Identifying Biomarkers Using Support Vector Machine to Understand the Racial Disparity in Triple-Negative Breast Cancer
Identifying Biomarkers Using Support Vector Machine to Understand the Racial Disparity in Triple-Negative Breast Cancer
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DOI：
10.1089/cmb.2022.0422
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发表时间：
2023-01-30
影响因子：
1.7
通讯作者：
Zelikovsky，Alex
中科院分区：
文献类型：
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作者：
Sahoo，Bikram;Pinnix，Zandra;Zelikovsky，Alex
三阴性乳腺癌（triple-negative breast cancer，TNBC）具有侵袭性和异质性肿瘤生物学特性，是一种临床预后差的乳腺癌。TNBC肿瘤中缺乏雌激素、孕激素和人表皮生长因子受体，导致临床治疗选择较少。非裔美国人（AA）女性的TNBC发病率高于欧洲裔美国人（EA）女性，临床结局较差。造成TNBC种族差异的重要因素是社会经济生活方式和肿瘤生物学。目前的研究考虑了三阴性乳腺癌样本种族信息的开源基因表达数据。我们实施了一个国家的最先进的分类支持向量机（SVM）的方法与经常性的特征消除方法的基因表达数据，以确定显着的生物标志物失调AA妇女和EA妇女。我们还包括斯皮尔曼的rho和沃德的链接方法在我们的功能选择工作流程。我们提出的方法生成24个特征/基因，可以对AA和EA样本进行98%的准确分类。我们还对24个特征/基因进行了Kaplan-Meier分析和对数秩检验。我们仅讨论了24个基因中2个基因（KLK 10和LRRC 37 A2）的表达失调与肿瘤进展的相关性，其中2个基因的生存率较差。我们相信，使用更多数量的RNA-seq基因表达数据进一步改进我们的方法将更准确地提供对TNBC种族差异的洞察。
With the properties of aggressive cancer and heterogeneous tumor biology, triple-negative breast cancer (TNBC) is a type of breast cancer known for its poor clinical outcome. The lack of estrogen, progesterone, and human epidermal growth factor receptor in the tumors of TNBC leads to fewer treatment options in clinics. The incidence of TNBC is higher in African American (AA) women compared with European American (EA) women with worse clinical outcomes. The significant factors responsible for the racial disparity in TNBC are socioeconomic lifestyle and tumor biology. The current study considered the open-source gene expression data of triple-negative breast cancer samples' racial information. We implemented a state-of-the-art classification Support Vector Machine (SVM) method with a recurrent feature elimination approach to the gene expression data to identify significant biomarkers deregulated in AA women and EA women. We also included Spearman's rho and Ward's linkage method in our feature selection workflow. Our proposed method generates 24 features/genes that can classify the AA and EA samples 98% accurately. We also performed the Kaplan–Meier analysis and log-rank test on the 24 features/genes. We only discussed the correlation between deregulated expression and cancer progression with a poor survival rate of 2 genes,KLK10andLRRC37A2, out of 24 genes. We believe that further improvement of our method with a higher number of RNA-seq gene expression data will more accurately provide insight into racial disparity in TNBC.