Advancing lung adenocarcinoma prognosis and immunotherapy prediction with a multi‐omics consensus machine learning approach

• Published in: Journal of cellular and molecular medicine 2024-07, Vol.28 (13), p.e18520-n/a
• Main Authors: Lin, Haoran, Zhang, Xiao, Feng, Yanlong, Gong, Zetian, Li, Jun, Wang, Wei, Fan, Jun
• Format: Article
• Language: English
• Subjects: Adenocarcinoma; Adenocarcinoma of Lung - genetics; Adenocarcinoma of Lung - immunology; Adenocarcinoma of Lung - pathology; Adenocarcinoma of Lung - therapy; Algorithms; Biomarkers, Tumor - genetics; Cluster analysis; Datasets; DNA methylation; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; Humans; Immunotherapy; Immunotherapy - methods; Learning algorithms; lung adenocarcinoma (LUAD); Lung cancer; Lung Neoplasms - diagnosis; Lung Neoplasms - genetics; Lung Neoplasms - immunology; Lung Neoplasms - mortality; Lung Neoplasms - pathology; Lung Neoplasms - therapy; Machine Learning; Metastases; MicroRNAs - genetics; miRNA; mRNA; Multiomics; multi‐omics consensus clusters (MOCs); Mutation; Original; overall survival (OS); Patients; Prognosis; Tumors
• ISSN: 1582-1838; 1582-4934; 1582-4934
• DOI: 10.1111/jcmm.18520

Lung adenocarcinoma (LUAD) is a tumour characterized by high tumour heterogeneity. Although there are numerous prognostic and immunotherapeutic options available for LUAD, there is a dearth of precise, individualized treatment plans. We integrated mRNA, lncRNA, microRNA, methylation and mutation data from the TCGA database for LUAD. Utilizing ten clustering algorithms, we identified stable multi‐omics consensus clusters (MOCs). These data were then amalgamated with ten machine learning approaches to develop a robust model capable of reliably identifying patient prognosis and predicting immunotherapy outcomes. Through ten clustering algorithms, two prognostically relevant MOCs were identified, with MOC2 showing more favourable outcomes. We subsequently constructed a MOCs‐associated machine learning model (MOCM) based on eight MOCs‐specific hub genes. Patients characterized by a lower MOCM score exhibited better overall survival and responses to immunotherapy. These findings were consistent across multiple datasets, and compared to many previously published LUAD biomarkers, our MOCM score demonstrated superior predictive performance. Notably, the low MOCM group was more inclined towards ‘hot’ tumours, characterized by higher levels of immune cell infiltration. Intriguingly, a significant positive correlation between GJB3 and the MOCM score (R = 0.77, p