Machine learning approach identifies inflammatory gene signature for predicting survival outcomes in hepatocellular carcinoma.

ABSTRACT:

Background

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related deaths worldwide, often linked to chronic inflammation. Our study aimed to probe inflammation pathways at the genetic level and pinpoint biomarkers linked to HCC patient survival.

Methods

We analyzed gene transcriptome data from 246 resectable stage I and II HCC patients from The Cancer Genome Atlas (TCGA). After selecting 917 inflammation-related genes (IRGs), we identified 104 differentially expressed genes (DEGs) through differential expression analysis. Two significant prognostic DEGs, S100A9 and PBK, were identified using LASSO and Cox regression, forming the basis of a risk score model. We conducted functional enrichment and immune landscape analyses, validated our findings on 170 patients f

SUBMITTER: Al-Bzour NN 

PROVIDER: S-EPMC11621542 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature