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Comparative study and meta-analysis of meta-analysis studies for the correlation of genomic markers with early cancer detection
Authors:Zoi?Lanara,Efstathia?Giannopoulou,Marta?Fullen,Evangelos?Kostantinopoulos,Jean-Christophe?Nebel,Haralabos?P?Kalofonos,George?P?Patrinos,Cristiana?Pavlidis  author-information"  >  author-information__contact u-icon-before"  >  mailto:chpavlidou@upatras.gr"   title="  chpavlidou@upatras.gr"   itemprop="  email"   data-track="  click"   data-track-action="  Email author"   data-track-label="  "  >Email author
Affiliation:1.Faculty of Mathematical, Physical and Natural Sciences, Department of Biological Sciences,University of Trieste,Trieste,Italy;2.School of Health Sciences, Department of Pharmacy,University of Patras, University Campus,Rio, Patras,Greece;3.Clinical Oncology Laboratory, Division of Oncology, Department of Medicine,University of Patras,Rio, Patras,Greece;4.School of Computing and Information Systems, Faculty of Science, Engineering and Computing,Kingston University,London,UK
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.
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