HIF1A gene Pro582Ser polymorphism and susceptibility to digestive tract cancers: a meta-analysis of case-control studies

Association of hypoxia-inducible factor 1α expression with susceptibility to hepatitis B virus-related hepatocellular carcinoma: A meta-analysis

Hypoxia-inducible factor 1α (HIF-1α) triggers tumorigenesis and progression in hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). Inconsistent findings have been reported on the influence of HIF-1α over-expression on the clinical outcomes of HBV-related HCC. This study aims to clarify the role of HIF-1α overexpression in the tumorigenesis and prognosis of HBV-induced HCC. Systematic and comprehensive search of online papers was carried out to elucidate the contribution of HIF-1α expression to susceptibility of HBV-induced HCC. STATA 12.0 software was utilized to analyze available data extracted from all eligible literature. Publication bias and sensitivity were comprehensively analyzed. A total of 23 published studies involving 2244 subjects were finally screened. The HIF-1α expression was remarkably upregulated in HBV-induced HCC tissues than in normal liver tissues, non-tumorous tissues, paraneoplastic tissues, and non-HBV HCC tissues. The high HIF-1α expression tended to be positively related to capsular infiltration (odds ratio (OR) 1.767; 95% confidence interval (CI) 1.058, 2.950). The HIF-1α expression was relevant to lymph node metastasis (OR 3.778; 95% CI 1.666, 8.568). High levels of HIF-1α expression tended to be closely implicated in portal vein invasion (OR 6.728, 95% CI 2.191, 20.656) but were irrelevant to alpha-fetoprotein, cirrhosis, Edmondson grading, tumor size, age, gender, and histological grade. Analysis of pooled data showed that HIF-1α was not statistically relevant to poor overall survival in HBV-related HCC. Our data provides compelling evidence that HIF-1α overexpression may imply a greater probability of invasion and metastasis in patients with HBV-induced HCC.

MUFFINN: cancer gene discovery via network analysis of somatic mutation data

A major challenge for distinguishing cancer-causing driver mutations from inconsequential passenger mutations is the long-tail of infrequently mutated genes in cancer genomes. Here, we present and evaluate a method for prioritizing cancer genes accounting not only for mutations in individual genes but also in their neighbors in functional networks, MUFFINN (MUtations For Functional Impact on Network Neighbors). This pathway-centric method shows high sensitivity compared with gene-centric analyses of mutation data. Notably, only a marginal decrease in performance is observed when using 10 % of TCGA patient samples, suggesting the method may potentiate cancer genome projects with small patient populations.