Vol.:(0123456789) Protoplasma https://doi.org/10.1007/s00709-025-02081-x ORIGINAL ARTICLE Identification of adipose‑proximal biomarkers in breast cancer using weighted gene co‑expression network analysis Mona N. BinMowyna 1  · Zhou Yanduo 2  · Hu Jianxin 3  · Nasser A. Elhawary 4  · Ahmad H. Mufti 4  · Samar N. Ekram 4  · Suad Hamdan Almasoudi 5  · Roaa MohammedTahir Kassim 5  · Liang Chengcheng 6 Received: 22 March 2025 / Accepted: 3 June 2025 © The Author(s), under exclusive licence to Springer-Verlag GmbH Austria, part of Springer Nature 2025 Abstract Breast cancer is a widely studied cancer that involves multiple complex molecular mechanisms in its development and pro- gression. To gain a deeper understanding of the molecular mechanisms of breast cancer and to search for potential therapeutic targets and prognostic markers, we performed an in-depth analysis of breast cancer gene expression data using weighted co-expression network analysis. First, we downloaded breast cancer-related gene expression data from public databases and performed weighted co-expression network analysis. Through the analysis, we identified the purple modules that are closely related to breast cancer and screened out 224 genes for further functional enrichment analysis. To construct the protein inter- actions network, we selected 90 of these genes for analysis after screening. The GO enrichment analysis mainly focused on the response of extracellular matrix organization to hormones, negative regulation of angiogenesis, positive regulation of cell proliferation, positive regulation of epithelial-to-mesenchymal transition, transforming growth factor β-receptor signal- ing pathway, localization of proteins to membranes, response to cortisol, positive regulation of protein kinase B signaling, and other biological processes. KEGG pathway enrichment analysis mainly includes PI3K-Akt signaling pathway, TGF-β signaling pathway, cell cycle, proteoglycan in cancer, MAPK signaling pathway, and many other cancer disease pathways. Finally, we screened the key genes in the protein interactions network using Cytoscape’s MCODE plug-in and identified nine key markers, namely THBS2, ACTA2, TIMP1, VCAN, TGFB2, FN1, BGN, CCN2, and TAGLN. These genes may play an important role in the pathogenesis of breast cancer providing new ideas for breast cancer treatment and prognosis. Keywords Bioinformatics · Breast cancer · Gene expression omnibus · Weighted gene co-expression network analysis Introduction Cancer is a disease characterized by the abnormal prolif- eration of cells. It can affect any organ or structure in the body and primarily consists of small cells that have lost their normal growth control mechanisms (Roy and Saikia 2016; Torre et al. 2016). In recent years, the incidence of cancer has been on the rise, likely linked to our increased exposure to various potential carcinogens and changes in lifestyle. As a result, cancer has become one of the major health concerns of the twenty-first century, with its prevalence becoming increasingly serious worldwide (Kroemer and Pouyssegur 2008). It is estimated that one in four people will develop cancer at some point in their lifetime, which poses signifi- cant concerns for both society and individuals (Srivastava et al. 2019). The characteristics of cancer are closely linked to its inter- nal metabolic mechanisms, although the causal relationship Handling Editor: Martina Sombetzki * Liang Chengcheng lcc20151120@nwafu.edu.cn 1 College of Life Sciences, Shaqra University, Shaqra, Saudi Arabia 2 Agricultural College, Xinyang Agriculture and Forestry University, Xinyang 464000, Henan, China 3 Xinyang Animal Husbandry and Veterinary Technology Service Center, Xinyang 464000, Henan, China 4 Department of Medical Genetics College of Medicine, Umm Al-Qura University, Mecca, Saudi Arabia 5 Department of Biology, College of Sciences, Umm Al-Qura University, 21955 Makkah, Saudi Arabia 6 College of Animal Science and Technology, Xinyang Agriculture and Forestry University, Xinyang 464000, Henan, China