Identification of Key Genes Related to CD8+ T-Cell Infiltration as Prognostic Biomarkers for Lung Adenocarcinoma

• Published in: Frontiers in oncology Vol. 11; p. 693353
• Main Authors: Du, Minjun, Liang, Yicheng, Liu, Zixu, Li, Xingkai, Liang, Mei, Zhou, Boxuan, Gao, Yushun
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 28-09-2021
• Subjects: bioinformatics analysis; CD8+ T cell; immune microenvironment; lung adenocarcinoma; multiplex immunohistochemistry; Oncology; multiplex immunohistochemistry; immune microenvironment; CD8+ T cell; bioinformatics analysis; lung adenocarcinoma
• ISSN: 2234-943X; 2234-943X
• DOI: 10.3389/fonc.2021.693353

CD8+ T cells are one of the central effector cells in the immune microenvironment. CD8+ T cells play a vital role in the development and progression of lung adenocarcinoma (LUAD). This study aimed to explore the key genes related to CD8+ T-cell infiltration in LUAD and to develop a novel prognosis model based on these genes.
With the use of the LUAD dataset from The Cancer Genome Atlas (TCGA), the differentially expressed genes (DEGs) were analyzed, and a co-expression network was constructed by weighted gene co-expression network analysis (WGCNA). Combined with the CIBERSORT algorithm, the gene module in WGCNA, which was the most significantly correlated with CD8+ T cells, was selected for the subsequent analyses. Key genes were then identified by co-expression network analysis, protein-protein interactions network analysis, and least absolute shrinkage and selection operator (Lasso)-penalized Cox regression analysis. A risk assessment model was built based on these key genes and then validated by the dataset from the Gene Expression Omnibus (GEO) database and multiple fluorescence
hybridization experiments of a tissue microarray.
Five key genes (MZT2A, ALG3, ATIC, GPI, and GAPDH) related to prognosis and CD8+ T-cell infiltration were identified, and a risk assessment model was established based on them. We found that the risk score could well predict the prognosis of LUAD, and the risk score was negatively related to CD8+ T-cell infiltration and correlated with the advanced tumor stage. The results of the GEO database and tissue microarray were consistent with those of TCGA. Furthermore, the risk score was higher significantly in tumor tissues than in adjacent lung tissues and was correlated with the advanced tumor stage.
This study may provide a novel risk assessment model for prognosis prediction and a new perspective to explore the mechanism of tumor immune microenvironment related to CD8+ T-cell infiltration in LUAD.