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

ABSTRACT: 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

SUBMITTER: Lin H 

PROVIDER: S-EPMC11221067 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature