CC BY 4.0 · Glob Med Genet 2023; 10(03): 144-158
DOI: 10.1055/s-0043-1768957
Original Article

Shared Genes and Molecular Mechanisms between Nonalcoholic Fatty Liver Disease and Hepatocellular Carcinoma Established by WGCNA Analysis

Juan He#
1   Traditional Chinese Medicine (ZHONG JING) School, Henan University of Chinese Medicine, Zhengzhou, Henan, People's Republic of China
Xin Zhang#
2   First School of Clinical Medicine, Henan University of Chinese Medicine, Zhengzhou, Henan, People's Republic of China
Xi Chen
2   First School of Clinical Medicine, Henan University of Chinese Medicine, Zhengzhou, Henan, People's Republic of China
Zongyao Xu
2   First School of Clinical Medicine, Henan University of Chinese Medicine, Zhengzhou, Henan, People's Republic of China
Xiaoqi Chen
2   First School of Clinical Medicine, Henan University of Chinese Medicine, Zhengzhou, Henan, People's Republic of China
Jiangyan Xu
1   Traditional Chinese Medicine (ZHONG JING) School, Henan University of Chinese Medicine, Zhengzhou, Henan, People's Republic of China
› Author Affiliations
Funding This research was funded by Henan Province Characteristic Backbone Discipline TCM Discipline Construction Project (STG-ZYXKY-2020019) and Henan Province Characteristic Backbone Discipline Chinese Medicine Second Batch Discipline Construction Project (STG-ZYX03-202126).


Background Hepatocellular carcinoma (HCC) is one of the leading causes of death from cancer worldwide. The histopathological features, risk factors, and prognosis of HCC caused by nonalcoholic fatty liver disease (NAFLD) appear to be significantly different from those of HCC caused by other etiologies of liver disease.

Objective This article explores the shared gene and molecular mechanism between NAFLD and HCC through bioinformatics technologies such as weighted gene co-expression network analysis (WGCNA), so as to provide a reference for comprehensive understanding and treatment of HCC caused by NAFLD.

Methods NAFLD complementary deoxyribonucleic acid microarrays (GSE185051) from the Gene Expression Omnibus database and HCC ribonucleic acid (RNA)-sequencing data (RNA-seq data) from The Cancer Genome Atlas database were used to analyze the differentially expressed genes (DEGs) between NAFLD and HCC. Then, the clinical traits and DEGs in the two disease data sets were analyzed by WGCNA to obtain W-DEGs, and cross-W-DEGs were obtained by their intersection. We performed subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) enrichment analyses of the cross-W-DEGs and established protein–protein interaction networks. Then, we identified the hub genes in them by Cytoscape and screened out the final candidate genes. Finally, we validated candidate genes by gene expression, survival, and immunohistochemical analyses.

Results The GO analysis of 79 cross-W-DEGs showed they were related mainly to RNA polymerase II (RNAP II) and its upstream transcription factors. KEGG analysis revealed that they were enriched predominantly in inflammation-related pathways (tumor necrosis factor and interleukin-17). Four candidate genes (JUNB, DUSP1, NR4A1, and FOSB) were finally screened out from the cross-W-DEGs.

Conclusion JUNB, DUSP1, NR4A1, and FOSB inhibit NAFLD and HCC development and progression. Thus, they can serve as potential useful biomarkers for predicting and treating NAFLD progression to HCC.

Ethics Approval and Consent to Participate


Consent for Publication


Availability of Data and Material

All the basic data involved in the analysis of this paper have been uploaded as Supplementary Files (SF), including the GSE185051 data set and the TCGA database gene expression matrix (SF 1, 2), and their differential gene matrix (SF 3, 4). The clinical phenotype information of the samples corresponding to the gene matrix (SF 5, 6) and the cross-W-DEGs of NAFLD and HCC (SF 7) were also included. KEGG, GO, PPI, and CytoHubba analysis results of cross-W-DEGs (SF 8, 9, 10, 11), the expression level validation results (SF 12, 13, 14), and the survival analysis results (SF 15, 16, 17, 18) of the candidate genes were also uploaded in the Supplementary Files.

Authors' Contributions

Z.X. realized the conception, data collection, and analysis in the article. H.J. performed the writing and editing. The remaining authors provided editing and writing assistance. All authors contributed to this article and read the submitted final version.

# These authors contributed equally to this work.

Publication History

Article published online:
10 July 2023

© 2023. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution License, permitting unrestricted use, distribution, and reproduction so long as the original work is properly cited. (

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