Machine learning with in silico analysis markedly improves survival prediction modeling in colon cancer patients.

ABSTRACT:

Background

Predicting the survival of cancer patients provides prognostic information and therapeutic guidance. However, improved prediction models are needed for use in diagnosis and treatment.

Objective

This study aimed to identify genomic prognostic biomarkers related to colon cancer (CC) based on computational data and to develop survival prediction models.

Methods

We performed machine-learning (ML) analysis to screen pathogenic survival-related driver genes related to patient prognosis by integrating copy number variation and gene expression data. Moreover, in silico system analysis was performed to clinically assess data from ML analysis, and we identified RABGAP1L, MYH9, and DRD4 as candidate genes. These three genes and tumor stages were used to generate survi

SUBMITTER: Lee CJ 

PROVIDER: S-EPMC10067044 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature