López Fernández, AurelioGallejones Eskubi, JanireDel Saz Navarro, Dulcenombre de MaríaGómez-Vela, Francisco Antonio2025-12-122025-12-122024-08-28López-Fernández, A., Gallejones-Eskubi, J., M. Saz-Navarro, D., A. Gómez-Vela, F. (2024). Breast Cancer Biomarker Analysis Using Gene Co-expression Networks. In: Rojas, I., Ortuño, F., Rojas, F., Herrera, L.J., Valenzuela, O. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2024. Lecture Notes in Computer Science(), vol 14849. Springer, Cham. https://doi.org/10.1007/978-3-031-64636-2_910.1007/978-3-031-64636-2_9https://hdl.handle.net/10433/25202Gene co-expression networks have emerged as a robust tool for conducting comprehensive analyses of gene expression patterns. These networks, constructed through inference algorithms, facilitate the exploration of various biological processes and enable the identification of novel biomarkers from which to explore new lines of disease research. This work found that breast cancer stromal cells are strongly dysregulated in genes related to modifications in cellular structures that hold stromal tissue cells together, inflammatory responses, and molecules implicated in immune system regulation. Finally, ANAPC11, LRFN5, COL8A2, TEX11, DOCK9, CPLX1, LONP2, and LAT2 biomarkers were suggested in the context of stromal breast tumors.application/pdfenGene expression analysisGene co-expression networksArtificial IntelligenceBioinformaticsconference outputrestricted access