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Single-Sample Networks Reveal Intra-Cytoband Co-Expression Hotspots in Breast Cancer Subtypes. Int. J. Mol. Sci. 2024, 25, 12163. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)ijms252212163  \nAcademic Editor: Andrea Nicolini  \nReceived: 12 October 2024  \nRevised: 31 October 2024  \nAccepted: 7 November 2024  \nPublished: 13 November 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Advanced Center for Chronic Diseases—ACCDiS, Facultad de Ciencias Químicas y Farmacéuticas, Universidad de Chile, Santiago 8330015, Chile; [bio.richard.ponce@gmail.com](bio.richard.ponce@gmail.com)  \n2 Escuela Profesional de Medicina, Facultad de Ciencias de la Salud, Universidad Nacional de Moquegua, Moquegua 180101, Peru  \n3 Computational Genomics Division, National Institute of Genomic Medicine, Mexico City 14610, Mexico; [patrick.losa90@gmail.com](patrick.losa90@gmail.com)  \n4 Unidad de Genómica Avanzada—UGA, Facultad de Ciencias Químicas y Farmacéuticas, Universidad de Chile, Santiago 8330015, Chile  \n* [Correspondence: vinicius.maracaja@uchile.cl](Correspondence: vinicius.maracaja@uchile.cl) (V.M.-C.); [jespinal@inmegen.gob.mx](jespinal@inmegen.gob.mx) (J.E.-E.)  \nAbstract: Breast cancer is a heterogeneous disease comprising various subtypes with distinct molecular characteristics, clinical outcomes, and therapeutic responses. This heterogeneity evidences significant challenges for diagnosis, prognosis, and treatment. Traditional genomic co-expression network analyses often overlook individual-specific interactions critical for personalized medicine. In this study, we employed single-sample gene co-expression network analysis to investigate the structural and functional genomic alterations across breast cancer subtypes (Luminal A, Luminal B, Her2-enriched, and Basal-like) and compared them with normal breast tissue. We utilized RNA-Seq gene expression data to infer gene co-expression networks. The LIONESS algorithm allowed us to construct individual networks for each patient, capturing unique co-expression patterns. We focused on the top 10,000 gene interactions to ensure consistency and robustness in our analysis. Network metrics were calculated to characterize the topological properties of both aggregated and single-sample networks. Our findings reveal significant fragmentation in the co-expression networks of breast cancer subtypes, marked by a change from interchromosomal (TRANS) to intrachromosomal (CIS) interactions. This transition indicates disrupted long-range genomic communication, leading to localized genomic regulation and increased genomic instability. Single-sample analyses confirmed that these patterns are consistent at the individual level, highlighting the molecular heterogeneity of breast cancer. Despite these pronounced alterations, the proportion of CIS interactions did not significantly correlate with patient survival outcomes across subtypes, suggesting limited prognostic value. Furthermore, we identified high-degree genes and critical cytobands specific to each subtype, providing insights into subtype-specific regulatory networks and potential therapeutic targets. These genes play pivotal roles in oncogenic processes and may represent important keys for targeted interventions. The application of single-sample co-expression network analysis proves to be a powerful tool","cbCaibOYt10Cj4gE","https://ap.wps.com/l/cbCaibOYt10Cj4gE","pdf",9263189,22,"English","# Abstract\n# 1. Introduction","[{\"question\":\"What challenge does breast cancer heterogeneity create for clinical management?\",\"answer\":\"Heterogeneity manifests across patients and within individual tumors, complicating diagnosis, prognosis, and treatment.\"},{\"question\":\"How does the study model patient-specific gene interactions?\",\"answer\":\"It uses single-sample gene co-expression network analysis from RNA-Seq data, constructing individual networks for each patient with the LIONESS algorithm.\"},{\"question\":\"What major network change is reported across breast cancer subtypes?\",\"answer\":\"The findings show a shift from interchromosomal (TRANS) to intrachromosomal (CIS) interactions, indicating disrupted long-range genomic communication.\"}]","Single-Sample Networks Reveal Intra-Cytoband Co-Expression Hotspots in Breast Cancer Subtypes | PDF",1789999574,55]