Senescence-Related Diagnostic Biomarkers in Gastric Carcinoma: Identification and Comprehensive Analysis Using a Machine Learning Algorithm

doi:10.21203/rs.3.rs-4005196/v1

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Senescence-Related Diagnostic Biomarkers in Gastric Carcinoma: Identification and Comprehensive Analysis Using a Machine Learning Algorithm

https://doi.org/10.21203/rs.3.rs-4005196/v1

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Recent research indicates that senescence plays a pivotal role in carcinogenesis. However, there is a lack of studies exploring the clinical significance and predictive capabilities of senescence-related genes (SRGs) in gastric carcinoma (GC). This study employs machine learning techniques to discern the diagnostic biomarker associated with senescence in GC. Moreover, it delves into an extensive evaluation of its immunological infiltration, biological function, and clinical relevance. Our analysis identified four SRGs (FEN1, HIF1A, PDGFRB, and PEX5) using a combination of least absolute shrinkage and selection operator regression, support vector machine recursive feature elimination, and the area under the curve metrics. Subsequently, these four SRGs were incorporated into a senescence-based prognostic signature termed “riskScore.” Notably, the riskScore demonstrated reliability and accuracy as an independent prognostic marker. We observed a robust association between the riskScore and tumor mutation burden, clinicopathological features, tumor immune microenvironment, and overall prognosis. Single-cell sequencing revealed heightened immune cell infiltration in the high-risk group. Furthermore, the riskScore emerged as a pivotal determinant guiding therapeutic decisions for GC, including immunotherapy and chemotherapy. The results strongly suggest the riskScore as the signature diagnostic biomarker for GC. These findings lay a robust foundation for GC treatments and hold promise for developing a rapid, non-invasive technique for disease monitoring and prognostic prediction.

Globally, gastric cancer (GC) stands fifth in terms of being the most prevalent malignancy and fourth in terms of the major contributor of cancer-related deaths, with the highest prevalence rates observed in East Asia(1). The development of reliable diagnostic and treatment methods for GC remains a paramount concern. Despite the availability of various therapies such as surgery, chemotherapy, immunotherapy, and targeted medicines, the recurrence rate remains substantial.

Accurate and timely diagnosis of GC is therefore essential for successful therapeutic interventions. Unfortunately, traditional markers like CEA have proven inadequate in providing useful diagnostic information. Consequently, there is a critical need to detect GC early and identify diagnostic markers with unique efficacy. Additionally, the quest for a biomarker to guide GC treatment emerges as particularly crucial.

The relationship between senescence and the onset and progression of GC is well-established. Senescence, first formally described in the 1960s by Hayflick and colleagues, involves the low proliferation potential exhibited by cultured normal human diploid fibroblasts(2). This irreversible growth arrest, known as cell senescence, is now recognized as a programmed response to various stressors, including oncogene activation, oxidative stress, DNA-damaging agents, telomere attrition, and others(3). The role of senescent cells in carcinogenesis remains poorly understood. However, they appear to proliferate more in precancerous lesions compared to their scarcity in malignant tumors(4). Premalignant cells may bypass the senescence response and progress to become aggressive, cancerous tumors(5). Notably, senescent cells secrete a cytokine collection, termed senescence-related secreted phenotype (SASP). These cytokines can be cleared by immune cells or create a microenvironment that stimulates tumor growth, enabling tumor metastasis and invasion(6, 7).

Modern technologies in the context of cancer, including machine learning (ML) algorithms, are being developed to handle the rising complexity and volume of multi-omics data(8, 9). ML, a rapidly evolving field of AI, enables computers to learn from data processing and enhance their ability to anticipate events without explicit programming(10). The incorporation of ML and traditional bioinformatics in the classification and identification of diagnostic biological markers can improve cancer biomarker recognition and provide new insights into cancer diagnosis and therapy(8, 11).

In the current study, a prognostic model (riskScore) was established by selecting four diagnostic markers from senescence-related genes (SRGs) using an ML algorithm. Subsequently, the riskScore was validated as an independent prognosis predictor, allowing the prediction of overall survival (OS) for patients with GC in the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Furthermore, We discovered associations between the riskScores of patients with GC and their somatic mutation status, tumor immune microenvironment (TIME), chemotherapy effectiveness, and immunotherapy success. The riskScore holds promise in aiding the diagnosis and selection of treatment programs.

Clinical data and tissue samples

Forty patients diagnosed with primary GC underwent surgical therapy at Huai’an Second People’s Hospital and Affiliated Huai’an Hospital of Xuzhou Medical University. Both GC and paracancerous tissues were obtained, totaling 40 samples for each. The principles of the Declaration of Helsinki were followed in the conductance of this study. The sample donors provided informed, written consent.

Collection and processing of raw data

We sourced transcriptomic and clinical data for all patients using the GEO (https://www.ncbi.nlm.nih.gov/gds) and TCGA (https://cancergenome.nih.gov/) platforms. The GEO data, derived from the GSE84437 microarray data, included patient clinical information. Baseline, survival, and pathology data from both GEO and TCGA cohorts were collected and analyzed. The TCGA database patients were considered the training set to establish the prognostic model, whereas the GEO database patients served as the validation set for model development and verification. A compilation of 296 SRGs was derived from the CellAge: Cell Senescence Gene Database (https://genomics.senescence.info/cells/).

Biomarker identification using support vector machine recursive feature elimination (SVM-RFE) and least absolute shrinkage and selection operator (LASSO) regression algorithms

LASSO is a technique that reduces regression coefficients to mitigate overfitting risks(12). This technique aids in the removal of redundant data and genes(13). Support vector machine (SVM), a widely-used microarray data classifier, is recognized for its efficacy in feature selection(14). SVM-RFE is an SVM-based feature selection technique(15), which identifies the best genes to minimize classification error while preventing overfitting(14). The glmnet program executed the LASSO regression strategy, whereas the e1071 package generated the SVM model. Both SVM-RFE and LASSO regression were employed as ML methods to determine markers for GC features based on the least cross-validation error.

Single-cell RNA-sequencing analysis (scRNA-seq)

The scRNA-seq atlas was constructed using data from GEO, Array Express, and is accessible through the Tumor Immune Single-cell Hub (TISCH) (http://tisch.comp-genomics.org/). TISCH facilitates direct comparisons across patient groups, treatments, responses, tissues, cell types, and cancer types through single-cell and cluster-level visualizations of gene expression data from various datasets. In the current study, we utilized TISCH datasets to evaluate the TIME heterogeneity of four SRGs in GSE167297, comparing single-cell data from five patients.

For riskScore research, the GEO database was used to obtain scRNA-seq data (GSE166635). The reference chip used was GSE166635, with expression data for all genes observed in < 300,000 cells, where red blood cells constituted < 3%. Individual cells possessed approximately 250–5000 genes. The first 10 genes exhibiting high variability simultaneously were selected. The top 2,000 genes with the greatest variations were highlighted in red.

Gene set enrichment analysis (GSEA)

The GSEA approach, known for its non-parametric and unsupervised nature, has the capability to convert a gene expression matrix into one that reflects characteristic expressions for a given gene set. GSEA was executed using R packages, namely “enrichplot,” “DOSE,” and “clusterProfiler.” With the “limma” program, we analyzed the statistics-related alterations in the expression matrix.

Analysis of TIME

To determine the TIME characteristics, the Microenvironment Cell Populations-counter method was used to quantify the concentrations of different immune cell types in every sample. The Wilcoxon rank-sum test was employed to investigate the correlation between riskScore and immune cell number. Further exploration of the immune status was conducted using single-sample GSEA (ssGSEA). Modeling the Tumor Immunological Dysfunction and Exclusion (TIDE) involved utilizing a superior algorithm to evaluate various tumor immune escape mechanisms(16).

Prognostic risk signature: development and validation

In a univariate Cox regression model, the SRGs (PDGFRB, PEX5, HIF1A, and FEN1) were examined to identify relevant prognostic genes. The riskScore formula was derived as follows:

$$\varvec{f}\left(\varvec{x}\right)=\sum \left(\mathbf{e}\mathbf{x}\mathbf{p} \mathbf{G}\mathbf{e}\mathbf{n}\mathbf{e}\mathbf{i}\times \mathbf{c}\mathbf{o}\mathbf{e}\mathbf{f}\mathbf{f}\mathbf{i}\mathbf{e}\mathbf{n}\mathbf{t} \mathbf{G}\mathbf{e}\mathbf{n}\mathbf{e}\mathbf{i}\right)$$

Following the selection of a suitable riskScore threshold, the TCGA set was categorized into high- and low-risk groups with the “surv cutpoint” R function. The accuracy of the prognostic models was evaluated via Kaplan–Meier (KM) analysis (from the “survival” package) and the receiver operating characteristic (ROC) curve (from the “timeROC” package). Significance of the ROC curve was determined using the area under the curve (AUC). The same analytical methodologies, riskScore algorithm, and threshold value were then employed for signature validation in the GEO cohort.

Analysis of chemotherapeutic agents and targeted medicines

The OncoPredict R package(17), formulated by Maeser et al, was used in the prediction of the response of patients with cancer to drugs in vivo. OncoPredict contrasts the genetic expression patterns of cancer cell lines and tissues from the Broad Institute Cancer Cell Line Encyclopedia Genomics of Drug Sensitivity in Cancer (GDSC; https://www.cancerrxgene.org/) and Cancer Cell Line Encyclopedia (https://portals.broadinstitute.org/cclelegacy/home) utilizing the IC₅₀ of medicines in cancer cell lines. T-tests (unpaired) compared drug sensitivity between the low-risk and high-risk groups for 198 medicines, with a significance level set at p < 0.05.

Quantitative real-time polymerase chain reaction (PCR)

Total RNA was isolated from forty paired GC and paracancerous tissues using TRIzol Reagent (Invitrogen, Carlsbad, CA, USA) according to package recommendations. The RNA isolation procedure involved: (a) cell rinsing with phosphate-buffered saline (3×); (b) cell lysis using 1 ml of TRIzol Reagent (10 min, on ice); (c) addition of 200 µl chloroform to each tube, followed by thorough shaking; (d) centrifugation after 5 min of chilling on ice (12,000 r/min for 15 min); (e) mixing in isopropanol (same volume) to the supernatant; (f) centrifugation following15 min of chilling on ice (12,000 r/min for 15 min); (g) mixing the precipitate with ethanol (v/v = 75%; 1 ml) and separation by centrifugation (12,000 r/min, 5 min); and (h) dissolution of the RNA precipitate by adding 20 µl of RNA enzyme-free water after discarding the ethanol. Subsequently, quantitative reverse transcription (qRT)-PCR analysis was conducted using a SYBR Premix ExTaq kit (TaKaRa) after reverse transcription of the total RNA of each sample into cDNA. The reverse transcription reaction system involved: (i) adding 5× gDNA Eraser buffer (2 µl), total RNA (1000 ng), and RNase-free water (10 µl) at 42°C for 2 min. (j) Subsequently, we added 5× Prime Script buffer (2 µl), RNase-free water (4 µl), PrimeScript RT Enzyme Mix I (1 µl), and RT Primer Mix (1 µl) to the reaction solution followed by 15 min of incubation at 37°C, 5s at 85°C, and storage at 4°C. The qRT-PCR reaction setup included 1 µl of cDNA, 2 µl of RNase-free water, 5 µl of SYBR buffer, and 4 µM of reverse and forward primers. With regard to gene quantification, GAPDH served as an endogenous control, and we performed a minimum of three biological and technical replicates for each gene.

Statistical analyses

We conducted statistical analyses using R (v4.0.5). Paired-sample t-tests or Wilcoxon rank-sum tests were implemented to validate significant variations across the groups. The Kruskal–Wallis test was utilized for testing significant variations between three or more groups. Spearman’s correlation analysis determined relevant coefficients between riskScore, tumor mutational burden (TMB), and immune checkpoint gene expression. Waterfall diagrams of gene mutation frequency were generated using the “maftools” program. A p-value of < 0.05 was used to indicate statistical significance.

Senescence-related feature biomarkers identified using an ML algorithm

Univariate analysis identified 27 genes associated with prognosis among the 296 SRGs obtained from CellAge (Fig. 1A). Subsequently, using two distinct ML algorithms—LASSO regression and SVM-RFE—we searched TCGA dataset for candidate GC diagnostic markers. LASSO regression enabled the reduction of differentially expressed genes (DEGs), revealing nine genes as diagnostic indicators of GC (Fig. 1B). Furthermore, 10 feature genes were discovered in DEGs using the SVM-RFE method (Fig. 1C). Eight diagnostic feature genes were determined via the intersection of the two algorithms (Fig. 1D). Finally, four genes with AUC values exceeding 0.9 were selected for further investigation: PDGFRB, PEX5, HIF1A, and FEN1.

Four SRG regulators in GC and their genetic variability

The role of four SRGs in GC was investigated, considering copy number variations (CNVs). The chromosome sites depicting CNV alterations in the four SRGs are illustrated in Fig. 2A. Examination of PDGFRB, PEX5, HIF1A, and FEN1 indicated a high prevalence of CNV mutations, with widespread amplification observed (Fig. 2B). Further analysis demonstrated overexpression of the four SRGs in GC samples (Fig. 2C). KM analysis (Fig. 2D-G) revealed that low expression of HIF1A and PDGFRB significantly increased OS in TCGA dataset, whereas low expression of PEX5 and FEN1 remarkably reduced OS compared to high expression. Thus, both genetic variation and expression in these four SRGs were identified as critical factors in the course of GC.

Single-cell correlation between four SRGs and the GC TIME

Senescence has an impact on immune cell infiltration into the TIME(18). Utilizing the TISCH database in STAD_GSE167297, we examined the single-cell expression of the four SRGs in the GC TIME. The Violin Chart in Fig. 3A illustrates high expression of the four SRGs, especially HIF1A, in CD8T cells, B cells, DC cells, mast cells, and macrophages/monocytes. Figures 3A-F show the expression of the four SRGs in various immune cell clusters, providing strong evidence of a link between the expression of these SRGs and immune cell infiltration in GC.

Development of a senescence-related signature

The univariate Cox regression model, processed using LASSO Cox regression, provided coefficients for the four SRGs selected based on minimal requirements (Fig. 4A, B). Subsequently, a quantitative indicator, riskScore, was derived using the formula: riskScore = (− 0.2029 × FEN1 expression) + (0.2358 × HIFA expression) + (0.2047 × PDGFRB expression) + (− 0.4524 × PEX5 expression). Using this formula, the riskScore for each patient was calculated, and two distinct groups (high-risk, n = 185; low-risk, n = 186) were obtained based on an optimal threshold value of 0.7752. Principal component analyses (PCA) revealed two distinct components for the two risk groups (Fig. 4C). Examination of clinicopathological characteristics indicated significant differences between high- and low-risk groups in terms of T stage, histologic grade, and pathologic stage in the TCGA cohort (Fig. 4D). KM analyses demonstrated a significantly longer OS in low-risk patients than in high-risk patients, whether using data from TCGA (Fig. 4E) or GEO (Fig. 4F) cohorts.

Verification of the senescence-related signature in the GEO dataset

To confirm the consistency and accuracy of the results, the riskScore model was applied to 431 patients with GC in the GEO dataset. Two subsets of TCGA data were generated using the same threshold value (= 0.7752): low-risk (193 patients) and high-risk (238 patients). Heatmaps of the four SRG expression patterns, riskScore distribution, and survival status of patients in TCGA-GC and GEO datasets demonstrated a similar trend (Figs. 5A-F).

Clinical significance of the senescence-related signature in the GEO and TCGA datasets

To assess the therapeutic implications of senescence, we examined the significance of the clinicopathological factors and riskScore. Univariate Cox regression analysis of TCGA-GC and GEO datasets revealed riskScore and pathological stage as significant risk factors (Fig. 6A, E). The riskScore functioned independently as a reliable prognostic marker in both TCGA-GC (hazard ratio (HR) = 2.335 (1.550–3.519); p = 0.001) (Fig. 6B) and GEO datasets (HR = 1.454 (1.124–1.881); p = 0.004) (Fig. 6F) according to multivariate Cox regression. KM analyses confirmed that the survival of low-risk patients was not significantly different from that of high-risk patients in early disease stages (Fig. 6C, G) but was significantly prolonged in late disease stages compared to high-risk patients (Fig. 6D, H).

Association between senescence-related signatures, somatic mutation, and immunological state

The formation of tumors is often triggered by mutation accumulation(19). The difference between low- and high-risk somatic mutations was investigated (Fig. 7A-B), revealing more pronounced immune-associated alterations in the high-risk population. In the high-risk group, TTN (46%), TP53 (37%), MUC16 (24%), LRP1B (20%), and ARID1A (23%) exhibited the highest mutation frequencies, whereas in the low-risk group, TTN (54%), TP53 (47%), MUC16 (36%), LRP1B (33%), and ARID1A (27%) showed the highest mutation frequencies. Patients were classified into high- and low-TMB groups based on the median TMB value, revealing a negative correlation between life expectancy and TMB levels in patients with GC (Fig. 7C, p = 0.020). Four distinct categories were used to divide the patients as per their RiskScore and the corresponding TMB threshold: high-TMB + low-risk, high-TMB + high-risk, low-TMB + high-risk, and low-TMB + low-risk. Prolonged OS rates were observed in the low-TMB + low-risk group compared to the high-TMB + high-risk group (Fig. 7D, p = 0.005). Since somatic mutations are strongly correlated with the immune microenvironment(20), the relationship between riskScore and immune function was explored. Using ssGSEA, the level of enrichment for various subsets of immune cells was determined, revealing higher infiltration levels of immune cells in high-risk patients, except for CD4 + T cells and neutrophils (Fig. 7E). Additionally, ssGSEA findings from TCGA-GC datasets suggested that the high-risk group exhibited enrichment most immune-associated functions (Fig. 7F). The TIDE score, a tool predicting the likelihood of tumor cells evading the immune system, was remarkably lower in the low-risk group, indicating greater potential benefit from immunotherapy (Fig. 7G).

Further validation of the connection between senescence-related signatures and immune status in single-cell sequencing

The preceding results were corroborated using single-cell sequencing, wherein 22,270 single cells were isolated after a rigorous quality check. The expression of the four SRGs in single-cell sequencing at high and low risk aligned with previous findings, showcasing upregulation of PDGFRB and HIF1A and downregulation of PEX5 and FEN1 in patients at high risk relative to those at low risk (Figure S2). Employing PCA for dimensionality reduction, the cells were categorized into 10 distinct clusters, including monocytes, neutrophils, CD8 + T cells, macrophages, T helper cells, CD4 + T cells, epithelial cells, mast cells, B cells, cancer cells, and endothelial cells (Fig. 8A, B). Consistent with prior research, the high-risk individuals exhibited increased levels of immune cells including monocytes, neutrophils, and CD8 + T cells, whereas the low-risk individuals showed a greater concentration of epithelial, cancer, and endothelial cells (Fig. 8C). The differences in cell proportions were statistically significant (Fig. 8D-I).

Sensitivity of senescence-related signatures to targeted therapy and chemotherapy

The primary treatment modalities for advanced GC include chemotherapy, immunotherapy, and targeted therapy(21). Given the established value of senescence-related signatures in predicting patient prognosis in advanced GC, we examined the association between riskScore and responses to chemotherapy and targeted treatments. Using the R package “OncoPredict,” we determined the IC₅₀ for various chemotherapeutic and targeted drugs. We found that the IC₅₀ values for sorafenib, bortezomib, gemcitabine, dabrafenib, oxaliplatin, lapatinib, cytarabine, gefitinib, paclitaxel, and 5-fluorouracil were higher in individuals at high risk than in those at low risk, suggesting lower sensitivity to these treatments (Fig. 9A-F). Thus, individuals at low risk might respond better to conventional chemotherapy and targeted therapies.

Significant gene sets between low- and high-risk groups identified via GSEA

GSEA identified several significant gene sets enriched in the high- and low-risk groups from TCGA-GC data. In the high-risk group, the calcium signaling pathway, hematopoietic cell lineage, hypertrophic cardiomyopathy, focal adhesion, and dilated cardiomyopathy were enriched (Fig. 10A). Conversely, the low-risk group exhibited enrichment in gene sets associated with cell cycle(22), DNA replication(23), glycerolipid metabolism(24), ribosomes(25), and spliceosomes(26) (Fig. 10B), all of which are associated with senescence.

Validation of SRG expression and alteration

To validate SRG expression (FEN1, HIF1A, PDGFRB, and PEX5) qRT-PCR was conducted. The findings demonstrated higher relative mRNA expression in GC tissues than in paracancerous tissues for FEN1, HIF1A, PDGFRB, and PEX5 (Fig. 11A-B). Consistent with the results for mRNA expression, the Human Protein Atlas (https://www.proteinatlas.org/) confirmed elevated protein levels of FEN1, HIF1A, and PDGFRB in GC tissues compared to normal tissues, except for PEX5 (Fig. 11C).

In this study, a combination of classical bioinformatics and ML algorithms was employed to identify four SRGs (FEN1, HIF1A, PDGFRB, and PEX5) as potential diagnostic biomarkers for GC. The constructed prognostic model using LASSO regression demonstrated practical clinical applications, classifying patients into low- and high-risk populations as per their riskScore. The high-risk group exhibited unfavorable OS, indicating a potential link between riskScore and carcinogenesis or negative prognostic events. These findings were independently validated in an external GEO cohort, strengthening the reliability of the results.

Senescence, characterized by permanent cell cycle arrest, plays dual roles in the immune response to tumors(27, 28). Senescent cells contribute to immune clearance and tissue remodeling, providing protection against cancer(29). However, the SASP factors released by these cells can suppress tumor immunity(30, 31). Our study highlights the importance of further research into the intricate relationship between cell senescence and tumor immune function in GC. The observed distinctions in immune cell infiltration between the two populations suggest a crucial role for SRGs. In the TCGA cohort, more immune cell types appeared to be infiltrating the high-risk groups. Thus, these findings confirm that SRGs critically regulate the GC immunological landscape. Single-cell sequencing validated the correlation between riskScore and immune cell infiltration, revealing higher infiltration levels in patients at high risk than those in individuals at low risk. Furthermore, we examined the dissimilarities between the high- and low-risk groups with regard to immune cell components. The dissimilar proportions of immune cell types between the groups suggest a potential impact of riskScore on patient prognosis through the TIME.

Patients with higher TMB generally exhibit more durable and effective responses to treatment. Our findings highlight that those at low risk had lower TMB than those at high risk, consistent with their higher immune-related alterations. These results align with previous data on immune cell infiltration.

Our study reveals the relevance of senescence-related characteristics in predicting patient prognosis in advanced GC. Immunotherapy, targeted therapy, and chemotherapy emerge as viable treatment options for advanced GC. To explore potential variations in the IC₅₀ of targeted therapeutic drugs and chemotherapeutic agents between low- and high-risk groups, we conducted an analysis using the GDSC database. The results confirm that individuals at high risk exhibit higher IC₅₀ values for 5-fluorouracil, oxaliplatin, bortezomib, cytarabine, dabrafenib, gefitinib, lapatinib, gemcitabine, paclitaxel, and sorafenib than those at lower risk. Building on our prior findings highlighting riskScore as an effective marker for predicting immunological status, we further investigated the TIDE score-based response rate of riskScore for immune checkpoint inhibitor (ICI) therapy. The TIDE score was notably higher in high-risk patients, implying a potentially reduced benefit from ICI treatment.

In summary, our utilization of ML algorithms identified senescence-related diagnostic biological markers in GC, revealing strong associations with immune infiltration, clinical outcomes, and biological function. This authenticates the notion that senescence plays a vital role in GC onset, emphasizing the necessity for further research in this domain. Moreover, the establishment of a robust predictive signature, riskScore, enables a comprehensive evaluation of cellular, molecular, and clinical features, allowing for the quantification and individualization of the phenotype of patients with GC. As an essential independent prognostic metric, riskScore aids in decision-making regarding chemotherapy, targeted treatment, and immunotherapy for patients with GC. However, our study acknowledges several limitations that warrant consideration. While the four SRGs obtained from CellAge reflect the significant role of senescence in GC to a certain extent, the complex senescence function and diverse genetic phenotypes suggest that these four genes may not entirely capture the significance of senescence in GC. Additional research is warranted to enhance our knowledge on the association of senescence with GC. Furthermore, to confirm the robustness of our prognostic model, multicenter, prospective, and large sample size studies—primarily retrospective research—are essential.

Ethics statement

This study was reviewed and approved by the Ethics Committee of the Subei People’s Hospital of Jiangsu Province, Clinical Medical College, Yangzhou University. The patients/participants provided their written informed consent to participate in this study.

Author contributions DT, JZ, MW, and HZ designed and implemented the research. XZ collated and analyzed the data. DT, JZ, MW, and HZ provided technical support. JZ provided the language polishing for this article. DT wrote the manuscript. XZ revised the manuscript. All authors contributed to the article and approved the submitted version.

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.

Funding

This work was supported by the Natural Science Research Programme of Huai'an (HAB202212), the Young Medical Talent Project of Jiangsu Province [QNRC2016424], the Six Talent Peaks Project of Jiangsu Province [WSW-220, WSW291], and Postgraduate Research & Practice Innovation Program of Jiangsu Province [SJCX23_2028]

Author Contribution

Acknowledgments

We thank The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), GeneCards, and TISCH for data support, gene set enrichment analysis (GSEA), and gene ontology (GO) for functional enrichment.

Data availability statement

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/ Supplementary Material.

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No competing interests reported.

highlight.docx
Supplementary1.docx
S1.jpg
Figure S1 (A) Expression of all genes occurs in < 300,000 cells, the number of red blood cells constitutes < 3% and individual cells possess around 250 - 5000 genes. (B) The names of the first 10 genes that exhibited high levels of variability all at the same time were tagged, and we selected the 3,000 genes that exhibited the highest variations and colored them red.
S2.jpg
Figure S2 Distribution of 4 SRGs in high- and low-risk groups analyzed using single-cell resolution. PDGFRB(A,E) and HIF1A(C,G) were upregulated while PEX5(B,F) and FEN1(D,H) were downregulated in the high-risk population relative to those at low risk

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Senescence-Related Diagnostic Biomarkers in Gastric Carcinoma: Identification and Comprehensive Analysis Using a Machine Learning Algorithm

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Figures

Introduction

Materials and methods

Results

Discussion

Declarations

Conflict of interest

Funding

Author Contribution

Acknowledgments

Data availability statement

References

Additional Declarations

Supplementary Files

Status:

Version 1