The role of senescence genes in the treatment, prognosis, and tumor microenvironment of gastric cancer

Abstract

Aim: Gastric cancer (GC) has a high incidence and poor prognosis. Senescence genes are suggested to participate in immune cell infiltration, thus affecting the immunotherapy of GC. In this research, we established a senescence-related GC model to explore and verify the role of senescence genes in the prognosis, treatment, and tumor microenvironment (TME) of GC.

Methods: The TCGA GC (TCGA-STAD) dataset was used to screen key senescence genes from differentially expressed genes (DEGs). A prognostic risk model was trained utilizing the TCGA-STAD dataset and validated using an external GEO dataset. The CIBERSORT algorithm was run to explore the relationship between senescence genes and TME. The chemotherapy drug sensitivities in GC patients were calculated utilizing R package pRRophetic.

Results: A total of 37 senescence-related DEGs were obtained. Five key senescence-related genes were further screened to establish a senescence-related risk model based on Cox regression. The survival status of GC patients in the high-risk group was found to be worse than that in the low-risk group. According to the results of gene set enrichment analysis, the senescence-related risk was mainly associated with cytokine activity, immune mechanism, and related pathways. By analyzing the sensitivity of common chemotherapy drugs in GC patients, it was revealed that the sensitivities of high-risk patients to Dasatinib, Lapatinib, and Pazopanib were lower than those of low-risk patients. The CIBERSORT algorithm was executed to analyze the TME in the high-risk group, revealing elevated levels of CD8 T cells, Macrophages M2, and resting Mast cells. In addition, decreased levels of resting memory CD4 T cells , resting NK cells, activated Dendritic cells, and activated Mast cells were also observed.

Conclusion: Senescence genes were related to the prognosis, response to chemotherapy drugs, and TME of GC. Our senescence-related risk model could forecast the survival of patients, their response to chemotherapy drugs, and the TME to a certain extent.

Keywords: Senescence gene; gastric cancer; immunotherapy; prognostic model; survival analysis; tumor microenvironment.

Figure 1

DEG analysis of paired samples. A. Volcano plot of DEGs. B. Heat map of 37 SRDEGs. DEG: Differentially expressed gene; SRDEG: senescence-related differentially expressed gene.

Figure 2

Gene set enrichment analysis. (A) GO enrichment analysis and (B) KEGG enrichment analysis of SRDEGs. SRDEG: senescence-related differentially expressed gene; GO: gene ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes.

Figure 3

Survival risk model performance in TCGA-STAD (A, B, D) and GSE84437 (C, E) cohort. (A) ROC curves of six-fold cross-validation. (B) ROC curve of the SRPRM in TCGA-STAD data. (C) ROC curve of the SRPRM in GSE84437 data. (D) Analysis of the predictive value of the model in TCGA-STAD cohort and (E) GSE84437 cohort. ROC: receiver operating characteristic; SRPRM: senescence-related prognostic survival risk model.

Figure 4

KM survival curves of TCGA cohort (A), GEO cohort (B), and the 5 key genes in the 2 cohorts (C). ①-⑤ correspond to KM survival curves of BUB1B, MMP1, IGFBP1, MMP12, and WNT2 in TCGA-STAD cohort and ⑥-⑩ in GSE84437 cohort, respectively.

Figure 5

Boxplots of IC₅₀ of Dasatinib, Lapatinib, Pazopanib, and Gefitinib between high-risk and low risk groups in (A) TCGA-STAD cohort and (B) GSE84437 cohort. IC₅₀: half-maximal inhibitory concentration.

Figure 6

Results of CIBERSORT algorithm. A. TME of TCGA-STAD cohort. B. TME of GSE84437 cohort. Samples were sorted according to the risk scores. The top half and the bottom half were the high-risk group and the low-risk group, respectively. TME: tumor microenvironment.