Data mining and machine learning approaches for the integration of genome-wide association and methylation data: methodology and main conclusions from GAW20

Identification of m6a-related signature genes in esophageal squamous cell carcinoma by machine learning method

Background: We aimed to construct and validate the esophageal squamous cell carcinoma (ESCC)-related m6A regulators by means of machine leaning. Methods: We used ESCC RNA-seq data of 66 pairs of ESCC from West China Hospital of Sichuan University and the transcriptome data extracted from The Cancer Genome Atlas (TCGA)-ESCA database to find out the ESCC-related m6A regulators, during which, two machine learning approaches: RF (Random Forest) and SVM (Support Vector Machine) were employed to construct the model of ESCC-related m6A regulators. Calibration curves, clinical decision curves, and clinical impact curves (CIC) were used to evaluate the predictive ability and best-effort ability of the model. Finally, western blot and immunohistochemistry staining were used to assess the expression of prognostic ESCC-related m6A regulators. Results: 2 m6A regulators (YTHDF1 and HNRNPC) were found to be significantly increased in ESCC tissues after screening out through RF machine learning methods from our RNA-seq data and TCGA-ESCA database, respectively, and overlapping the results of the two clusters. A prognostic signature, consisting of YTHDF1 and HNRNPC, was constructed based on our RNA-seq data and validated on TCGA-ESCA database, which can serve as an independent prognostic predictor. Experimental validation including the western and immunohistochemistry staining were further successfully confirmed the results of bioinformatics analysis. Conclusion: We constructed prognostic ESCC-related m6A regulators and validated the model in clinical ESCC cohort as well as in ESCC tissues, which provides reasonable evidence and valuable resources for prognostic stratification and the study of potential targets for ESCC.

Fibrate pharmacogenomics: expanding past the genome

Fibrates are a medication class prescribed for decades as 'broad-spectrum' lipid-modifying agents used to lower blood triglyceride levels and raise high-density lipoprotein cholesterol levels. Such lipid changes are associated with a decrease in cardiovascular disease, and fibrates are commonly used to reduce risk of dangerous cardiovascular outcomes. As with most drugs, it is well established that response to fibrate treatment is variable, and this variation is heritable. This has motivated the investigation of pharmacogenomic determinants of response, and multiple studies have discovered a number of genes associated with fibrate response. Similar to other complex traits, the interrogation of single nucleotide polymorphisms using candidate gene or genome-wide approaches has not revealed a substantial portion of response variation. However, recent innovations in technological platforms and advances in statistical methodologies are revolutionizing the use and integration of other 'omes' in pharmacogenomics studies. Here, we detail successes, challenges, and recent advances in fibrate pharmacogenomics.