Identification of hub genes related to CD4+ memory T cell infiltration with gene co-expression network predicts prognosis and immunotherapy effect in colon adenocarcinoma.

Abstract

Background: CD4+ memory T cells (CD4+ MTCs), as an important part of the microenvironment affecting tumorigenesis and progression, have rarely been systematically analyzed. Our purpose was to comprehensively analyze the effect of CD4+ MTC infiltration on the prognosis of colon adenocarcinoma (COAD). Methods: Based on RNA-Seq data, weighted gene co-expression network analysis (WGCNA) was used to screen the CD4+ MTC infiltration genes most associated with colon cancer and then identify hub genes and construct a prognostic model using the least absolute shrinkage and selection operator algorithm (LASSO). Finally, survival analysis, immune efficacy analysis, and drug sensitivity analysis were performed to evaluate the role of the prognostic model in COAD. Results: We identified 929 differentially expressed genes (DEGs) associated with CD4+ MTCs and constructed a prognosis model based on five hub genes (F2RL2, TGFB2, DTNA, S1PR5, and MPP2) to predict overall survival (OS) in COAD. Kaplan-Meier analysis showed poor prognosis in the high-risk group, and the analysis of the hub gene showed that overexpression of TGFB2, DTNA, S1PR5, or MPP2 was associated with poor prognosis. Clinical prediction nomograms combining CD4+ MTC-related DEGs and clinical features were constructed to accurately predict OS and had high clinical application value. Immune efficacy and drug sensitivity analysis provide new insights for individualized treatment. Conclusion: We constructed a prognostic risk model to predict OS in COAD and analyzed the effects of risk score on immunotherapy efficacy or drug sensitivity. These studies have important clinical significance for individualized targeted therapy and prognosis.

• Authors: Lingxue Tang, Sheng Yu, Qianqian Zhang, Yinlian Cai, Wen Li, Senbang Yao, Huaidong Cheng
• Journal: Frontiers in genetics (Front Genet) Vol. 13 Pg. 915282 ( 2022) ISSN: 1664-8021 [Print] Switzerland
• PMID: 36105107 (Publication Type: Journal Article)
• Copyright: Copyright © 2022 Tang, Yu, Zhang, Cai, Li, Yao and Cheng.