Research Paper Volume 15, Issue 10 pp 4444—4464
Development and validation of a novel T cell proliferation-related prognostic model for predicting survival and immunotherapy benefits in melanoma
- 1 Department of Dermatology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China
- 2 Key Laboratory of Dermatology, Anhui Medical University, Ministry of Education, Hefei, Anhui 230022, China
- 3 Inflammation and Immune-Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui 230022, China
- 4 Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China
- 5 Affiliated Chuzhou Hospital of Anhui Medical University, The First People’s Hospital of Chuzhou, Chuzhou, Anhui 230022, China
- 6 Department of Biochemistry and Molecular Biology, Metabolic Disease Research Center, School of Basic Medicine, Anhui Medical University, Hefei, Anhui 230022, China
Received: February 28, 2023 Accepted: May 9, 2023 Published: May 24, 2023
https://doi.org/10.18632/aging.204748How to Cite
Copyright: © 2023 Chen 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.
Abstract
Background: T cell plays a crucial role in the occurrence and progression of Skin cutaneous melanoma (SKCM). This research aims to identify the actions of T cell proliferation-related genes (TRGs) on the prognosis and immunotherapy response of tumor patients.
Method: The clinical manifestation and gene expression data of SKCM patients were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. T cell proliferation-related molecular subtypes were identified utilizing consensus clustering. Subsequently, Cox and Lasso regression analysis was conducted to identify six prognostic genes, and a prognostic signature was constructed. A series of experiments, such as qRT-PCR, Western blotting and CCK8 assay, were then conducted to verify the reliability of the six genes.
Results: In this study, a grading system was established to forecast survival time and responses to immunotherapy, providing an overview of the tumoral immune landscape. Meanwhile, we identified six prognostic signature genes. Notably, we also found that C1RL protein may inhibit the growth of melanoma cell lines.
Conclusion: The scoring system depending on six prognostic genes showed great efficiency in predicting survival time. The system could help to forecast prognosis of SKCM patients, characterize SKCM immunological condition, assess patient immunotherapy response.
Abbreviations
SKCM: Skin cutaneous melanoma; TRGs: T cell proliferation-related genes; TCGA: The Cancer Genome Atlas; GEO: Gene Expression Omnibus; TME: tumor microenvironment; DFS: disease-free survival; OS: overall survival (OS); ICIs: Immune checkpoint inhibitors; ATC: Adoptive T-cell; TMB: tumor mutation burden; DEGs: differentially expressed genes; ICIs: Immune checkpoint inhibitors; CC: cellular components; BP: biological processes; GTEx: Genotype-Tissue Expression; CNV: copy number variation; GSVA: gene set variation analysis; ssGSEA: single-sample gene set enrichment analysis; KEGG: Kyoto Encyclopedia of Genes and Genomes; CTLA4: Cytotoxic T lymphocyte-associated antigen 4; PD-L1: PD-1 ligand 1; GO: Gene Ontology; LASSO: Least absolute shrinkage and selection operator; PD-1: Programmed cell death protein 1; PD-L1: PD-1 ligand 1.