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

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          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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          Most cited references202

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          Cancer genetics and epigenetics: two sides of the same coin?

          Epigenetic and genetic alterations have long been thought of as two separate mechanisms participating in carcinogenesis. A recent outcome of whole exome sequencing of thousands of human cancers has been the unexpected discovery of many inactivating mutations in genes that control the epigenome. These mutations have the potential to disrupt DNA methylation patterns, histone modifications, and nucleosome positioning and hence, gene expression. Genetic alteration of the epigenome therefore contributes to cancer just as epigenetic process can cause point mutations and disable DNA repair functions. This crosstalk between the genome and the epigenome offers new possibilities for therapy. Copyright © 2012 Elsevier Inc. All rights reserved.
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            Stat3: linking inflammation to epithelial cancer - more than a "gut" feeling?

            Inflammation is an important environmental factor that promotes tumourigenesis and the progression of established cancerous lesions, and recent studies have started to dissect the mechanisms linking the two pathologies. These inflammatory and infectious conditions trigger immune and stromal cell release of soluble mediators which facilitate survival and proliferation of tumour cells in a paracrine manner. In addition, (epi-)genetic mutations affecting oncogenes, tumour-suppressor genes, chromosomal rearrangements and amplifications trigger the release of inflammatory mediators within the tumour microenvironment to promote neoplastic growth in an autocrine manner. These two pathways converge in tumour cells and result in activation of the latent signal transducer and activator of transcription 3 (Stat3) which mediates a transcriptional response favouring survival, proliferation and angiogenesis. The abundance of cytokines that activate Stat3 within the tumour microenvironment, which comprises of members of the interleukin (IL) IL6, IL10 and IL17/23 families, underpins a signaling network that simultaneously promotes the growth of neoplastic epithelium, fuels inflammation and suppresses the host's anti-tumour immune response. Accordingly, aberrant and persistent Stat3 activation is a frequent observation in human cancers of epithelial origin and is often associated with poor outcome. Here we summarize insights gained from mice harbouring mutations in components of the Stat3 signaling cascade and in particular of gp130, the shared receptor for the IL6 family of cytokines. We focus on the various feed-back and feed-forward loops in which Stat3 provides the signaling node in cells of the tumour and its microenvironment thereby functionally linking excessive inflammation to neoplastic growth. Although these observations are particularly pertinent to gastrointestinal tumours, we suggest that the tumour's addiction to persistent Stat3 activation is likely to also impact on other epithelial cell-derived cancers. These insights provide clues to the judicious interference of the gp130/Stat3 signaling cascade in therapeutically targeting cancer.
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              Autocrine and paracrine regulation by cytokines and growth factors in melanoma.

              Tumour development and progression involves the expression of oncogenes and inactivation of tumour suppressor genes, leading to the appearance of multiple malignant characteristics. Malignant melanoma cells express different growth factors and cytokines and their receptors in respective stages of tumour progression, which by autocrine and paracrine effects enable them to grow autonomously and confer competence to metastasis. Autocrine growth factors (bFGF, MGSA/GRO, IL-8 and sometimes IL-6, PDGF-A, IL-10) produced by melanoma cells stimulate proliferation of the producing cell itself, while paracrine growth factors (for example PDGF, EGF, TGF-beta, IL-1, GM-CSF, IGF-I, NGF, VEGF) modulate the microenvironment to the benefit of tumour growth and invasion. Paracrine effects include angiogenesis, stroma formation, modulation of host immune response, activation of proteolytic enzymes, adhesion or motility and metastasis formation. Some growth factors have inhibitory effects on melanocytes and early lesions (IL-1, IL-6, TGF-beta, OSM, TNF and IFN) but not on advanced stage melanomas, and in some cases they switch to autocrine stimulator (IL-6, TGF-beta). Understanding the involvement of different growth factors and cytokines in the molecular mechanism of melanoma progression will help to provide an insight into new future therapeutic approaches for melanoma. Copyright 2000 Academic Press.
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                Author and article information

                Contributors
                Journal
                Hum Genomics
                Hum. Genomics
                Human Genomics
                BioMed Central
                1473-9542
                1479-7364
                2013
                5 June 2013
                : 7
                : 1
                : 14
                Affiliations
                [1 ]Faculty of Mathematical, Physical and Natural Sciences, Department of Biological Sciences, University of Trieste, Trieste, 34128, Italy
                [2 ]School of Health Sciences, Department of Pharmacy, University of Patras, University Campus, Rio, Patras 26504, Greece
                [3 ]Clinical Oncology Laboratory, Division of Oncology, Department of Medicine, University of Patras, Rio, Patras, 26504, Greece
                [4 ]School of Computing and Information Systems, Faculty of Science, Engineering and Computing, Kingston University, London, SW15 3DW, UK
                Article
                1479-7364-7-14
                10.1186/1479-7364-7-14
                3686617
                23738773
                6e2503f5-eed4-4ccc-b014-e3280079316a
                Copyright ©2013 Lanara et al.; licensee BioMed Central Ltd.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 25 March 2013
                : 1 May 2013
                Categories
                Primary Research

                Genetics
                cancer,meta-analysis,gene,association,interaction,single-nucleotide polymorphism,alleles,clustering
                Genetics
                cancer, meta-analysis, gene, association, interaction, single-nucleotide polymorphism, alleles, clustering

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