An efficient model for auxiliary diagnosis of hepatocellular carcinoma based on gene expression programming.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide. The early diagnosis of HCC is greatly helpful to achieve long-term disease-free survival. However, HCC is usually difficult to be diagnosed at an early stage. The aim of this study was to create the prediction model to diagnose HCC based on gene expression programming (GEP). GEP is an evolutionary algorithm and a domain-independent problem-solving technique. Clinical data show that six serum biomarkers, including gamma-glutamyl transferase, C-reaction protein, carcinoembryonic antigen, alpha-fetoprotein, carbohydrate antigen 153, and carbohydrate antigen 199, are related to HCC characteristics. In this study, the prediction of HCC was made based on these six biomarkers (195 HCC patients and 215 non-HCC controls) by setting up optimal joint models with GEP. The GEP model discriminated 353 out of 410 subjects, representing a determination coefficient of 86.28% (283/328) and 85.37% (70/82) for training and test sets, respectively. Compared to the results from the support vector machine, the artificial neural network, and the multilayer perceptron, GEP showed a better outcome. The results suggested that GEP modeling was a promising and excellent tool in diagnosis of hepatocellular carcinoma, and it could be widely used in HCC auxiliary diagnosis. Graphical abstract The process to establish an efficient model for auxiliary diagnosis of hepatocellular carcinoma.

Meta-analysis reveals an association between signal transducer and activator of transcription-4 polymorphism and hepatocellular carcinoma risk.

AIM: Hepatocellular carcinoma (HCC) is one of the most common causes of cancer-related mortality worldwide. Signal transducer and activator of transcription (STAT) proteins play a multitude of important functions in liver pathophysiology. Recent studies have indicated associations of rs7574865 single nucleotide polymorphism (SNP) in the STAT4 gene with various autoimmune diseases. The association between STAT4 polymorphism and the risk of HCC has been analyzed in several studies, but results remain inconsistent. This study used a meta-analysis approach to comprehensively investigate the correlation between STAT4 polymorphism and HCC risk based on previously published reports. METHODS: Studies were searched from the databases of PubMed, EMBase, Web of Science, and the Chinese National Knowledge Infrastructure up to 31 December 2015. The meta-analysis was carried out based on the statement of Preferred Reporting Items for Systematic Reviews and Meta-Analyses. RESULTS: Eight published studies, consisting of 7503 HCC patients (cases) and 13 831 individuals without HCC (controls), were included in the present study. Meta-analysis of the included studies revealed that STAT4 rs7574865 polymorphism contributed to the risk of HCC under all four genetic models, consisting of the allelic model (G vs. T: odds ratio [OR], 1.25; 95% confidence interval [CI], 1.19-1.30), the dominant effect model (GG + GT vs. TT: OR, 1.52; 95% CI, 1.26-1.84), the recessive effect model (GG vs. GT + TT: OR, 1.35; 95% CI, 1.21-1.50), and the co-dominant effect model (GG vs.. TT: OR, 1.72; 95% CI, 1.42-2.10) comparisons. No publication bias was indicated from either visualization of the funnel plot or Egger's test. CONCLUSION: A significantly increased risk of HCC associated with the rs7574865 G was found. The rs7574865 polymorphism might be used as one risk factor for HCC.