Uncovering Prognosis-Related Genes and Pathways by Multi-Omics Analysis in Lung Cancer.

ABSTRACT: Lung cancer is one of the leading causes of death worldwide. Therefore, understanding the factors linked to patient survival is essential. Recently, multi-omics analysis has emerged, allowing for patient groups to be classified according to prognosis and at a more individual level, to support the use of precision medicine. Here, we combined RNA expression and miRNA expression with clinical information, to conduct a multi-omics analysis, using publicly available datasets (the cancer genome atlas (TCGA) focusing on lung adenocarcinoma (LUAD)). We were able to successfully subclass patients according to survival. The classifiers we developed, using inferred labels obtained from patient subtypes showed that a support vector machine (SVM), gave the best classification results, with an accuracy

SUBMITTER: Asada K 

PROVIDER: S-EPMC7225957 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature