Identification of potential crucial genes associated with the pathogenesis and prognosis of liver hepatocellular carcinoma.

Publisher: BMJ Pub. Group Country of Publication: England NLM ID: 0376601 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1472-4146 (Electronic) Linking ISSN: 00219746 NLM ISO Abbreviation: J Clin Pathol Subsets: MEDLINE

Publication: London : BMJ Pub. Group
Original Publication: London : British Medical Association

Gene Expression Profiling* ; Transcriptome*; Biomarkers, Tumor/*genetics ; Carcinoma, Hepatocellular/*genetics ; Liver Neoplasms/*genetics; Carcinoma, Hepatocellular/mortality ; Carcinoma, Hepatocellular/pathology ; Carcinoma, Hepatocellular/therapy ; Clinical Decision-Making ; Computational Biology ; Databases, Genetic ; Gene Expression Regulation, Neoplastic ; Gene Regulatory Networks ; Humans ; Liver Neoplasms/mortality ; Liver Neoplasms/pathology ; Liver Neoplasms/therapy ; Oligonucleotide Array Sequence Analysis ; Predictive Value of Tests ; Prognosis ; Protein Interaction Maps

Aims: Liver hepatocellular carcinoma (LIHC) is the main manifestation of primary liver cancer, with low survival rate and poor prognosis. Medical decision-making process of LIHC is so complex that new biomarkers for diagnosis and prognosis have yet to be explored, this study aimed to identify the genes involved in the pathophysiology of LIHC and biomarkers that can be used to predict the prognosis of LIHC.
Methods: Six Gene Expression Omnibus (GEO) datasets selected from GEO were screened and integrated to find out the differential expression genes (DEGs) obtained from LIHC and normal hepatic tissues. The Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes pathway enrichment analysis of DEGs was implemented by DAVID. The Protein-protein interaction network was performed via STRING. In addition, Cox regression model was used to construct a gene prognostic signature.
Results: We ascertained 10 hub genes, nine of them (CDK1, CDC20, CCNB1, Thymidylate synthetase, Nuclear division cycle80, NUF2, MAD2L1, CCNA2 and BIRC5) as biomarkers of progression in LIHC patients. We also build a six gene prognosis signature (SOCS2, GAS2L3, NLRP5, TAF3, UTP11 and GAGE2A), which can be implemented to predict over survival effectively.
Conclusions: We revealed promising genes that may participate in the pathophysiology of LIHC, and found available biomarkers for LIHC prognosis prediction, which were significant for researchers to further understand the molecular basis of LIHC and direct the synthesis medicine of LIHC.
Competing Interests: Competing interests: None declared.
(© Author(s) (or their employer(s)) 2021. No commercial re-use. See rights and permissions. Published by BMJ.)

Keywords: biomarkers; carcinoma; liver neoplasms; tumor

0 (Biomarkers, Tumor)

Date Created: 20201002 Date Completed: 20210802 Latest Revision: 20210802

20240829

10.1136/jclinpath-2020-206979

33004423