Diagnosis of HCC for patients with cirrhosis using miRNA profiles of the tumor-surrounding tissue - A statistical model based on stepwise penalized logistic regression

Abstract

The presence of hepatocellular carcinoma (HCC) is a significant complication of cirrhosis because it changes the prognosis and the treatment of the patients. By now, contrast-enhanced CT and MR scans are the most reliable tools for the diagnosis of HCC; however, in some cases, a biopsy of the tumor is necessary for the final diagnosis. The aim of the study was to develop a diagnostic tool using the microRNA (miRNA) profiles of the tissue surrounding the HCC tumor combined with clinical parameters in statistical models. At a transplantation setting, 32 patients with HCC and cirrhosis (B) were compared to 22 patients suffering from cirrhosis only (A). The diagnosis and exclusion of HCC was confirmed following the histopathological examination of the explanted liver. The HCC patients were significantly older than the patients with cirrhosis only (B: 60.6 and A: 49.9, p<0.001) and showed higher levels of ALT (A: 0.76μkat/l, B: 1.02μkat/, p=0.006) and AFP (A: 5.8ng/ml, B: 70.3ng/ml, p<0.001), whereas the bilirubin levels were higher in the cirrhosis only group (p=0.002). Using age (cut-off 50.23years) and AFP (cut-off 4.2ng/ml) thresholds, the levels of expression of miR-1285-3p and miR-943 differentiated between the patients with HCC and cirrhosis from those with cirrhosis only with an accuracy of 96.3%. This is the first report about the use of stepwise penalized logistic regression and decision tree analyses of miRNA expressions in the tumor-surrounding tissue combined with clinical parameters for the diagnosis of HCC.

Keywords: Biomarkers; HCC; Stepwise penalized logistic regression; Transplantation; Tumor associated cirrhosis.