Clinical factors such as age, gender, alcohol use, and age-at-infection influence the progression to cirrhosis but cannot accurately predict the risk of developing cirrhosis in patients with chronic hepatitis C (CHC). The aim of this study was to develop a predictive signature for cirrhosis in Caucasian patients. All patients had well-characterized liver histology and clinical factors; DNA was extracted from whole blood for genotyping. We validated all significant markers from a genome scan in the training cohort, and selected 361 markers for the signature building. Using a "machine learning" approach, a signature consisting of markers most predictive for cirrhosis risk in Caucasian patients was developed in the training set (N = 420). The Cirrhosis Risk Score (CRS) was calculated to estimate the risk of developing cirrhosis for each patient. The CRS performance was then tested in an independently enrolled validation cohort of 154 Caucasian patients. A CRS signature consisting of 7 markers was developed for Caucasian patients. The area-under-the-ROC curves (AUC) of the CRS was 0.75 in the training cohort. In the validation cohort, AUC was only 0.53 for clinical factors, increased to 0.73 for CRS, and 0.76 when CRS and clinical factors were combined. A low CRS cutoff of <0.50 to identify low-risk patients would misclassify only 10.3% of high-risk patients, while a high cutoff of >0.70 to identify high-risk patients would misclassify 22.3% of low-risk patients.
Conclusion: CRS is a better predictor than clinical factors in differentiating high-risk versus low-risk for cirrhosis in Caucasian CHC patients. Prospective studies should be conducted to further validate these findings.