Moreover, the TME contributes to
therapeutic resistance, which is a major challenge in cancer treatment, as hypoxia within
the TME can trigger the expression of drug-resistance genes, and stromal cells may secrete
factors that protect cancer cells from chemotherapy and radiation therapy [22].
Studies have shown that interactions between tumor-associated macrophages and
cancer-associated fibroblasts (CAFs) can enable cancer cells to develop resistance to gem-
citabine and paclitaxel in pancreatic and breast cancers, primarily through IGF-1/2 sig-
naling pathways [
23
,
24
]. Additionally, while natural killer (NK) cells are highly effective
in eliminating cancer cells, their cytotoxic activity is significantly diminished by expo-
sure to the TGF-
β
secreted by CAFs. This occurs through the miR-183-mediated disrup-
Pharmaceuticals 2025,18, 75 5 of 38
tion of DAP12 transcription, ultimately enhancing cancer cell survival and resistance to
chemotherapy [25].
Pharmaceuticals 2025,18, x FOR PEER REVIEW 5of 39
sponses [20], and the expression of immune checkpoint proteins like PD-L1 on cancer
cells further dampens T cell function [11].
The TME also facilitates metastasis through mechanisms such as ECM remodeling
and EMT, allowing cancer cells to detach from the primary tumor, invade nearby tissues,
and establish secondary tumors in distant locations [21]. Moreover, the TME contributes
to therapeutic resistance, which is a major challenge in cancer treatment, as hypoxia
within the TME can trigger the expression of drug-resistance genes, and stromal cells
may secrete factors that protect cancer cells from chemotherapy and radiation therapy
[22].
Studies have shown that interactions between tumor-associated macrophages and
cancer-associated fibroblasts (CAFs) can enable cancer cells to develop resistance to
gemcitabine and paclitaxel in pancreatic and breast cancers, primarily through IGF-1/2
signaling pathways [23,24]. Additionally, while natural killer (NK) cells are highly effec-
tive in eliminating cancer cells, their cytotoxic activity is significantly diminished by ex-
posure to the TGF-β secreted by CAFs. This occurs through the miR-183-mediated dis-
ruption of DAP12 transcription, ultimately enhancing cancer cell survival and resistance
to chemotherapy [25].
Figure 2. Key processes in tumor progression and therapeutic resistance. The illustration highlights
five critical processes within the TME that drive tumor progression and resistance to therapy: (A)
EMT-driven invasiveness, (B) VEGF-promoted angiogenesis, (C) immune evasion through im-
mune cell manipulation, (D) ECM restructuring leading to metastasis, and (E) the development of
resistance due to hypoxia and ECM changes.
2.2. Cellular Heterogeneity in the TME
Figure 2. Key processes in tumor progression and therapeutic resistance. The illustration high-
lights five critical processes within the TME that drive tumor progression and resistance to therapy:
(A) EMT-driven
invasiveness, (B) VEGF-promoted angiogenesis, (C) immune evasion through im-
mune cell manipulation, (D) ECM restructuring leading to metastasis, and (E) the development of
resistance due to hypoxia and ECM changes.
2.2. Cellular Heterogeneity in the TME
The TME is highly heterogeneous, comprising a wide variety of cell types, including
cancer-associated fibroblasts (CAFs), immune cells, and endothelial cells. This cellular
diversity profoundly influences tumor behavior and treatment outcomes (Figure 3).
CAFs can originate from multiple sources, including resident fibroblasts, mesenchy-
mal stem cells, or epithelial cells undergoing EMT under tumor influence [
3
]. CAFs can
be categorized into subpopulations based on their functions and phenotypes. Myofibrob-
lastic CAFs (myCAFs) are characterized by high
α
-SMA expression and are key players
in ECM remodeling, which enhances tumor invasiveness [
26
]. On the other hand, in-
flammatory CAFs (iCAFs) secrete inflammatory cytokines like IL-6 and IL-8, fostering an
immunosuppressive environment that aids tumor progression [27].
The functional heterogeneity of CAFs is regulated by specific signaling pathways
and their cellular origins. For example, IL-1-induced JAK/STAT signaling promotes iCAF
formation, while TGF-
β
signaling opposes this process, encouraging the differentiation
of myCAFs [
27
]. This interplay underscores the complexity of CAF functions and their
potential as therapeutic targets.
The immune cells within the TME also display significant heterogeneity. CTLs and
Tregs represent two T cell subtypes with opposing roles. CTLs are vital for anti-tumor im-
Pharmaceuticals 2025,18, 75 6 of 38
munity but are often suppressed within the TME [
16
]. In contrast, Tregs suppress immune
responses and facilitate tumor immune evasion, correlating with worse prognoses [
28
]. Sim-
ilarly, TAMs exhibit functional plasticity, with M1-like TAMs promoting anti-tumor activity,
while M2-like TAMs enhance tumor growth by driving angiogenesis, tissue remodeling,
and immune suppression [29].
Pharmaceuticals 2025,18, x FOR PEER REVIEW 6of 39
The TME is highly heterogeneous, comprising a wide variety of cell types, including
cancer-associated fibroblasts (CAFs), immune cells, and endothelial cells. This cellular
diversity profoundly influences tumor behavior and treatment outcomes (Figure 3).
CAFs can originate from multiple sources, including resident fibroblasts, mesen-
chymal stem cells, or epithelial cells undergoing EMT under tumor influence [3]. CAFs
can be categorized into subpopulations based on their functions and phenotypes. Myo-
fibroblastic CAFs (myCAFs) are characterized by high α-SMA expression and are key
players in ECM remodeling, which enhances tumor invasiveness [26]. On the other hand,
inflammatory CAFs (iCAFs) secrete inflammatory cytokines like IL-6 and IL-8, fostering
an immunosuppressive environment that aids tumor progression [27].
Figure 3. Heterogeneity in the TME and cancer-associated fibroblasts (CAFs). The figure showcases
the diverse cell types in the TME, including myofibroblastic CAFs (myCAFs) and inflammatory
CAFs (iCAFs) with distinct roles; the dual nature of immune cells like CTLs and Tregs; and the role
of abnormal endothelial cells in fostering an environment conducive to tumor growth.
The functional heterogeneity of CAFs is regulated by specific signaling pathways
and their cellular origins. For example, IL-1-induced JAK/STAT signaling promotes iCAF
formation, while TGF-β signaling opposes this process, encouraging the differentiation of
myCAFs [27]. This interplay underscores the complexity of CAF functions and their po-
tential as therapeutic targets.
The immune cells within the TME also display significant heterogeneity. CTLs and
Tregs represent two T cell subtypes with opposing roles. CTLs are vital for anti-tumor
immunity but are often suppressed within the TME [16]. In contrast, Tregs suppress
immune responses and facilitate tumor immune evasion, correlating with worse prog-
noses [28]. Similarly, TAMs exhibit functional plasticity, with M1-like TAMs promoting
anti-tumor activity, while M2-like TAMs enhance tumor growth by driving angiogenesis,
tissue remodeling, and immune suppression [29].
Figure 3. Heterogeneity in the TME and cancer-associated fibroblasts (CAFs). The figure showcases
the diverse cell types in the TME, including myofibroblastic CAFs (myCAFs) and inflammatory CAFs
(iCAFs) with distinct roles; the dual nature of immune cells like CTLs and Tregs; and the role of
abnormal endothelial cells in fostering an environment conducive to tumor growth.
2.3. Single-Cell Analysis of Immune Cell Populations in the TME
Single-cell multi-omics technologies have transformed our understanding of biological
systems by providing high-resolution views of cellular diversity and functionality. By
integrating genomic, transcriptomic, proteomic, and metabolomic data at the single-cell
level, this approach is particularly valuable in studying the TME. It offers detailed insights
into the molecular mechanisms of cancer, uncovering rare cell populations and crucial
interactions driving therapy resistance and disease progression. This advancement not
only deepens our knowledge of tumor biology but also supports the development of
personalized treatment strategies tailored to each patient’s unique tumor landscape [
30
,
31
].
Single-cell RNA sequencing (scRNA-seq) has especially revolutionized the profiling
of immune cells within the TME, allowing for the dissection of immune cell heterogeneity
and the identification of rare and functionally distinct subpopulations that were previously
undetectable [
32
]. The throughput of single-cell RNA sequencing (scRNA-seq) has signifi-
cantly improved, allowing the analysis of hundreds of thousands of cells per experiment
while reducing costs. Consequently, the adoption of scRNA-seq in research has grown, with
advanced methods like microfluidic-, microwell-, and droplet-based technologies, in situ
barcoding, and spatial transcriptomics driving this trend [
33
]. The main steps of scRNA-seq
Pharmaceuticals 2025,18, 75 7 of 38
include single-cell isolation, cell lysis, reverse transcription (RNA to cDNA conversion),
cDNA amplification, and library preparation. Among these, single-cell capture, reverse
transcription, and cDNA amplification are the most technically demanding. Advances in
sequencing platforms have also led to rapid and diverse improvements in RNA-seq library
preparation techniques [34,35].
For example, scRNA-seq has revealed diverse T cell populations within the TME,
including exhausted, cytotoxic, and regulatory T cells (Tregs). These findings highlight
how the immune landscape within tumors is not homogeneous but rather a mosaic of cell
types with varying states of activation and function [36].
Another study used single-cell RNA sequencing (scRNA-seq) to map the immune
landscape of hepatocellular carcinoma (HCC) across tumors, lymph nodes, blood, and
ascites in 16 patients. Tumors were enriched with immunosuppressive regulatory T cells
(Tregs) and exhausted CD8+ T cells (Tex), while proliferative T cells were abundant in
ascites. Two macrophage states were identified: MDSC-like macrophages and TAM-like
macrophages, the latter being linked to poor prognosis and inflammatory markers like
SLC40A1 and GPNMB. Migratory LAMP3+ dendritic cells (DCs) were also identified,
interacting with Tregs and Tex through immune checkpoints like PD-L1 and promoting T
cell dysfunction. These findings reveal mechanisms of immune suppression, migration,
and evasion, offering therapeutic insights [37].
Beyond T cells, scRNA-seq has provided insights into the complexity of myeloid
cells in the TME, such as macrophages and dendritic cells. These cells can either promote
tumor growth through immunosuppressive actions or boost anti-tumor immunity. For
instance, TAMs exhibit a spectrum of phenotypic states ranging from pro-inflammatory M1
macrophages to immunosuppressive M2 macrophages, highlighting the delicate balance
between tumor-promoting and tumor-suppressing functions within the TME [38].
For instance, using single-cell RNA sequencing (scRNA-seq) in a study, distinct
populations of T cells, B cells, and myeloid cells were identified, each contributing
uniquely to tumor progression and immune modulation [
39
]. Exhausted CD8+ T cells
(
subclusters C8 and C9
) were marked by inhibitory receptors like PDCD1 (PD-1) and LAG3,
significantly impairing their cytotoxic activity within tumors. Regulatory CD4+ T cells
(Tregs) also contributed to immune suppression through markers like FOXP3 and CTLA4.
While naïve T cells were prevalent in normal tissues, tumor-associated regulatory B cells
(Bregs) promoted progression by suppressing T cell function and enhancing angiogenesis
pathways [40].
Myeloid cells exhibited notable heterogeneity, with tumor-associated macrophages
(TAMs) adopting M2-like pro-tumoral phenotypes, enriched in angiogenesis and glycolysis
pathways. TAMs in subcluster C2 were strongly associated with tumor progression, while
dendritic cells (DCs) in subcluster DC2 were crucial for antigen presentation and T cell
activation [
41
]. Monocytes, primarily found in the blood, infiltrated tissues and differen-
tiated into macrophages. This complex interplay among immune subtypes underscores
the immunosuppressive nature of the TME and provides valuable insights into potential
therapeutic strategies, such as targeting exhausted T cells or modulating TAM and Breg
activity to enhance anti-tumor immunity [42].
A key hallmark of cancer is its ability to evade immune surveillance. Several mecha-
nisms enable this immune evasion, contributing to the immunosuppressive TME (Figure 4).
One significant mechanism involves the expression of immune checkpoint molecules like
PD-1, CTLA-4, and PD-L1, which inhibit T cell activation, allowing cancer cells to escape im-
mune detection. Recent single-cell studies have mapped the dynamics of these checkpoints
within the TME, uncovering their role in sustaining immune suppression [43].
Pharmaceuticals 2025,18, 75 8 of 38
Pharmaceuticals 2025,18, x FOR PEER REVIEW 8of 39
tory B cells (Bregs) promoted progression by suppressing T cell function and enhancing
angiogenesis pathways [40].
Myeloid cells exhibited notable heterogeneity, with tumor-associated macrophages
(TAMs) adopting M2-like pro-tumoral phenotypes, enriched in angiogenesis and gly-
colysis pathways. TAMs in subcluster C2 were strongly associated with tumor progres-
sion, while dendritic cells (DCs) in subcluster DC2 were crucial for antigen presentation
and T cell activation [41]. Monocytes, primarily found in the blood, infiltrated tissues and
differentiated into macrophages. This complex interplay among immune subtypes un-
derscores the immunosuppressive nature of the TME and provides valuable insights into
potential therapeutic strategies, such as targeting exhausted T cells or modulating TAM
and Breg activity to enhance anti-tumor immunity [42].
A key hallmark of cancer is its ability to evade immune surveillance. Several
mechanisms enable this immune evasion, contributing to the immunosuppressive TME
(Figure 4). One significant mechanism involves the expression of immune checkpoint
molecules like PD-1, CTLA-4, and PD-L1, which inhibit T cell activation, allowing cancer
cells to escape immune detection. Recent single-cell studies have mapped the dynamics of
these checkpoints within the TME, uncovering their role in sustaining immune suppres-
sion [43].
Figure 4. Immune evasion mechanisms in the tumor microenvironment (TME). (A) Immune
checkpoint inhibition: Tumor cells exploit immune checkpoints, such as PD-1/PD-L1 interactions,
to suppress T cell activation and evade immune responses. This pathway directly impairs effector T
cell function, allowing tumors to grow unchecked. (B) Immunosuppressive cytokines and growth
factors in TME: Tumor-secreted factors, including TGF-β, IL-10, and VEGF, induce differentiation
of immune-suppressive cell populations such as regulatory T cells (Tregs) and myeloid-derived
suppressor cells (MDSCs). These cells release reactive oxygen species (ROS) and other suppressive
signals, further promoting immune suppression and hindering T cell-mediated tumor elimination.
Another study utilized single-cell RNA sequencing (scRNA-seq) to define two major
tumor microenvironment (TME) paerns in lung adenocarcinoma, validated for prog-
nostic relevance using bulk RNA-seq data from 533 patients in The Cancer Genome Atlas
(TCGA) cohort. The N
3
MC (normal-like microenvironment) paern, enriched in nor-
Figure 4. Immune evasion mechanisms in the tumor microenvironment (TME). (A) Immune check-
point inhibition: Tumor cells exploit immune checkpoints, such as PD-1/PD-L1 interactions, to
suppress T cell activation and evade immune responses. This pathway directly impairs effector T
cell function, allowing tumors to grow unchecked. (B) Immunosuppressive cytokines and growth
factors in TME: Tumor-secreted factors, including TGF-
β
, IL-10, and VEGF, induce differentiation
of immune-suppressive cell populations such as regulatory T cells (Tregs) and myeloid-derived
suppressor cells (MDSCs). These cells release reactive oxygen species (ROS) and other suppressive
signals, further promoting immune suppression and hindering T cell-mediated tumor elimination.
Another study utilized single-cell RNA sequencing (scRNA-seq) to define two major
tumor microenvironment (TME) patterns in lung adenocarcinoma, validated for prognostic
relevance using bulk RNA-seq data from 533 patients in The Cancer Genome Atlas (TCGA)
cohort. The N
3
MC (normal-like microenvironment) pattern, enriched in normal-like myofi-
broblasts and non-inflammatory immune cells, was associated with better overall survival
(hazard ratio [HR]: 0.50; 95% confidence interval [CI]: 0.35–0.71, p= 0.0001). Conversely, the
CP
2
E (cancer-progressive environment) pattern, characterized by cancer-associated myofi-
broblasts, pro-inflammatory macrophages, and exhausted T cells, correlated with reduced
survival (HR: 2.0; CI: 1.4–2.9, p< 0.001). These patterns were derived from scRNA-seq data
comprising 114,489 high-quality single-cell transcriptomes, linking cellular diversity to
patient outcomes [44].
The Single-cell Assay for Transposase-Accessible Chromatin using sequencing
(scATAC-seq) is a powerful tool for exploring chromatin accessibility at the single-cell
level, enabling the identification of regulatory DNA elements and transcription factor activ-
ity. In cancer research, it has been instrumental in dissecting tumor heterogeneity, revealing
regulatory networks in malignant cells, and understanding the immune microenvironment.
Specifically, in the tumor microenvironment (TME), scATAC-seq provides valuable insights
into the epigenetic landscapes of immune cells, such as exhausted T cells, which play a
critical role in tumor progression and responses to immunotherapy. By offering an unbi-
ased view of chromatin dynamics, this method facilitates the discovery of key regulatory
elements across diverse cell populations [45].
A study used scATAC-seq to analyze over 200,000 single cells from blood and basal
cell carcinoma (BCC), including 37,818 tumor biopsy cells from pre- and post-PD-1 block-
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ade therapy. Distinct immune, stromal, and tumor cell clusters were identified, with a
notable post-therapy expansion of CD8+ exhausted T cells (TEx), constituting over 90%
of the TEx population. Chromatin remodeling linked to therapy response was evident,
with
4598 TEx-specific
cis-elements identified. Additionally, a shared regulatory program
between TEx and T follicular helper (Tfh) cells was observed, driven by transcription
factors BATF, IRF4, and NFATC1. These findings highlight scATAC-seq’s role in un-
covering regulatory mechanisms in the tumor microenvironment and advancing cancer
immunotherapy [46].
Moreover, the TME is rich in immunosuppressive cytokines and growth factors,
such as TGF-
β
, IL-10, and VEGF, which further enhance immune evasion. These factors
inhibit the activity of effector T cells while promoting the development of Tregs and
myeloid-derived suppressor cells (MDSCs). For example, IL-10 and TGF-
β
encourage Treg
differentiation, which in turn suppresses anti-tumor immune responses [
47
]. Similarly,
MDSCs, which are prevalent in the TME, can impair T cell function by producing reactive
oxygen species (ROS) and nitric oxide (NO), further weakening the immune response [
48
].
The tumor microenvironment (TME) enables immune evasion through hypoxia,
metabolic competition, and structural barriers. Hypoxia-driven HIF-1
α
upregulates im-
mune checkpoints like PD-L1, impairs CTLs and NK cells, and recruits regulatory T
cells. Cancer cells produce lactate, suppressing immune cells and attracting MDSCs. The
disorganized vasculature limits immune infiltration, while extracellular vesicles deliver
suppressive molecules. Additionally, cancer cells outcompete immune cells for essential nu-
trients, weakening their function. These mechanisms create an immunosuppressive niche
that promotes tumor growth and therapy resistance, highlighting the need for targeted
strategies [40].
Insights gained from the single-cell analysis of the TME’s immune landscape have
opened new avenues for targeted immunotherapies. One promising approach involves
immune checkpoint inhibitors (ICIs) that block PD-1, PD-L1, and CTLA-4, reactivating
exhausted T cells and enhancing anti-tumor immunity. ICIs have demonstrated significant
efficacy to treat cancers like melanoma and non-small cell lung cancer [
49
]. Additionally,
targeting the immunosuppressive cytokine network is another emerging strategy. Inhibitors
targeting TGF-
β
and IL-10 are currently in clinical trials, aiming to reduce immunosup-
pression and boost T cell activity [
11
]. Moreover, targeting specific immunosuppressive
cell populations, such as MDSCs and Tregs, represents a novel therapeutic approach, with
drugs and antibodies designed to deplete or modulate these cells under investigation [
50
].
Further innovations include chimeric antigen receptor T cell (CAR-T) therapies and
cancer vaccines that harness the patient’s immune system to target specific cancer anti-
gens. These therapies are increasingly being refined based on molecular and cellular
profiles obtained from single-cell analyses, enhancing their efficacy while minimizing side
effects [
51
]. Ongoing research in single-cell multi-omics holds great promise to develop
more personalized and effective cancer treatments, ultimately improving patient outcomes.
2.4. Metabolic Reprogramming in the TME
Metabolic reprogramming within the TME is another critical factor influencing tumor
progression and therapeutic resistance. The metabolic interactions between cancer cells
and stromal cells are key to supporting tumor growth and impact the efficacy of therapies.
Cancer cells undergo metabolic shifts to sustain rapid proliferation and survival under
stressful conditions, a phenomenon known as metabolic reprogramming. This process
includes an increased reliance on glycolysis, glutaminolysis, and fatty acid synthesis [
12
].
However, cancer cells do not operate in isolation—they engage in metabolic crosstalk
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with stromal cells, like CAFs, immune cells, and endothelial cells, creating a cooperative
environment that supports tumor growth.
A notable metabolic interaction within the TME is the “reverse Warburg effect”. In
contrast to the classical Warburg effect—where cancer cells depend heavily on glycolysis
even when oxygen is available—the reverse Warburg effect involves stromal cells, par-
ticularly CAFs, undergoing aerobic glycolysis to produce lactate and pyruvate. Cancer
cells then absorb these metabolites and use them for oxidative phosphorylation (OXPHOS),
enabling sustained ATP production and tumor growth under challenging conditions [52].
Additionally, adipocytes in the TME supply fatty acids to cancer cells through lipolysis.
These fatty acids are then used in beta-oxidation, which is essential for energy production
and supports the rapid division of cancer cells [
53
]. This metabolic symbiosis allows cancer
cells to adapt to nutrient fluctuations within the TME.
Metabolic reprogramming also plays a pivotal role in therapeutic resistance. For
example, the acidic environment created by enhanced glycolysis in cancer cells can decrease
the uptake and effectiveness of weakly basic chemotherapeutic drugs [
54
]. Furthermore,
altered metabolic states within the TME can affect immune cell functionality, further
contributing to resistance. High lactate levels, a byproduct of aerobic glycolysis, can inhibit
CTLs and NK cells, compromising anti-tumor immunity [6].
Cancer cells can also develop resistance to targeted therapies through metabolic
adaptations. For example, resistance to BRAF inhibitors in melanoma has been linked to
a shift towards increased mitochondrial biogenesis and reliance on OXPHOS, allowing
cancer cells to survive despite inhibition of the MAPK pathway [
55
]. Similarly, breast
cancer cells rely more on fatty acid synthesis and storage, contributing to resistance against
HER2-targeted therapies [56].
A crucial aspect of metabolic reprogramming is its role in maintaining cancer stem
cells (CSCs), which are a subpopulation of cells with self-renewal capabilities that drive
tumor recurrence [
57
]. CSCs often exhibit a unique metabolic profile, relying heavily on
OXPHOS and fatty acid oxidation, which makes them resistant to conventional therapies
that target rapidly dividing cells. Targeting the metabolic vulnerabilities of CSCs is an
emerging strategy for overcoming resistance and preventing tumor relapse [58].
Understanding these metabolic interactions and their contribution to therapeutic
resistance is key to developing more effective cancer treatments. Future research should
focus on identifying the specific metabolic pathways involved and designing targeted
therapies that disrupt these networks, ultimately leading to better patient outcomes.
2.5. Therapeutic Resistance Mediated by the TME
The tumor microenvironment (TME) plays a critical role in the development of thera-
peutic resistance in cancer (Figure 5). The interactions among various components within
the TME significantly influence how tumors respond to treatments, including chemother-
apy, radiotherapy, and targeted therapies.
Chemotherapy resistance: The TME contributes to chemotherapy resistance through
several mechanisms. One major factor is the presence of hypoxia within tumors. Under low
oxygen conditions, hypoxia-inducible factors (HIFs) are stabilized, leading to the activation
of genes that promote angiogenesis, glycolysis, and resistance to cell death (apoptosis) [
59
].
This hypoxic environment can reduce the efficacy of chemotherapy drugs that rely on
oxygen to generate reactive oxygen species (ROS) for DNA damage [
60
]. Additionally,
CAFs in the TME secrete growth factors and cytokines like IL-6 and TGF-
β
, which activate
survival pathways in cancer cells, further contributing to drug resistance [
61
]. The dense
ECM produced by CAFs also acts as a physical barrier, preventing chemotherapeutic agents
from penetrating deep into the tumor [62].
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Understanding these metabolic interactions and their contribution to therapeutic
resistance is key to developing more effective cancer treatments. Future research should
focus on identifying the specific metabolic pathways involved and designing targeted
therapies that disrupt these networks, ultimately leading to beer patient outcomes.
2.5. Therapeutic Resistance Mediated by the TME
The tumor microenvironment (TME) plays a critical role in the development of
therapeutic resistance in cancer (Figure 5). The interactions among various components
within the TME significantly influence how tumors respond to treatments, including
chemotherapy, radiotherapy, and targeted therapies.
Figure 5. The tumor microenvironment (TME) and therapeutic resistance. This figure highlights the
complex ways the tumor microenvironment (TME) contributes to therapeutic resistance. The TME,
made up of cancer-associated fibroblasts (CAFs), immune cells, and the extracellular matrix (ECM),
poses significant barriers to treatment. CAFs alter the ECM using cytokines like IL-6 and TGF-β,
creating a physical barrier that hampers drug delivery while triggering survival pathways through
integrin receptors. In nutrient-deprived areas, hypoxia-induced factors (HIFs) weaken the effec-
tiveness of radiotherapy and chemotherapy by promoting drug efflux and resistance. The TME also
recruits immune-suppressive cells like myeloid-derived suppressor cells (MDSCs) and regulatory
T cells (Tregs), which release IL-10 and TGF-β, further stalling the immune response and increasing
resistance to treatment. Additionally, CAFs communicate directly with tumor cells through gap
Figure 5. The tumor microenvironment (TME) and therapeutic resistance. This figure highlights
the complex ways the tumor microenvironment (TME) contributes to therapeutic resistance. The
TME, made up of cancer-associated fibroblasts (CAFs), immune cells, and the extracellular matrix
(ECM), poses significant barriers to treatment. CAFs alter the ECM using cytokines like IL-6 and
TGF-
β
, creating a physical barrier that hampers drug delivery while triggering survival pathways
through integrin receptors. In nutrient-deprived areas, hypoxia-induced factors (HIFs) weaken the
effectiveness of radiotherapy and chemotherapy by promoting drug efflux and resistance. The TME
also recruits immune-suppressive cells like myeloid-derived suppressor cells (MDSCs) and regulatory
T cells (Tregs), which release IL-10 and TGF-β, further stalling the immune response and increasing
resistance to treatment. Additionally, CAFs communicate directly with tumor cells through gap
junctions, helping the tumor thrive. These interlinked mechanisms within the TME highlight the
complexity of cancer resistance, presenting crucial targets for overcoming therapeutic challenges.
Radiotherapy resistance: Hypoxia within the TME also contributes to radiotherapy
resistance, as oxygen is a key radiosensitizer that enhances the DNA-damaging effects of
radiation. In hypoxic conditions, HIFs drive the expression of genes that promote DNA
repair and reduce oxidative stress, making cancer cells less susceptible to radiation [
63
].
Furthermore, the immunosuppressive environment within the TME, driven by cells like
MDSCs and Tregs, can inhibit the activation of anti-tumor immune responses following
radiotherapy, reducing its effectiveness [
64
]. Additionally, cytokines such as IL-10 and
TGF-
β
secreted by TME components further dampen the immune response, contributing
to radioresistance [65].
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Targeted therapy resistance: The TME can also undermine the effectiveness of targeted
therapies. For instance, stromal cells may secrete growth factors and cytokines that activate
alternative survival pathways in cancer cells, allowing them to bypass the inhibitory effects
of targeted therapies. An example is the secretion of hepatocyte growth factor (HGF) by
stromal cells, which can activate the MET signaling pathway, providing a survival route
for cancer cells even when EGFR-targeted therapies are applied [
66
]. Additionally, the
ECM can sequester and inactivate targeted drugs, reducing their availability to cancer
cells. Hyaluronan, a component of the ECM, can bind to therapeutic antibodies, decreasing
their efficacy [
67
]. The stiffness and density of the ECM also influence how well targeted
therapies can penetrate the tumor, contributing to resistance [68].
2.6. Role of ECM and Cell–Cell Communication in Resistance
The extracellular matrix (ECM) plays a pivotal role in therapeutic resistance by provid-
ing structural support and delivering biochemical signals to cancer cells. The ECM consists
of proteins, glycoproteins, and proteoglycans that create a scaffold for tissue architecture.
In the TME, CAFs actively remodel the ECM, altering its composition and mechanical
properties [68].
The ECM contributes to therapeutic resistance through several mechanisms. First,
its dense and fibrous nature can impede the penetration of therapeutic agents, limiting
their access to cancer cells. This physical barrier effect is especially problematic for large
molecules like monoclonal antibodies and nanoparticles [
62
]. Second, the biochemical
composition of the ECM influences signaling pathways that enhance cell survival and
resistance. Integrins and other ECM receptors on cancer cell surfaces can activate pathways
like PI3K/AKT and MAPK, which are linked to survival, proliferation, and resistance to
cell death [67].
Cell–cell communication within the TME is also crucial to promote therapeutic resis-
tance. Cancer cells engage in direct communication with stromal, immune, and endothelial
cells through cell contacts and secreted factors. For example, CAFs can form gap junctions
with cancer cells, facilitating the direct transfer of ions, metabolites, and signaling molecules
that enhance survival and resistance [
61
]. Moreover, exosomes and other extracellular vesi-
cles released by TME cells carry proteins, lipids, and nucleic acids that modulate signaling
pathways in recipient cancer cells, further contributing to resistance [63].
2.7. Insights into Tumor Microenvironments and Therapy
Recent studies have expanded our understanding of tumor immune microenviron-
ments and their implications for treatment. Key findings include the identification of
P2RY6 as an immune marker in lung adenocarcinoma, where its high expression is linked
to immune cell infiltration and poor survival outcomes, suggesting its potential as a ther-
apeutic target [
69
]. Additionally, CXCR6+ CD8 T cells have been identified as pivotal in
the immune response to hepatocellular carcinoma, serving as predictive markers for better
immunotherapy outcomes [
70
]. In gastric cancer, ALKBH1 expression has been associated
with tumor-associated macrophages, influencing tumor progression and immune evasion,
making it a potential target for enhancing anti-tumor immunity [
71
]. Furthermore, a single-
cell multi-omics analysis of desmoplastic small round cell tumors (DSRCTs) has revealed
their heterogeneous nature and diverse microenvironmental interactions driving tumor
behavior and resistance [72].
Advanced multi-omics techniques have also mapped critical molecular and metabolic
pathways involved in immune evasion and tumor progression, providing new insights for
therapeutic strategies [
73
]. Recent research highlights the significance of TP53-related gene
signatures in predicting the prognosis and characterizing the TME in bladder cancer, which
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could guide personalized treatments [
74
]. Additionally, studies suggest that targeting
circadian pathways may improve therapeutic efficacy and survival in certain cancers [
75
].
Insights from these studies underscore the growing importance of understanding the
TME’s role in therapy and resistance, guiding the development of more effective and
personalized approaches.
2.8. Recent Advances and Future Directions in TME Research
Recent research has brought forth significant discoveries regarding tumor microen-
vironments (TMEs) and their influence on cancer diagnosis, prognosis, and treatment
strategies. Among these findings, the identification of m7G modification patterns linked
with TME infiltration in clear cell renal cell carcinoma offers new avenues for therapeutic
targeting [
76
]. Similarly, a three-gene panel has been identified for diagnosing and pre-
dicting the prognosis of thyroid papillary carcinoma, providing insights into its immune
microenvironment [
77
]. Additionally, OAS1 has emerged as a vital biomarker across sev-
eral cancer types, playing a critical role in prognosis, immune response, and therapeutic
targeting [78].
Another significant discovery is NNMT’s role as a metabolic regulator in esophageal
squamous cell carcinoma, contributing to metastasis and presenting a potential therapeutic
target [
79
]. Moreover, the development of CAMML, a computational tool for immune
cell-typing and stemness analysis in single-cell RNA sequencing, has enhanced our under-
standing of tumor immunology and its impact on therapy [80].
2.9. New Therapeutic Strategies and Emerging Biomarkers
Recent research highlights the potential of targeting specific pathways and cell pop-
ulations within the TME to improve cancer treatment outcomes. For instance, studies
have identified the presence of a germinal center-like environment in HLA-DR-positive
metastatic melanoma, which could enhance anti-tumor immune responses [
81
]. In smoking-
related bladder cancer, cancer-associated fibroblasts were found to facilitate immune
evasion and epithelial–mesenchymal transition (EMT), making them promising thera-
peutic targets [
82
]. Additionally, immune and metabolic profiling in lung adenocarci-
noma has revealed biomarkers for predicting immunotherapy responses, while the role
of CXCL12 in breast cancer progression has been recognized as a potential target for
immunotherapy [83,84].
Furthermore, uric acid has been identified as a key factor influencing the immune
microenvironment in bladder cancer, suggesting its potential as a prognostic biomarker [
85
].
Cellular senescence has also been recognized as a critical contributor to cancer progres-
sion across various tumor types, and multi-omics analyses from needle core biopsies in
glioblastoma have provided new insights into tumor biology and therapeutic targets [86].
3. Current Strategies and Therapeutic Approaches
As the understanding of the TME deepens, the development of therapeutic strategies
has increasingly focused on targeting the complex interactions between cancer cells and
their surrounding environment. The TME plays a critical role in tumor progression, immune
evasion, and resistance to conventional therapies, making it a prime target for innovative
treatment approaches. Key therapeutic approaches include immune checkpoint inhibitors,
targeting CAFs, anti-angiogenic therapies, modulating the ECM, and reprogramming
immune cells within the TME (Figure 6).
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tic targets [82]. Additionally, immune and metabolic profiling in lung adenocarcinoma has
revealed biomarkers for predicting immunotherapy responses, while the role of CXCL12 in
breast cancer progression has been recognized as a potential target for immunotherapy
[83,84].
Furthermore, uric acid has been identified as a key factor influencing the immune
microenvironment in bladder cancer, suggesting its potential as a prognostic biomarker
[85]. Cellular senescence has also been recognized as a critical contributor to cancer pro-
gression across various tumor types, and multi-omics analyses from needle core biopsies in
glioblastoma have provided new insights into tumor biology and therapeutic targets [86].
3. Current Strategies and Therapeutic Approaches
As the understanding of the TME deepens, the development of therapeutic strate-
gies has increasingly focused on targeting the complex interactions between cancer cells
and their surrounding environment. The TME plays a critical role in tumor progression,
immune evasion, and resistance to conventional therapies, making it a prime target for
innovative treatment approaches. Key therapeutic approaches include immune check-
point inhibitors, targeting CAFs, anti-angiogenic therapies, modulating the ECM, and
reprogramming immune cells within the TME (Figure 6).
Figure 6. Targeting the TME for cancer therapy. This figure outlines major therapeutic strategies for
addressing the tumor microenvironment (TME) in cancer treatment. (1) Immune checkpoint in-
hibitors (ICIs): anti-PD-1 antibodies block PD-1/PD-L1 interactions, boosting T cell activation and
promoting tumor cell death. (2) Targeting cancer-associated fibroblasts (CAFs): galunisertib, a drug
that blocks TGF-β signaling, prevents fibroblasts from turning into CAFs, thereby reducing the
tumor’s support system. (3) Anti-angiogenesis: bevacizumab targets VEGF to halt abnormal blood
vessel formation, restoring normal vasculature and enhancing drug delivery. (4) Modulating ECM:
Figure 6. Targeting the TME for cancer therapy. This figure outlines major therapeutic strategies
for addressing the tumor microenvironment (TME) in cancer treatment. (1) Immune checkpoint
inhibitors (ICIs): anti-PD-1 antibodies block PD-1/PD-L1 interactions, boosting T cell activation
and promoting tumor cell death. (2) Targeting cancer-associated fibroblasts (CAFs): galunisertib, a
drug that blocks TGF-
β
signaling, prevents fibroblasts from turning into CAFs, thereby reducing
the tumor’s support system. (3) Anti-angiogenesis: bevacizumab targets VEGF to halt abnormal
blood vessel formation, restoring normal vasculature and enhancing drug delivery. (4) Modulating
ECM: the
enzyme PEGPH20 breaks down hyaluronan in the ECM, allowing better drug penetration
into the tumor’s core. (5) Targeting immune cells: reprogramming myeloid-derived suppressor cells
(MDSCs) via drugs that target the CCR2 pathway can alleviate immunosuppression and restore a
robust anti-tumor immune response. These strategies, taken together, offer a comprehensive approach
to overcoming the protective barriers of the TME and improving cancer treatment outcomes.
3.1. Immune Checkpoint Inhibitors (ICIs)
Immune checkpoint inhibitors have transformed cancer treatment by focusing on
proteins that block T cell activation. Medications like pembrolizumab and nivolumab
disrupt PD-1/PD-L1 interactions, enabling the immune system aggressively to target and
destroy tumor cells [
87
]. These inhibitors have shown success in various cancers, including
melanoma, lung cancer, and renal cell carcinoma [43].
3.2. Targeting CAFs
CAFs play a role in tumor growth and spread by remodeling the ECM and secreting
growth factors. Targeting CAFs includes inhibitors of fibroblast activation protein (FAP)
and pathways like TGF-
β
and hedgehog [
3
]. For example, the TGF-
β
inhibitor galunisertib
has shown promise in curbing CAF activity and boosting chemotherapy effectiveness in
pancreatic cancer [88].
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3.3. Anti-Angiogenic Therapies
Tumors encourage new blood vessel formation through angiogenesis to sustain their
growth. Anti-angiogenic therapies like bevacizumab target VEGF signaling to disrupt
vascular development. These treatments, combined with chemotherapy, have been used in
cancers such as colorectal and ovarian cancer [89].
3.4. Modulating the ECM
The ECM offers structural support and controls cell signaling within the TME. Tar-
geting ECM components like collagen and hyaluronan can change the tumor’s physical
properties, making it more susceptible to treatment. The enzyme PEGPH20, which de-
grades hyaluronan, has demonstrated potential to improve drug delivery and survival in
pancreatic cancer [90].
3.5. Targeting Immune Cells
Reprogramming immune cells within the TME, such as tumor-associated macrophages
(TAMs) and myeloid-derived suppressor cells (MDSCs), can shift the environment toward a
more anti-tumor response. Drugs targeting CSF-1R and CCR2 pathways are being explored
to either reduce or reprogram TAMs [91].
4. Applications in Cancer Research and Therapy
The fast-paced advancements in single-cell multi-omics technologies are fundamen-
tally reshaping how we understand cancer biology. These innovations allow scientists
to measure multiple molecular aspects like genomics, transcriptomics, proteomics, and
metabolomics within individual cells all at once. Such a holistic approach offers new levels
of insight into cellular diversity, how tumors interact with their surrounding environment,
and the mechanisms that cause resistance to treatment.
One of the significant leaps forward is the combination of single-cell RNA sequencing
(scRNA-seq) with other omics layers. Techniques like CITE-seq (Cellular Indexing of
Transcriptomes and Epitopes by sequencing) and REAP-seq (RNA Expression and Protein
Sequencing) merge transcriptomic and proteomic analysis, allowing researchers to explore
both gene expression and protein levels within the same cells [
92
,
93
]. These approaches
have unveiled new types of cells and states within tumors, deepening our grasp of the
functional variety of cancer cells.
In addition, progress in single-cell ATAC-seq (Assay for Transposase-Accessible Chro-
matin using sequencing) has expanded our understanding of chromatin accessibility at a
cellular level, revealing the regulatory blueprints behind gene expression in cancer [
94
].
The integration of single-cell ATAC-seq with scRNA-seq helps uncover how chromatin
structure influences gene activity, shedding light on the role of epigenetic changes in
cancer progression.
Spatial transcriptomics (ST) has significantly advanced our understanding of the tumor
microenvironment (TME), revealing conserved transcriptional features in tumor cores, such
as hypoxia-driven metabolic reprogramming and immune evasion markers, associated
with poor prognosis. In contrast, peripheral regions show greater immune infiltration,
highlighting spatial immune–tumor interactions that may guide treatment strategies [
95
].
Additionally, the integration of ST with single-cell RNA sequencing (scRNA-seq) has
revealed immune-suppressive and stromal niches, providing insights into tumor–stroma
interactions and immune evasion mechanisms that contribute to tumor progression and
therapeutic resistance [96].
In glioblastoma, spatial heterogeneity in therapy resistance was linked to increased hy-
poxia, epithelial-to-mesenchymal transition (EMT), and reduced cytotoxic T cell proximity.
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Tumor evolution mapping also identified region-specific proliferation and differentiation
patterns that traditional sequencing methods could not detect [
97
]. Together, these studies
highlight the transformative potential of ST in identifying critical TME interactions and
guiding personalized therapeutic strategies.
Another study highlighted the application of single-cell omics in understanding vascu-
logenic mimicry (VM) in clear cell renal cell carcinoma (ccRCC). By integrating single-cell
RNA sequencing (scRNA-seq) with bulk RNA data, researchers identified a six-gene signa-
ture (L1CAM,TEK,CLDN4,EFNA1,SERPINF1, and MALAT1) as key prognostic markers.
These genes were linked to tumor invasion, angiogenesis, and immune modulation within
the tumor microenvironment (TME). The six-gene risk model effectively stratified patients
by survival outcomes and highlighted the potential for combining anti-angiogenic ther-
apies with immunotherapy-targeting VM. This study underscores single-cell omics as a
powerful tool for identifying biomarkers and guiding personalized treatment strategies in
ccRCC [98].
Another emerging area is single-cell metabolomics, which provides a closer look at
the metabolic states of individual cells. With the help of mass spectrometry-based single-
cell metabolomics, scientists can now track metabolites within individual cells, directly
connecting metabolic shifts to specific cancer cell behaviors [
99
]. The potential for these
single-cell multi-omics technologies in cancer research and treatment is immense.
These advanced technologies are crucial for mapping tumor heterogeneity and evolu-
tion, identifying distinct cell subpopulations, and tracing their evolutionary paths. This
is key to understanding how genetic and epigenetic variations within tumors drive pro-
gression and resistance to treatment. By tracking cancer cell evolution, researchers can
identify the rise of resistant clones, guiding strategies to prevent or combat resistance [
100
].
Moreover, single-cell multi-omics allows for an in-depth study of the tumor microenviron-
ment (TME), which includes immune cells, stromal cells, and cancer-associated fibroblasts.
Understanding how these cells interact with cancer cells is vital for developing treatments
that reshape the TME to boost anti-tumor immune responses. For example, scRNA-seq
has helped identify unique immune cell subtypes within tumors, unveiling potential im-
munotherapy targets. These findings highlight the importance of single-cell multi-omics in
driving personalized cancer care and enhancing patient outcomes [101] (Figure 7).
Personalizing cancer therapy with single-cell multi-omics is transforming patient
care. By analyzing individual tumors at the single-cell level, doctors can pinpoint the
molecular changes driving cancer in each patient, leading to more targeted treatments that
minimize resistance and work more effectively. This precision is particularly valuable in
rare cancers, where discovering actionable mutations and pathways can inform the use of
precise inhibitors [
102
]. Additionally, single-cell multi-omics is making it possible to detect
early warning signs and predict cancer outcomes. Biomarkers identified in blood or other
bodily fluids provide a non-invasive way to catch the disease early and monitor its progress
over time. For instance, analyzing circulating tumor cells (CTCs) using these cutting-
edge techniques reveals signatures linked to metastasis. These breakthroughs emphasize
the game-changing potential of single-cell multi-omics in enhancing personalized cancer
treatment and making early detection more accessible [103].
The ongoing development and application of single-cell multi-omics technologies will
shape the future of cancer research and therapy. These approaches offer a comprehensive
view of cancer biology at unparalleled resolution, opening up new pathways for personal-
ized and targeted treatments. As these technologies become more widely accessible and
integrated, their impact on transforming cancer care and improving patient outcomes will
only increase.
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portance of single-cell multi-omics in driving personalized cancer care and enhancing
patient outcomes [101] (Figure 7).
Personalizing cancer therapy with single-cell multi-omics is transforming patient
care. By analyzing individual tumors at the single-cell level, doctors can pinpoint the
molecular changes driving cancer in each patient, leading to more targeted treatments
that minimize resistance and work more effectively. This precision is particularly valua-
ble in rare cancers, where discovering actionable mutations and pathways can inform the
use of precise inhibitors [102]. Additionally, single-cell multi-omics is making it possible
to detect early warning signs and predict cancer outcomes. Biomarkers identified in
blood or other bodily fluids provide a non-invasive way to catch the disease early and
monitor its progress over time. For instance, analyzing circulating tumor cells (CTCs)
using these cuing-edge techniques reveals signatures linked to metastasis. These
breakthroughs emphasize the game-changing potential of single-cell multi-omics in en-
hancing personalized cancer treatment and making early detection more accessible [103].
Figure 7. The applications of single-cell multi-omics. This illustration highlights how single-cell
multi-omics analysis is revolutionizing cancer research and personalized medicine. By analyzing
patient-derived samples with multi-layered approaches (genomics, transcriptomics, proteomics,
and metabolomics), researchers gain deep insights into the TME, exposing cellular diversity and
the molecular pathways which are driving cancer progression and drug resistance. Key applica-
tions include detecting biomarkers, mapping tumor diversity, overcoming treatment resistance,
and enabling personalized treatment strategies.
The ongoing development and application of single-cell multi-omics technologies
will shape the future of cancer research and therapy. These approaches offer a compre-
hensive view of cancer biology at unparalleled resolution, opening up new pathways for
personalized and targeted treatments. As these technologies become more widely acces-
Figure 7. The applications of single-cell multi-omics. This illustration highlights how single-cell
multi-omics analysis is revolutionizing cancer research and personalized medicine. By analyzing
patient-derived samples with multi-layered approaches (genomics, transcriptomics, proteomics, and
metabolomics), researchers gain deep insights into the TME, exposing cellular diversity and the
molecular pathways which are driving cancer progression and drug resistance. Key applications
include detecting biomarkers, mapping tumor diversity, overcoming treatment resistance, and
enabling personalized treatment strategies.
Recent advancements in understanding the TME using single-cell multi-omics have
greatly deepened our knowledge of cancer progression and treatment resistance. The TME,
made up of cancer cells, stromal cells, immune cells, and the extracellular matrix, plays
a pivotal role in shaping tumor behavior and response to therapies. Key findings high-
light the cellular diversity within the TME, with single-cell sequencing uncovering varied
populations of cancer-associated fibroblasts, immune cells like T cells and macrophages,
and endothelial cells, each displaying distinct functional states that contribute differently
to tumor growth and treatment responses [
104
]. Moreover, single-cell transcriptomics
has provided detailed insights into the immune landscape of tumors, identifying specific
immune cell subsets and their interactions with cancer cells, which illuminate immune
evasion strategies. For instance, exhausted T cells, characterized by the expression of in-
hibitory receptors like PD-1 and CTLA-4, are prevalent in the TME and are associated with
a poor prognosis. Additionally, tracking therapy-induced changes at the single-cell level
has offered valuable clues about how cancer cells adapt to treatments, aiding the design of
combination therapies to prevent or delay resistance. Fresh tissue multi-omics profiling in
conventional chondrosarcoma has helped classify immune subtypes and identify potential
candidates for immunotherapy, enhancing treatment approaches [8].
Single-cell metabolomics has revealed the metabolic diversity within tumors, demon-
strating how different metabolic profiles in cancer and stromal cells contribute to the overall
reprogramming of the TME. This reprogramming supports cancer cell growth and survival
in low-oxygen environments, and plays a role in resistance to therapy [9].
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5. Clinical Trials
This section reviews a series of studies employing advanced multi-omics techniques,
such as single-cell RNA sequencing, spatial transcriptomics, and comprehensive genomic
analyses, to investigate various cancers. These studies aim to identify novel biomarkers,
understand immune landscape dynamics, and develop personalized treatment strategies,
ultimately enhancing patient outcomes. By dissecting the molecular and cellular intricacies
of different cancers, these investigations pave the way for more precise and effective
therapeutic interventions. There are several clinical trials focusing on the role of the tumor
microenvironment (TME) in cancer progression and therapeutic resistance, with insights
from single-cell multi-omics technologies (Table 1).
Table 1. Clinical trials employing single-cell multi-omics approach to treat different types of cancer.
Study Title Reg. No. Country Pts. No. Cancer Type Description Ref.
Single-cell RNA sequencing
reveals the tumor
microenvironment and
facilitates strategic choices to
circumvent treatment failure
in a chemorefractory bladder
cancer patient.
DOI:
10.1186/s13073-020-
00741-6 Korea 1 Bladder cancer
This study uses single-cell RNA
sequencing to analyze the tumor
microenvironment of a chemorefractory
bladder cancer patient, aiming to
identify strategic choices to overcome
treatment failure
[105]
Mechanism of Response to
IMFINZI Neoadjuvant
Therapy in Non-small Cell
Lung Cancer Patients Based
on Multiple-omics Models.
NCT04646837 China 20 Non-small lung
cancer
This single-center, exploratory study
examines the impact of neoadjuvant
PD-1 monoclonal antibody therapy by
analyzing blood and tumor samples
before and after treatment.
[106]
Analysis of the
Microenvironment of Lung
Cancer and Exploration of the
Mechanism of Resistance
to Immunotherapy
NCT05636605 China 200 Lung cancer
Investigators used multi-omics
analyses, including genomics,
proteomics, single-cell RNA sequencing,
and spatial transcriptomics on tumor
tissue and blood. The goal was to
analyze tumor heterogeneity, map the
lung cancer microenvironment, and
explore mechanisms of sensitivity and
resistance to
anti-PD1/PD-L1 antibodies.
[107]
Improving Personalised
Glioblastoma Care by Stem
Cell Analysis, Omics
(Including Immunomics) and
Artificial Intelligence
Approaches
NCT05941234 Italy 120 Glioblastoma
This study aimed to integrate in-depth
multi-omics with clinical data to
discover immune markers in
glioblastoma patients. It considered age
and sex differences, predicted
prognosis, defined key
life/environmental factors, and guided
AI-driven personalized treatments to
improve care and quality of life.
[108]
Multi-omics Tumor Evolution
Model of NSCLC NCT05352035 China 300 Non-small cell lung
cancer
The purpose of this study was to
determine the evolutionary mechanism
of early-stage non-small cell lung cancer
and establish an accurate prognostic
model and a recurrence monitoring
system using multi-omics analysis,
which can be helpful for the individual
and whole management of lung cancer
patients and improve the
overall prognosis.
[109]
Multi-omics Characterization
of Pancreatic Neuroendocrine
Tumors and Carcinomas NCT05234450 France 300 Pancreatic cancer
Researchers aimed to identify distinct
subgroups within pancreatic
neuroendocrine tumors and carcinomas,
using integrated multi-omics analysis.
Their approach involved
next-generation sequencing methods
like RNAseq, along with the MCP
Counter tool to quantify eight immune
cell populations in TME.
[110]
Investigation of Tumor
Microenvironment After
CRPC Along With Before and
After Neoadjuvant Therapy
for Prostate Cancer
NCT05522907 China 1000 Prostate cancer
In a retrospective study, investigators
perform multi-omics analysis
(including whole exome, RNAseq),
immune cell characterization, and
biopsy samples from prostate cancer
primary biopsy, ADT neoadjuvant, and
CRPC biopsy samples in the biobank.
[111]
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Table 1. Cont.
Study Title Reg. No. Country Pts. No. Cancer Type Description Ref.
Neuroendocrine Neoplasm
Based on Multi-omics
Integrated Analysis NCT04931446 China 200 Neuroendocrine
neoplasm
This project analyzed the molecular
biological characteristics of NEN based
on multi-omics, developing an
exclusive NEN multi-omics big data
platform, and carrying out molecular
subtypes and potential targets
predictions to improve the therapeutic
effect of neuroendocrine tumors.
[112]
Single-cell RNA Sequencing
Resolves the Regulatory Role
of HBV on the Hepatocellular
Carcinoma Immune
Microenvironment
NCT05677724 China 20 Primary liver cancer
and HBV
With the help of single-cell sequencing
technology, this study focused on
elucidating the influence of
HBV-induced hepatocellular carcinoma
cell metabolic changes on
microenvironment remodeling. This
study provides a more accurate
diagnosis and treatment method for
HBV-induced hepatocellular carcinoma.
[113]
Tumor Microenvironment in
Ovarian Cancer (MICO) NCT06272240 Italy 50 Ovarian cancer
This research aimed to create ovarian
cancer organoids to explore interactions
and molecular pathways between
tumor cells, immune cells, and the local
microbiota in a controlled
laboratory setting.
[114]
Deep, Multi-omics
Phenotyping to Predict
Response, Resistance and
Recurrence to Adjuvant
Atezolizumab Plus
Bevacizumab in Resected
Hepatocellular Carcinoma
(EMPHASIS)
NCT05516628 Singapore 30 Hepatocellular
carcinoma
This study explored the molecular
complexity of hepatocellular carcinoma
(HCC) to find predictive biomarkers for
personalized therapies. Through two
clinical studies with Atezolizumab plus
Bevacizumab and Yttrium-90, it
collected longitudinal biosamples. The
goal was to understand the tumor
microenvironment, biomarker
co-localization, and their impact on
treatment response and recurrence
in HCC.
[115]
Comparison of the Breast
Tumor Microenvironment NCT03165487 United States 30 Triple-negative
breast cancer
The project aimed to define a molecular
profile of tumor stroma using “normal”
adjacent breast tissue collected before
and after intraoperative radiation
therapy during breast conserving
surgery. This research specifically
focused on patients with luminal A and
triple-negative breast cancer, where
IORT is a standard treatment.
[116]
Deciphering the Immune
Microenvironment at the
Forefront of Tumor
Aggressiveness by
Constructing a Regulatory
Network with Single-Cell and
Spatial Transcriptomic Data.
DOI:
10.3390/genes15010100
China - Breast cancer
This study constructed intercellular
gene regulatory networks using
single-cell RNA sequencing and spatial
transcriptomics data to understand the
immune microenvironment at the
invasive front of ER-positive
breast cancer.
[117]
6. Exploring New Cancer Treatment Avenues via Multi-Omics Analysis
Research has identified a protective barrier within liver cancer tumors, made up of
specific immune and support cells. This barrier prevents immune cells from reaching the
tumor, reducing the effectiveness of immunotherapy [
118
]. Recent breakthroughs have
uncovered ways to disrupt this barrier, improving the immunotherapy response in early
trials. Simultaneously, a novel type of cell death called cuproptosis has been connected to
immunosuppressive environments in several cancers [
119
]. Cuproptosis-related genes have
been linked to poor outcomes and immune evasion, suggesting that therapies targeting
both the physical tumor barrier and cuproptosis could be promising [
120
]. Additionally,
the single-cell analysis of testicular and pancreatic cancers has uncovered unique immune
cell groups with signs of exhaustion, offering clues to combat tumor progression. The
power of single-cell technologies lies in revealing the complex tumor environment, aiding
the creation of new treatments [121].
Studies in pancreatic cancer have highlighted the diversity within tumor-associated
neutrophils, with some subtypes driving tumor growth by increasing glycolysis—a pro-
Pharmaceuticals 2025,18, 75 20 of 38
cess fueled by a specific transcription factor. These findings offer potential therapeutic
targets [122].
Despite progress in treating hepatocellular carcinoma (HCC) and clear cell renal
cell carcinoma (ccRCC), challenges remain. HCC’s immunosuppressive environment,
with exhausted T cells and suppressive macrophages, continues to resist treatment ef-
forts. In ccRCC, although models have been developed to predict outcomes, translating
these insights into real-world benefits is difficult due to the tumor’s genetic and cellular
complexity [37].
Although single-cell technologies have made significant strides in mapping tumor
cells and their environments, there is still much to learn about how these elements interact
to drive cancer growth. While we now have potential molecular targets and a clearer
understanding of cell populations linked to tumor progression, the exact mechanisms are
still murky. This gap in knowledge hinders the development of more effective treatments.
Emerging insights from multi-omics studies emphasize the intricate relationships
between tumor cells, immune cells, and the surrounding environment. For instance,
targeting the molecular driver SERPINE2 in renal cell carcinoma, understanding the role
of tumor stromal cells in colorectal cancer, and mapping ligand–receptor networks in
squamous cell carcinoma all underscore the importance of the tumor environment in cancer
progression and immune escape [123].
A comprehensive understanding of T cell exhaustion across cancers remains a chal-
lenge. While distinct profiles linked to patient outcomes have been identified, the specific
mechanisms driving immune escape are still unclear. This ongoing research highlights the
need for a deeper exploration into the interactions between tumor cells, immune cells, and
their environment [124].
7. Integrating Multi-Omics into Cancer Therapeutic Strategies
Recent studies have spotlighted various factors that are pivotal in cancer develop-
ment and treatment. For example, research has identified LSM1 as a key player in breast
cancer metabolism and macrophage infiltration in tumors, indicating its therapeutic poten-
tial [
125
]. Similarly, CLIC1 has been highlighted as both a promising therapeutic target and
a biomarker in gliomas. Novel diagnostic markers and inflammatory profiles have been
identified in renal angiomyolipoma linked to tuberous sclerosis complex, paving the way
for more precise therapeutic approaches [
126
]. Regulatory T cells are also shown to affect
the tumor immune environment and responses to immunotherapy in triple-negative breast
cancer, suggesting their potential as therapeutic targets [
127
,
128
]. Furthermore, Apolipopro-
tein E’s role in alternative splicing and its influence on the immune microenvironment in
kidney renal clear cell carcinoma have positioned it as a promising therapeutic target [
129
].
Advancements in our understanding of tumor microenvironments and strategies
for treating diverse cancers have also been substantial [
130
]. For instance, certain tumor
microenvironment-related genes have emerged as predictors of prognosis in pancreatic
cancer. Additionally, a classifier focusing on macrophage differentiation and multi-omics
data has been developed to enhance treatment strategies for papillary thyroid cancer [
131
].
The role of IMMT in breast cancer is now recognized, emphasizing its value as a potential
theragnostic marker [
132
]. Another noteworthy development is a risk model centered on
interferon regulatory factors that guides immunotherapy for clear cell renal carcinoma [
133
].
Recent studies have also explored immunogenic cell death pathways in gastric adenocarci-
noma and identified PFKFB3 as a major factor in sunitinib resistance for papillary renal cell
carcinoma, offering it as a potential target for improved treatment responses [134,135].
New research has greatly enhanced our grasp of cancer biology and treatment options.
Single-cell profiling, for instance, has shed light on the complex biology and environ-
Pharmaceuticals 2025,18, 75 21 of 38
ment within primary brain tumors, guiding more targeted treatment approaches [
136
].
Additionally, a novel classifier based on G protein-coupled receptors and the tumor mi-
croenvironment has been developed for predicting survival and immunotherapy responses
in melanoma [
137
]. Moreover, the roles of toll-like receptors across different cancers
have been emphasized for their relevance in tumor progression and potential therapeutic
avenues [
138
]. Functional state-based molecular subtypes of gastric cancer have been
identified, supporting the adoption of personalized treatment approaches [
139
]. Tools
like GBMdeconvoluteR have also been introduced, offering a more accurate analysis of
neoplastic and immune cell proportions in glioblastoma [
140
]. Through multi-omics analy-
ses, basement membrane genes have been identified as novel biomarkers and therapeutic
targets in uveal melanoma [141].
Several studies have revealed significant findings with potential implications for can-
cer prognosis and treatment. For instance, FABP5 has emerged as a critical gene in gliomas,
linked to liquid–liquid phase separation and suggested as a prognostic marker and ther-
apeutic target [
142
]. AIM2 inflammasomes have been highlighted in various cancers as
potential targets for immunotherapy [
143
]. Machine learning models have been developed
to assess perineural invasion risks in head and neck squamous cell carcinoma and to iden-
tify macrophage-related therapeutic modules in colorectal cancer [
144
]. CD73 has also
surfaced as a prognostic biomarker and potential immunotherapy target in intrahepatic
cholangiocarcinoma [
145
]. Insights from advanced imaging and multi-omics integration
have unveiled cell type-specific features in breast cancer and crucial GPCR gene markers
in lung adenocarcinoma, paving the way for more effective targeted therapies [
146
,
147
].
Molecular characteristics of parthanatos in gastric cancer and a gene enrichment score
related to glycerolipid metabolism in colon cancer have also been linked to prognosis,
offering new therapeutic pathways [
148
,
149
]. Additionally, studies on arginine biosyn-
thesis genes point to new strategies for overcoming tumor immune evasion and therapy
resistance [150].
The understanding of cancer biology and possible therapeutic interventions has been
further enriched by recent findings across several cancer types. For instance, MYC signaling
in lung adenocarcinoma has been identified as a key driver of tumor progression and a vi-
able therapeutic target [
151
]. In metastatic melanoma, pyroptosis-related markers offer new
possibilities for enhancing immunotherapy outcomes [
152
]. Macrophage-specific cathepsin
in clear cell renal cell carcinoma has also been identified as a prognostic marker [
153
]. Stud-
ies on colorectal cancer have shown that precursor exhausted CD8+ T cells are linked to
survival rates and responses to immunotherapy [
154
]. Additionally, a prognostic signature
for pancreatic adenocarcinoma based on ubiquitination-related mRNA and lncRNA has
been proposed [
155
]. Single-cell and multi-omics approaches have further revealed how
Euphorbiae Humifusae Herba induces mitochondrial dysfunction in non-small-cell lung
cancer, while gamma-delta T lymphocytes have shown potential for improving the tumor
microenvironment and immunotherapy outcomes in cervical cancer [
156
,
157
]. Moreover, a
multi-omics pan-cancer analysis has identified CREB5 as a key biomarker for prognosis
and immunotherapy responses in glioma [158].
Recent studies have highlighted key insights into cancer progression and therapy.
Glycogen synthase 1 has been identified as a crucial target for inducing disulfidptosis in
triple-negative breast cancer, pointing to new therapeutic possibilities [
159
]. The heterogene-
ity of macrophages in glioma patients has been linked to tumor progression and therapy
outcomes [
160
]. Transcriptomics and deep multiplex imaging have mapped bone marrow
metastasis in neuroblastoma, uncovering potential therapeutic targets [
161
]. T-bet has also
been identified as a suppressor of malignant B cell proliferation in chronic lymphocytic
leukemia, suggesting its therapeutic relevance [
162
]. Dysregulated neurogenesis-related
Pharmaceuticals 2025,18, 75 22 of 38
genes in the tumor immune microenvironment have been proposed as potential biomarkers
and therapeutic targets [
163
]. Additionally, pyroptosis-related markers have been identi-
fied as critical for optimizing neoadjuvant immunotherapy in gastric cancer, which could
enhance treatment outcomes [164].
Recent research continues to shed light on the intricacies of cancer progression and
novel therapeutic strategies. For example, Glycogen synthase kinase 1 has been identified
as a critical target for inducing disulfidptosis in triple-negative breast cancer, offering new
potential avenues for treatment [
159
]. Additionally, the diversity of macrophages in glioma
patients has been linked to differences in tumor progression and therapeutic responses [
160
].
Transcriptomics and deep multiplex imaging have also been employed to map bone marrow
metastasis in neuroblastoma, revealing promising therapeutic targets [
161
]. In chronic
lymphocytic leukemia, T-bet has been shown to suppress malignant B cell proliferation,
positioning it as a potential therapeutic target [
162
]. Dysregulation of neurogenesis-related
genes within the tumor immune microenvironment has emerged as a promising area for
the identification of biomarkers and therapeutic targets [
163
]. Lastly, key pyroptosis-related
markers have been identified as essential for optimizing neoadjuvant immunotherapy in
gastric cancer, with the potential to improve treatment outcomes [164].
8. Prognostic Biomarkers and Therapeutic Targets
Recent breakthroughs have significantly refined cancer diagnosis and treatment meth-
ods. For instance, novel approaches in lung adenocarcinoma uncovered immune sup-
pression subtypes, leading to the development of a prognostic signature that deepens the
understanding of tumor progression [
165
]. Similarly, in osteosarcoma, an m7G-related
signature now helps predict both the prognosis and response to immunotherapy, paving
the way for more personalized treatment strategies [
166
]. Moreover, the creation of a
Precancer Atlas, designed to detect early tissue changes, promises to enhance preventive
measures and early intervention efforts [
167
]. Notably, a prognostic signature based on
necroptosis-related genes has been introduced for cervical cancer, offering innovative pa-
tient stratification techniques [
168
]. Further research on hepatocellular carcinoma explored
the relationship between disulfidptosis and the immune microenvironment, pointing to
fresh immunotherapeutic strategies [
169
]. Additionally, the significance of C1QC+ and
SPP1+ tumor-associated macrophages in cervical cancer was highlighted, and single-cell
analyses revealed evolutionary mimicry across breast cancer subtypes, providing a new
lens for classification and treatment [170,171].
Recent findings also underscore a deeper grasp of cancer progression and emerging
therapeutic targets. A variety of genes and molecular mechanisms that shape prognosis,
immune microenvironments, and drug responsiveness have been uncovered across several
cancer types. For instance, the ADAMTS gene family, galectins, and FXYD5 have been
spotlighted as critical biomarkers, while research notes the co-regulation of tumor-stromal
heterogeneity by MUC16 and TP53 in pancreatic adenocarcinoma [
172
–
174
]. Additionally,
immune cell-related gene expression models now provide deeper insights into tumor
immune dynamics, while the role of FTO in promoting tumor neovascularization via
m6A modification removal has been highlighted [
175
,
176
]. For glioblastoma, CpG island
methylation patterns present a novel classification system, revealing potential therapeutic
targets for improved outcomes [177].
Diverse therapeutic strategies continue to be explored across different cancer types.
For instance, research has delved into genes and pathways associated with vasculogenic
mimicry, immune microenvironments, and cancer-associated fibroblasts, all aimed at sti-
fling cancer progression and enhancing treatment outcomes [
178
]. Systems pharmacology
approaches have also emerged as promising methods to boost the effectiveness of PD-
Pharmaceuticals 2025,18, 75 23 of 38
1/PD-L1 blockade, a key cancer immunotherapy [
179
]. Additionally, cuproptosis-related
genes in hepatocellular carcinoma and variations in surface protein abundance in metastatic
melanoma have shed light on new avenues for targeted therapies [
180
]. The role of the
CCN family in regulating cell interactions and immune evasion in glioma, along with the
impact of JAG1 on immune microenvironments and resistance to immunotherapy in lung
adenocarcinoma, have been identified as promising areas for targeted
treatment [181,182]
.
Moreover, antigen-specific T cell receptor clusters across various cancers present opportu-
nities for personalized immunotherapy approaches [
183
]. These advancements collectively
enhance our understanding of cancer biology and open new pathways for more effective
therapeutic solutions.
Further investigations into prognostic biomarkers and therapeutic targets have yielded
valuable insights across multiple cancer types. In gastric cancer, for instance, YWHAE has
been flagged as a key gene in ferroptosis, offering new therapeutic possibilities [
184
]. Simi-
larly, in head and neck squamous cell carcinoma, SLC25A17 emerged as a novel biomarker
for forecasting prognosis and immune microenvironment changes [
185
]. A multi-omics
profiling of fresh tissues in conventional chondrosarcoma classified immune subtypes,
informing immunotherapy candidates and treatment strategies [
186
]. A polyamine gene
expression score was also developed to predict the prognosis and treatment response in
clear cell renal cell carcinoma, pointing to personalized therapy options [
187
]. Further-
more, inhibiting PI3K/mTOR has been found to remodel the tumor microenvironment and
sensitize pS6high uterine leiomyosarcoma to PD-1 blockade, suggesting new therapeutic
approaches [
188
]. Different head and neck squamous cell carcinoma subtypes were also
identified based on mononuclear phagocyte system-related multi-omics features, offering
insights into prognosis and treatment responses [
189
]. These approaches have been applied
to investigate immune-checkpoint blockade (ICB) therapies in human cancers. A study
utilizing previously published single-cell transcriptomics, epigenomics, and TCR data
revealed that tumor-reactive T cells responded differently to ICB across various tumor
types. Notably, the presence of CXCL13+ CD8+ T cells was associated with a favorable
response to ICB [190].
Single-cell multi-omics has been utilized to evaluate chimeric antigen receptor (CAR) T
cell therapy in acute lymphoblastic leukemia by integrating transcriptomics and proteomics
data. This approach identified molecular differences underlying complete remission, non-
responsiveness, and relapse. The analysis highlighted TH2 cell pathways and associated
genes as potential targets for sustaining long-term remission, particularly over five years,
following CAR T cell immunotherapy [
191
]. Lastly, machine learning has been employed to
study regulated cell death pathways in glioma, proving effective in predicting both patient
prognosis and immunotherapy outcomes [192].
The discovery of various prognostic biomarkers and therapeutic targets continues
to advance cancer research. For example, one study identified B7-H3 as a prognostic
biomarker in head and neck squamous cell carcinoma, correlating it with the response to im-
mune checkpoint blockade therapies [
193
]. Another highlighted aldehyde dehydrogenase
2 as a key player in mediating interactions between Treg-mediated immunosuppression
and hepatocellular carcinoma, offering fresh targets for therapy [
194
]. Furthermore, the
reciprocal interaction between Th17 cells and mesothelial cells, promoting adhesion in
ovarian cancer metastasis, hints at new treatment options [
195
]. PANoptosis-related mark-
ers in glioma also provide critical insights into prognosis and tumor microenvironment
understanding. Additional studies found that ALG3 suppresses CD8+ T cell infiltration
by inhibiting chemokine secretion, impacting the efficacy of 5-fluorouracil in cancer ther-
apy [
196
]. Another study formulated a hypoxia prognostic signature for glioblastoma
multiforme using bulk and single-cell RNA-seq data, refining patient stratification methods
Pharmaceuticals 2025,18, 75 24 of 38
for treatment [
197
]. Finally, the mitochondrial AAA protease gene has been identified as a
prognostic marker linked to immune infiltration in ovarian cancer, revealing potential new
treatment paths [198].
9. Advances in Multi-Omics and Personalized Cancer Therapy
Recent strides in cancer research have revealed the vital role that genetic and molecular
markers play in refining cancer prognosis, immune interactions, and therapeutic outcomes
across a variety of cancers. For instance, a combination of single-cell and bulk RNA sequenc-
ing has pinpointed a gene signature linked to neoadjuvant chemotherapy in breast cancer,
which offers a way to predict survival and treatment response, thereby paving the way for
more tailored treatment strategies [
199
]. Multi-omics investigations also uncovered that
CD276 stands as a significant prognostic marker in several cancers, including glioblastoma,
shedding light on new possibilities for targeted therapies [
200
]. In colorectal cancer, studies
revealed that CTSB+ macrophages impede memory immune responses in liver metastases,
offering potential therapeutic avenues to enhance immune responses [
201
]. Moreover,
integrating multi-omics with machine learning has revealed disulfidptosis patterns in lung
adenocarcinoma, which could predict immunotherapy outcomes and assist in personaliz-
ing treatment plans [
202
]. Collectively, these findings underscore the growing significance
of sophisticated genetic and molecular analyses in shaping individualized therapies and
boosting patient outcomes across various cancer types.
Continued exploration has further uncovered key biomarkers and cellular behaviors
in different cancers, offering critical insights for personalized treatment approaches. In pan-
creatic cancer, TLR2 has been identified as a prognostic marker, highlighting the variability
in NETosis that could guide customized treatments [
203
]. High-resolution transcriptomics
in lung adenocarcinoma identified a cell subtype marked by the expression of CXCL13, EP-
STI1, and CDK1, suggesting possible therapeutic targets [
204
]. For early-stage endometrial
cancer, a newly discovered oncogenic cluster with low TRAP1 and CAMSAP3 expression
was linked to more aggressive behavior and a poorer prognosis [
205
]. These collective
insights emphasize the value of integrating multi-omics data to deepen our understanding
of cancer biology and enhance patient-specific therapeutic strategies.
Research has also highlighted pivotal findings in different cancer types through ad-
vanced genetic and molecular approaches. Hypoxia-related genes have emerged as key
factors in TACE-refractory hepatocellular carcinoma, impacting the prognosis, immune
characteristics, and drug resistance [
206
]. In lung adenocarcinoma, a gene signature linked
to cuproptosis and anoikis was developed to predict the prognosis and immune infiltra-
tion [
207
]. Meanwhile, in lower-grade glioma, a mitochondrial RNA modification-based
signature was identified, improving patient stratification and prognosis prediction [
208
].
In cervical cancer, hypoxia subtypes and the immunosuppressive factor S100A2 were rec-
ognized as new therapeutic targets [
209
]. Moreover, an integrative multi-omics analysis,
coupled with machine learning, revealed significant GPCR gene features in lung adeno-
carcinoma, offering new perspectives for targeted therapies. Single-cell profiling methods
have also shed light on the biology and microenvironment of primary brain tumors, aiding
in the development of more precise therapies [
146
]. These findings collectively highlight the
essential role of multi-omics in personalizing cancer treatments and enhancing
patient care
.
Further insights from multi-omics analyses continue to shape our understanding of
cancer biology, prognosis, and treatment strategies. For example, fresh tissue profiling
in conventional chondrosarcoma helped classify immune subtypes and identified can-
didates for immunotherapy, presenting new therapeutic possibilities [
186
]. In head and
neck squamous cell carcinoma, B7-H3 emerged as a new prognostic marker and predic-
tor for immune checkpoint blockade response, offering fresh therapeutic targets [
193
].
Pharmaceuticals 2025,18, 75 25 of 38
Aldehyde dehydrogenase 2 was found to be a key factor in the immunosuppressive envi-
ronment of hepatocellular carcinoma, suggesting new therapeutic interventions [
194
]. In
glioma, predictors linked to PANoptosis were identified, providing insights into progno-
sis and tumor microenvironment characteristics, thereby guiding personalized treatment
approaches [
210
]. These studies collectively underscore the importance of multi-omics tech-
niques in pinpointing biomarkers and crafting targeted therapies that ultimately improve
cancer treatment and patient outcomes.
Genomics, transcriptomics, proteomics, and metabolomics are pivotal