Identification of prognosis-related key genes in hepatocellular carcinoma based on bioinformatics analysis.
10.11817/j.issn.1672-7347.2025.240354
- Author:
Qian XIE
1
;
Yingshan ZHU
2
;
Ge HUANG
2
;
Yue ZHAO
2
Author Information
1. Department of Clinical Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510000, China. xieqian@gdph.org.cn.
2. Department of Clinical Laboratory Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou 510000, China.
- Publication Type:Journal Article
- Keywords:
bioinformatics;
differentially expressed genes;
hepatocellular carcinoma;
key genes;
prognostic biomarkers
- MeSH:
Humans;
Carcinoma, Hepatocellular/mortality*;
Liver Neoplasms/pathology*;
Prognosis;
Computational Biology/methods*;
Protein Interaction Maps/genetics*;
Biomarkers, Tumor/genetics*;
Gene Expression Regulation, Neoplastic;
Gene Expression Profiling;
Gene Ontology;
Databases, Genetic
- From:
Journal of Central South University(Medical Sciences)
2025;50(2):167-180
- CountryChina
- Language:English
-
Abstract:
OBJECTIVES:Hepatocellular carcinoma is one of the most common primary malignant tumors with the third highest mortality rate worldwide. This study aims to identify key genes associated with hepatocellular carcinoma prognosis using the Gene Expression Omnibus (GEO) database and provide a theoretical basis for discovering novel prognostic biomarkers for hepatocellular carcinoma.
METHODS:Hepatocellular carcinoma-related datasets were retrieved from the GEO database. Differentially expressed genes (DEGs) were identified using the GEO2R tool. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using the Database for Annotation, Visualization, and Integrated Discovery (DAVID). A protein-protein interaction (PPI) network was constructed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING), and key genes were identified using Cytoscape software. The University of Alabama at Birmingham Cancer Data Analysis Resource (UALCAN) was used to analyze the expression levels of key genes in normal and hepatocellular carcinoma tissues, as well as their associations with pathological grade, clinical stage, and patient survival. The Human Protein Atlas (THPA) was used to further validate the impact of key genes on overall survival. Expression levels of key genes in the blood of hepatocellular carcinoma patients were evaluated using the expression atlas of blood-based biomarkers in the early diagnosis of cancers (BBCancer).
RESULTS:A total of 78 DEGs were identified from the GEO database. GO and KEGG analyses indicated that these genes may contribute to hepatocellular carcinoma progression by promoting cell division and regulating protein kinase activity. Sixteen key genes were screened via Cytoscape and validated using UALCAN and THPA. These genes were overexpressed in hepatocellular carcinoma tissues and were associated with disease progression and poor prognosis. Finally, BBCancer analysis showed that ASPM and NCAPG were also elevated in the blood of hepatocellular carcinoma patients.
CONCLUSIONS:This study identified 16 key genes as potential prognostic biomarkers for hepatocellular carcinoma, among which ASPM and NCAPG may serve as promising blood-based markers for hepatocellular carcinoma.
