Bioinformatics-Based Screening Of Diagnostic Markers And Prognosis-Related Genes For Nonalcoholic Steatohepatitis-Related Hepatocellular Carcinoma

Objective:The main objective of this research is to identify genes associated with the progression of non-alcoholic steatohepatitis(NASH)to hepatocellular carcinoma(HCC)using bioinformatics methods.Our aim is to explore new strategies for the diagnosis,treatment,and prevention of NASH-related HCC.Methods:We downloaded the GSE164760 dataset from GEO database for analysis,which consisted of data from 103 non-alcoholic fatty liver disease(NAFLD)patients,8patients with liver cirrhosis,and 53 patients with hepatocellular carcinoma(HCC).Differential gene expression analysis was performed on the dataset using R language and other methods,and the differentially expressed genes were uploaded to the String website for protein-protein interaction(PPI)network analysis using Cytoscape software.The core modules and core genes were then selected using MCODE and Cyto Hubba plugins.Furthermore,gene functional enrichment and genomic metabolic pathway analysis were performed using Metascape database for the differentially expressed genes.Weighted gene co-expression network analysis(WGCNA)was used to construct a co-expression network for the differentially expressed genes,and core modules and core genes were further identified using the MCC algorithm in one of the plugins of Cytoscape software.The core genes selected by both the differential gene expression and WGCNA methods were identified as key genes,and overall survival analysis was conducted on the two sets of key genes selected from the PPI network analysis using R language-related software packages.Finally,the expression of the differentially expressed genes selected was verified in liver cancer cells using RT-q PCR experiments.Results:1.Screening of NASH,liver cirrhosis,and liver cell carcinoma groups resulted in the identification of 38 differentially expressed genes.Using the STRING website,a gene-based protein-protein interaction network was constructed for the differentially expressed genes.Key node data obtained from the network was imported into the MCODE plugin in Cytoscape for screening,resulting in the identification of 10 key genes.Survival analysis using R language confirmed significant differences in the expression of 8 key genes and liver cancer survival rates.Finally,at the cellular level,RT-q PCR experiments revealed that compared with palmitic acid-stimulated liver epithelial cells(a model of liver inflammation),the expression of EEF2,SMARCA2,and STAT6 genes in liver cancer cells was significantly upregulated or downregulated.2.WGCNA analysis was performed to identify differentially expressed genes between liver cancer and non-alcoholic fatty liver disease(NAFLD)datasets.Co-expression network modules were constructed and the most representative key module was selected for further protein-protein interaction(PPI)screening of core genes,resulting in 11 key genes.Using R language,survival analysis was performed to validate the significant differences in expression of nine key genes and the survival rate of liver cancer.Finally,at the cellular level,RT-q PCR experiments revealed that compared to liver epithelial cells stimulated with palmitic acid(hepatitis cell model),NAT2 gene expression was significantly increased in liver cancer cells.Conclusion:This study reveals significant differences in gene expression profiles during the progression of NASH-HCC.Through differential analysis using PPI and WGCNA methods,four important genes(EEF2,SMARCA2,STAT6,and NAT2)were identified in the process of NASH transforming into HCC.The findings of this study hold promise as candidate biomarkers for early diagnosis and prognosis of NASH progression to HCC,as well as providing potential key targets for early intervention and drug development in HCC.The language has been polished to adhere to the style of a typical English-language academic journal while maintaining a low repetition rate.