Identification of novel dysregulated key genes in Breast cancer through high throughput ChIP-Seq data analysis
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 mech...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2017-06-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-017-03534-x |
| _version_ | 1819042324134494208 |
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| author | Utkarsh Raj Imlimaong Aier Rahul Semwal Pritish Kumar Varadwaj |
| author_facet | Utkarsh Raj Imlimaong Aier Rahul Semwal Pritish Kumar Varadwaj |
| author_sort | Utkarsh Raj |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-21T09:39:04Z |
| format | Article |
| id | doaj.art-ef319c78b07349a1a3fd543560ce1d2c |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-21T09:39:04Z |
| publishDate | 2017-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-ef319c78b07349a1a3fd543560ce1d2c2022-12-21T19:08:32ZengNature PortfolioScientific Reports2045-23222017-06-017111110.1038/s41598-017-03534-xIdentification of novel dysregulated key genes in Breast cancer through high throughput ChIP-Seq data analysisUtkarsh Raj0Imlimaong Aier1Rahul Semwal2Pritish Kumar Varadwaj3Department of Bioinformatics & Applied Sciences, Indian Institute of Information TechnologyDepartment of Bioinformatics & Applied Sciences, Indian Institute of Information TechnologyDepartment of Bioinformatics & Applied Sciences, Indian Institute of Information TechnologyDepartment of Bioinformatics & Applied Sciences, Indian Institute of Information TechnologyAbstract 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.https://doi.org/10.1038/s41598-017-03534-x |
| spellingShingle | Utkarsh Raj Imlimaong Aier Rahul Semwal Pritish Kumar Varadwaj Identification of novel dysregulated key genes in Breast cancer through high throughput ChIP-Seq data analysis Scientific Reports |
| title | Identification of novel dysregulated key genes in Breast cancer through high throughput ChIP-Seq data analysis |
| title_full | Identification of novel dysregulated key genes in Breast cancer through high throughput ChIP-Seq data analysis |
| title_fullStr | Identification of novel dysregulated key genes in Breast cancer through high throughput ChIP-Seq data analysis |
| title_full_unstemmed | Identification of novel dysregulated key genes in Breast cancer through high throughput ChIP-Seq data analysis |
| title_short | Identification of novel dysregulated key genes in Breast cancer through high throughput ChIP-Seq data analysis |
| title_sort | identification of novel dysregulated key genes in breast cancer through high throughput chip seq data analysis |
| url | https://doi.org/10.1038/s41598-017-03534-x |
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