G6PD and machine learning algorithms as prognostic and diagnostic indicators of liver hepatocellular carcinoma.

ABSTRACT:

Background

Liver Hepatocellular carcinoma (LIHC) exhibits a high incidence of liver cancer with escalating mortality rates over time. Despite this, the underlying pathogenic mechanism of LIHC remains poorly understood.

Materials & methods

To address this gap, we conducted a comprehensive investigation into the role of G6PD in LIHC using a combination of bioinformatics analysis with database data and rigorous cell experiments. LIHC samples were obtained from TCGA, ICGC and GEO databases, and the differences in G6PD expression in different tissues were investigated by differential expression analysis, followed by the establishment of Nomogram to determine the percentage of G6PD in causing LIHC by examining the relationship between G6PD and clinical features, and the subsequent

SUBMITTER: Li F 

PROVIDER: S-EPMC10829225 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature