RT Journal Article
SR Electronic
T1 Identification of potential crucial genes associated with the pathogenesis and prognosis of liver hepatocellular carcinoma
JF Journal of Clinical Pathology
JO J Clin Pathol
FD BMJ Publishing Group Ltd and Association of Clinical Pathologists
SP 504
OP 512
DO 10.1136/jclinpath-2020-206979
VO 74
IS 8
A1 Laner Shi
A1 Xin Shang
A1 Kechao Nie
A1 Zhiqin Lin
A1 Meisi Zheng
A1 Miao Wang
A1 Haoyu Yuan
A1 Zhangzhi Zhu
YR 2021
UL http://jcp.bmj.com/content/74/8/504.abstract
AB 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.Data are available in a public, open access repository. Data are available on reasonable request. All data relevant to the study are included in the article or uploaded as supplemental information. The datasets (GEO data) and (TCGA LIHC data) for this study can be found in the GEO (https://www.ncbi.nlm.nih.gov/) and TCGA (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga)