ArticleScientific reports2017
Identification of novel dysregulated key genes in Breast cancer through high throughput ChIP-Seq data analysis.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Enhancer regulation in cancer: from epigenetics to mArchives of pharmacal research · 2025Review
- Mapping Multi-factor-mediated Chromatin Interactions to Assess Dysregulation of Lung Cancer-related Genes.Genomics, proteomics & bioinformatics · 2023Article
- Prevalence and characteristics of mouse mammary tumor virus-like virus associated breast cancer in China.Infectious agents and cancer · 2021Article
- Genetic co-expression networks contribute to creating predictive model and exploring novel biomarkers for the prognosis of breast cancer.Scientific reports · 2021Article
- Exploring novel key regulators in breast cancer network.PloS one · 2018Article
- Onco-Multi-OMICS Approach: A New Frontier in Cancer Research.BioMed research international · 2018Review
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is the most common cancer in women both in the developed and less developed countries, and it imposes a considerable threat to human health. Therefore, in order to develop effective targeted therapies against Breast cancer, a deep understanding of its underlying molecular mechanisms is required. The application of deep transcriptional sequencing has been found to be reported to provide an efficient genomic assay to delve into the insights of the diseases and may prove to be useful in the study of Breast cancer. In this study, ChIP-Seq data for normal samples and Breast cancer were compared, and differential peaks identified, based upon fold enrichment (with P-values obtained via t-tests). The Protein-protein interaction (PPI) network analysis was carried out, following which the highly connected genes were screened and studied, and the most promising ones were selected. Biological pathway involved in the process were then identified. Our findings regarding potential Breast cancer-related genes enhances the understanding of the disease and provides prognostic information in addition to standard tumor prognostic factors for future research.
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