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

Jiajie Chen^1,^2,³, Daiyue Wang^1,^2,³, Shixin Chan⁴, Qingqing Yang^1,^2,³, Chen Wang^1,^2,³, Xu Wang⁴, Rui Sun⁴, Yu Gui^1,^2,³, Shuling Yu^1,^2,³, Jinwei Yang⁴, Haoxue Zhang^1,^2,³, Xiaomin Zhang⁶, Kechao Tang⁶, Huabing Zhang^5,⁶, Shengxiu Liu^1,^2,³

¹Department of Dermatology, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China
²Key Laboratory of Dermatology, Anhui Medical University, Ministry of Education, Hefei, Anhui 230022, China
³Inflammation and Immune-Mediated Diseases Laboratory of Anhui Province, Hefei, Anhui 230022, China
⁴Department of General Surgery, The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui 230022, China
⁵Affiliated Chuzhou Hospital of Anhui Medical University, The First People’s Hospital of Chuzhou, Chuzhou, Anhui 230022, China
⁶Department of Biochemistry and Molecular Biology, Metabolic Disease Research Center, School of Basic Medicine, Anhui Medical University, Hefei, Anhui 230022, China

* Equal contribution

Received: February 28, 2023Accepted: May 9, 2023Published: May 24, 2023

https://doi.org/10.18632/aging.204748

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.