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pmid:33584787
Effect of N6-Methyladenosine Regulators on Progression and Prognosis of Triple-Negative Breast Cancer.
Shanshan Wang · Xuan Zou · Yajie Chen · William C Cho · Xiang Zhou
Frontiers in genetics · 2020 doi:10.3389/fgene.2020.580036
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Experiment
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Complete file
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Thus, the prognostic value of m6A RNA methylation in TNBC deserves exploration. Methods: The expression levels of the 13 m6A methylation regulators were compared between the 98 TNBC tumor samples and normal tissue samples based on the transcriptome profiles from The Cancer Genome Atlas (TCGA). The association between the m6A regulators and patients' overall survival was assessed by Kaplan-Meier survival analysis and Cox regression analysis. Lasso regression analysis was conducted to construct a prognostic model based on the m6A methylation system. The prognostic performance of the identified model was validated in GSE88847 and GSE135565 datasets. A nomogram combining the TNM stage and the m6A prognostic model was further constructed for the survival prediction of TNBC patients. Results: The m6A regulator genes were remarkably dysregulated in TNBC tumor tissues, with ALKBH5, YTHDF2, HNRNPC, KIAA1429, and RBM15 significantly up-regulated and FTO, YTHDC1, YTHDC2, METTL3, METTL14, and ZC3H13 significantly down-regulated (P < 0.01). The expression level of ALKBH5 was an independent unfavorable prognostic factor (HR = 3.327, P = 0.006), while METTL14 (HR = 0.425, P = 0.009) was an independent favorable prognostic factor for TNBC patients. A prognostic model consisting of ALKBH5 and METTL14 was therefore proposed displaying higher accuracy of risk prediction when combined with TNM stage with an AUC of 0.791. The prognostic value of the identified signature remained consistent within the two external validation datasets. Conclusion: The m6A methylation regulators were significantly dysregulated in TNBC tissues and could constitute a novel prognostic signature for the survival prediction of TNBC patients.", "claims": [{"id": "claim-method", "statement": "Lasso (least absolute shrinkage and selection) regression analysis using the glmnet (Friedman et al., 2010) and caret packages (https://CRAN.R-project.org/package=caret) was performed for variable selection.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33584787", "locator": "sections[4] \"Construction of the Prognostic Model\" paragraph 2", "quote": "Lasso (least absolute shrinkage and selection) regression analysis using the glmnet (Friedman et al., 2010) and caret packages (https://CRAN.R-project.org/package=caret) was performed for variable selection."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. 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