Comparative study and meta-analysis of meta-analysis studies for the correlation of genomic markers with early cancer detection

Abstract

A large number of common disorders, including cancer, have complex genetic traits, with multiple genetic and environmental components contributing to susceptibility. A literature search revealed that even among several meta-analyses, there were ambiguous results and conclusions. In the current study, we conducted a thorough meta-analysis gathering the published meta-analysis studies previously reported to correlate any random effect or predictive value of genome variations in certain genes for various types of cancer. The overall analysis was initially aimed to result in associations (1) among genes which when mutated lead to different types of cancer (e.g. common metabolic pathways) and (2) between groups of genes and types of cancer. We have meta-analysed 150 meta-analysis articles which included 4,474 studies, 2,452,510 cases and 3,091,626 controls (5,544,136 individuals in total) including various racial groups and other population groups (native Americans, Latinos, Aborigines, etc.). Our results were not only consistent with previously published literature but also depicted novel correlations of genes with new cancer types. Our analysis revealed a total of 17 gene-disease pairs that are affected and generated gene/disease clusters, many of which proved to be independent of the criteria used, which suggests that these clusters are biologically meaningful.

Data Mining the Protein Data Bank to Identify and Characterise Chameleon Coil Sequences that Form Symmetric Homodimer β-Sheet Interfaces

“…To explore combinations of features whish may allow discriminating chameleon fragments, unsupervised clustering was performed using different sets of features. More specifically, data were processed using a general purpose clustering tool, CLUTO [16], which has been used in a variety of bioinformatics applications [2,8,12,14]. In order to give each feature equal weight, a normalization process is applied.…”

Nutrigenomics 2.0: The Need for Ongoing and Independent Evaluation and Synthesis of Commercial Nutrigenomics Tests' Scientific Knowledge Base for Responsible Innovation

“…The methodology used by Pavlidis et al (2015) has solid precedence, rigorously peer-reviewed, and already been published in reputable forums as OMICS: A Journal of Integrative Biology as well as Human Genomics, the official journal of the Human Genome Organisation (HUGO) (Lanara et al, 2013).…”

“…Similar to other peer-reviewed meta-analysis work published elsewhere (Lanara et al, 2013), profile comparison and grouping were performed using a state-of-the-art bioinformatics tool, CLUTO (Rasmussen et al, 2003). More specifically, hierarchical agglomerative clustering was carried out to produce a binary tree representing similarities between profiles and highlighting possible groupings.…”

Meta-Analysis of Genes in Commercially Available Nutrigenomic Tests Denotes Lack of Association with Dietary Intake and Nutrient-Related Pathologies

“…Note that we also investigated variants/genes displaying opposite effects. As in our previous work (Lanara et al, 2013), profile comparison and grouping were performed using a state-of-the-art data-mining tool, CLUTO (Rasmussen et al, 2003). More specifically, hierarchical agglomerative clustering was carried out to produce a binary tree representing similarities between profiles and highlighting possible groupings.…”