Dataset Information

[Identifying critical state of breast cancer cell differentiation based on landscape dynamic network biomarkers].

ABSTRACT: Breast cancer is a malignant tumor with the highest morbidity and mortality in female in recent years, and it is a complex disease that affects human health. Studies have shown that dynamic network biomarkers (DNB) can effectively identify critical states at which complex diseases such as breast cancer change from a normal state to a disease state. However, the traditional DNB method requires data from multiple samples in the same disease state, which is usually unachievable in clinical diagnosis. This paper quantitatively analyzes the time series data of MCF-7 breast cancer cells and finds the DNB module of a single sample in the time series based on landscape DNB (L-DNB) method. Then, a comprehensive index is constructed to detect its early warning signals to determine the critical state of breast cancer cell differentiation. The results of this study may be of great significance for the prevention and early diagnosis of breast cancer. It is expected that this paper can provide references for the related research of breast cancer.

SUBMITTER: Zhao H

PROVIDER: S-EPMC9927596 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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[Identifying critical state of breast cancer cell differentiation based on landscape dynamic network biomarkers].

Zhao Hongqian H Gao Jie J

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi 20200401 2

Breast cancer is a malignant tumor with the highest morbidity and mortality in female in recent years, and it is a complex disease that affects human health. Studies have shown that dynamic network biomarkers (DNB) can effectively identify critical states at which complex diseases such as breast cancer change from a normal state to a disease state. However, the traditional DNB method requires data from multiple samples in the same disease state, which is usually unachievable in clinical diagnosi ...[more]

PMID: 32329283

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Project description:BackgroundMining gene regulatory network (GRN) is an important avenue for addressing cancer mechanism. Mutations in cancer genome perturb GRN and cause a rewiring in an orchestrated network. Hence, the exploration of gene regulatory network rewiring is significant to discover potential biomarkers and indicators for discriminating cancer phenotypes.ResultsHere, we propose a new bioinformatics method of identifying biomarkers based on network rewiring in different states. It firstly reconstructs GRN in different phenotypic conditions from gene expression data with a priori background network. We employ the algorithm based on path consistency algorithm and conditional mutual information to delete false-positive regulatory interactions between independent nodes/genes or not closely related gene pairs. And then a differential gene regulatory network (D-GRN) is constructed from the rewiring parts in the two phenotype-specific GRNs. Community detection technique is then applied for D-GRN to detect functional modules. Finally, we apply logistic regression classifier with recursive feature elimination to select biomarker genes in each module individually. The extracted feature genes result in a gene set of biomarkers with impressing ability to distinguish normal samples from controls. We verify the identified biomarkers in external independent validation datasets. For a proof-of-concept study, we apply the framework to identify diagnostic biomarkers of breast cancer. The identified biomarkers obtain a maximum AUC of 0.985 in the internal sample classification experiments. And these biomarkers achieve a maximum AUC of 0.989 in the external validations.ConclusionIn conclusion, network rewiring reveals significant differences between different phenotypes, which indicating cancer dysfunctional mechanisms. With the development of sequencing technology, the amount and quality of gene expression data become available. Condition-specific gene regulatory networks that are close to the real regulations in different states will be established. Revealing the network rewiring will greatly benefit the discovery of biomarkers or signatures for phenotypes. D-GRN is a general method to meet this demand of deciphering the high-throughput data for biomarker discovery. It is also easy to be extended for identifying biomarkers of other complex diseases beyond breast cancer.

Dataset Information

[Identifying critical state of breast cancer cell differentiation based on landscape dynamic network biomarkers].

Publications

[Identifying critical state of breast cancer cell differentiation based on landscape dynamic network biomarkers].

Similar Datasets

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