Identify of Potential Genetic Biomarkers for Hepatitis C Virus Related Hepatocellular Carcinoma

Objectives: Hepatocellular carcinoma (HCC) is a considerable global health concern. This study attempts to analyze gene expression data between liver tissues with HCV-related HCC and healthy liver tissues to identify potential biomarkers that contribute to HCC development.
Methods: We analyzed RNA sequencing data from liver tissues with HCV-related HCC and healthy liver tissues in this study. We retrieved the dataset from NCBI using the code GSE140845. We performed gene expression analysis using the Limma software package in the R programming language, defining genes as differentially expressed if they had a log2 fold change (log2FC) > 1 and a p > 0.05. We conducted data visualization using scatter plots, UMAP, volcano plots, and mean difference (MD) plots.
Results: A total of 20,868 genes were analyzed between the HCV-HCC and healthy liver tissue groups, and 3,303 genes were found to be significantly differentially expressed. Genes such as AKR1B10, MUC13, SLC22A11, and SPINK1 showed upregulation in the HCV-HCC group, whereas CNDP1, IGFALS, PVALB, and CLEC4M showed downregulation. These genes have the potential to serve as biomarkers and play critical roles in understanding the mechanisms of HCC development.
Conclusion: This study highlights the differential regulation of genes associated with HCV-HCC, emphasizing their potential roles in the pathogenesis of HCC. Notably, the identified biomarkers hold promise as therapeutic targets. These findings may contribute to personalized medicine approaches and enable the development of novel strategies for the prevention and treatment of HCC.