Clinical Implication of EGF A61G Polymorphism in the Risk of Non Small Cell Lung Adenocarcinoma Patients: A Case Control Study

Association between polymorphisms of epidermal growth factor 61 and susceptibility of lung cancer: A meta-analysis

To explore the association between epidermal growth factor (EGF) 61A/G polymorphism and lung cancer.All eligible case-control studies published up to August, 2019 were identified by searching PubMed, The excerpta medica database, China Academic Journals Full-text Database, China Biology Medicine, China National Knowledge Infrastructure, China Science and Technology Journal Database, and Wanfang databases. Two researchers independently identified the literature, extracted data, and evaluated quality according to inclusion and exclusion criteria. Meta-analysis was performed by Stata 15.0.A total of 6 studies is included, including 1487 cases and 2044 control subjects. Compared with allele A, allele G was considered to have no association with the risk of lung cancer, odds ratio = 1.07 (95% confidence interval: 0.98-1.15). GG recessive genotype, GG + GA dominant genotype, GG homozygote genotype and GA heterozygote genotype were found out that all of them are not associated with the risk of lung cancer. No association between EGF 61A/G polymorphism and lung cancer was found out by ethnical subgroup analysis. However, in view of the limitations of this study, such as the results of quantitative and sensitivity analysis may be lack of accuracy, so the conclusions of allele model and recessive gene model should be made carefully.It suggested that there was no association between polymorphism of EGF 61A/G and susceptibility of lung cancer.

EGF+61 A>G polymorphism is not associated with lung cancer risk in the Brazilian population

Epidermal growth factor (EGF) and its receptor (EGFR) play an important role in lung carcinogenesis. A functional single nucleotide polymorphism (SNP) in EGF promoter region (EGF+61 A>G-rs4444903) has been associated with cancer susceptibility. Yet, in lung cancer, the EGF+61 A>G role is unclear. The aim of this study was to evaluate the risk of lung cancer associated with EGF+61 A>G SNP in the Brazilian population. For that, 669 lung cancer patients and 1104 controls were analyzed. EGF+61 A>G genotype was assessed by PCR-RFLP and TaqMan genotyping assay. Both patients and controls were in Hardy-Weinberg equilibrium. As expected, uni- and multivariate analyses showed that tobacco consumption and age were significant risk factors for lung cancer. The genotype frequencies in lung cancer patients were 27.3% of AA, 47.4% of AG and 25.3% of GG, and for controls were 25.3% of AA, 51.6% of AG and 23.1% of GG. The allele frequencies were 51.1% of A and 48.9% of G for both cases and controls. No significant differences for the three genotypes (AA, AG and GG-codominant model) were observed between cases and controls. We then grouped AG and GG (recessive model) genotypes, as well as AA and AG (dominant model), and again, no significant differences were also found. This is the largest study to explore EGF+61 A>G polymorphism association with lung cancer risk and suggests that this SNP is not a risk factor for lung cancer in the Brazilian population.

MaxMIF: A New Method for Identifying Cancer Driver Genes through Effective Data Integration

Identification of a few cancer driver mutation genes from a much larger number of passenger mutation genes in cancer samples remains a highly challenging task. Here, a novel method for distinguishing the driver genes from the passenger genes by effective integration of somatic mutation data and molecular interaction data using a maximal mutational impact function (MaxMIF) is presented. When evaluated on six somatic mutation datasets of Pan-Cancer and 19 datasets of different cancer types from TCGA, MaxMIF almost always significantly outperforms all the existing state-of-the-art methods in terms of predictive accuracy, sensitivity, and specificity. It recovers about 30% more known cancer genes in 500 top-ranked candidate genes than the best among the other tools evaluated. MaxMIF is also highly robust to data perturbation. Intriguingly, MaxMIF is able to identify potential cancer driver genes, with strong experimental data support. Therefore, MaxMIF can be very useful for identifying or prioritizing cancer driver genes in the increasing number of available cancer genomic data.