Machine learning-based analysis identifies a 13-gene prognostic signature to improve the clinical outcomes of colorectal cancer.

ABSTRACT:

Background

Colorectal cancer (CRC) is a common intestinal malignancy worldwide, posing a serious threat to public health. Due to its high heterogeneity, prognosis and drug response of different CRC patients vary widely, limiting the effectiveness of traditional treatment. Therefore, this study aims to construct a novel CRC prognostic signature using machine learning algorithms to assist in making informed clinical decisions and improving treatment outcomes.

Methods

Gene expression matrix and clinical information of CRC patients were obtained from the The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Then, genes with prognostic value were identified through univariate Cox regression analysis. Next, nine machine learning algorithms, including least ab

SUBMITTER: Xun D 

PROVIDER: S-EPMC11565104 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature