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A prognostic model based on
autophagy-and senescence-
related genes for gastric cancer:
implications for immunotherapy
and personalized treatment
Shuming Chen
†
, Xiaoxi Han
†
, Yangyang Lu, Shasha Wang,
Yuanyuan Fang, Chuanyu Leng, Xueying Sun, Xin Li,
Wensheng Qiu*and Weiwei Qi*
Department of Oncology, The Affiliated Hospital of Qingdao University, Qingdao, China
Background: The process of human aging is accompanied by an increased
susceptibility to various cancers, including gastric cancer. This heightened
susceptibility is linked to the shared molecular characteristics between aging
and tumorigenesis. Autophagy is considered a critical mediator connecting aging
and cancer, exerting a dynamic regulatory effect in conjunction with cellular
senescence during tumor progression. In this study, a combined analysis of
autophagy- and senescence-related genes was employed to comprehensively
capture tumor heterogeneity.
Methods: The gene expression profiles and clinical data for GC samples were
acquired from TCGA and GEO databases. Differentially expressed autophagy-
and senescence-related genes (DEASRGs) were identified between tumor and
normal tissues. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and
Genomes (KEGG) pathway analyses were carried out to provide insights into
biological significance. A prognostic signature was established using univariate
Cox and LASSO regression analyses. Furthermore, consensus clustering analyses
and nomograms were employed for survival prediction. TME and drug sensitivity
analyses were conducted to compare differences between the groups. To
predict immunotherapy efficacy, the correlations between risk score and
immune checkpoints, MSI, TMB, and TIDE scores were investigated.
Results: A fourteen-gene prognostic signature with superior accuracy was
constructed. GC patients were stratified into three distinct clusters, each
exhibiting significant variations in their prognosis and immune
microenvironments. Drug sensitivity analysis revealed that the low-risk group
demonstrated greater responsiveness to several commonly used
chemotherapeutic agents for gastric cancer, including oxaliplatin. TME analysis
further indicated that the high-risk group exhibited increased immune cell
infiltration, upregulated expression of ICs, and a higher stromal score,
suggesting a greater capacity for immune evasion. In contrast, the low-risk
group was characterized by a higher proportion of microsatellite instability-
high (MSI-H) cases, an elevated TIDE score, and a greater TMB, indicating a
higher likelihood of benefiting from immunotherapy. In addition, Single-cell
sequencing demonstrated that TXNIP was expressed in epithelial cells. Cellular
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OPEN ACCESS
EDITED BY
Zhaofeng Liang,
Jiangsu University, China
REVIEWED BY
Jian-Rong Sun,
Beijing University of Chinese Medicine, China
You Guo,
First Affiliated Hospital of Gannan Medical
University, China
Shuzhao Chen,
First Affiliated Hospital of Shantou University
Medical College, China
*CORRESPONDENCE
Wensheng Qiu
wsqiuqdfy@qdu.edu.cn
Weiwei Qi
qwwqdfy@126.com
†
These authors have contributed
equally to this work and share
first authorship
RECEIVED 11 October 2024
ACCEPTED 03 March 2025
PUBLISHED 20 March 2025
CITATION
Chen S, Han X, Lu Y, Wang S, Fang Y, Leng C,
Sun X, Li X, Qiu W and Qi W (2025) A
prognostic model based on autophagy-and
senescence-related genes for gastric cancer:
implications for immunotherapy and
personalized treatment.
Front. Oncol. 15:1509771.
doi: 10.3389/fonc.2025.1509771
COPYRIGHT
© 2025 Chen, Han, Lu, Wang, Fang, Leng, Sun,
Li, Qiu and Qi. This is an open-access article
distributed under the terms of the Creative
Commons Attribution License (CC BY). The
use, distribution or reproduction in other
forums is permitted, provided the original
author(s) and the copyright owner(s) are
credited and that the original publication in
this journal is cited, in accordance with
accepted academic practice. No use,
distribution or reproduction is permitted
which does not comply with these terms.
TYPE Original Research
PUBLISHED 20 March 2025
DOI 10.3389/fonc.2025.1509771
experiments preliminarily verified that TXNIP could promote the proliferation and
migration of gastric cancer cells.
Conclusion: This study presents a robust predictive model for GC prognosis
using autophagy- and senescence-related genes, demonstrating its ability to
predict immune infiltration, immunotherapy effectiveness, and guide
personalized treatment.
KEYWORDS
autophagy, senescence, immunotherapy, TXNIP, gastric cancer
1 Introduction
Globally, gastric cancer remains the second leading cause of
cancer-related death and the fourth most common cancer (1).
Despite a high incidence and mortality rate, the prevalence of GC
varies significantly across different regions and individuals (1,2),
indicating its substantial heterogeneity (3). While recent
advancements in GC diagnosis and treatment have been
considerable, the traditional prognostic system based on tumor
stage and histological grade is increasingly inadequate for capturing
the observed clinical heterogeneity (4). In the era of precision
medicine, developing novel diagnostic and prognostic models
based on patients’molecular signatures and clinical characteristics
holds significant promise.
Cancer, including gastric cancer, is well acknowledged as a
disease associated with ageing. As individuals age, chronic
inflammation, and the accumulation of senescent cells collectively
contribute to an environment conducive to cancer formation (5). At
the cellular level, senescence is characterized by the irreversible
arrest of cell proliferation in response to cellular stress (6). During
the initial phases of carcinogenesis, cellular senescence is frequently
regarded as a protective mechanism that prevents the proliferation
of potentially malignant cells. Senescent cells, however, secrete the
senescence-associated secretory phenotype (SASP), which
comprises a variety of pro-inflammatory cytokines and
chemokines. These secretory components enhance the malignant
characteristics of tumor cells and accelerate their immune evasion
mechanisms (7,8). Therefore, comprehending the dual function of
senescence and the complex interactions between senescent cells
and tumor cells is essential for the formulation of innovative anti-
cancer strategies.
Autophagy is recognized as a critical link between aging and
cancer (9). It is a highly conserved cellular catabolic process that
facilitates the recycling of cellular components through lysosomal
degradation (10). During the initial phases of tumorigenesis,
autophagy acts as a tumor-suppressive mechanism by eliminating
damaged organelles, preserving genomic stability, and promoting
cellular senescence (11). In contrast, in advanced tumors,
autophagy aids in the survival of senescent cells through
metabolic reprogramming. Concurrently, the SASP is activated,
releasing pro-inflammatory factors such as IL-6 and TGF-b, which
modify the tumor microenvironment (TME), promoting immune
evasion and facilitating metastasis (8). However, senescence-related
signals can also influence autophagic activity through a feedback
loop. Consequently, autophagy and cellular senescence engage in
a dynamic, bidirectional regulatory relationship during
tumor progression.
In summary, the present study sought to identify a gene signature
incorporating autophagy and senescence factors to accurately predict
the prognosis of GC. A fourteen-gene signature was constructed
using univariate Cox regression and LASSO regression. Additionally,
the predictive performance of the model was further enhanced
through the establishment of a nomogram. A detailed analysis was
performed on gastric cancer subtypes, immune cell infiltration, the
distribution of ICs, gene mutation differences, and drug sensitivity
differences in the TCGA cohort. In addition, cellular function
experiments preliminarily verified the role of TXNIP in gastric
cancer. Collectively, this research has the potential to uncover novel
characteristic genes that serve as reliable prognostic biomarkers for
the personalized treatment of GC patients.
2 Materials and methods
2.1 Data sources
The raw data was downloaded from TCGA and GEO databases.
Duplicate samples and those lacking essential clinical characteristics
or survival information were removed. The training cohort
consisted of 410 STAD samples and 10 gastric normal samples
from TCGA. The validation cohort was selected to be the GSE66229
dataset (12), which was verified using the GPL570 platform. To
eliminate discrepancies caused by batch effects and ensure research
integrity and reliability, COMBAT was used when merging GEO
data. In the survival analyses, patients were included based on the
availability of survival status and survival time, with a minimum
survival time of 30 days. The list of genes associated with autophagy
and senescence was obtained from GeneCards datasets (13).
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2.2 Analysis of differentially
expressed genes
To identify common genes, Venny 2.1.0 was employed. DEGs
were detected using the “limma”package in R. A cutoff of |logFC| ≥
1 and a FDR < 0.05 were applied to determine significant DEGs. A
volcano plot was drawn by the “pheatmap”package in R to visually
represent the DEGs.
2.3 Functional annotation and
enrichment analyses
The “clusterProfiler”package was employed to conduct GO and
KEGG analyses. These analyses identified the biological functions
and pathways associated with the DEASRGs, providing insights
into their biological significance. Furthermore, the functional
profiles of the different risk groups were assessed using Gene Set
Enrichment Analysis (GSEA) to detect any relevant changes.
2.4 Consensus clustering to identify
DEASRG clusters
DEASRGs were utilized to conduct consensus cluster analysis
using the “ConsensusClusterPlus”package in R. Employing optimal
k-means clustering, STAD patients were categorized into three
distinct groups. Principal component analysis (PCA) was
implemented to differentiate these clusters. We used the
“estimate”package to determine particular scores in tumor tissue
for assessing the extent of infiltration by stroma and immune cells.
2.5 Construction and verification of
autophagy- and senescence-related risk
score signature
Univariate Cox analysis was employed to screen core prognostic
DEASRGs and we further assessed their copy number variation
(CNV) alterations. Subsequently, LASSO regression was utilized to
select genes for constructing the prognostic signature. Through the
calculation of the following algorithm, we obtained corresponding
risk score for every single patient.
Riskscore =o
n
i=1
(Coefi ∗Expi)
The variables n, Coefi, and Expi represent the signature gene
number, the risk weighting coefficient index, and the expression
level of the signature gene, respectively.
The median risk score in the TCGA cohort was used to
distinguish the high-risk group from the low-risk group. KM
survival curves were generated to compare prognosis between the
groups. We constructed a ROC curve to compare the Area Under
the Curve (AUC) value of the risk score and several clinical
markers. Risk curves and survival status analyses were performed
to evaluate the efficiency of model in high- and low-risk groups.
Additionally, we conducted PCA analysis to visualize the
distribution of patients.
2.6 The modification of predictive
signature-nomogram
To enhance predictive power the signature, a nomogram was
established incorporating risk scores and clinical features. Variables
within the nomogram, including age, gender, M stage, T stage, N
stage, clinical stage, and risk score, were assigned points based on
their relative prognostic contributions. Individual patient scores
were calculated by summing these points. The calibration curve
allowed us to evaluate the predictive capability of the nomogram
across various survival periods. Decision curve analysis (DCA) was
used to evaluate the clinical benefits brought by the model.
2.7 Stratified analysis of
clinicopathological features
To investigate the association between the novel signature and
various clinical factors, subgroup analyses were conducted within
the TCGA cohort. Available clinicopathological features were
extracted for further analysis. Moreover, survival curves were also
plotted across distinct clinical subgroups.
2.8 Screening of sensitive drugs
Drug sensitivity assessments was performed using data obtained
from the Genomics of Drug Sensitivity in Cancer (GDSC) public
database (14). The “oncoPredict”package was employed to
calculate the half-maximal inhibitory concentration (IC
50
) values
for therapeutic drug.
2.9 Immune cell infiltration and
immunotherapy response
The CIBERSORT algorithm enabled us to accurately assess the
composition of infiltrating immune cell types within patient’s
tumor sample (15). To assess potential treatment response based
on proportions of immune cell, we examined the expression of
immune checkpoint genes within the two subgroups. Furthermore,
we employed the tumor immune dysfunction and exclusion (TIDE)
algorithm to obtain TIDE scores, dysfunction scores, and exclusion
scores for each tumor sample. Tumor purity was evaluated using the
ESTIMATE algorithm. The stromal score represents the proportion
of stromal cells, while the immune score reflects the balance of
immune cell populations. Finally, we visualized the distribution
differences between risk groups and microsatellite status (MSI)
states using boxplots.
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2.10 Mutation analysis
We downloaded somatic mutation data of training group via
the UCSC Xena browser. This data was then visualized using
waterfall charts from the “maftools”Rpackage.Next,we
computed the tumor mutational burden (TMB) score for every
single sample and investigated its correlation with risk score.
2.11 Visualization of protein-protein
interaction networks
Associations between these model genes were constructed by
STRING database. A PPI network was built using interaction scores
higher than 0.15 and P < 0.05 as the significant threshold. Genes
with interaction scores greater than 0.15 were selected to construct
PPI network. Cytohubba plugin was utilized to estimate the MCC
score, Stress score, Degree score and Closeness score. In this
analysis, genes with the same score were considered to be
sequenced equally.
2.12 Single-cell data analysis
The raw expression profiling of GSE112302 was retrieved from
the GEO dataset. The data pertaining to normal tissue were omitted,
while the data corresponding to tumor tissue were utilized for
subsequent analysis. The employment of the “Seurat”program was
necessary for performing data quality control, PCA, and t-
Distributed Stochastic Neighbor Embedding (t-SNE) visualization
are all reliant on the utilization of the “Seurat”package. The
“SingleR”package was vital for annotating the cell types in
each cluster.
2.13 Cell culture
The gastric cancer cell lines (SGC7901, AGS, HGC27) and the
human normal gastric mucosal cell line GES-1 were procured from
Pricella Life Science & Technology Co., Ltd. These cell lines were
cultured in RPMI-1640 medium (Pricella Life Science &
Technology Co., Ltd) supplemented with 10% Fetal Bovine Serum
(FBS, from Shanghai Life-iLab Biotech Co., Ltd.), and containing
penicillin and streptomycin.
2.14 Cell viability assay
The growth of AGS and HGC27 cells was assessed using the
MTT assay. Cells were seeded at a stable density in a 24-well plate and
incubated. Subsequently, 0.5 mg/mL MTT solution (M158055,
Aladdin) was added to each well at 24, 72, and 120 hours,
respectively. After 3-4 hours of incubation at 37°C, the supernatant
was removed, and dimethyl sulfoxide was added to dissolve the
formazan precipitate. The absorbance of the resulting solution was
measured at 490 nm using a microplate spectrophotometer.
2.15 Colony formation assay
To assess the proliferation of AGS and HGC27 cells, a cell
cloning assay was performed. Cells were seeded into a six-well plate
and incubated under standard conditions for 10-14 days.
Subsequently, the supernatant was removed, and cells were fixed
with 4% paraformaldehyde (30072418, China National
Pharmaceutical Group Chemical Reagent Co., Ltd). Following
fixation, cells were stained with crystal violet, and the number of
colonies formed was manually counted.
2.16 Migration assay
To assess the migration of AGS and HGC27 cells, a cell scratch
assay was performed. Cells were seeded in a 24-well plate and
incubated for 2-3 days until reaching approximately 90%
confluency. A wound was created on the cell monolayer using a
200 μL pipette tip. Cell migration into the wound area was observed
and evaluated after 48 hours.
2.17 Western blot
Use cell lysis buffer (containing 20mM Tris (pH 7.5), 150mM
NaCl, 1% Triton X-100, and other components) to lyse cell samples.
Protein concentration in the collected lysates was determined using
the BCA quantification method. Subsequently, 25-40 μg of protein
was separated on an SDS-PAGE gel, followed by transfer to a
polyvinylidene fluoride (PVDF) membrane. The membrane was
incubated overnight at 4°C with primary antibodies against TXNIP
and GAPDH. Afterward, the membrane was incubated with a
secondary antibody at room temperature for 2 hours. Protein
bands were visualized using ECL chemiluminescence.
2.18 Statistical analysis
The work in this study was primarily performed by R software
(version 4.3.1). Univariate and multivariate Cox regression analysis
were employed to evaluate the independent prognostic significance
of variables. For survival analysis, the Kaplan-Meier method was
employed to plot the survival curves of different risk groups, and the
Log-rank test was utilized to evaluate the significance of survival
differences between groups. Regarding the comparison of
continuous data, the independent Student’s t-test was carried out
for normally distributed data between two groups, while the
Wilcoxon test was applied for non - normally distributed
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continuous variables. The Spearman’s correlation coefficient was
computed to evaluate the associations between two variables.
Statistical significance was determined by setting the threshold at
p < 0.05 for all analyses.
3 Results
3.1 Identification of autophagy- and
senescence-related prognostic DEGs in
STAD and functional enrichment analysis
Firstly, a general workflow was constructed to outline the entire
analysis process (Figure 1). Supplementary table 1 displayed a
fundamental table containing baseline data of certain participants
in this study. These participants have available survival status and
survival time (≥30 days), and their T, N, M and clinical stage are
clearly defined. As illustrated in the Venn plot (Figure 2A), 685
overlapping genes were obtained by intersecting 2269 autophagy-
related genes and 4136 senescence-related genes from the
GeneCards database with TCGA-STAD genes. Through
differential expression analysis of these overlapping genes, 161
autophagy- and senescence-related DEGs (DEASRGs) were
filtered comparing normal and tumor groups (Figure 2B). To
provide a clearer understanding of the functional properties of
DEASRGs in STAD, GO enrichment analysis was performed. The
GO terms for biological processes and molecular functions revealed
that the DEASRGs were mainly involved in the regulation of mitotic
cell cycle phase transition, regulation of response to DNA damage
stimulus, and ubiquitin-like protein ligase binding (Figure 2C).
Additionally, KEGG pathway analysis was conducted to investigate
the possible mechanistic pathways associated with these DEGs,
including the cell cycle, cellular senescence, and PI3K-Akt signaling
pathway (Figure 2D).
3.2 Construction and validation of the
autophagy- and senescence-
related signature
Through univariate Cox regression analysis, we identified 29
genes from the initial 161 DEASRGs as potential prognostic factors
for STAD patients (Figure 2E). We investigated CNV alterations in
these 29 genes, revealing predominantly copy number gains.
However,COL3A1,HMGB2,DNMT1,CASP2,EZH2,DCN,
TP53, LMNB2, TTF2, LMNB1, TNFRSF10B and UHRF1
exhibited a greater frequency of CNV losses (Figure 2F). The
chromosomal location of CNV alterations for these 29 genes is
depicted in Figure 2G. Finally, LASSO regression analysis further
reduced the gene set to 14 (Figures 3A,B).
After calculating risk scores, patients were categorized into
high- and low-risk groups using the median risk score as a
threshold. KM survival analysis demonstrated a statistically better
OS for the individuals classified as low risk (Figures 3C,D). The risk
FIGURE 1
Study workflow.
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score exhibited a negative correlation with patient survival,
indicating that a higher risk score predicts lower overall survival
and higher mortality (Figures 3E,F).Similaranalyseswere
conducted on the validation cohort (Figures 3G,H). Figures 3I,J
presents gene expression profiles of the prognostic model genes as
heatmaps. Our model demonstrated superior prognostic accuracy
for gastric cancer patients compared to traditional clinical
indicators in TCGA cohort (Figure 3K). Time-dependent ROC
curves with AUCs of 0.645, 0.673, and 0.721 at 1, 3, and 5 years
further validated the model’sefficiency (Figure 3L). In the
GSE66229 dataset, our model outperformed most clinical
indicators (Figure 3M), with AUCs of 0.627, 0.648, and 0.633 at
1, 3, and 5 years (Figure 3N). After that, we employed GSEA
method to investigate disparities in biological functionality between
patients classified as high and low risk (Supplementary Figure S1).
Interestingly, the findings indicated that the biological functions of
the high-risk group were intricately linked to the composition and
specific activities of the extracellular matrix, while the low-risk
group was primarily enriched in cell cycle, certain activities related
to DNA and mitochondria. Univariate and multivariate Cox
analyses confirmed the risk score as a significantly independent
predictor of gastric cancer in the TCGA-STAD and GSE66229
datasets (Supplementary Figures S2A-D). Next, we examined the
association between risk scores and clinical factors. The findings
suggested that the younger populations (<65) had considerably
higher risk scores (Supplementary Figure S2E). In addition, the
differences in risk scores among gender, T stage, N stage, M stage,
and clinical stage were not statistically significant (Supplementary
Figures S2F-J). Moreover, Supplementary Figure S3 illustrated the
difference in survival rates among high- and low-risk patients across
several clinical subgroups.
3.3 Identification of three subtypes by
consensus clustering
Based on the expression of DEASRGs, we employed the
Consensus Cluster algorithm to identify three distinct patient
subtypes within the TCGA-STAD cohort, designated as clusters 1,
2, and 3 (Figure 4A). A PCA plot visualized the transcriptional
differences among the three clusters in a three-dimensional space
(Figure 4B). Survival analyses revealed a significant survival
advantage for patients in cluster 1 (Figure 4C). A Sankey diagram
illustrated patient transitions among gene clusters, risk groups, and
survival status, demonstrating higher survival rates in cluster 1 and
low-risk group (Figure 4D). The risk score in cluster 1 exhibited a
statistically significant decrease compared to the other two clusters
(Figure 4E). We subsequently assessed inter-cluster variations in the
immunological microenvironment (Figures 4F-H). Notably, cluster
2 exhibited considerably higher expression levels of most ICs than
the other clusters (Figure 4I), suggesting a suboptimal response to
immunotherapy. A heatmap comparing immune cell infiltration
patterns across clusters using algorithms from multiple platforms is
presented in Figure 4J. C2 exhibited the highest overall immune cell
FIGURE 2
Identification of core prognostic genes and enrichment analysis. (A) Venn diagram illustrating the intersection of 685 genes associated with
autophagy, senescence, and STAD. (B) Volcano plot of DEASRGs based on intersected genes. (C) GO functional annotation of DEASRGs. (D) KEGG
enrichment analysis of DEASRGs. (E) Univariate Cox regression analysis identifying 29 genes. (F) Frequencies of CNV gain and loss among 29
prognostic genes. (G) Circular plots visualizing chromosome distributions of core prognostic genes.
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infiltration, consistent with the findings from the ESTIMATE
algorithm (Figure 4G). However, elevated expression of M2
macrophages, myeloid-derived suppressor cells, and tumor-
associated fibroblasts was detected in C2 across multiple
platforms, suggesting that this subgroup resides in an
immunosuppressive microenvironment.
3.4 Establishing a predictive nomogram
To enhance the clinical applicability and predictive accuracy of
our signature, a nomogram was constructed incorporating risk
score and other clinical indicators (Figures 5A,E). Calibration
curves exhibited robust concordance between the expected and
observed survival probability at 1, 3, and 5 years (Figures 5B,F),
indicating high nomogram accuracy and reliability. DCA curve
revealed that the nomogram exhibited larger net benefit compared
to the nomogram without prognostic signature (nomogram
excluding ASRG) and other factors (Figures 5C,G). ROC curve
demonstrated superior predictive accuracy of the nomogram
compared to other factors, such as nomogram excluding ASRG,
risk score, gender, age, and TNM stage, with AUC values of 0.691 in
the training set and 0.826 in the validation set (Figures 5D,H). The
above results indicated that the incorporation of prognostic
signature contributed to enhancing the superiority of
the nomogram.
3.5 Relationship between ASRG signature
and drug sensitivity
Resistance to therapeutic medications is a common challenge in
cancer therapy, often leading to poor drug efficacy and worse
clinical outcomes in gastric cancer. To enhance therapeutic
benefits, we figured out whether the ASRG signature could
accurately predict drug sensitivity in the training cohort. By
FIGURE 3
Development and verification the ASRGs signature. (A) LASSO regression model selection curve with log(l) on the x-axis and partial likelihood
deviance on the y-axis. (B) Coefficients of the LASSO regression model. (C, D) KM survival curves of OS. (E, G) Survival curves of patients with GC. (F,
H) Distribution of survival status based on risk score. (I, J) Heatmaps of gene expression for the prognostic model genes. (K, M) Comparison of ROC
curves. (L, N) ROC curve using temporal information (time-dependent ROC curves).
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utilizing the “oncoPredict”package, we estimated IC
50
values for
198 drugs in all patients. The high-risk patients showed markedly
elevated IC
50
values for Oxaliplatin, Paclitaxel, Cisplatin, Docetaxel,
5-Fluorouracil, and Afatinib, which were positively correlated with
risk scores. This suggested that individuals with lower risk scores
might exhibit a more favorable response to therapies containing
these medications (Figures 6A-F). Gemcitabine, Camptothecin,
KRAS (G12C) Inhibitor, Dabrafenib, and Sorafenib also exhibited
increased IC
50
values in the high-risk group (Figure 6G).
Conversely, SB505124, JQ1, IGF1R, JAK, and NU7441 had higher
IC
50
values in the low-risk patients, implying a poorer response to
these drugs (Figure 6H).
FIGURE 4
Association of the prognostic signature with gene clusters and immunological features. (A) The heat map display of consensus clustering is
categorized into three cluster (C1 = 277; C2 = 63; C3 = 26). (B) PCA showing the perfect separation of C1, C2 and C3. (C) KM survival curves with
three distinct clusters. (D) A Sankey diagram illustrating the link between gene clusters, risk group, and survival status. (E) Variations in risk score
among the three gene subtypes. (F-H) ESTIMATE algorithm results for three gene clusters. (I) Expression of immune checkpoints related genes. (J)
The heat map depicting variations in immune cell infiltration as determined using TIMER, CIERSORT, quanTIseq, MCPcounter, xCell, and
EPIC algorithms.
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3.6 Immunological features of
the signature
The TME, composed of diverse immune cells, cancer-associated
fibroblasts (CAFs), endocrine cells, extracellular matrix (ECM)
components, and other elements, significantly influences
tumorigenesis. Disrupting the tumor immune tolerance feedback
loop by targeting the TME is a promising strategy to enhance cancer
therapy (16). To examine the correlation between our signature and
immune infiltration, we employed the CIBERSORT algorithm to
determine the composition of tumor-infiltrating immune cells in
STAD (Figure 7A). Comparative analysis of immune cell
distribution between high- and low-risk groups revealed
significant differences. Plasma cells were notably reduced in the
FIGURE 5
Establishment and validation of the nomogram. (A, E) A nomogram was established to forecast the 1-year, 3-year, and 5-year OS. (B, F) Calibration
plots illustrating the agreement of predicted survival rates compared to the actual observed survival rates. (C, G) A DCA was carried out to compare
the net benefits of the nomogram incorporating the prognostic signature, the nomogram excluding the prognostic signature, and other factors. (D,
H) The AUC was employed to compare the predictive accuracy of the nomogram with other prognostic markers.
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high-risk patients, whereas naïve B cells, activated NK cells
monocytes, resting dendritic cells, and resting mast cells were
increased (Figure 7B). Moreover, elevated expression of multiple
ICs in high-risk patients suggested increased susceptibility to
immune evasion (Figure 7C).
Subsequently, we examined the correlation between microsatellite
status and risk score. Figure 7D indicated that patients with MSI-H,
known for increased immunotherapy sensitivity, exhibited lower risk
scores. As expected, the MSI-H prevalence was considerably higher in
low-risk patients (30%) (Figure 7E). To predict immune system evasion,
FIGURE 6
Drug sensitivity in the TCGA cohort. (A-F) The IC
50
of Oxaliplatin (A), Paclitaxel (B), Cisplatin (C), Docetaxel (D), 5-Fluorouracil (E), Afatinib (F) were
considerably lower in the low-risk group, and there was a favorable correlation between the IC
50
values of these drugs and the risk score. The
difference in drug sensitivity showing the IC
50
of Gemcitabine, Camptothecin, KRAS (G12C) Inhibitor, Dabrafenib, Sorafenib drugs were significantly
higher in high-risk groups (G), while the IC
50
of SB505124, JQ1, IGF1R, JAK, NU7441 were significantly higher in low-risk groups (H).
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we performed TIDE analysis. As shown in Figure 7F, individuals at high
risk expressed elevated TIDE scores, indicating a greater risk of
immunological escape and reduced immunotherapy responsiveness.
The ESTIMATE algorithm revealed significantly elevated stromal,
immune, and estimate scores in the high-risk group, positively
correlated with the risk score. Conversely, a negative correlation
between tumor purity and the risk score was observed (Figures 7G,H).
3.7 Correlation of risk model with TMB
Human tumors exhibit varying levels of somatic mutations
collectively termed TMB, which has been linked to immunotherapy
efficacy (17,18). To investigate the association between risk score
and gene mutation, we analyzed simple nucleotide variation data
from TCGA. Figures 8A,Bpresent the top 20 genes with the highest
frequency of mutations in two groups. TTN, TP53, MUC16,
ARID1A, and LRP1B emerged as the five most prominent
mutated genes. TMB analysis revealed an inverse relationship
between TMB and risk score (Figures 8C,D). Spearman
correlation analysis further differentiated clusters based on TMB
and risk score (Figure 8E). Significantly, the high TMB had superior
survival rates compared to the group with a low TMB (Figure 8F).
An integrated survival analysis, including both TMB and risk
groups (Figure 8G), demonstrated that GC patients with high
TMB and low risk scores presented the most favorable outcome.
FIGURE 7
Immune microenvironment analysis and prediction for immunotherapy. (A) Heat map of immune cell distribution in the immune microenvironment
of GC patients in training cohort. (B) Disparities in the allocation of various immune cells within the TME. (C) Differences of ICs expression. (D) The
distribution of risk scores under three different microsatellite states. (E) The proportion of MSS, MSI-L, and MSI-H in different risk groups. (F)
Comparative analysis of the TIDE score in both low- and high-risk populations. (G) ESTIMATE algorithm results for different risk groups. (H)
Correlation analysis between four indicators of the ESTIMATE algorithm and the risk score. (*, **, ***, and ns represent p < 0.05, p < 0.01, p < 0.001,
and “not statistically”, respectively.).
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3.8 PPI network
To investigate the interaction of model genes, we constructed a
PPI network. Next, the results of the STRING database were
exported into Cytoscape for further analysis to obtain the hub
gene (Supplementary Figure S4A). Then, by intersecting the top 7
hub genes determined by MCC, Stress, Degree and Closeness
algorithms in cytohub plug-in, we identified6coregenes
(Supplementary Figure S4B-F). Moreover, the impact of IRAK1,
SERPINE1, KIT, CXCL1, CD36, TXNIP on prognosis of STAD was
analyzed by GEPIA online tools (Supplementary Figure S5). The
findings indicated that lower expression levels of SERPINE1, KIT,
CD36 and TXNIP were associated with longer OS, while differences
in IRAK1 and CXCL1 expression levels did not have a statistically
significant effect on prognosis.
3.9 Single-cell analysis of the model genes
We selected scRNA-seq data from GSE112302 dataset for
further analysis of the model gene. To ensure the reliability of the
single-cell data, we applied a filter to exclude genes expressed in
fewer than three cells and cells expressing fewer than 50 genes
(Supplementary Figure S6A). The correlation of sequencing depth
with mitochondrial content and gene number was shown in the
Supplementary Figure S6B. Subsequently, the data was standardized
and the top 1,500 genes with significant intercellular coefficients of
variation were extracted for further analysis (Supplementary Figure
S6C). We then employed PCA analysis to reduce the dimensionality
of the data (Supplementary Figure S7A). Supplementary Figures
S7B-C illustrates the characteristic genes of the initial four principal
components in the PCA analysis. We chose the initial 14 PCA
FIGURE 8
Assessment of TMB and genetic mutation profile. (A, B) The waterfall plot illustrated difference of somatic mutation characteristics. (C) The TMB
difference between the two groups. (D, E) The correlation map demonstrates the associations between various risk groups and gene clusters with
TMB. (F) Comparison of the survival probability. (G) An examination of survival rates was conducted on three groups of patients, combining their risk
group and TMB group.
Chen et al. 10.3389/fonc.2025.1509771
Frontiers in Oncology frontiersin.org12
components with a significance level of p<0.05 for subsequent
analysis (Supplementary Figure S7D). The t-Distributed
Stochastic Neighbor Embedding (tSNE) algorithm was applied to
classify the cells into six distinct clusters, illustrating the global
distribution of the single-cell transcriptomes (Supplementary
Figure S8A). Each cluster represents a distinct cell population.
Supplementary Figure S8B depicted, in the form of a heat map,
the top 10 genes exhibiting the most substantial variances within
each cluster. The distribution and expression of key model genes are
visualized in Supplementary Figures S8C,D.Furthermore,
Supplementary Figure S8E visualizes the expression of prognostic
genes identified through the PPI network, including SERPINE1,
KIT, CD36, and TXNIP, across the clusters: IRAK1 was
significantly expressed in Clusters 4 and 5; SERPINE1 and KIT
were not highly expressed in any of the clusters; CXCL1 showed
high expression in Clusters 1 and 5; CD36 was predominantly
expressed in Cluster 5; and TXNIP was most highly expressed in
Clusters 0 and 2. Cell type annotation (Supplementary Figure S8F)
reveals that the six clusters can be broadly classified into two major
cell types: Clusters 0-4 primarily represent epithelial cells, while
Cluster 5 is primarily composed of monocytes. Based on these
findings, we conclude that IRAK1 and CXCL1 are expressed in both
epithelial cells and monocytes, CD36 is predominantly expressed in
monocytes, and TXNIP is mainly expressed in epithelial cells.
3.10 Knockdown of TXNIP inhibits the
growth of gastric cancer cells
We initially examined TXNIP protein expression levels in
gastric cancer cell lines SGC7901, AGS, and HGC27, as well as in
normal human gastric mucosal epithelial cells GES-1. TXNIP
protein expression was significantly higher in AGS and HGC27
cells compared to GES-1 cells (Figure 9A). To investigate the
biological role of TXNIP in gastric cancer, we employed lentiviral
transduction to knock down TXNIP gene expression in AGS and
FIGURE 9
Knockdown of TXNIP inhibits the growth of gastric cancer cells. (A) Western blot revealed TXNIP expression levels in GES-1, SGC7901, AGS, HGC27
cell lines (n=3). (B) TXNIP protein expression was evaluated by western blot in AGS and HGC27 cells silenced by TXNIP-sh1, TXNIP-sh2, and TXNIP-
sh3 (n=3). (C, D) Cell viability of AGS and HGC27 cell lines treated with lentivirus (shControl, TXNIP-sh3) was determined (n=3). (E, F) AGS and
HGC27 cell lines cloning after lentivirus treatment (shControl, TXNIP-sh3). (G, H) Determination of migration ability of AGS and HGC27 cell lines
treated with lentivirus (shControl, TXNIP-sh3) (n=3). Scale bar: 250 mm. The data were represented as mean ± standard deviation **p<0.01,
***p<0.001, and ****p<0.0001, with significant differences compared to the control group.
Chen et al. 10.3389/fonc.2025.1509771
Frontiers in Oncology frontiersin.org13
HGC27 cells. Based on knockdown efficiency, shRNA3 was selected
for subsequent experiments (Figure 9B). MTT assays revealed that
TXNIP knockdown significantly inhibited the growth of AGS and
HGC27 cells, with a marked reduction in cell viability on days three
and five (Figures 9C,D). Furthermore, colony formation and
migration assays demonstrated that TXNIP knockdown
suppressed the clonal formation and migratory capacity of AGS
and HGC27 cells (Figures 9E-H). Overall, these results indicate that
TXNIP exerts an oncogenic role in gastric cancer.
4 Discussion
Gastric cancer, originating from the epithelial cells of the gastric
mucosa, is a globally prevalent and highly lethal malignant tumor
(1,19). While the morbidity and mortality rates of GC have
declined in recent decades, largely attributed to advancements in
multimodal treatments, China continues to bear 44% of the global
disease burden, and overall patient survival remains a critical
concern (20). Given the substantial heterogeneity of gastric
cancer, personalized treatment is considered the optimal
approach to reduce mortality and prolong survival. Advances in
sequencing and bioinformatics technologies are empowering
clinicians to refine patient assessments for personalized care.
While cancer and aging have traditionally been studied as distinct
entities, a growing body of evidence underscores the intimate link
between them, suggesting that cancer is an aging-related disease (21–
24). Impaired macroautophagy and cellular senescence, both
hallmarks of aging, exert context-dependent oncosuppressive and
pro-tumorigenic influences (5). Furthermore, previous research has
established the pivotal role of autophagy-senescence crosstalk in
regulating tumor initiation and progression (25–29). By integrating
autophagy- and senescence-related genes, we developed a novel
prognostic signature that demonstrates exceptional predictive
power and offers novel avenues for identifying potential therapeutic
interventions in GC patients.
While autophagy has been investigated in GC using
bioinformatics approaches (30–34), this study established a novel
connection between autophagy and senescence to develop a robust
prognostic model, further characterizing the TME, predicting
immunotherapy efficacy, and assessing drug responsiveness in GC
patients. Initially, common genes were identified among autophagy-
related genes, senescence-related genes, and STAD-associated genes
through intersection analysis. Subsequent differential expression
analysis of these intersecting genes yielded 161 DEASRGs.
Functional enrichment analyses revealed significant enrichment of
these DEGs incell cycleand carcinogenesis pathways. Univariate Cox
regression identified 29 prognosis-associated genes, with frequent
copy number variations confirming the critical involvement of
ASRGs in GC lesions. LASSO regression selected 14 variables
(PIM1, ITGB4, SPARC, CASP2, LMNB2, SERPINE1, TXNIP,
UHRF1, IRAK1, KIT, CD36, CXCL1, ZFP36, MAP4K4) for
inclusion in the final prognostic signature. KM curves revealed a
statistically significant decrease in overall survival for individuals
classified as high-risk. Time-dependent ROC curve validated the
signature’s predictive performance, exhibiting high accuracy.
Multivariate Cox regression confirmed the independent prognostic
value of the derived risk scores. These findings suggest autophagy and
senescence as potential therapeutic targets for GC, with the novel
signature serving as a predictor of prognosis. To further explore
ASRG-related modifications in GC, patients were classified into three
distinct subtypes with significant prognostic differences based on
gene expression profiles. This suggests three different ASRG-related
modification modes in GC, each with unique clinical and
immunological characteristics. Nomograms incorporating
clinicopathological variables and the signature provided a
comprehensive perspective on the predictive potential of ASRGs.
External validation using the GSE66229 dataset confirmed the
robustness of the prognostic risk model and nomogram.
We then conducted a comparative analysis of TME variations
within risk subgroups. As a dynamic and complex ecosystem
composed of various extracellular components and cell types, the
crosstalk between cellular components and tumor cells is a critical
factor in cancer pathogenesis and has emerged as a potential
therapeutic target (35). Immune checkpoints (ICs) analysis
indicated an immunosuppressive TME in the high-risk group.
NKcellsserveasanessentialpartintheinnateimmune
response, capable of operating independently without prior
sensitization. They can eliminate tumor cells through antibody-
dependent cell-mediated cytotoxicity (ADCC) and trigger an
adaptive immune response by releasing pro-inflammatory
cytokines and chemokines (36).
Previous studies have demonstrated that a high abundance of
NK cell infiltration within the TME was associated with favorable
prognosis in certain malignancies (37). NK cells directly kill tumor
cells. Additionally, NK cells can express death receptors, such as
FasL, which bind to Fas on the tumor cell surface, triggering
apoptosis (38). NK cells also secrete cytokines like IFN-gand
TNF-a. Recent studies have indicated that IFN-gcan upregulate
MHC-I expression on the surface of tumor cells, thereby increasing
their susceptibility to immune cell-mediated recognition (39). In
contrast, TNF-adirectly induces apoptosis in tumor cells. NKG2D
is a stimulatory receptor located on the surface of NK cells. While
NKG2D ligands are downregulated in normal tissues, their
expression rapidly increases upon malignant transformation (40).
Consequently, NKG2D is an ideal target for chimeric antigen
receptor (CAR)-T cell therapy (41). Additionally, a research team
has developed 70CAR-iNK cells, which express CD70-targeted
CAR molecules (42). Dendritic cells (DCs) play a crucial role in
the TME, serving as antigen-presenting cells that initiate specific
immune responses. Beyond this, they also regulate the function of
other immune cells. Studies have shown that IL-12 secreted by DCs
can promote the differentiation of T cells into Th1 cells (43).
Moreover, mature DCs can inhibit Treg activity by upregulating
co-stimulatory molecules, thereby restoring the body’s anti-tumor
immune response (44).
In the contemporary medical landscape, chemotherapy efficacy
for GC has plateaued, while targeted therapies benefit only a small
Chen et al. 10.3389/fonc.2025.1509771
Frontiers in Oncology frontiersin.org14
subset (10-12%) of the population. Immunotherapy, exemplified by
programmed cell death protein 1 (PD-1) inhibitor antibodies, has
demonstrated significant progress in GC treatment (45,46). Previous
research on immunotherapy response predictors has primarily
focused on patients with elevated MSI, increased PD-L1 expression,
higher tumor mutation burden, and Epstein-Barr virus positivity.
However, identifying patients who benefit from immunotherapy may
require additional clinical and molecular markers.
Microsatellites are DNA sequences composed of short, tandemly
repeated units (typically 1 to 6 base pairs) with a high mutation rate.
MSI arises from errors in DNA replication due to defective mismatch
repair machinery, resulting in insertions or deletions within
microsatellite sequences (47). MSI-H tumors exhibit increased
immunogenicity across various tumor types, leading to an immune
response from tumor-infiltrating lymphocytes (TILs). This
heightened immunogenicity is responsible for the susceptibility of
MSI-H tumors to immunotherapy.
Our signature identified a 4.3-fold higher proportion of MSI-H in
the low-risk group, suggesting superior immunotherapy efficacy for
the low-risk population. Immune checkpoint inhibitors (ICIs), a
group of molecules expressed on immune cells that modulate
immune activation, are central to immunotherapy (48). Analysis of
ICs within the two risk subgroups revealed significantly increased
ICIs expression in the high-risk populations. Consistent with these
findings, the low-risk patients exhibited a considerably higher TMB.
A higher TMB correlates with increased neoantigen presentation and
enhanced T-cell recognition, leading to improved ICIs outcomes (49).
Furthermore, TIDE scores corroborated these observations. The
high-risk group demonstrated markedly elevated stromal scores.
Excessive stromal components in the high-risk group might impair
ICIs efficacy by impeding the infiltration of TILs and other immune
cells into tumors (50,51). Taken together, our novel signature
provides a new perspective for accurately identifying individuals
who may benefit from immunotherapy.
Four genes significantly associated with prognosis in GEPIA
analysis were selected for further analysis. SERPINE1 promotes the
proliferation and division of gastric cancer cells by upregulating
positive cell cycle regulators, such as Cyclin D1 (52). Additionally,
SERPINE1 can indirectly enhances the migratory and invasive
abilities of gastric cancer cells by inhibiting plasminogen
activators, like tPA and uPA (52). CD36 functions as a fatty acid
transporter and plays a crucial role in metabolic reprogramming. By
facilitating the uptake of fatty acids, CD36 supports the growth and
drug resistance of gastric cancer cells (53). In gastric cancer,
particularly in gastrointestinal stromal tumors (GISTs), mutations
in the KIT gene are frequently observed. These mutations activate
signaling pathways, including MAPK and PI3K/Akt, that promote
cell survival, proliferation, migration, and invasion (54). As such,
KIT inhibitors, are currently being investigated in clinical trials for
their therapeutic potential in gastric cancer (55,56).TXNIP
functions primarily as a molecule that binds to TRX to regulate
ROS and oxidative stress within cells. ROS are closely related to the
initiation and development of autophagy and senescence. Increased
expression of TXNIP can enhance the cytotoxicity of chemotherapy
drugs by modulating ROS levels (57). Furthermore, overexpression
of TXNIP leads to the upregulation of angiogenesis-related proteins
and promotes an angiogenic phenotype (58). The NLRP3
inflammasome is involved in immune responses in various
cancers, and numerous studies have highlighted the link between
TXNIP and NLRP3 inflammasome activation (59). TXNIP is
crucial for the maturation of NK cells and the function of DCs in
the tumor microenvironment, thus influencing anti-tumor
immunity (60–62). Initially, TXNIP was considered a potential
tumor suppressor gene. Nevertheless, the findings obtained from
diverse tumor studies utilizing varied methodologies exhibit
paradox, suggesting that the role of TXNIP can be variable upon
the specific tumor type and stage. These findings indicate that the
involvement of TXNIP in cancer is intricate Some studies have
demonstrated decreased TXNIP expression in several cancer types.
Song et al. reported that TXNIP antisense cDNA transfection in
melanoma cells reduced FasL and CD44 cytokine expression,
confirming TXNIP’s role in promoting melanoma cell apoptosis
and inhibiting tumor growth (63). In breast cancer, TXNIP
knockdown increased Ki-67 expression (a marker of cell
proliferation) and decreased p27 (a cell cycle regulatory protein),
leading to enhanced breast cancer cell growth in vitro and in vivo
(64–66). Furthermore, TXNIP mediates acetylation inhibitor-
induced suppression of hepatocellular carcinoma by triggering
potassium deprivation (67). The tumor-suppressive mechanism of
TXNIP in lung cancer is likely attributed to its promotion of A2BR
degradation and inhibition of cRaf/Erk signaling (68). In contrast,
Elevated expression of TXNIP may also contribute to worse
prognosis in some types of cancer.
For instance, in hepatocellular carcinoma (HCC) and renal
clear cell carcinoma, the overexpression of TXNIP promotes
angiogenesis and the spread of cancer cells (58,69). Studies have
also noted that lung cancer patients with high levels of TXNIP
expression had reduced rates of progression-free survival (70).
These studies demonstrate that the effects of TXNIP on tumors
are characterized by tumor heterogeneity.
However, the precise role of TXNIP in GC remains poorly
understood. TXNIP protein expression correlates with the
prognosis of GC patients. A Pan-cancer analysis indicated a
connection between TXNIP and an unfavorable outcome in
gastric cancer (71). To elucidate TXNIP’s role in GC progression,
this study downregulated TXNIP protein expression in gastric
cancer cell lines, resulting in significant inhibition of cell viability,
proliferation, and migration. Given previous findings on TXNIP’s
involvement in ROS homeostasis, metabolic response, and immune
function, TXNIP emerges as a promising therapeutic target for
cancer treatment (71–74).
Despite these promising findings, our study has inherent
limitations. Primarily, the research relied on publicly available
databases, necessitating prospective, large-scale real-world
investigations to validate the model’s generalizability. While we
have experimentally confirmed key findings, additional exploration
is necessary to clarify the underlying mechanisms governing the
interplay between autophagy, senescence, and tumorigenesis.
Additionally, the model’s complexity, involving many genes,
hinders its practical application and necessitates optimization.
Chen et al. 10.3389/fonc.2025.1509771
Frontiers in Oncology frontiersin.org15
Conclusively, our study identified an entirely novel, fourteen-
gene predictive signature associated with autophagy and senescence
in GC patients, validated in an independent cohort. This prognostic
model reliably and consistently predicts GC patient survival,
providing a foundation for personalized treatment strategies.
Additionally, our findings suggest that alterations in immune cell
infiltration within the TME may underlie gastric cancer
development. These results offer valuable insights for future
research on GC prognosis and personalized therapy.
Data availability statement
The original contributions presented in the study are included
in the article/Supplementary Material. Further inquiries can be
directed to the corresponding authors.
Ethics statement
Ethical approval was not required for the studies on humans in
accordance with the local legislation and institutional requirements
because only commercially available established cell lines were used.
Author contributions
SC: Conceptualization, Formal Analysis, Investigation,
Methodology, Software, Writing –original draft. XH:
Conceptualization, Methodology, Validation, Writing –original
draft, Writing –review & editing. YL: Data curation, Formal
Analysis, Methodology, Writing –review & editing. SW: Data
curation, Funding acquisition, Investigation, Writing –review &
editing. YF: Conceptualization, Methodology, Writing –review &
editing. CL: Formal Analysis, Writing –original draft. XS: Data
curation, Writing –review & editing. XL: Conceptualization,
Visualization, Writing –review & editing. WQiu: Funding
acquisition, Project administration, Supervision, Writing –review
&editing.WQi:Fundingacquisition, Methodology, Project
administration, Supervision, Writing –review & editing.
Funding
The author(s) declare that financial support was received for the
research and/or publication of this article. This research was funded
by Beijing Science and Technology Innovation Medical
Development Foundation (Grant No. KC2021-JX-0186-145);
China Zhongguancun Precision Medicine Science and
Technology Foundation (Grant No. GXZDH13); Qingdao Key
Clinical Specialty Elite Discipline.
Acknowledgments
We acknowledge public database for providing their platforms and
contributors for uploading their meaningful datasets. We thank Home
for Researchers editorial team (www.home-for-researchers.com) for
language editing service. We also thank the associate editor and the
reviewers for their useful feedback that improved this paper.
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Generative AI statement
The author(s) declare that no Generative AI was used in the
creation of this manuscript.
Publisher’s note
All claims expressed in this article are solely those of the authors
and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the
reviewers. Any product that may be evaluated in this article, or
claim that may be made by its manufacturer, is not guaranteed or
endorsed by the publisher.
Supplementary material
The Supplementary Material for this article can be found online
at: https://www.frontiersin.org/articles/10.3389/fonc.2025.1509771/
full#supplementary-material
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