Identification of Lung Adenocarcinoma Subtypes by Using Growth Hormone-Releasing Hormone-Related Genes and Establishment of Signature to Predict Prognosis and Guide Immunother

Abstract

Background: Growth hormone-releasing hormone (GH-RH) and its antagonists are believed to influence the progression of varying tumor diseases. However, the specific effects of GH-RH-related genes on lung adenocarcinoma (LUAD) are yet to be deciphered. Methods: GH-RH-related gene set data, LUAD transcriptome data, and clinical data were available for download at GeneCards, TCGA, and GEO databases. R software was implemented to finish differential, regression, cluster, survival, and tumor microenvironment analyses. Drugs associated with model genes were predicted using CellMiner database. Results: 781 LUAD samples (TCGA-LUAD: 600 cases; GSE50081: 181 cases) and data for 1555 GH-RH-related gene sets were obtained from public datasets. Two LUAD subtypes with different GH-RH gene expressions were identified through cluster analysis. Significant differences were unveiled in prognosis between the two subtypes. A prognostic risk scoring model was generated based on genes screened from the PPI network, and afterward, the model was validated. The model comprised 7 genes, specifically CLCA1, CYP17A1, DKK1, IGF2BP1, IGFBP1, RPE65, and VGF. Immune-related analysis revealed significant differences in immune cell infiltration levels between high and low-risk groups (P<0.05), with mast cells and neutrophils showing significantly higher infiltration levels in the low-risk population than the other group. Conclusion: The 7-gene signature in the current study is of paramount importance for predicting overall survival and immune microenvironment of LUAD patients.