Identification of Key Genes in HCV-induced HCC at Early Stage on the Background of HCV-cirrhosis

Abstract

Background: Hepatocellular carcinoma (HCC) is one of the most common and deadly malignant tumors worldwide. Hepatitis C virus (HCV)-induced cirrhosis (HCV-cirrhosis) is one of the leading causes of HCC occurrence. Due to the lack of effective biomarkers, most patients with HCV-induced HCC (HCV-HCC) are at advanced stages when they are first diagnosed. Our study aims to employ transcriptomic profiling dataset to identify potential biomarkers that can predict HCV-HCC at early stage on the background of HCV-cirrhosis. Methods: The dataset incorporating HCV-cirrhosis and HCV-HCC subjects at different stages from Gene Expression Omnibus was analyzed to identify gene signature-related to HCV-HCC on the background of HCV-cirrhosis. Multiple-genes based risk score-related prediction model was established to predict HCV-HCC samples at different stages, and receiver-operating characteristic (ROC) curve analysis was used to select the best cutoff value of risk scores for prediction and to evaluate the prediction model. Samples with risk scores higher than cutoff value were defined as high score group.Results: Highly clustered 20 genes were identified and were all gradually increased as HCV-HCC progressed from HCV-cirrhosis to very advanced HCC. Compared with HCV-cirrhosis, the prevalence of HCV-HCC at any of the stages in high score group was 100%. The risk score-related prediction model based on the 20 genes-biomarker got the accuracy of over 95.2 % with area under ROC (AUC) over 0.94. To implement the biomarker easily in real life and make it economical, we tried to limit the gene numbers. Interestingly, CDKN3 and FAM83D were significantly decreased in HCV-cirrhosis tissues as compared to normal tissues and then increased as HCV-HCC progressed. The results were attractive that the prediction model based on this 2 genes-biomarker indicated that prevalence of HCV-HCC in high score group was still 100% and got the predictive accuracy of over 95.2 % with AUC over 0.96, revealing even a better performance.Conclusion: Our study indicated a 2-genes-based biomarker that could identify HCV-HCC at earlier stages on the background of HCV-cirrhosis, which might be a promising biomarker for early diagnosis of HCV-HCC and potential novel treatment target.