Research Paper Advance Articles

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

Xiaogang Shen1, *, , Meng Wang2, *, , Wenxi Chen3, , Yu Xu3, , Qiaoxia Zhou3, , Tengfei Zhu3, , Guoqiang Wang3, , Shangli Cai3, , Yusheng Han3, , Chunwei Xu4, , Wenxian Wang5, , Lei Meng6, , Hao Sun7, &, ,

  • 1 Departments of gastrointestinal surgery, Sichuan Academy of Medical Sciences and Sichuan Provincial People’s Hospital, Chengdu, China
  • 2 Department of General Surgery, The Third People’s Hospital of Chengdu, Affiliated Hospital of Southwest Jiaotong University, Chengdu, China
  • 3 Burning Rock Biotech, Guangzhou, China
  • 4 Institute of Basic Medicine and Cancer (IBMC), Chinese Academy of Sciences, Hangzhou, China
  • 5 Department of Clinical Trial, The Cancer Hospital of the University of Chinese Academy of Sciences (Zhejiang Cancer Hospital), Hangzhou, China
  • 6 Department of Surgical Oncology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi’an, China
  • 7 Department of Gastrointestinal Cancer Center, Chongqing University Cancer Hospital, Chongqing, China
* Equal contribution

Received: July 26, 2022       Accepted: February 2, 2023       Published: February 16, 2023
How to Cite

Copyright: © 2023 Shen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


As one of the prevalent tumors worldwide, gastric cancer (GC) has obtained sufficient attention in its clinical management and prognostic stratification. Senescence-related genes are involved in the tumorigenesis and progression of GC. A machine learning algorithm-based prognostic signature was developed from six senescence-related genes including SERPINE1, FEN1, PDGFRB, SNCG, TCF3, and APOC3. The TCGA-STAD cohort was utilized as a training set while the GSE84437 and GSE13861 cohorts were analyzed for validation. Immune cell infiltration and immunotherapy efficacy were investigated in the PRJEB25780 cohort. Data from the genomics of drug sensitivity in cancer (GDSC) database revealed pharmacological response. The GSE13861 and GSE54129 cohorts, single-cell dataset GSE134520, and The Human Protein Atlas (THPA) database were utilized for localization of the key senescence-related genes. Association of a higher risk-score with worse overall survival (OS) was identified in the training cohort (TCGA-STAD, P<0.001; HR = 2.03, 95% CI, 1.45–2.84) and the validation cohorts (GSE84437, P = 0.005; HR = 1.48, 95% CI, 1.16–1.95; GSE13861, P = 0.03; HR = 2.23, 95% CI, 1.07–4.62). The risk-score was positively correlated with densities of tumor-infiltrating immunosuppressive cells (P < 0.05) and was lower in patients who responded to pembrolizumab monotherapy (P = 0.03). Besides, patients with a high risk-score had higher sensitivities to the inhibitors against the PI3K-mTOR and angiogenesis (P < 0.05). Expression analysis verified the promoting roles of FEN1, PDGFRB, SERPINE1, and TCF3, and the suppressing roles of APOC3 and SNCG in GC, respectively. Immunohistochemistry staining and single-cell analysis revealed their location and potential origins. Taken together, the senescence gene-based model may potentially change the management of GC by enabling risk stratification and predicting response to systemic therapy.


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.