ArticlePloS one2014
Prediction of potential cancer-risk regions based on transcriptome data: towards a comprehensive view.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 23 citations in OpenAlex.
- Identification of the key regulating genes of diminished ovarian reserve (DOR) by network and gene ontology analysis.Molecular biology reports · 2016Pooled it
- Predicting breast cancer risk using interacting genetic and demographic factors and machine learning.Scientific reports · 2020Article
- Oral mucosa tissue gene expression profiling before, during, and after radiation therapy for tonsil squamous cell carcinoma.PloS one · 2018Article
- Integration of machine learning and meta-analysis identifies the transcriptomic bio-signature of mastitis disease in cattle.PloS one · 2018Article
- Network analysis revealed aurora kinase dysregulation in five gynecological types of cancer.Oncology letters · 2018Article
- Computational Systems Biology Approach Predicts Regulators and Targets of microRNAs and Their Genomic Hotspots in Apoptosis Process.Molecular biotechnology · 2016Article
- Isolation, Characterization, Cryopreservation of Human Amniotic Stem Cells and Differentiation to Osteogenic and Adipogenic Cells.PloS one · 2016Article
- Unique ability of pandemic influenza to downregulate the genes involved in neuronal disorders.Molecular biology reports · 2015Article
- Prognostic role of miR-200c in various malignancies: a systematic review and meta-analysis.International journal of clinical and experimental medicine · 2015Article
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Corrections and comments
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Authors and funding
5 authors at 5 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A novel integrative pipeline is presented for discovery of potential cancer-susceptibility regions (PCSRs) by calculating the number of altered genes at each chromosomal region, using expression microarray datasets of different human cancers (HCs). Our novel approach comprises primarily predicting PCSRs followed by identification of key genes in these regions to obtain potential regions harboring new cancer-associated variants. In addition to finding new cancer causal variants, another advantage in prediction of such risk regions is simultaneous study of different types of genomic variants in line with focusing on specific chromosomal regions. Using this pipeline we extracted numbers of regions with highly altered expression levels in cancer condition. Regulatory networks were also constructed for different types of cancers following the identification of altered mRNA and microRNAs. Interestingly, results showed that GAPDH, LIFR, ZEB2, mir-21, mir-30a, mir-141 and mir-200c, all located at PCSRs, are common altered factors in constructed networks. We found a number of clusters of altered mRNAs and miRNAs on predicted PCSRs (e.g.12p13.31) and their common regulators including KLF4 and SOX10. Large scale prediction of risk regions based on transcriptome data can open a window in comprehensive study of cancer risk factors and the other human diseases.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.