The association of variants in PNPLA3 and GRP78 and the risk of developing hepatocellular carcinoma in an Italian population

// Daniele Balasus 1, * , Michael Way 2, * , Caterina Fusilli 3 , Tommaso Mazza 3 , Marsha Y. Morgan 2 , Melchiorre Cervello 4 , Lydia Giannitrapani 1 , Maurizio Soresi 1 , Rosalia Agliastro 5 , Manlio Vinciguerra 2, 6 , Giuseppe Montalto 1, 4 1 Biomedical Department of Internal Medicine and Medical Specialties, University of Palermo, Palermo, Italy 2 Institute for Liver & Digestive Health, Division of Medicine, Royal Free Campus, University College London, London, UK 3 IRCCS Casa Sollievo della Sofferenza, Bioinformatics Unit, San Giovanni Rotondo (FG), Italy 4 Institute of Biomedicine and Molecular Immunology, National Research Council (C.N.R.), Palermo, Italy 5 Immunohematology and Transfusion Medicine Unit, “Civico” Reference Regional Hospital, Palermo, Italy 6 Center for Translational Medicine (CTM), International Clinical Research Center (ICRC), St. Anne's University Hospital, Brno, Czech Republic * These authors have contributed equally to this work Correspondence to: Daniele Balasus, email: d77balasus@gmail.com Manlio Vinciguerra, email: m.vinciguerra@ucl.ac.uk Keywords: hepatocellular carcinoma, hepatitis C virus, single nucleotide polymorphisms, risk factors, genetic variants Received: July 26, 2016      Accepted: November 07, 2016      Published: November 24, 2016 ABSTRACT 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.