Skip to Main Content
 

Global Search Box

 
 
 
 

Files

File List


Qamar.Alsabi.thesis.19.pdf (1.18 MB)

ETD Abstract Container

Abstract Header

Characterizing Basal-Like Triple Negative Breast Cancer using Gene Expression Analysis: A Data Mining Approach.

Alsabi, Qamar
http://rave.ohiolink.edu/etdc/view?acc_num=wright1578936915199438

Abstract Details

2019, Master of Science in Biomedical Engineering (MSBME), Wright State University, Biomedical Engineering.
Triple-negative breast cancer (TNBC) is characterized by the absence of expression of the estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 (HER2). Therefore, TNBC is unresponsive to targeted hormonal therapies, which limits treatment options to nonselective chemotherapeutic agents. Basal-like breast cancers (BLBCs) represent a subset of about 70% of TNBCs, more frequently affecting younger patients, being more prevalent in African-American women and significantly more aggressive than tumors of other molecular subtypes, with high rates of proliferation and extremely poor clinical outcomes. Proper classification of BLBCs using current pathological tools has been a major challenge. Although TNBCs have many BLBC characteristics, the relationship between clinically defined TNBC and the gene expression profile of BLBC is not fully examined. The purpose of this study is to assemble publicly-available TNBC gene expression datasets generated by Affymetrix gene chips and define a set of genes, or gene signature, that can classify TNBC samples between BLBC and Non-BLBC subtypes. We compiled over 3,500 breast cancer gene expression profiles from several individual publicly available datasets and extracted Affymetrix gene expression data for 580 TNBC cases. Several popular data mining methods along with dimensionality reduction and feature selection techniques were applied to the resultant dataset to build predictive models to understand molecular characteristics and mechanisms associated with BLBCs and to classify them more accurately according to important features extracted through microarray data analysis of BLBC and Non-BLBC cases. Our result can lead to proper identification and diagnosis of BLBCs, which can potentially direct clinical implications by dictating the most effective therapy.
Jaime Ramirez-Vick, Ph.D. (Advisor)
Nasim Nosoudi, Ph.D. (Committee Member)
Amir Zadeh, Ph.D. (Committee Member)
73 p.
Biomedical Engineering; Genetics
Triple-negative breast cancer; TNBC; Basal-like breast cancer; BLBC; gene expression datasets; gene signature; Affymetrix gene chips; Affymetrix gene expression; data mining;

Recommended Citations

Citations

  • Alsabi, Q. (2019). Characterizing Basal-Like Triple Negative Breast Cancer using Gene Expression Analysis: A Data Mining Approach. [Master's thesis, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1578936915199438

    APA Style (7th edition)

  • Alsabi, Qamar. Characterizing Basal-Like Triple Negative Breast Cancer using Gene Expression Analysis: A Data Mining Approach. 2019. Wright State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=wright1578936915199438.

    MLA Style (8th edition)

  • Alsabi, Qamar. "Characterizing Basal-Like Triple Negative Breast Cancer using Gene Expression Analysis: A Data Mining Approach." Master's thesis, Wright State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=wright1578936915199438

    Chicago Manual of Style (17th edition)

wright1578936915199438
458
© 2019, some rights reserved.Creative Commons License Characterizing Basal-Like Triple Negative Breast Cancer using Gene Expression Analysis: A Data Mining Approach. by Qamar Alsabi is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by Wright State University and OhioLINK.