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Intertumoral heterogeneity of tumor epithelial cells in lung adenocarcinomas. A UMAPs based on the top 20 principal components of all epithelial single-cell transcriptomes color-coded by tissue type, cell type and patient, and quantification of epithelial cell types per tissue type, AT1, alveolar type 1 cells, AT2, alveolar type 2 cells. B Average gene expression of selected marker genes for normal epithelial cell types. C Differentially expressed genes in tumor epithelial cells grouped by patients, maximum top ten genes showed per patient, for patient color code see (A). D Immunohistochemical staining of proteins encoded by selected differentially expressed genes indicated by black arrowheads in (C). E Mean pathway activity scores of tumor epithelial cells grouped by patient. F Distribution of histological subtypes, (G) mean module scores of normal epithelial cell type gene signatures, and (H) mean pathway activity scores of tumor epithelial cells sorted along principal component 1 (PC1). F, G, H Principal component analysis based on gene expression of all tumor epithelial single-cell transcriptomes; schematic depiction of tumor cell signature module scores along PC1.
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Recent developments in immuno-oncology demonstrate that not only cancer cells, but also the tumor microenvironment can guide precision medicine. A comprehensive and in-depth characterization of the tumor microenvironment is challenging since its cell populations are diverse and can be important even if scarce. To identify clinically relevant microe...
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... to unsorted singlecell RNA sequencing, yielding 114,489 high-quality transcriptomes after quality control and filtering (Fig. 1A, Supplementary Fig. 1A, B). Evaluation of consecutive H&E stained tissue sections showed tumor morphology ranging from well differentiated lepidic to poorly differentiated sarcomatoid growth patterns (Supplementary Fig. ...
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... as normal or tumor cell clusters, based on tissue origin ( Supplementary Fig. 3B), which was largely congruent with the copy-number status of cells ( Supplementary Fig. 4A, B), and demonstrated a tumor purity >90%. Within the normal cell clusters, we found alveolar type 1 and 2, club, ciliated, and even a small cluster of neuroendocrine cells ( Fig. 2A), which were characterized by expression of typical individual marker genes ( [13,14]. The club cell cluster also expressed basal cell marker genes such as NGFR and KRT5 indicating an admixture of small amounts of basal cells (Fig. 2B). Tumor cell clusters segregated from normal cell clusters and were mainly patient-specific, ...
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... cell clusters, we found alveolar type 1 and 2, club, ciliated, and even a small cluster of neuroendocrine cells ( Fig. 2A), which were characterized by expression of typical individual marker genes ( [13,14]. The club cell cluster also expressed basal cell marker genes such as NGFR and KRT5 indicating an admixture of small amounts of basal cells (Fig. 2B). Tumor cell clusters segregated from normal cell clusters and were mainly patient-specific, indicating intertumoral heterogeneity ( Fig. 2A). This was underlined by a variety of genes differentially expressed across tumors such as EGFR, TFF3, CDKN2A, and SFTPA2 (Fig. 2C, black arrowheads), correlating with protein expression as shown ...
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... by expression of typical individual marker genes ( [13,14]. The club cell cluster also expressed basal cell marker genes such as NGFR and KRT5 indicating an admixture of small amounts of basal cells (Fig. 2B). Tumor cell clusters segregated from normal cell clusters and were mainly patient-specific, indicating intertumoral heterogeneity ( Fig. 2A). This was underlined by a variety of genes differentially expressed across tumors such as EGFR, TFF3, CDKN2A, and SFTPA2 (Fig. 2C, black arrowheads), correlating with protein expression as shown by immunostaining (Fig. 2D). We quantified oncogenic signal strengths by pathway target gene signature expression and found highly variable ...
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... as NGFR and KRT5 indicating an admixture of small amounts of basal cells (Fig. 2B). Tumor cell clusters segregated from normal cell clusters and were mainly patient-specific, indicating intertumoral heterogeneity ( Fig. 2A). This was underlined by a variety of genes differentially expressed across tumors such as EGFR, TFF3, CDKN2A, and SFTPA2 (Fig. 2C, black arrowheads), correlating with protein expression as shown by immunostaining (Fig. 2D). We quantified oncogenic signal strengths by pathway target gene signature expression and found highly variable activities for EGFR, TGFβ, JAK/STAT, Hypoxia, and PI3K signaling across the different patients (Fig. 2E). These signal strengths ...
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... cell clusters segregated from normal cell clusters and were mainly patient-specific, indicating intertumoral heterogeneity ( Fig. 2A). This was underlined by a variety of genes differentially expressed across tumors such as EGFR, TFF3, CDKN2A, and SFTPA2 (Fig. 2C, black arrowheads), correlating with protein expression as shown by immunostaining (Fig. 2D). We quantified oncogenic signal strengths by pathway target gene signature expression and found highly variable activities for EGFR, TGFβ, JAK/STAT, Hypoxia, and PI3K signaling across the different patients (Fig. 2E). These signal strengths were largely unrelated to the mitotic activity of tumor epithelial cells (Supplementary Fig. ...
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... such as EGFR, TFF3, CDKN2A, and SFTPA2 (Fig. 2C, black arrowheads), correlating with protein expression as shown by immunostaining (Fig. 2D). We quantified oncogenic signal strengths by pathway target gene signature expression and found highly variable activities for EGFR, TGFβ, JAK/STAT, Hypoxia, and PI3K signaling across the different patients (Fig. 2E). These signal strengths were largely unrelated to the mitotic activity of tumor epithelial cells (Supplementary Fig. 3C). p53 signaling was significantly reduced in tumors harboring TP53 mutations, whereas pathway activity scores for EGFR and MAPK signaling were not significantly higher in KRAS-mutated compared to KRAS-wildtype tumors ...
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... patient subgroups. In order to emphasize similarities between tumors, epithelial transcriptomes were embedded in lowdimensional UMAPs (4, 6, 8 instead of 20 dimensions). Here, tumor cells clustered by histological subtype rather than by Supplementary Fig. 3E), and the first principal component (PC1) displayed a gradient along histological grades (Fig. 2F, Supplementary Fig. 3E, F). Interestingly, SCGB3A1 and SCGB3A2 (Fig. 2C, white arrowheads), two genes that were previously associated with lung development [15], were positively correlated with PC1 ( Supplementary Fig. 3G, arrowheads). Moreover, gene signature scores of normal lung cell types [13] along PC1 showed a strong positive ...
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... transcriptomes were embedded in lowdimensional UMAPs (4, 6, 8 instead of 20 dimensions). Here, tumor cells clustered by histological subtype rather than by Supplementary Fig. 3E), and the first principal component (PC1) displayed a gradient along histological grades (Fig. 2F, Supplementary Fig. 3E, F). Interestingly, SCGB3A1 and SCGB3A2 (Fig. 2C, white arrowheads), two genes that were previously associated with lung development [15], were positively correlated with PC1 ( Supplementary Fig. 3G, arrowheads). Moreover, gene signature scores of normal lung cell types [13] along PC1 showed a strong positive correlation with gene expression profiles of alveolar type 1 and 2 as well ...
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... arrowheads), two genes that were previously associated with lung development [15], were positively correlated with PC1 ( Supplementary Fig. 3G, arrowheads). Moreover, gene signature scores of normal lung cell types [13] along PC1 showed a strong positive correlation with gene expression profiles of alveolar type 1 and 2 as well as club cells (Fig. 2G). Together, this indicates that PC1 reflects the degree of differentiation of tumor epithelial cells. Hence, the top 30 genes positively and negatively correlated with PC1 were defined as an "alveolar/club-like" and "undifferentiated" tumor cell signature, respectively ( Fig. 2F-H, Supplementary Fig. 3G, H). While tumor cells with ...
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... expression profiles of alveolar type 1 and 2 as well as club cells (Fig. 2G). Together, this indicates that PC1 reflects the degree of differentiation of tumor epithelial cells. Hence, the top 30 genes positively and negatively correlated with PC1 were defined as an "alveolar/club-like" and "undifferentiated" tumor cell signature, respectively ( Fig. 2F-H, Supplementary Fig. 3G, H). While tumor cells with different degrees of differentiation exhibited no clear differences in mitotic activity ( Supplementary Fig. 3I), we found high pathway activity scores for JAK/STAT, Hypoxia, EGFR and TGFβ signaling in "undifferentiated", and high scores for PI3K signaling in "alveolar/club cell-like" ...
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... While tumor cells with different degrees of differentiation exhibited no clear differences in mitotic activity ( Supplementary Fig. 3I), we found high pathway activity scores for JAK/STAT, Hypoxia, EGFR and TGFβ signaling in "undifferentiated", and high scores for PI3K signaling in "alveolar/club cell-like" tumor epithelial cells, respectively (Fig. 2H). We conclude that tumor epithelial cells of different lung adenocarcinoma patients exhibit transcriptional patterns along a spectrum ranging from undifferentiated to alveolar/club cell-like phenotypes correlating with distinct oncogenic pathway ...
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Although the value of tumor-infiltrating lymphocytes is well known, the clinical relevance of an increased immune response, specifically in breast cancer, has not been investigated across large cohorts of patients using computational algorithms. Our hypothesis stated that an enhanced immune response is associated with an improvement in outcomes. To...
Citations
... 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]. ...
Recent developments in single-cell multi-omics technologies have provided the ability to identify diverse cell types and decipher key components of the tumor microenvironment (TME), leading to important advancements toward a much deeper understanding of how tumor microenvironment heterogeneity contributes to cancer progression and therapeutic 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 reprogramming 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, revealing 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.
... Next, we looked at transcriptional differences between the ON and OFF groups through differential expression. Differentially expressed genes included cytokines and growth factors Ccl4, Cxcl2, Tgfb, and Cxcl14; extracellular matrix remodeling components Timp3, Col4a1, and Fbln5; and growth arrest-specific 6 (Gas6), which is upregulated during growth arrest (16,17) (Figure 3, E and F, and Supplemental Figure 3D). Given the driver mutation in our mouse model, we also looked at genes that are upregulated in fibroblasts in KRAS G12D -driven pancreatic cancer (18). ...
Lung cancer is the leading cause of cancer deaths in the United States. New targeted therapies against the once-deemed undruggable oncogenic KRAS are changing current therapeutic paradigms. However, resistance to targeted KRAS inhibitors almost inevitably occurs; resistance can be driven by tumor cell-intrinsic changes or by changes in the microenvironment. Here, we utilized a genetically engineered mouse model of KRASG12D-driven lung cancer that allows for inducible and reversible expression of the oncogene: activation of oncogenic KRASG12D induces tumor growth; conversely, inactivation of KRASG12D causes tumor regression. We showed that in addition to regulating cancer cell growth and survival, oncogenic KRAS regulated the transcriptional status of cancer-associated fibroblasts and macrophages in this model. Utilizing ex vivo approaches, we showed that secreted factors from cancer cells induced the expression of multiple cytokines in lung fibroblasts, and in turn drove expression of immunosuppressive factors, such as arginase 1, in macrophages. In summary, fibroblasts emerged as a key source of immune regulatory signals, and a potential therapeutic target for improving the efficacy of KRAS inhibitors in lung cancer.
... Single-cell sequencing is a high-throughput technology that enables detailed analysis of epigenomic, transcriptomic, and genomic characteristics at the single-cell level. This technology has found broad applications across oncology, immunology, neuroscience, and other fields [9][10][11]. Due to the structural and functional complexity of brain cells, isolating individual cells for single-cell analysis remains challenging. ...
Background
The role of neurons in central post-stroke pain (CPSP) following thalamic hemorrhage remains unclear. This study aimed to identify key genes associated with post-thalamic hemorrhage pain and to explore their functions in neurons. Single-nucleus RNA sequencing (snRNA-seq) data from a mouse model was used for this analysis.
Methods
First, snRNA-seq data were analyzed to identify cell types associated with CPSP induced by thalamic hemorrhage. Differentially expressed genes (DEGs) in neurons were then screened between control and model groups, followed by the construction of a protein-protein interaction (PPI) network for the DEGs. CytoNCA was used to assess node connectivity in the PPI network, and the top 5 key genes were identified. Subsequently, transcription factor (TF)-mRNA and miRNA-mRNA networks were constructed, and small-molecule drugs potentially targeting these key genes were predicted. Finally, the expression differences of key genes in neurons were compared between the model and control groups.
Results
A total of 13 cell clusters were identified, categorized into 8 cell types: T cells, endothelial cells, monocytes, neural progenitor cells (NPCs), microglia, astrocytes, neurons, and oligodendrocytes. A total of 228 DEGs were detected in neurons when comparing the model group with the control group. The PPI network of the DEGs consisted of 126 nodes and 209 edges, identifying the top 5 key genes: Dlgap1, Cacna1c, Gria2, Hsp90ab1, and Gapdh. The miRNA-mRNA network included 68 miRNA-mRNA pairs, 62 miRNAs, and 5 mRNAs, while the TF-mRNA network consisted of 66 TF-mRNA pairs, 56 TFs, and 5 mRNAs. Drug prediction identified 110 small-molecule drugs (e.g., purpurogallin, nifedipine, and novobiocin) potentially targeting these key genes. Additionally, Cacna1c were significantly upregulated in model mice.
Conclusion
This study identified the role of key genes in thalamic hemorrhage-induced CPSP through snRNA-seq, providing a scientific basis for further exploration of the molecular mechanisms underlying CPSP.
... In our study, we incorporated 10 samples from the dataset by Philip Bischoff and colleagues [26], which comprised 5 samples from individuals with normal lung tissue and 5 from patients with Lung Adenocarcinoma (LUAD). We leveraged the Seurat R package for the analysis of single-cell RNA sequencing data. ...
Background
Efferocytosis (ER) refers to the process of phagocytic clearance of programmed dead cells, and studies have shown that it is closely related to tumor immune escape.
Methods
This study was based on a comprehensive analysis of TCGA, GEO and CTRP databases. ER-related genes were collected from previous literature, univariate Cox regression was performed and consistent clustering was performed to categorize lung adenocarcinoma (LUAD) patients into two subgroups. Lasso regression and multivariate Cox regression analyses were used to construct ER-related prognostic features, and multiple immune infiltration algorithms were used to assess the correlation between the extracellular burial-related risk score (ERGRS) and tumor microenvironment (TME). And the key gene HAVCR1 was identified by deep learning, etc. Finally, pan-cancer analysis of the key genes was performed and in vitro experiments were conducted to verify the promotional effect of HAVCR1 on LUAD progression.
Results
A total of 33 ER-related genes associated with the prognosis of LUAD were identified, and the prognostic signature of ERGRS was successfully constructed to predict the overall survival (OS) and treatment response of LUAD patients. The high-risk group was highly enriched in some oncogenic pathways, while the low-ERGRS group was highly enriched in some immune-related pathways. In addition, the high ERGRS group had higher TMB, TNB and TIDE scores and lower immune scores. The low-risk group had better immunotherapeutic response and less likelihood of immune escape. Drug sensitivity analysis revealed that BRD-K92856060, monensin and hexaminolevulinate may be potential therapeutic agents for the high-risk group. And ERGRS was validated in several cohorts. In addition, HAVCR1 is one of the key genes, and knockdown of HAVCR1 in vitro significantly reduced the proliferation, migration and invasion ability of lung adenocarcinoma cells.
Conclusion
Our study developed a novel prognostic signature of efferocytosis-related genes. This prognostic signature accurately predicted survival prognosis as well as treatment outcome in LUAD patients and explored the role of HAVCR1 in lung adenocarcinoma progression.
... The bulk RNA-seq data of LUAD used in this study from The Cancer Genome Atlas (TCGA) database. The single-cell RNA-seq data of LUAD collected from Bischoff etc. research (https://doi.org/10.24433/CO.0121060.v1.) [17]. We identi ed 522 samples of bulk RNA-seq data in this study, including 331 samples without lymphatic metastases (N0) and 187 samples with lymphatic metastases (N1, N2, N3). ...
Objective Lung adenocarcinoma (LUAD) is the most prevalent histological subtype of lung cancer, and lymph node metastasis serves as a significant prognostic risk factor. The identification of molecular biomarkers associated with lymph node metastasis holds paramount importance in the prevention and treatment strategies for this condition. Methods We identified the GFBP1 as the biomarker with the highest risk for lymph node metastasis by bioinformatical analysis. And we conducted the cell proliferation, invasion, and migration assays in H1975 and H1299 cells by overexpressing IGFBP1. The single-cell-RNA-sequence data indicated that IGFBP1 facilitates the progression of LUAD cells through the MAPK signaling pathway. Subsequently, western blot analysis was performed to validate these findings, while the ERK inhibitor U0126 was employed for cellular experiments and in vivo verification to elucidate the precise biological function of IGFBP1. Results IGFBP1 emerged as the most prominent biomarker for lymph node metastasis risk. Difference was shown in immunohistochemistry, univariate and multivariate Cox regression analyses. The vitro experiments confirmed that the overexpression of IGFBP1 in H1299 and H1975 cells can significantly promote proliferation, migration and invasion capacities. Western blot analysis validated that IGFBP1 overexpression substantially increased p-ERK expression levels. The use of the ERK inhibitor U0126 in subcutaneous tumor formation demonstrated that U0126 effectively suppressed both proliferation and invasion in animal model. Conclusions IGFBP1 indicates the promotion of lymph node metastasis in LUAD by facilitating tumor proliferation, invasion, and migration through modulation of the MAPK-ERK signaling pathway. Targeting this pathway exhibits significant potential for inhibiting tumor progression.
... To determine whether malignant cells exhibit a higher baseline pctMT, we analyzed pctMT levels in both tumor microenvironment (TME) and malignant cells across nine different studies: lung adenocarcinoma (LUAD), small cell lung (SCLC), renal cell (RCC), breast (BRCA), prostate, nasopharyngeal carcinoma (NPC), uveal melanoma, and primary and metastatic pancreatic cancers [4,[21][22][23][24][25][26][27][28], spanning the total of 439,507 cells across 151 patients, including 155,573 malignant cells (Fig. 1). We conducted extensive initial quality control (QC) without applying pctMT-based filtering. ...
... We analyzed nine single-cell cancer datasets [4,[21][22][23][24][25][26][27][28] across 151 patients and 439,507 cells from various cancer types, categorizing cells by their percentage of mitochondrial-encoded gene RNA counts (pctMT), with cells above 15% designated as high mitochondrial content cells (HighMT). First, we examined potential links between pctMT and common artifacts, including dissociation-induced stress. ...
... . CC-BY-NC 4.0 International license available under a (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made We performed stringent quality control on a patient level across the nine included studies: uveal melanoma [28], small cell lung cancer (SCLC) [24], lung adenocarcinoma (LUAD) [27], renal clear cell cancer (RCC) [4], breast cancer (BRCA) [25], prostate cancer [21], nasopharyngeal carcinoma [26], pancreatic [23] and metastatic pancreatic cancer [22]. We followed the standard processing guidelines described at https://www.sc-best-practices.org/preprocessing_visualization/quality_control.html, ...
Background
Single-cell transcriptomics has transformed our understanding of cellular diversity in biological systems. However, systematic noise, often introduced by low-quality cells, can obscure biological signals if not properly accounted for. Thus, one of the common quality control steps involves filtering out cells with a high percentage of mitochondrial RNA counts (pctMT), as high pctMT typically indicates cell death. Yet, commonly used filtering thresholds, primarily derived from studies on healthy tissues, may be overly stringent for malignant cells, which often naturally exhibit higher baseline mitochondrial gene expression. We analyzed public single-cell RNA-seq and spatial data to investigate if malignant cells with high pctMT are viable and functionally significant subpopulations.
Results
We analyzed nine single-cell RNA-seq datasets from uveal melanoma, breast, lung, kidney, head and neck, prostate, and pancreatic cancers, including 439,507 cells from 151 patients. Malignant cells exhibited significantly higher pctMT than nonmalignant cells without a significant increase in dissociation-induced stress signature scores. Malignant cells with high pctMT showed metabolic dysregulation, including increased xenobiotic metabolism, which is implicated in cancer therapeutic response. Our analysis of pctMT in cancer cell lines uncovered associations with resistance and sensitivity to certain classes of drugs. Additionally, we observed a link between pctMT and malignant cell transcriptional heterogeneity as well as patient clinical features.
Conclusions
This study provides a detailed exploration of the functional characteristics of malignant cells with elevated pctMT, challenging current quality control practices in single-cell RNA-seq analyses of tumors. Our findings have the potential to improve data interpretation and refine the biological conclusions of future cancer studies.
... Previous studies primarily emphasized the impact of Ku70 in cancer cells on sensitivity to radiotherapy and chemotherapy (2,13). Using the transcriptome of LUAD from four public single-cell RNA-sequencing data (14)(15)(16)(17), we unraveled the heterogeneity of XRCC6 expression across different cell types. Strikingly, XRCC6 exhibited high expression in T/NK cells of LUAD cancer ( Figure 1D). ...
Ku70, a DNA repair protein, binds to the damaged DNA ends and orchestrates the recruitment of other proteins to facilitate repair of DNA double-strand breaks. Besides its essential role in DNA repair, several studies have highlighted non-classical functions of Ku70 in cellular processes. However, its function in immune homeostasis and anti-tumor immunity remains unknown. Here, we discovered a marked association between elevated Ku70 expression and unfavorable prognosis in lung adenocarcinoma, focusing specifically on increased Ku70 levels in tumor-infiltrated Treg cells. Using a lung-colonizing tumor model of in mice with Treg-specific Ku70 deficiency, we demonstrated that deletion of Ku70 in Treg cells led to a stronger anti-tumor response and slower tumor growth due to impaired immune-suppressive capacity of Treg cells. Furthermore, we confirmed that Ku70 played a critical role in sustaining the suppressive function of human Treg cells. We found that Ku70 bound to FOXP3 and occupied FOXP3-bound genomic sites to support its transcriptional activities. These findings not only unveil a non-homologous end joining (NHEJ)-independent role of Ku70 crucial for Treg suppressive function, but also underscore the potential of targeting Ku70 as an effective strategy in cancer therapy, aiming to both restrain cancer cells and enhance pulmonary anti-tumor immunity.
... A higher score denotes greater enrichment of the corresponding functional state. We based our selection of gene sets for M1 and M2 modules on the macrophage functional gene set 15 , which encompasses established markers and signature genes characterizing these distinct functional states. ...
Sepsis-induced acute lung injury (ALI), characterized by severe hypoxemia and pulmonary leakage, remains a leading cause of mortality in intensive care units. The exacerbation of ALI during sepsis is largely attributed to uncontrolled inflammatory responses and endothelial dysfunction. Emerging evidence suggests an important role of Z-DNA binding protein 1 (ZBP1) as a sensor in innate immune to drive inflammatory signaling and cell death during infections. However, the role of ZBP1 in sepsis-induced ALI has yet to be defined. We utilized ZBP1 knockout mice and combined single-cell RNA sequencing with experimental validation to investigate ZBP1’s roles in the regulation of macrophages and lung endothelial cells during sepsis. We demonstrate that in sepsis, ZBP1 deficiency in macrophages reduces mitochondrial damage and inhibits glycolysis, thereby altering the metabolic status of macrophages. Consequently, this metabolic shift leads to a reduction in the differentiation of macrophages into pro-inflammatory states and decreases macrophage pyroptosis triggered by activation of the NLRP3 inflammasome. These changes significantly weaken the inflammatory signaling pathways between macrophages and endothelial cells and alleviate endothelial dysfunction and cellular damage. These findings reveal important roles for ZBP1 in mediating multiple pathological processes involved in sepsis-induced ALI by modulating the functional states of macrophages and endothelial cells, thereby highlighting its potential as a promising therapeutic target.
... 56 Additionally, proinflammatory monocyte-derived macrophages become significantly infiltrated in patients with early-stage lung cancer. 57 Sinjab et al. found that CD8+ T cells and the inflammatory signature were significantly reduced in early-stage lung cancer tissues and adjacent normal tissues. 58 In this study, we found that individuals in the higher LCDS group had significantly fewer CD4+ T cells and CD8+ T cells and significantly more macrophages than those in the lower LCDS group. ...
Lung cancer continues to be the leading cause of cancer‐related mortality worldwide. Early detection and a comprehensive understanding of tumor‐immune interactions are crucial for improving patient outcomes. This study aimed to develop a novel biomarker panel utilizing peripheral blood transcriptomics and machine learning algorithms for early lung cancer diagnosis, while simultaneously providing insights into tumor‐immune crosstalk mechanisms. Leveraging a training cohort (GSE135304), we employed multiple machine learning algorithms to formulate a Lung Cancer Diagnostic Score (LCDS) based on peripheral blood transcriptomic features. The LCDS model's performance was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC) in multiple validation cohorts (GSE42834, GSE157086, and an in‐house dataset). Peripheral blood samples were obtained from 20 lung cancer patients and 10 healthy control subjects, representing an in‐house cohort recruited at the Sixth People's Hospital of Chengdu. We employed advanced bioinformatics techniques to explore tumor‐immune interactions through comprehensive immune infiltration and pathway enrichment analyses. Initial screening identified 844 differentially expressed genes, which were subsequently refined to 87 genes using the Boruta feature selection algorithm. The random forest (RF) algorithm demonstrated the highest accuracy in constructing the LCDS model, yielding a mean AUC of 0.938. Lower LCDS values were significantly associated with elevated immune scores and increased CD4+ and CD8+ T‐cell infiltration, indicative of enhanced antitumor‐immune responses. Higher LCDS scores correlated with activation of hypoxia, peroxisome proliferator‐activated receptor (PPAR), and Toll‐like receptor (TLR) signaling pathways, as well as reduced DNA damage repair pathway scores. Our study presents a novel, machine learning‐derived peripheral blood transcriptomic biomarker panel with potential applications in early lung cancer diagnosis. The LCDS model not only demonstrates high accuracy in distinguishing lung cancer patients from healthy individuals but also offers valuable insights into tumor‐immune interactions and underlying cancer biology. This approach may facilitate early lung cancer detection and contribute to a deeper understanding of the molecular and cellular mechanisms underlying tumor‐immune crosstalk. Furthermore, our findings on the relationship between LCDS and immune infiltration patterns may have implications for future research on therapeutic strategies targeting the immune system in lung cancer.
... The scRNA-seq data of four histologic patterns was downloaded from a prior study (14), which includes one lepidic sample, one papillary sample, two acinar samples and two solid samples. ...
... To further elucidate the molecular heterogeneity of four histologic patterns, we obtained scRNA-seq data of six LUAD samples (one lepidic, one papillary, two acinar and two solid) from a prior study (14). After quality control, 35,107 cells remained for subsequent analysis. ...
Introduction
Lung adenocarcinoma, a prevalent and lethal malignancy globally, is characterized by significant tumor heterogeneity and a complex tumor immune microenvironment during its histologic pattern progression. Understanding the intricate interplay between tumor and immune cells is of paramount importance as it could potentially pave the way for the development of effective therapeutic strategies for lung adenocarcinoma.
Methods
In this study, we run comparative analysis of the single-cell transcriptomic data derived from tumor tissues exhibiting four distinct histologic patterns, lepidic, papillary, acinar and solid, in lung adenocarcinoma. Furthermore, we conducted immunofluorescence assay and spatial transcriptomic sequencing to validated the spatial co-localization of typical co-inhibitory factors.
Results and Discussion
Our analysis unveiled several co-inhibitory receptor-ligand interactions, including PD1-PDL1, PVR-TIGIT and TIGIT-NECTIN2, that potentially exert a pivotal role in recruiting immunosuppressive cells such as M2 macrophages and Tregs into LUAD tumor, thereby establishing immunosuppressive microenvironment and inducing T cells to exhaustion state. Furthermore, The expression level of these co-inhibitory factors, such as NECTIN2 and PVR, were strongly correlated with low immune infiltration, unfavorable patient clinical outcomes and limited efficacy of immunotherapy. We believe this study provides valuable insights into the heterogeneity of molecular, cellular interactions leading to immunosuppressive microenvironment during the histological progression of lung adenocarcinoma. The findings could facilitate the development of novel immunotherapy for lung cancer.
