Senescence-related genes define prognosis, immune contexture, and pharmacological response in gastric cancer

Results

Analysis of differentially expressed genes for potential prognostic signature

Baseline characteristics of the patients used in the training and validation sets were depicted in Supplementary Table 1. We first tried to identify senescence-related differentially expressed genes (DEGs) in patients with GC. In total, 1,396 DEGs between tumor and non-tumorous tissues in the cancer genome atlas-stomach adenocarcinoma (TCGA-STAD) cohort were identified (Figure 1A). Of these, 36 genes were senescence-related genes (Figure 1B). The chromosomal locations of these senescence-related DEGs are shown in Figure 1C. We also demonstrated the mutations in the 36 senescence-related DEGs in GC patients and the top 20 most mutated senescence-related DEGs in Figure 1D. The mutational frequency of TP53 was the highest (46%) followed by PIK3CA (16%, Figure 1D).

Figure 1. Identification of the candidate senescence-related DEGs in the TCGA-STAD. (A) Differentially expressed genes depicted by the volcano plot (red, up-regulated; blue, down-regulated in GC). (B) Heatmap depicting the mRNA levels of the 36 senescence-related DEGs between GC tissues and adjacent normal tissues. (C) Locations of the 36 senescence-related DEGs in chromosomes (red, up-regulated; blue, down-regulated in GC). (D) The mutation frequency of top 20 DEGs. (E) Bubble diagram demonstrated the top 6 enriched KEGG pathways of the 36 senescence-related DEGs. (F) GO enrichment analysis of the 36 senescence-related DEGs via biological process (BP), cellular component (CC) and molecular function (MF).

According to the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, these DEGs were mainly enriched in cell cycle regulation, homologous recombination, base excision repair, and P53 pathway (P < 0.05, Figure 1E). As expected, the 36 senescence-related DEGs were involved in DNA replication, telomere maintenance, negative cell cycle regulation, and DNA metabolism (P < 0.05, Figure 1F), which are consist in pathways related to cell cycle and cellular senescence. These findings collectively suggested the potential association between the senescence-related DEGs and the tumorigenesis of GC.

Prognostic model construction and validation

Of the 36 senescence-related DEGs, six senescence-related DEGs were identified due to their association with overall survival (OS) as continuous variables in the TCGA-STAD cohort (P < 0.05, Figure 2A, Supplementary Table 2). For instance, poorer OS was observed in patients with higher expression of SERPINE1 (P < 0.001; hazard ratio (HR) = 1.93; 95% confidence interval (95% CI), 1.38–2.71; Figure 2B), while patients with high expression of FEN1 exhibited improved OS (P = 0.003;HR, 0.61; 95% CI, 0.44–0.85; Figure 2C).

Figure 2. Model construction and validation. (A) Potential prognostic valued of each senescence-related genes in the overall survival (OS) of gastric cancer (GC). (B, C) Kaplan-Meier curves comparing the OS between patients with high and low expressions of SERPINE1 (B) and FEN1 (C), respectively. (D–L) Heatmap, Kaplan-Meier curves, and ROC curves depicting the gene expression patterns, survival status, and prognostic valued of the model in the TCGA-STAD (D–F), the GSE84437 (G–I), and the GSE13861 (J–L), respectively.

Based on the mRNA levels of these six genes, a risk-score was then developed and defined as follows: risk-score = (0.196 × SERPINE1) + (0.120 × APOC3) + (0.090 × SNCG) + (0.015 × PDGFRB) – (0.128 × TCF3) – (0.133 × FEN1). Assigned with a risk-score, patients were stratified into high- or low-risk groups by the median value in the cohort. Patients in the high-risk group had higher expression of SERPINE1, APOC3, PDGFRB, and SNCG and lower expression of FEN1 and TCF3 (P < 0.001, Figure 2D). In the TCGA-STAD cohort, the low-risk group exhibited improved OS (P < 0.001; HR = 2.03; 95% CI, 1.45–2.84; Figure 2E). The 1-, 3-, and 5-year area under curves (AUCs) of the risk-score were 0.639, 0.678, and 0.681, respectively (Figure 2F). These results were further verified in two validation cohorts (GSE84437 and GSE13861). Patients with higher risk had higher levels of SERPINE1, APOC3, PDGFRB, and SNCG, and lower FEN1 and TCF3 expressions (GSE84437: Figure 2G, GSE13861: Figure 2J, P < 0.01), together with worse OS (GSE84437: P = 0.005; HR = 1.48, 95% CI, 1.13–1.95; Figure 2H; GSE13861: P = 0.03; HR = 2.23, 95% CI, 1.07–4.62; Figure 2K). The signature predicted 1-, 3-, and 5-year OS with AUCs of 0.608, 0.590, and 0.606 in the GSE84437 cohort, and 0.705, 0.583 (Figure 2I), and 0.586 in the GSE13861 cohort (Figure 2L), respectively.

Univariable and multivariable Cox regression analysis was conducted to examine the independence of the novel prognostic signature. After adjusted for key covariates including TNM stage and age, the signature remained robust in OS differentiation in the TCGA-STAD cohort (P < 0.001; HR = 2.23, 95% CI, 1.57–3.12; Table 1), the GSE84437 cohort (P = 0.02; HR = 1.40, 95% CI, 1.07–1.85; Table 1), and the GSE13861 cohort (P = 0.10; HR = 1.87, 95% CI, 0.87–4.03; Table 1). The results concerning the independence of the six-gene signature were consistent between the three cohorts, indicating the robustness of our model in predicting prognosis.

Table 1. Univariable and multivariable Cox regression in TCGA-STAD and GSE84437 cohorts.

Parameter	Univariable analysis		Multivariable analysis
	HR (95% CI)	P value	HR (95% CI)	P value
TCGA-STAD cohort
Age (≥65 vs. <65)	1.49 (1.06–2.10)	0.02	1.67 (1.17–2.37)	0.01
Sex (male vs. female)	1.35 (0.95–1.94)	0.10
Tumor stage (I and II vs. III and IV)	1.65 (1.09–2.49)	0.02	1.78 (1.16–2.74)	0.01
EBV infection (positive vs. negative)	0.94 (0.48–1.85)	0.86
MSI (MSI-H vs. MSI-L and MSS)	1.94 (0.53–7.11)	0.19
TP53 (mutation vs. wildtype)	0.65 (0.32–0.84)	0.06
Asian (yes vs. no)	0.59 (0.37–0.95)	0.03	0.54 (0.33–0.87)	0.01
SMARCA4 (mutation vs. wildtype)	0.45 (0.16–1.28)	0.13
Risk score (high-risk vs. low-risk)	2.03 (1.45–2.84)	<0.001	2.23 (1.57–3.12)	<0.001
GSE84437 cohort
Age (≥65 vs. <65)	1.37 (1.04–1.81)	0.02	0.73 (0.56–0.97)	0.03
Sex (male vs. female)	1.24 (0.91–1.77)	0.17
Tumor stage (I and II vs. III and IV)	3.71 (1.90–7.24)	<0.001	0.28 (0.14–0.54)	<0.001
Risk score (high-risk vs. low-risk)	1.48 (1.13–1.95)	0.005	1.40 (1.07–1.85)	0.02
GSE13861 cohort
Age (≥65 vs. <65)	1.20 (0.58–2.52)	0.62
Sex (male vs. female)	1.27 (0.59–2.73)	0.55
Tumor stage (I and II vs. III and IV)	7.70 (2.32–25.54)	<0.001	7.12 (2.14–23.70)	<0.001
Risk score (high-risk vs. low-risk)	2.24 (1.04–4.83)	0.04	1.87 (0.87–4.03)	0.1
Abbreviations: EBV: Epstein-Barr virus; MSI: microsatellite instability; MSS: microsatellite stability.

Role of the senescence-related risk-score in mutational events, immunoinfiltration, and response to systemic therapy

We further explored the underlying difference between risk groups based on the senescence-related DEGs. Higher-risk patients had fewer mutations in LRP1B, SYNE1, and ARID1A (Supplementary Figure 1A, 1B), while those with lower risk-scores obtained a increased tumor mutational burden (TMB) (P < 0.001, Supplementary Figure 1C), suggesting that the low-risk group might be immune-sensitive since a high TMB might be linked to an inflammatory tumor immune microenvironment (TIME) and preferable sensitivity to immune checkpoint inhibitors (ICIs) [16]. Thus, we further studied the correlations of the absolute densities of 22 types of immune cells with the signature. Positive correlation was identified between the risk-score and the infiltration levels of the immune cells related to an immunosuppressive microenvironment (Figure 3A), e.g., M2 macrophage (Rho = 0.36, P < 0.001), resting memory CD4⁺ T cell (Rho = 0.33, P < 0.001), naïve B cells (Rho = 0.18, P = 0.003), and resting mast cells (Rho = 0.16, P = 0.01). Consistent with the risk-score, expression of SERPINE1, PDGFRB, and SNCG were also positively associated with M2 macrophage, resting memory CD4⁺ T cell, and naïve B cells, while expression of FEN1 and TCF3 were negatively associated with M2 macrophage, resting memory CD4⁺ T cell, and resting mast cells (Figure 3A).

Figure 3. Correlation between the six-gene signature and tumor immune features. (A) Correlation between immune infiltration and the risk-score by Spearman analysis. (B) Comparison of the risk-score between responders and non-responders to immunotherapy in PRJEB25780 cohort. (C) Time-dependent ROC curve for the risk-score in predicting response to immunotherapy (D) Heatmap showing the Spearman analysis results of the risk-score in drug sensitivity.

Given this, we further investigated the potential of the risk-score in predicting response to ICIs based on a cohort of patients with advanced GC treated with pembrolizumab monotherapy. Patients who responded to pembrolizumab had a lower risk-score than those who didn’t (P = 0.03, Figure 3B), and an association between a low risk-score and objective response was observed (AUC = 0.707, 95% CI, 0.517–0.897; Figure 3C).

Besides ICIs, we examined the correlations between the risk-score and the anti-tumor efficacy of multiple treatments in GC cell lines (Figure 3D). As for chemotherapeutic drugs, a high risk-score was correlated with decreased sensitivities to gemcitabine, doxorubicin, and etoposide, etc. As for targeted agents, the correlations were observed between a high risk-score and increased sensitivities to the inhibitors targeting phosphatidylinositol 3-kinase /mammalian target of rapamycin (PI3K/mTOR), poly ADP-ribose polymerase (PARP), Polo-like kinase (PLK), Src, and vascular endothelial growth factor receptor (VEGFR). Taken together, these results may indicate the role of the risk-score in predicting response to systemic therapy in GC.

Expression of the genes involved in the senescence-related risk-score

To further explore the potential roles of the key genes involved in the senescence-related risk-score in the tumorigenesis and development of GC by comparing the expression of SERPINE1, FEN1, PDGFRB, SNCG, TCF3, and APOC3 between tumor and normal tissues. Based on the mRNA expression profile of GC tumor and adjacent normal tissues from the GSE13861 and GSE54129 cohorts, FEN1, PDGFRB, SERPINE1, and TCF3 were up-regulated in tumor tissues (Figure 4A), which coincide with their risk roles in the senescence-related signature. In contrast, the lower expression levels of APOC3 and SNCG in tumor (Figure 4A), together with their protective roles in the prognostic signature, have further revealed their potential suppressor functions in GC.

Figure 4. Validation of the key genes expression in GC tissue and single cell localization. (A) Expression of SERPINE1, FEN1, PDGFRB, SNCG, TCF3, and APOC3 between GC tumor and normal tissues in the GSE54129 cohort. (B) Seven cell types identified in the gastric cancer GSE134520 dataset by single cell RNA sequencing (scRNA-seq) profiles and the calculation of uniform manifold approximation and projection (UMAP). (C–H) Immunohistochemistry staining in stomach normal tissues (left, upper) and gastric tumor tissues (left, lower), and scRNA-seq expression levels of APOC3 (C), FEN1 (D), PDGFRB (E), SERPINE1 (F), SNCG (G), and TCF3 (H), respectively.

Besides, the data of single-cell sequencing and immunohistochemistry (IHC) staining revealed the distribution of the 6 senescence-based risk-score-related genes in GC. According to the single-cell RNA sequencing profile of the GSE134520 dataset, a total of 9 cell types were identified (Figure 4B). APOC3 was mainly expressed in pit mucous cells, while SNCG expression was enriched in myofibroblasts and tumor cells. The expressions of FEN1 and TCF3 were mainly enriched in pit mucous and grand mucous cells, while SERPINE1 was mainly expressed in tumor cells. Expression of PDGFRB was comparable in almost all cell types except mast cells. (Figure 4C–4H).

Materials and Methods

Data and study design

The transcriptomic, genomic, and survival data of 348 GC and 31 controls were retrieved from the TCGA-STAD (Data Release 31.0) cohort database. The transcriptomic and clinical data of 431 GC samples and 45 GC patients treated with pembrolizumab monotherapy were collected from the GSE84437 and the PRJEB25780, respectively. The expression matrix of tumor and normal tissues from the GSE54129 cohort (111 GC samples and 21 normal controls) and the GSE13861 cohort (66 GC samples and 19 normal controls), respectively. The single-cell RNA sequencing data of 13 fresh human tissue samples from nine GC patients were retrieved from the GSE134520 dataset. The process and specific cohorts used in the analysis were depicted in Figure 5.

Figure 5. Study design.

Analysis of senescence-related DEGs and functional enrichment

The gene expression profiles were analyzed in tumorous and normal samples of the TCGA-STAD cohort via the “limma” R package (Version 3.28.14), and genes with a false discovery rate (FDR) <0.05 were selected as DEGs. Gene ontology (GO) and KEGG analysis were conducted for functional enrichment of the DEGs utilizing the “clusterProfiler” R package (Version 3.0.4).

Model development and verification

The prognostic value of every single senescence-related gene was examined by univariable cox regression using the R package “survival” according to the log₂ (Fragments Per Kilobase Million + 1) value of each gene. The LASSO method (the “glmnet” R package, Version 4.3) was utilized for model construction in the training set (the TCGA-STAD cohort) based on the DEGs with a significant association with OS [40]. The number of genes input for model construction was selected according to the minimum penalty parameter (λ) by ten-fold cross-validation. The risk-score was determined as follows:

$$Riskscore={\displaystyle \sum _{i=1}^{n}expi\times \beta i}$$

“n” depicted the number of genes involved in the model, while“expi” and “βi” represents the mRNA level and regression coefficient of gene i, respectively.

Assigned with a risk-score, patients were stratified into high- or low-risk groups by the median value in the cohort. The R package “survival” (version 3.4.0) was applied for survival analysis comparing the OS between the high- and low-risk groups. The prognostic value of the model was evaluated with the AUC and C-index values, and visualized by the receiver operating characteristic (ROC) curve by the “timeROC” R package (Version 0.4). The GSE84437 and the GSE13861 cohorts were utilized for validation.

Tumor immune infiltration analysis

Based on the cell types categorized by the deconvolution approach in CIBERSORT [41], the density of immune cells in tumor was identified. [42]. The potential association of the risk-score and TIME was analyzed by Spearman correlation.

Association analysis of the risk-score and drug sensitivities

We analyzed the response to pembrolizumab monotherapy in GC patients from the PRJEB25780 cohort. Based on the genomics of drug sensitivities in cancer (GDSC) database (https://www.cancerrxgene.org), we calculated the correlations (Spearman correlation analysis) of the half maximal inhibitory concentration (IC50) with the mRNA expression and the risk-score. The results are obtained by the “pRRophetic” (Version 4.0.2) and the “ggplot2” (Version 3.3.6) R packages. P values were adjusted by the FDR method.

Expression verification and localization of the senescence-related genes

The mRNA levels of the 6 genes involved in the signature were compared between GC tumors and normal tissues from two more cohorts (GSE54129, 111 GC and 21 controls; GSE13861, 66 GC and 19 controls), together with the expression levels compared among different cell types in GC tissues from the GSE134520 dataset, with annotation from the Tumor Immune Single-cell Hub (TISCH) database for cell identification. IHC staining figures in GC tissues and normal gastric samples were obtained from The Human Protein Atlas database (THPA, https://www.proteinatlas.org/).

Statistical analysis

Statistical results generated in this study were conducted in R (Version 3.6.0), SPSS (Version 23.0), and GraphPad Prism (Version 8). Wilcox test was used to analyze the association between the senescence-related gene signature and immune characteristics. Survival analyses were conducted by the Log-rank test, with visualization by the Kaplan-Meier (KM) curves. The independence of the prognostic signature was verified by univariable and multivariable Cox regression, with the input of significant variables into the multivariable analysis by P < 0.05. The accuracy of the signature was examined and depicted by the area under the curve (AUC). If not stated above, P < 0.05 illustrated statistical significance.

Abbreviations

GC: gastric cancer; LASSO: least absolute shrinkage and selection operator; TCGA-STAD: the cancer genome atlas-stomach adenocarcinoma; GDSC: genomics of drug sensitivity in cancer; OS: overall survival; SASP: senescence-related secretory phenotype; DEGs: differentially expressed genes; FDR: false discovery rate; KEGG: Kyoto Encyclopedia of Genes and Genomes; HR: hazard ratio; 95% CI: 95% confidence interval; AUC: area under curve; TMB: tumor mutational burden; ICI: immune checkpoint inhibitor; TIME: tumor immune microenvironment; PI3K/Mtor: 3-kinase /mammalian target of rapamycin; PARP: poly ADP-ribose polymerase; PLK: Polo-like kinase; VEGFR: vascular endothelial growth factor receptor; IHC: immunohistochemistry; GI: gastrointestinal; RT-PCR: reverse transcription-polymerase chain reaction; TAM: tumor-associated macrophage; GO: gene ontology; CC: cellular component; MF: molecular function; BP: biological pathway; ROC: receiver operating characteristic; CIBERSORT: cell-type identification by estimating relative subsets of RNA transcripts; IC50: half maximal inhibitory concentration; TISCH: Tumor Immune Single-cell Hub; THPA: The Human Protein Atlas; KM: Kaplan-Meier.

Author Contributions

XS and CJ served as co-first authors, each with equal contribution to the manuscript. WC, YX, TZ, and QZ developed the concept and designed the study. GW and SC performed the statistical analysis. All authors participated in the acquisition, analysis, interpretation of data, drafting of the manuscript, and approval of the submitted version. HS and LM performed a critical revision of the manuscript for important intellectual content. HS supervised the study.

Conflicts of Interest

We have no conflicts of interest to disclose, except the employment of WC, YX, TZ, QZ, GW, SC, YH by Burning Rock Biotech.

Funding

This study was funded by The Science and Technology Project of The Health Planning Committee of Sichuan Municipality (21PJ147) and the 2018 Entrepreneurial Leading Talent of Guangzhou Huangpu District and Guangzhou Development District (2022-L023).

Editorial Note

This corresponding author has a verified history of publications using a personal email address for correspondence.

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