Generic placeholder image

Current Cancer Therapy Reviews

Editor-in-Chief

ISSN (Print): 1573-3947
ISSN (Online): 1875-6301

Review Article

Bioinformatics Approach for Data Capturing: The Case of Breast Cancer

Author(s): Ramji Gupta*, Nidhi Kala, Aravinda Pai and Rishabha Malviya*

Volume 17 , Issue 4 , 2021

Published on: 02 February, 2021

Page: [261 - 266] Pages: 6

DOI: 10.2174/1573394717666210203112941

Price: $65

Abstract

Background: With the rapid evolution in advanced computer systems and various statistical algorithms, it is now a days possible to analyze complex biological data. Bioinformatics is an interface between computational and biological assemblies. It is applied in various fields of biological as well as medical sciences.

Aim: The manuscript aims to summarize the developments in the field of breast cancer research through the applications of bioinformatics.

Methods: Various search engines like google, science direct, Scopus, PubMed, etc., were used for the literature survey.

Results: It describes the bioinformatics analysis tools and models, which include mainly artificial neural network models.

Conclusion: Bioinformatics is the evolutionary approach that is used for the capturing of data from the various case studies related to breast cancer.

Keywords: Bioinformatics, software, simulation, breast cancer, computational method, estrogen receptor.

Graphical Abstract
[1]
Oshi M, Takahashi H, Tokumaru Y, et al. G2M cell cycle pathway score as a prognostic biomarker of metastasis in estrogen receptor (ER)-positive breast cancer. Int J Mol Sci 2020; 21(8): 2921-38.
[http://dx.doi.org/10.3390/ijms21082921] [PMID: 32331421]
[2]
Lal S, McCart Reed AE, de Luca XM, Simpson PT. Molecular signatures in breast cancer. Methods 2017; 131: 135-46.
[http://dx.doi.org/10.1016/j.ymeth.2017.06.032] [PMID: 28669865]
[3]
Gendoo DMA. Bioinformatics and computational approaches for analyzing patient-derived disease models in cancer research. Comput Struct Biotechnol J 2020; 18: 375-80.
[http://dx.doi.org/10.1016/j.csbj.2020.01.010] [PMID: 32128067]
[4]
Latha NR, Rajan A, Nadhan R, et al. Gene expression signatures: A tool for analysis of breast cancer prognosis and therapy. Crit Rev Oncol Hematol 2020; 151: 102964-99.
[http://dx.doi.org/10.1016/j.critrevonc.2020.102964] [PMID: 32464482]
[5]
Lam SW, Jimenez CR, Boven E. Breast cancer classification by proteomic technologies: Current state of knowledge. Cancer Treat Rev 2014; 40(1): 129-38.
[http://dx.doi.org/10.1016/j.ctrv.2013.06.006] [PMID: 23891266]
[6]
Wu JR, Zhao Y, Zhou XP, Qin X. Estrogen receptor 1 and progesterone receptor are distinct biomarkers and prognostic factors in estrogen receptor-positive breast cancer: Evidence from a bioinformatic analysis. Biomed Pharmacother 2020; 121: 109647-57.
[http://dx.doi.org/10.1016/j.biopha.2019.109647] [PMID: 31733575]
[7]
Zaheed O, Samson J, Dean K. A bioinformatics approach to identify novel long, non-coding RNAs in breast cancer cell lines from an existing RNA-sequencing dataset. Noncoding RNA Res 2020; 5(2): 48-59.
[http://dx.doi.org/10.1016/j.ncrna.2020.02.004] [PMID: 32206740]
[8]
Kong Q, Ma Y, Yu J, Chen X. Predicted molecular targets and pathways for germacrone, curdione, and furanodiene in the treatment of breast cancer using a bioinformatics approach. Sci Rep 2017; 7(1): 15543.
[http://dx.doi.org/10.1038/s41598-017-15812-9] [PMID: 29138518]
[9]
Huang L, Zhao S, Frasor JM, Dai Y. An integrated bioinformatics approach identifies elevated cyclin E2 expression and E2F activity as distinct features of tamoxifen resistant breast tumors. PLoS One 2011; 6(7): e22274.
[http://dx.doi.org/10.1371/journal.pone.0022274] [PMID: 21789246]
[10]
Roy D, Morgan M, Yoo C, et al. Integrated bioinformatics, environmental epidemiologic and genomic approaches to identify environmental and molecular links between endometriosis and breast cancer. Int J Mol Sci 2015; 16(10): 25285-322.
[http://dx.doi.org/10.3390/ijms161025285] [PMID: 26512648]
[11]
Shamsi R, Seifi-Alan M, Behmanesh A, Omrani MD, Mirfakhraie R, Ghafouri-Fard S. A bioinformatics approach for identification of miR-100 targets implicated in breast cancer. Cell Mol Biol 2017; 63(10): 99-105.
[http://dx.doi.org/10.14715/cmb/2017.63.10.16] [PMID: 29096749]
[12]
Hu Y, Zhang S, Yu J, Liu J, Zheng S. SELDI-TOF-MS: The proteomics and bioinformatics approaches in the diagnosis of breast cancer. Breast 2005; 14(4): 250-5.
[http://dx.doi.org/10.1016/j.breast.2005.01.008] [PMID: 16085230]
[13]
Ryall KA, Kim J, Klauck PJ, et al. An integrated bioinformatics analysis to dissect kinase dependency in triple negative breast cancer. BMC Genomics 2015; 16(12)(Suppl. 12): S2.
[http://dx.doi.org/10.1186/1471-2164-16-S12-S2] [PMID: 26681397]
[14]
Habashy HO, Powe DG, Glaab E, et al. RERG (Ras-like, oestrogen-regulated, growth-inhibitor) expression in breast cancer: A marker of ER-positive luminal-like subtype. Breast Cancer Res Treat 2011; 128(2): 315-26.
[http://dx.doi.org/10.1007/s10549-010-1073-y] [PMID: 20697807]
[15]
Núñez-Marrero A. Assessing the role of the interleukin-12/STAT4 axis in breast cancer by a bioinformatics approach. Int J Sci Basic Appl Res 2019; 48(2): 38-52.
[PMID: 32467824]
[16]
Sonntag J, Bender C, Soons Z, et al. Reverse phase protein array based tumor profiling identifies a biomarker signature for risk classification of hormone receptor-positive breast cancer. Transl Proteom 2014; 2: 52-9.
[http://dx.doi.org/10.1016/j.trprot.2014.02.001]
[17]
Niida A, Smith AD, Imoto S, et al. Integrative bioinformatics analysis of transcriptional regulatory programs in breast cancer cells. BMC Bioinformatics 2008; 9(1): 404-17.
[http://dx.doi.org/10.1186/1471-2105-9-404] [PMID: 18823535]
[18]
Ha KC, Lalonde E, Li L, et al. Identification of gene fusion transcripts by transcriptome sequencing in BRCA1-mutated breast cancers and cell lines. BMC Med Genomics 2011; 4(1): 75-87.
[http://dx.doi.org/10.1186/1755-8794-4-75] [PMID: 22032724]
[19]
Barh D, Parida S, Parida BP, Viswanathan G. Let-7, miR-125, miR-205, and miR-296 are prospective therapeutic agents in breast cancer molecular medicine. Gene Ther Mol Biol 2008; 12(2): 189.
[20]
Johnson J, Thijssen B, McDermott U, Garnett M, Wessels LF, Bernards R. Targeting the RB-E2F pathway in breast cancer. Oncogene 2016; 35(37): 4829-35.
[http://dx.doi.org/10.1038/onc.2016.32] [PMID: 26923330]
[21]
Yotsukura S, Karasuyama M, Takigawa I, Mamitsuka H. A bioinformatics approach for understanding genotype–phenotype correlation in breast cancer. In: Big Data Analytics in Genomics. Cham: Springer 2016; pp. 397-428.
[http://dx.doi.org/10.1007/978-3-319-41279-5_13]
[22]
Zhang Y, Li Y, Wang Q, et al. Identification of an lncRNA‑miRNA‑mRNA interaction mechanism in breast cancer based on bioinformatic analysis. Mol Med Rep 2017; 16(4): 5113-20.
[http://dx.doi.org/10.3892/mmr.2017.7304] [PMID: 28849135]
[23]
Fang E, Zhang X. Identification of breast cancer hub genes and analysis of prognostic values using integrated bioinformatics analysis. Cancer Biomark 2017; 21(1): 373-81.
[PMID: 29081411]
[24]
Sepandi M, Taghdir M, Rezaianzadeh A, Rahimikazerooni S. Assessing breast cancer risk with an artificial neural network. Asian Pac J Cancer Prev 2018; 19(4): 1017-9.
[PMID: 29693975]
[25]
Janghel RR, Shukla A, Tiwari R, Kala R. Breast cancer diagnosis using artificial neural network models. The 3rd International Conference on Information Sciences and Interaction Sciences. 23-25 June; Chengdu, China. 2010; pp. 89-94.
[http://dx.doi.org/10.1109/ICICIS.2010.5534716]
[26]
Wang H, Zheng B, Yoon SW, Ko HS. A support vector machine-based ensemble algorithm for breast cancer diagnosis. Eur J Oper Res 2018; 267(2): 687-99.
[http://dx.doi.org/10.1016/j.ejor.2017.12.001]
[27]
Jia B, Zhao X, Wang Y, Wang J, Wang Y, Yang Y. Prognostic roles of MAGE family members in breast cancer based on KM-Plotter Data. Oncol Lett 2019; 18(4): 3501-16.
[http://dx.doi.org/10.3892/ol.2019.10722] [PMID: 31516568]
[28]
Cerami E, Gao J, Dogrusoz U, et al. The cBio cancer genomics portal: An open platform for exploring multidimensional cancer genomics data. Cancer Discov 2012; 2(5): 401-4.
[http://dx.doi.org/10.1158/2159-8290.CD-12-0095] [PMID: 22588877]
[29]
Shah SS, Senapati S, Klacsmann F, et al. Current technologies and recent developments for screening of HPV-associated cervical and oropharyngeal cancers. Cancers (Basel) 2016; 8(9): 1-27.
[http://dx.doi.org/10.3390/cancers8090085] [PMID: 27618102]
[30]
Lancashire LJ, Lemetre C, Ball GR. An introduction to artificial neural networks in bioinformatics--application to complex microarray and mass spectrometry datasets in cancer studies. Brief Bioinform 2009; 10(3): 315-29.
[http://dx.doi.org/10.1093/bib/bbp012] [PMID: 19307287]

Rights & Permissions Print Export Cite as
© 2022 Bentham Science Publishers | Privacy Policy