Screening and Identification of Four Prognostic Genes Related to Immune Infiltration and G-Protein Coupled Receptors Pathway in Lung Adenocarcinoma - Wang, Yan; Qiu, Liwei; Chen, Yu; Zhang, Xia; Yang, Peng; Xu, Feng (Frontiers in Oncology, 2021)
Background: Lung adenocarcinoma (LUAD) is a common malignant tumor with the highest morbidity and mortality worldwide. The degree of tumor immune infiltration and clinical prognosis depend on immune-related genes, but their interaction with the tumor immune microenvironment, the specific mechanism driving immune infiltration and their prognostic value are still not very clear. Therefore, the aim of this work was focused on the elucidation of these unclear aspects. Methods: TCGA LUAD samples were divided into three immune infiltration subtypes according to the single sample gene set enrichment analysis (ssGSEA), in which the associated gene modules and hub genes were screened by weighted correlation network analysis (WGCNA). Four key genes related to immune infiltration were found and screened by differential expression analysis, univariate prognostic analysis and Lasso-COX regression, and their PPI network was constructed. Finally, a Nomogram model based on the 4 genes and tumor stages was constructed and confirmed in two GEO data sets. Results: Among the three subtypes high, medium and low immune infiltration subtype, the survival rate of the patients in the high one was higher than the rate in the other two subtypes. The 4 key genes related to LUAD immune infiltration subtypes were CD69, KLRB1, PLCB2 and P2RY13. The PPI network revealed that the downstream genes of the G-protein coupled receptors (GPCRs) pathway were activated by these 4 genes through the S1PR1. The risk score signature based on these 4 genes could distinguish high and low-risk LUAD patients with different prognosis. The Nomogram constructed by risk score and clinical tumor stage showed a good ability to predict the survival rate of LUAD patients. The universality and robustness of the Nomogram was confirmed by two GEO datasets. Conclusions: The prognosis of LUAD patients could be predicted by the constructed risk score signature based on the 4 genes, making this score a potential independent biomarker. The screening, identification and analysis of these four genes could contribute the understanding of GPCRs and LUAD immune infiltration, thus guiding the formulation of more effective immunotherapeutic strategies.