Robust biomarker discovery for hepatocellular carcinoma from high-throughput data by multiple feature selection methods.

ABSTRACT:

Background

Hepatocellular carcinoma (HCC) is one of the most common cancers. The discovery of specific genes severing as biomarkers is of paramount significance for cancer diagnosis and prognosis. The high-throughput omics data generated by the cancer genome atlas (TCGA) consortium provides a valuable resource for the discovery of HCC biomarker genes. Numerous methods have been proposed to select cancer biomarkers. However, these methods have not investigated the robustness of identification with different feature selection techniques.

Methods

We use six different recursive feature elimination methods to select the gene signiatures of HCC from TCGA liver cancer data. The genes shared in the six selected subsets are proposed as robust biomarkers. Akaike information criterion

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC8386074 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature