Single-Nucleotide Polymorphisms Promote Dysregulation Activation by Essential Gene Mediated Bio-Molecular Interaction in Breast Cancer

BackgroundBreast cancer (BRCA) is a malignant tumor with a high mortality rate and poor prognosis in patients. However, understanding the molecular mechanism of breast cancer is still a challenge.Materials and MethodsIn this study, we constructed co-expression networks by weighted gene co-expression...

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Autores principales:	Xue Wang, Zihui Zhao, Xueqing Han, Yutong Zhang, Yitong Zhang, Fenglan Li, Hui Li
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	breast cancer differential expression survival analysis single-nucleotide polymorphisms machine learning Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/b17a3dd0d9084a57b683301a4dbdcff7
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spelling	oai:doaj.org-article:b17a3dd0d9084a57b683301a4dbdcff72021-12-02T06:04:23ZSingle-Nucleotide Polymorphisms Promote Dysregulation Activation by Essential Gene Mediated Bio-Molecular Interaction in Breast Cancer2234-943X10.3389/fonc.2021.791943https://doaj.org/article/b17a3dd0d9084a57b683301a4dbdcff72021-12-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fonc.2021.791943/fullhttps://doaj.org/toc/2234-943XBackgroundBreast cancer (BRCA) is a malignant tumor with a high mortality rate and poor prognosis in patients. However, understanding the molecular mechanism of breast cancer is still a challenge.Materials and MethodsIn this study, we constructed co-expression networks by weighted gene co-expression network analysis (WGCNA). Gene-expression profiles and clinical data were integrated to detect breast cancer survival modules and the leading genes related to prognostic risk. Finally, we introduced machine learning algorithms to build a predictive model aiming to discover potential key biomarkers.ResultsA total of 42 prognostic modules for breast cancer were identified. The nomogram analysis showed that 42 modules had good risk assessment performance. Compared to clinical characteristics, the risk values carried by genes in these modules could be used to classify the high-risk and low-risk groups of patients. Further, we found that 16 genes with significant differential expressions and obvious bridging effects might be considered biological markers related to breast cancer. Single-nucleotide polymorphisms on the CYP24A1 transcript induced RNA structural heterogeneity, which affects the molecular regulation of BRCA. In addition, we found for the first time that ABHD11-AS1 was significantly highly expressed in breast cancer.ConclusionWe integrated clinical prognosis information, RNA sequencing data, and drug targets to construct a breast cancer–related risk module. Through bridging effect measurement and machine learning modeling, we evaluated the risk values of the genes in the modules and identified potential biomarkers for breast cancer. The protocol provides new insight into deciphering the molecular mechanism and theoretical basis of BRCA.Xue WangZihui ZhaoXueqing HanYutong ZhangYitong ZhangFenglan LiHui LiFrontiers Media S.A.articlebreast cancerdifferential expressionsurvival analysissingle-nucleotide polymorphismsmachine learningNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENFrontiers in Oncology, Vol 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	breast cancer differential expression survival analysis single-nucleotide polymorphisms machine learning Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	breast cancer differential expression survival analysis single-nucleotide polymorphisms machine learning Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Xue Wang Zihui Zhao Xueqing Han Yutong Zhang Yitong Zhang Fenglan Li Hui Li Single-Nucleotide Polymorphisms Promote Dysregulation Activation by Essential Gene Mediated Bio-Molecular Interaction in Breast Cancer
description	BackgroundBreast cancer (BRCA) is a malignant tumor with a high mortality rate and poor prognosis in patients. However, understanding the molecular mechanism of breast cancer is still a challenge.Materials and MethodsIn this study, we constructed co-expression networks by weighted gene co-expression network analysis (WGCNA). Gene-expression profiles and clinical data were integrated to detect breast cancer survival modules and the leading genes related to prognostic risk. Finally, we introduced machine learning algorithms to build a predictive model aiming to discover potential key biomarkers.ResultsA total of 42 prognostic modules for breast cancer were identified. The nomogram analysis showed that 42 modules had good risk assessment performance. Compared to clinical characteristics, the risk values carried by genes in these modules could be used to classify the high-risk and low-risk groups of patients. Further, we found that 16 genes with significant differential expressions and obvious bridging effects might be considered biological markers related to breast cancer. Single-nucleotide polymorphisms on the CYP24A1 transcript induced RNA structural heterogeneity, which affects the molecular regulation of BRCA. In addition, we found for the first time that ABHD11-AS1 was significantly highly expressed in breast cancer.ConclusionWe integrated clinical prognosis information, RNA sequencing data, and drug targets to construct a breast cancer–related risk module. Through bridging effect measurement and machine learning modeling, we evaluated the risk values of the genes in the modules and identified potential biomarkers for breast cancer. The protocol provides new insight into deciphering the molecular mechanism and theoretical basis of BRCA.
format	article
author	Xue Wang Zihui Zhao Xueqing Han Yutong Zhang Yitong Zhang Fenglan Li Hui Li
author_facet	Xue Wang Zihui Zhao Xueqing Han Yutong Zhang Yitong Zhang Fenglan Li Hui Li
author_sort	Xue Wang
title	Single-Nucleotide Polymorphisms Promote Dysregulation Activation by Essential Gene Mediated Bio-Molecular Interaction in Breast Cancer
title_short	Single-Nucleotide Polymorphisms Promote Dysregulation Activation by Essential Gene Mediated Bio-Molecular Interaction in Breast Cancer
title_full	Single-Nucleotide Polymorphisms Promote Dysregulation Activation by Essential Gene Mediated Bio-Molecular Interaction in Breast Cancer
title_fullStr	Single-Nucleotide Polymorphisms Promote Dysregulation Activation by Essential Gene Mediated Bio-Molecular Interaction in Breast Cancer
title_full_unstemmed	Single-Nucleotide Polymorphisms Promote Dysregulation Activation by Essential Gene Mediated Bio-Molecular Interaction in Breast Cancer
title_sort	single-nucleotide polymorphisms promote dysregulation activation by essential gene mediated bio-molecular interaction in breast cancer
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/b17a3dd0d9084a57b683301a4dbdcff7
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Single-Nucleotide Polymorphisms Promote Dysregulation Activation by Essential Gene Mediated Bio-Molecular Interaction in Breast Cancer

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