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Combined mRNAs and clinical factors model on predicting prognosis in patients with triple-negative breast cancer

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  • Yanjun Hu
  • Dehong Zou

Abstract

Objective: Triple-negative breast cancer (TNBC) is aggressive cancer usually diagnosed in young women with no effective prognosis prediction model to use. The present study was performed to develop a useful prognostic model for predicting overall survival (OS) for TNBC patients. Methods: The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) databases were used as training and validation data sets, respectively, in which the gene expression levels and clinical prognostic information of TNBC were collected. Differentially expressed genes (DEGs) between TNBC and non-TNBC (NTNBC) were identified with the thresholds of false discovery rate 1. DEGs in AmiGO2 and the Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were retained for further study. Univariate, multivariate Cox, and logistic regression analysis were conducted for detecting DEG signature with the threshold of log-rank P

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  • Yanjun Hu & Dehong Zou, 2021. "Combined mRNAs and clinical factors model on predicting prognosis in patients with triple-negative breast cancer," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-17, December.
    • Handle: RePEc:plo:pone00:0260811
    DOI: 10.1371/journal.pone.0260811
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    1. Lee, Jae Won & Lee, Jung Bok & Park, Mira & Song, Seuck Heun, 2005. "An extensive comparison of recent classification tools applied to microarray data," Computational Statistics & Data Analysis, Elsevier, vol. 48(4), pages 869-885, April.
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