Development of an immune-related gene signature applying Ridge method for improving immunotherapy responses and clinical outcomes in lung adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) is a major cause of cancer mortality. Considering the critical role of tumor infiltrating lymphocytes in effective immunotherapy, this study was designed to screen molecular markers related to tumor infiltrating cells in LUAD, aiming to improve immunotherapy response during LUAD therapy. Methods: The ConsensusClusterPlus method was used for clustering immune molecular subtypes of LUAD. Immune cell infiltration and immunotherapeutic potential in each subtype was evaluated employing single-sample gene set enrichment analysis (ssGSEA), Tumor Immune Dysfunction and Exclusion (TIDE), and Immunophenoscore (IPS). Immune-related co-expression modules were classified by weighted gene co-expression network analysis (WGCNA) analysis. The sequencing data of immune-related genes were comprehensively analyzed by introducing a new computational framework and 10 machine learning algorithms (a total of 101 combinations) to determine the prognostic genes, which were further combined to develop an immune prognostic signature (IMMPS) using the stepCox and Ridge methods. The expression of the signature genes was validated by quantitative real-time PCR (qRT-PCR). Results: Samples from The Cancer Genome Atlas dataset (TCGA-LUAD) were divided into two subtypes (immunosuppressive subgroup C1 and immune-activated subgroup C2); notably, the C2 subgroup was more likely to benefit from immunotherapy ( Conclusions: Our results contributed to a better understanding of immunological characteristics of LUAD. The IMMPS could serve as a promising tool for improving the clinical outcome of patients suffering from LUAD.