Research Paper Volume 16, Issue 3 pp 2494—2516

Risk model based on genes regulating the response of tumor cells to T-cell-mediated killing in esophageal squamous cell carcinoma

Xun Zhang1,2, *, , Chuting Yu1,2, *, , Siwei Zhou1,2, , Yanhui Zhang1,2, , Bo Tian1,2, , Yan Bian1,2, , Wei Wang1,2, , Han Lin1,2, , Luo-Wei Wang1,2, ,

  • 1 Department of Gastroenterology, Changhai Hospital, Naval Medical University, Shanghai, China
  • 2 National Clinical Research Center for Digestive Diseases, Shanghai, China
* Equal contribution and co-first author

Received: September 29, 2023       Accepted: December 26, 2023       Published: February 1, 2024
How to Cite

Copyright: © 2024 Zhang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Immune checkpoint inhibitors (ICIs) represent a promising therapeutic approach for esophageal squamous cell carcinoma (ESCC). However, the subpopulations of ESCC patients expected to benefit from ICIs have not been clearly defined. The anti-tumor cytotoxic activity of T cells is an important pharmacological mechanism of ICIs. In this study, the prognostic value of the genes regulating tumor cells to T cell-mediated killing (referred to as GRTTKs) in ESCC was explored by using a comprehensive bioinformatics approach. Training and validation datasets were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), respectively. A prognostic risk scoring model was developed by integrating prognostic GRTTKs from TCGA and GEO datasets using a ridge regression algorithm. Patients with ESCC were divided into high- and low-risk groups based on eight GRTTKs (EIF4H, CDK2, TCEA1, SPTLC2, TMEM209, RGP1, EIF3D, and CAPZA3) to predict overall survival in the TCGA cohort. Using Kaplan-Meier curves, receiver operating characteristic curves, and C-index analysis, the high reliability of the prognostic risk-scoring model was certified. The model scores served as independent prognostic factors, and combining clinical staging with risk scoring improved the predictive value. Patients in the high-risk group exhibited abundant immune cell infiltration, including immune checkpoint expression, antigen presentation capability, immune cycle gene expression, and high tumor inflammation signature scores. The high-risk group exhibited a greater response to immunotherapy and neoadjuvant chemotherapy than the low-risk group. Drug sensitivity analysis demonstrated lower IC50 for AZD6244 and PD.0332991 in high-risk groups and lower IC50 for cisplatin, ATRA, QS11, and vinorelbine in the low-risk group. Furthermore, the differential expression of GRTTK-related signatures including CDK2, TCEA1, and TMEM209 were verified in ESCC tissues and paracancerous tissues. Overall, the novel GRTTK-based prognostic model can serve as indicators to predict the survival status and immunotherapy response of patients with ESCC, thereby providing guidance for the development of personalized treatment strategies.


GRTTKs: genes regulating the response of tumor cells to T-cell-mediated killing; TCGA: The Cancer Genome Atlas; GEO: Gene Expression Omnibus; ESCC: esophageal squamous cell carcinoma; PCA: Principal component analysis; OS: overall survival; ROC: receiver operating characteristic; AUC: area under the ROC curve. GO: Gene Ontology; KEGG: Kyoto Encyclopedia of Genes and Genomes; ssGSEA: single-sample gene set enrichment analysis; TIS: tumor inflammation signature; TIDE: Tumor Immune Dysfunction and Exclusion; GEPIA: Gene Expression Profiling Interactive Analysis.