Hepatocellular carcinoma (HCC) has one of the worst prognoses amongst all malignancies. It commonly arises in patients with established liver disease and the diagnosis often occurs at an advanced stage. Genetic variations, such as single nucleotide polymorphisms (SNPs), may alter disease risk and thus may have use as predictive markers of disease outcome. The aims of this study were (i) to assess the association of two SNPs, rs430397 in GRP78 and rs738409 in PNPLA3 with the risk of developing HCC in a Sicilian association cohort and, (ii) to use a machine learning technique to establish a predictive combinatorial phenotypic model for HCC including rs430397 and rs738409 genotypes and clinical and laboratory attributes. The controls comprised of 304 healthy subjects while the cases comprised of 170 HCC patients the majority of whom had hepatitis C (HCV)-related cirrhosis. Significant associations were identified between the risk of developing HCC and both rs430397 (p=0.0095) and rs738409 (p=0.0063). The association between rs738409 and HCC was significantly stronger in the HCV positive cases. In the best prediction model, represented graphically by a decision tree with an acceptable misclassification rate of 17.0%, the A/A and G/A genotypes of the rs430397 variant were fixed and combined with the three rs738409 genotypes; the attributes were age, sex and alcohol. These results demonstrate significant associations between both rs430397 and rs738409 and HCC development in a Sicilian cohort. The combinatorial predictive model developed to include these genetic variants may, if validated in independent cohorts, allow for earlier diagnosis of HCC.
- Journal Article