A novel prognostic model based on multi-omics features predicts the prognosis of colon cancer patients.

ABSTRACT:

Background

As a common malignant tumor in the colon, colon cancer (CC) has high incidence and recurrence rates. This study is designed to build a prognostic model for CC.

Methods

The gene expression dataset, microRNA-seq dataset, copy number variation (CNV) dataset, DNA methylation dataset, and transcription factor (TF) dataset of CC were downloaded from UCSC Xena database. Using limma package, the differentially methylated genes (DMGs), and differentially expressed genes (DEGs) and miRNAs (DEMs) were identified. Based on random forest method, prognostic model for each omics dataset were constructed. After the omics features related to prognosis were selected using logrank test, the prognostic model based on multi-omics features was built. Finally, the clinical phenotypes co

SUBMITTER: Yang H 

PROVIDER: S-EPMC7336766 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature