Identifying genes for establishing a multigenic test for hepatocellular carcinoma surveillance in hepatitis C virus-positive cirrhotic patients.

In this study, we used the Affymetrix HG-U133A version 2.0 GeneChips to identify genes capable of distinguishing cirrhotic liver tissues with and without hepatocellular carcinoma by modeling the high-dimensional dataset using an L(1) penalized logistic regression model, with error estimated using N-fold cross-validation. Genes identified by gene expression microarray included those that have important links to cancer development and progression, including VAMP2, DPP4, CALR, CACNA1C, and EGR1. In addition, the selected molecular markers in the multigenic gene expression classifier were subsequently validated using reverse transcriptase-real time PCR, and an independently acquired gene expression microarray dataset was downloaded from Gene Expression Omnibus. The multigenetic classifier derived herein did similarly or better than standard abdominal ultrasonography and serum alpha-fetoprotein, which are currently used for hepatocellular carcinoma surveillance. Because early hepatocellular carcinoma diagnosis increases survival by increasing access to therapeutic options, these molecular markers may prove useful for early diagnosis of hepatocellular carcinoma, especially if prospectively validated and translated into gene products that can be reproducibly and reliably tested noninvasively.