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. 2022 Jan 14;23:6. doi: 10.1186/s12863-021-01015-9

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© The Author(s) 2022

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 1 — A comprehensive assessment of breast cancer progression outline. The first step is data cleaning and normalization, the second step is Differential co-expression network (DCRN) reconstruction for four stages, and the third step is the computational approach for scoring and extracting breast cancer-related stage-specific (BCSS) subnetworks for each stage. In the fourth step, the survival analyses were implemented for four BCSS subnetworks, and a risk model fitted to data. In step five, the stage-related genes were detected; in step six, the topological changes, called re-wiring, among prognostic genes were assessed across stages. In step seven, the core biological pathways for stage-specific subnetworks were detected; in step eight, the breast cancer-specific prognostic genes were detected, and finally in step nine, the computational validation were implemented