Identification of seven-gene marker to predict the survival of patients with lung adenocarcinoma using integrated multi-omics data analysis.

ABSTRACT:

Background

The mechanism of cancer occurrence and development could be understood with multi-omics data analysis. Discovering genetic markers is highly necessary for predicting clinical outcome of lung adenocarcinoma (LUAD).

Methods

Clinical follow-up information, copy number variation (CNV) data, single nucleotide polymorphism (SNP), and RNA-Seq were acquired from The Cancer Genome Atlas (TCGA). To obtain robust biomarkers, prognostic-related genes, genes with SNP variation, and copy number differential genes in the training set were selected and further subjected to feature selection using random forests. Finally, a gene-based prediction model for LUAD was validated in validation datasets.

Results

The study filtered 2071 prognostic-related genes and 230 genomic vari

SUBMITTER: Zhang S 

PROVIDER: S-EPMC8841135 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature