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

Growth hormone-releasing hormone (GH-RH) and its antagonists are believed to influence the progression of various tumor diseases. However, the specific effects of GH-RH-related genes on lung adenocarcinoma (LUAD) are yet to be deciphered. GH-RH-related gene set data, LUAD transcriptome data, and clinical data were available for download at the GeneCards, TCGA, and GEO databases. R software was used to conduct differential, regression, cluster, survival, gene expression, and tumor microenvironment analyses. Drugs associated with model genes were predicted using the CellMiner database. Expression of CYP17A1, IGF2BP1, IGFBP1 and VGF in LUAD cells was assayed via qRT-PCR. A total of 781 LUAD samples (TCGA-LUAD: 600; GSE50081: 181) and data on 1,555 GH-RH-related gene sets were obtained from public datasets. Two LUAD subtypes with different GH-RH gene expression were identified through cluster analysis. Significant differences were unveiled in prognosis between the two subtypes. A prognostic risk-scoring model was generated with 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. CYP17A1 expression was low in LUAD, while IGF2BP1, IGFBP1, and VGF expression was high. Immune-related analysis revealed significant differences in immune cell infiltration levels between the high- and low-risk groups (P < 0.05), with mast cells and neutrophils showing significantly higher infiltration levels in the low-risk population. The 7-gene signature in the current study is of paramount importance for forecasting overall survival and the immune microenvironment of LUAD patients.