A comprehensive review and meta-analysis of risk factors for statin-induced myopathy.

PURPOSE: To aid prescribers in assessing a patient's risk for statin-induced myopathy (SIM), we performed a comprehensive review of currently known risk factors and calculated aggregated odds ratios for each risk factor through a meta-analysis. METHODS: This meta-analysis was done through four phases: (1) Identification of the relevant primary literature; (2) abstract screening using inclusion and exclusion criteria; (3) detailed review and data extraction; and (4) synthesis and statistical analysis. RESULTS: Out of 44 papers analyzed from 836 papers searched from MEDLINE, 18 different potential risk factors were collected, divided into three categories: three demographics (11 papers), ten clinical factors (31 papers), and five pharmacogenetics/biomarkers (12 papers). Risk factors significant for myopathy and/or rhabdomyolysis included age, gender, diabetes, renal impairment, cardiovascular disease, certain interacting drugs, and mutations of the SLCO1B1 gene, which encodes a transporter protein in the liver. Several factors, such as gender, race, cardiovascular disease, and the GATM gene, which encodes a protein for creatine synthesis, appeared to be protective in terms of the outcomes of interest. CONCLUSIONS: This comprehensive assessment of risk factors can help support clinicians in reducing the incidence of SIM in their patient population on statins.

Comprehensive comparison of molecular portraits between cell lines and tumors in breast cancer.

BACKGROUND: Proper cell models for breast cancer primary tumors have long been the focal point in the cancer's research. The genomic comparison between cell lines and tumors can investigate the similarity and dissimilarity and help to select right cell model to mimic tumor tissues to properly evaluate the drug reaction in vitro. In this paper, a comprehensive comparison in copy number variation (CNV), mutation, mRNA expression and protein expression between 68 breast cancer cell lines and 1375 primary breast tumors is conducted and presented. RESULTS: Using whole genome expression arrays, strong correlations were observed between cells and tumors. PAM50 gene expression differentiated them into four major breast cancer subtypes: Luminal A and B, HER2amp, and Basal-like in both cells and tumors partially. Genomic CNVs patterns were observed between tumors and cells across chromosomes in general. High C > T and C > G trans-version rates were observed in both cells and tumors, while the cells had slightly higher somatic mutation rates than tumors. Clustering analysis on protein expression data can reasonably recover the breast cancer subtypes in cell lines and tumors. Although the drug-targeted proteins ER/PR and interesting mTOR/GSK3/TS2/PDK1/ER_P118 cluster had shown the consistent patterns between cells and tumor, low protein-based correlations were observed between cells and tumors. The expression consistency of mRNA verse protein between cell line and tumors reaches 0.7076. These important drug targets in breast cancer, ESR1, PGR, HER2, EGFR and AR have a high similarity in mRNA and protein variation in both tumors and cell lines. GATA3 and RP56KB1 are two promising drug targets for breast cancer. A total score developed from the four correlations among four molecular profiles suggests that cell lines, BT483, T47D and MDAMB453 have the highest similarity with tumors. CONCLUSIONS: The integrated data from across these multiple platforms demonstrates the existence of the similarity and dissimilarity of molecular features between breast cancer tumors and cell lines. The cell lines only mirror some but not all of the molecular properties of primary tumors. The study results add more evidence in selecting cell line models for breast cancer research.

Bi-EB: Empirical Bayesian Biclustering for Multi-Omics Data Integration Pattern Identification among Species.

Although several biclustering algorithms have been studied, few are used for cross-pattern identification across species using multi-omics data mining. A fast empirical Bayesian biclustering (Bi-EB) algorithm is developed to detect the patterns shared from both integrated omics data and between species. The Bi-EB algorithm addresses the clinical critical translational question using the bioinformatics strategy, which addresses how modules of genotype variation associated with phenotype from cancer cell screening data can be identified and how these findings can be directly translated to a cancer patient subpopulation. Empirical Bayesian probabilistic interpretation and ratio strategy are proposed in Bi-EB for the first time to detect the pairwise regulation patterns among species and variations in multiple omics on a gene level, such as proteins and mRNA. An expectation-maximization (EM) optimal algorithm is used to extract the foreground co-current variations out of its background noise data by adjusting parameters with bicluster membership probability threshold Ac; and the bicluster average probability p. Three simulation experiments and two real biology mRNA and protein data analyses conducted on the well-known Cancer Genomics Atlas (TCGA) and The Cancer Cell Line Encyclopedia (CCLE) verify that the proposed Bi-EB algorithm can significantly improve the clustering recovery and relevance accuracy, outperforming the other seven biclustering methods-Cheng and Church (CC), xMOTIFs, BiMax, Plaid, Spectral, FABIA, and QUBIC-with a recovery score of 0.98 and a relevance score of 0.99. At the same time, the Bi-EB algorithm is used to determine shared the causality patterns of mRNA to the protein between patients and cancer cells in TCGA and CCLE breast cancer. The clinically well-known treatment target protein module estrogen receptor (ER), ER (p118), AR, BCL2, cyclin E1, and IGFBP2 are identified in accordance with their mRNA expression variations in the luminal-like subtype. Ten genes, including CCNB1, CDH1, KDR, RAB25, PRKCA, etc., found which can maintain the high accordance of mRNA-protein for both breast cancer patients and cell lines in basal-like subtypes for the first time. Bi-EB provides a useful biclustering analysis tool to discover the cross patterns hidden both in multiple data matrixes (omics) and species. The implementation of the Bi-EB method in the clinical setting will have a direct impact on administrating translational research based on the cancer cell screening guidance.

Impact of demographic factors on gastrointestinal symptoms using population pyramid.

BACKGROUND: Gastrointestinal diseases are maladies that produce multiple symptoms. Suffering from these symptoms attributes people to an illness and self-treat or seek medical care. The objective of this study is to determine the relation between gastrointestinal symptoms and demographic factors using population pyramid. METHODS: This study was a cross-sectional survey conducted from May 2006 to December 2007 Tehran province, Iran. The questionnaire included personal and family characteristics such as age, gender, and educational level. In addition to this, interviewers asked about 10 GI symptoms. Comparison prevalence of GI symptoms among age groups was made via population pyramid. All analysis carried out using SPSS version 16. RESULTS: A total of 30,334 subjects were included in the study. The prevalence of GI symptoms increased with increasing age. Moreover, women had higher prevalence of symptoms and the GI symptoms were more prevalent in married individuals than single one. CONCLUSION: Age is dynamic in the sense that individuals continually age. It is the timing and sequencing of such changes that could be incorporated into analyses of health. So age pyramid is a flexible way to evaluate the effect of this factor on health in population.