Lina Wang, Jie Ma, Fei Tang
Lung adenocarcinoma (LUAD), the most common lung cancer subtype, exhibits a poor prognosis. Although mitotic catastrophe can be induced in LUAD, leading to cell death, the prognostic and biological significance of mitotic catastrophe-related genes (MCRGs) in this disease remains unclear. Differentially expressed genes (DEGs) associated with LUAD were screened from the TCGA-LUAD cohort, and overlapping genes were defined by intersecting DEGs with MCRGs. Candidate prognostic genes were selected via machine learning, after which a multigene prognostic model was constructed. An eight-candidate prognostic signature comprising PDGFB, LDHA, ZEB2, H2AX, FKBP4, DMD, ANXA2, and S100B was established, demonstrating acceptable predictive reliability in training with 2-year, 3-year and 5-year area under the curves (AUCs) of 0.723, 0.715, and 0.625, respectively, in the training set (bootstrap-corrected C-index: 0.699, 95% CI: 0.657-0.742) and consistent validation in GSE31210 (AUCs: 0.812, 0.766, and 0.814). Low-risk patients exhibited significantly better survival outcomes, enhanced immune infiltration, higher immune and stromal scores, increased immunophenoscores, and more active cancer immunity cycles. Significant differences in tumor mutation burden and drug sensitivity were observed between the high- and low-risk groups. Quantitative polymerase chain reaction(qPCR) validated the transcriptomic profiles of six candidate outcome-related genes in clinical samples. A nomogram incorporating the risk score and clinical stage showed good predictive performance. These findings demonstrate that the eight MCRG-related candidate genes have strong potential to predict patient outcomes and stratify risk in LUAD, with distinct immune microenvironment characteristics between risk groups.