Identification of differentially expressed genes in peripheral blood mononuclear cells of patients with hepatocellular carcinoma and its regulatory network analysis.
- Author:
Yongzhi LUN
1
;
Jie SUN
1
Author Information
1. Department of Laboratory Medicine, School of Pharmacy and Medical Technology, Putian University, Putian 351100, Fujian Province, China.
- Publication Type:Journal Article
- MeSH:
Carcinoma, Hepatocellular;
physiopathology;
Gene Expression Regulation, Neoplastic;
Gene Regulatory Networks;
Humans;
Leukocytes, Mononuclear;
metabolism;
Liver Neoplasms;
physiopathology
- From:
Journal of Zhejiang University. Medical sciences
2019;48(2):148-157
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE:To identify the differentially expressed genes (DEGs) in peripheral blood mononuclear cells (PBMC) of patients with hepatocellular carcinoma (HCC) and to analyze their regulatory network.
METHODS:The DEGs in PBMCs of HCC patients were screened based on GEO database. The functional enrichment analysis and interaction analysis were carried out for DEGs. MCODE algorithm was used to screen core genes of DEGs, and the mirDIP and starBase online tools were used to predict upstream miRNAs and lncRNAs of the core genes.
RESULTS:A total of 265 DEGs with a high credibility were identified, which were mainly enriched in the biological activity, such as regulation of cell proliferation, metabolic regulation, cell communication and signaling, and inflammatory diseases according to Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, and the two analyses were correlated. Four diagnostic candidate genes were identified, including FUS RNA binding protein, C-X-C motif chemokine ligand 8, cullin 1 and RNA polymerase Ⅱ subunit H. Subsequently, 10 miRNAs, 1 lncRNAs and 38 circRNAs were predicted, and finally a lncRNA/circRNA-miRNA-mRNA-pathway regulatory networks was constructed.
CONCLUSIONS:The diagnostic candidate genes and its regulatory network in HCC PBMC have been identified based on data mining, which could provide potential tumor biomarkers for early diagnosis and treatment of HCC.
