ArticleFrontiers in oncology2022
Screening of co-pathogenic genes of non-alcoholic fatty liver disease and hepatocellular carcinoma.
Article in Frontiers in oncology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Identification of circRNA/lncRNA-miRNA-Transcription Factor-mRNA Networks to Identify Potential Biomarkers in Response to Neoadjuvant Therapy for Breast Cancer.Breast cancer (Dove Medical Press) · 2026Article
- PRKAA2 mediates the pathogenesis of metabolic dysfunction-associated steatotic liver disease via PI3K/AKT signaling pathway.Human genomics · 2025Article
- Identification and Validation of Biomarkers in Metabolic Dysfunction-Associated Steatohepatitis Using Machine Learning and Bioinformatics.Molecular genetics & genomic medicine · 2025Article
- To explore the prognostic efficacy and mechanism of ABCC5 clinical scoring model in hepatocellular carcinoma.Frontiers in oncology · 2025Article
- Integrating molecular, biochemical, and immunohistochemical features as predictors of hepatocellular carcinoma drug response using machine-learning algorithms.Frontiers in molecular biosciences · 2024Article
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Abstract
Background: Non-alcoholic fatty liver disease (NAFLD) is a risk factor for hepatocellular carcinoma (HCC). However, its carcinogenic mechanism is still unclear, looking for both diseases' transcriptome levels, the same changes as we are looking for NAFLD may provide a potential mechanism of action of HCC. Thus, our study aimed to discover the coexisting pathogenic genes of NAFLD and HCC. Methods: We performed a variance analysis with public data for both diseases. At the same time, weighted gene correlation network analysis (WGCNA) was used to find highly correlated gene modules in both diseases. The darkturquoise gene module was found to be highly correlated with both diseases. Based on the diagnosis related module genes and the differential genes of the two diseases, we constructed diagnostic and prognostic models by logistic regression, univariate Cox regression, and LASSO regression. Public datasets verified the results. Meanwhile, we built a competing endogenous RNA (ceRNA) network based on the model genes and explored the related pathways and immune correlation involved in the two diseases by using Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and gene set enrichment analyses. Immunohistochemistry was used to verify the different expression of ABCC5 and TUBG1 among the normal liver, NAFLD, and HCC tissues. Sodium palmitate/sodium oleate was used to establish high-fat cell models, and Real Time Quantitative Polymerase Chain Reaction (RT-qPCR) was used to verify the messenger RNA (mRNA) expression of ABCC5 in lipidization cells. Results: A total of 26 upregulated genes and 87 downregulated genes were found using limma package identification analysis. According to WGCNA, the darkturquoise gene module was highly correlated with the prognosis of both diseases. The coexisting genes acquired by the two groups were only three central genes, that is, ABCC5, DHODH and TUBG1. The results indicated that the diagnostic and prognostic models constructed by ABCC5 and TUBG1 genes had high accuracy in both diseases. The results of immunohistochemistry showed that ABCC5 and TUBG1 were significantly overexpressed in NAFLD and HCC tissues compared with normal liver tissues. The Oil Red O staining and triglyceride identified the successful construction of HepG2 and LO2 high-fat models using PA/OA. The results of RT-qPCR showed that the lipidization of LO2 and HepG2 increased the mRNA expression of ABCC5. Conclusions: The gene model constructed by ABCC5 and TUBG1 has high sensibility and veracity in the diagnosis of NAFLD as well as the diagnosis and prognosis of HCC. ABCC5 and TUBG1 may play an important role in the development of NAFLD to HCC. In addition, lipidization could upregulate the mRNA expression of ABCC5 in HCC.
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