A large cohort study identifying a novel prognosis prediction model for lung adenocarcinoma through machine learning strategies.

ABSTRACT:

Background

Predicting lung adenocarcinoma (LUAD) risk is crucial in determining further treatment strategies. Molecular biomarkers may improve risk stratification for LUAD.

Methods

We analyzed the gene expression profiles of LUAD patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). We initially used three distinct algorithms (sigFeature, random forest, and univariate Cox regression) to evaluate each gene's prognostic relevance. Survival related genes were then fitted into the least absolute shrinkage and selection operator (LASSO) model to build a risk prediction model for LUAD. After 100,000 times of calculation and model construction, a 16-gene-based prediction model capable of classifying LUAD patients into high-risk and low-risk groups was succ

SUBMITTER: Li Y 

PROVIDER: S-EPMC6729062 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature