ANALYSIS OF DIFFERENTIALLY EXPRESSED GENES IN BREAST CANCER SAMPLES FROM THE SEQUENCE READ ARCHIVE (SRA)
DOI:
https://doi.org/10.47820/recima21.v5i3.4955Keywords:
Gene Expression Profiling, Computational Biology, Breast Neoplasms, RNA-seqAbstract
Breast cancer (BC) is a highly prevalent disease in women with millions of new cases each year. Among the technological advances, RNA-seq technology stands out, which has allowed us to better understand gene expression, making it possible to unveil protein interactions between early and recurrent (post-mastectomy) breast tumors. New tools based on bioinformatics have emerged to follow the advancement of sequencing, with the main examples being the online analysis platforms Galaxy and WebGestalt. Additionally, the Sequence Read Archive (SRA) was established as a public repository for next-generation sequence data, as was the use of the Gene Expression Omnibus (GEO) functional genomic data repository. In this work, using total RNA sequencing analysis, it was possible to demonstrate generalized comparisons of early-stage CM with recurrent CM. Furthermore, Gene Ontology (GO), KEGG and Reactome were used to evaluate the functional relationships and improved pathways between early-stage CM and post-mastectomy recurrent CM. In conclusion, through the development of this study it was possible to discover new biomarkers that could be used as future therapeutic targets, enabling a better diagnosis and prognosis in BC aiming to improve the overall survival of patients.
Downloads
References
AFGAN, Enis et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update. Nucleic Acids Research, v. 46, n. W1, p. W537–W544, 2018. DOI: https://doi.org/10.1093/nar/gky379
CAMPÊLO DE SOUSA, Maisa; CAMPÊLO DE SOUSA, Camila. Diagnóstico de câncer de mama por exames genéticos: uma revisão de literatura (Diagnosis of breast cancer by genetic exams: a literature review). Brazilian Journal of health Review Braz. J. Hea. Rev, Teresina & Codó, n. 2, p. 1786–1797, 2020. DOI: https://doi.org/10.34119/bjhrv3n2-039
CHEN, Jiarui et al. KEGG-expressed genes and pathways in triple negative breast cancer. Medicine (Baltimore), v. 99, n. 18, e19986, 2020. Doi: 10.1097 / MD.0000000000019986. PMCID: PMC7440132. PMID: 32358373. DOI: https://doi.org/10.1097/MD.0000000000019986
COSTA-SILVA, Juliana; DOMINGUES, Douglas; LOPES, Fabricio Martins. RNA-Seq differential expression analysis: An extended review and a software tool. PLoS ONE, New Jersey (EUA), 21 dec. 2017. DOI: https://doi.org/10.1371/journal.pone.0190152
KEENE, Kimberly S. et al. Molecular determinants of post-mastectomy breast cancer recurrence. NPJ Breast Cancer, v. 4, n. 34, 2018. Doi: 10.1038 / s41523-018-0089-z. PMCID: PMC6185974. PMID: 30345349.
KLOET, Frans M. van der; et al. Increased comparability between RNA-Seq and microarray data by utilization of gene sets. PLoS Comput Biol., v. 16, n. 9, e1008295, 2020. Doi: 10.1371 / journal.pcbi.1008295. PMCID: PMC7549825. PMID: 32997685. DOI: https://doi.org/10.1371/journal.pcbi.1008295
LEINONEN, Rasko; SUGAWARA, Hideaki; SHUMWAY, Martin. The sequence read archive. Nucleic Acids Research, v. 39, n. 1, p. 3, 2011. DOI: https://doi.org/10.1093/nar/gkq1019
LIAO, Yuxing et al. WebGestalt 2019: gene set analysis toolkit with revamped UIs and APIs. Nucleic acids research, v. 47, n. W1, p. W199–W205, 2019. DOI: https://doi.org/10.1093/nar/gkz401
OSHLACK, Alicia; ROBINSON, Mark; YOUNG, Matthew. From RNA-seq Reads to Differential. Genome Biology, Parkville, Australia, p. 10, 2010. DOI: https://doi.org/10.1186/gb-2010-11-12-220
PAL, Bhupinder et al. A single-cell RNA expression atlas of normal, preneoplastic and tumorigenic states in the human breast. EMBO J., v. 40, n. 11, e107333, 2021. Doi: 10.15252 / embj.2020107333. PMCID: PMC8167363. PMID: 33950524. DOI: https://doi.org/10.15252/embj.2020107333
PARSONS, Joseph; FRANCAVILLA, Chiara. ‘Omics Approaches to Explore the Breast Cancer Landscape. Front Cell Dev Biol., v. 7, n. 395, 2020. Doi: 10.3389 / fcell.2019.00395. PMCID: PMC6987401. PMID: 32039208. DOI: https://doi.org/10.3389/fcell.2019.00395
RAO, Arunagiri Kuha Deva Magendhra; et al. Identification of lncRNAs associated with early-stage breast cancer and their prognostic implications. Mol Oncol., v 13, n. 6, p. 1342–1355, 2019. Doi: 10.1002 / 1878- 0261.12489. PMCID: PMC6547626. PMID: 30959550. DOI: https://doi.org/10.1002/1878-0261.12489
RODRIGUEZ-ESTEBAN, Raul; JIANG, Xiaoyu. Differential gene expression in disease: a comparison between high-throughput studies and the literature. BMC Medical Genomics, v. 10, n. 59, 2017. DOI: https://doi.org/10.1186/s12920-017-0293-y
SIMPSON, Peter T. et al. Molecular evolution of breast cancer. Journal of Pathology, 2005. DOI: https://doi.org/10.1002/path.1691
STUPNIKOV, A. et al. Robustness of differential gene expression analysis of RNA-seq. Comput Struct Biotechnol J., v. 19, p. 3470–3481, 2021. Doi: 10.1016/j.csbj.2021.05.040. PMCID: PMC8214188. PMID: 34188784. DOI: https://doi.org/10.1016/j.csbj.2021.05.040
THE GENE ONTOLOGY CONSORTIUM. The Gene Ontology resource: enriching a GOld mine. Nucleic Acids Research, v. 49, n. D1, p. D325-D334, 2021. https://doi.org/10.1093/nar/gkaa1113. DOI: https://doi.org/10.1093/nar/gkaa1113
TIAN, Zelin et al. Identification of Important Modules and Biomarkers in Breast Cancer Based on WGCNA. Onco Targets Ther., v. 13, p. 6805–6817, 2020. Doi: 10.2147 / OTT.S258439. PMCID: PMC7367932. PMID: 32764968. DOI: https://doi.org/10.2147/OTT.S258439
WU, Shaocheng et al. Cellular, transcriptomic and isoform heterogeneity of breast cancer cell line revealed by full-length single-cell RNA sequencing. Comput Struct Biotechnol J., v. 18, p. 676–685, 2020. Doi: 10.1016 / j.csbj.2020.03.005. PMCID: PMC7114460. PMID: 32257051. DOI: https://doi.org/10.1016/j.csbj.2020.03.005
ZHANG, Fan et al. Identification of novel alternative splicing biomarkers for breast cancer with LC/MS/MS and RNA-Seq. BMC Bioinformatics, v. 21, n. 541, 2020. Doi: 10.1186 / s12859-020-03824-8. PMCID: PMC7713335. PMID: 33272210.
ZHAO, Yingwen et al. A Literature Review of Gene Function Prediction by Modeling Gene Ontology. Front Genet., v. 11, n. 400, 2020. Doi: 10.3389 / fgene.2020.00400. PMCID: PMC7193026. PMID: 32391061. DOI: https://doi.org/10.3389/fgene.2020.00400
Downloads
Published
License
Copyright (c) 2024 RECIMA21 - Revista Científica Multidisciplinar - ISSN 2675-6218
This work is licensed under a Creative Commons Attribution 4.0 International License.
Os direitos autorais dos artigos/resenhas/TCCs publicados pertecem à revista RECIMA21, e seguem o padrão Creative Commons (CC BY 4.0), permitindo a cópia ou reprodução, desde que cite a fonte e respeite os direitos dos autores e contenham menção aos mesmos nos créditos. Toda e qualquer obra publicada na revista, seu conteúdo é de responsabilidade dos autores, cabendo a RECIMA21 apenas ser o veículo de divulgação, seguindo os padrões nacionais e internacionais de publicação.