乳腺癌以其复杂性和多样性为特征，对理解其基础生物学提出了重大挑战。在本研究中，我们采用基因共表达网络分析来研究乳腺癌亚型和正常乳腺组织中的基因组成和功能模式。我们的目标是阐明在转录水平上区分这些肿瘤的详细免疫学特征，并探讨它们对诊断和治疗的影响。该分析确定了九个不同的基因模块簇，每个簇代表乳腺癌亚型和正常组织内独特的转录特征。有趣的是，虽然一些簇在正常组织和某些亚型之间的基因组成上表现出高度相似性，但其他簇却表现出较低的相似性和共同特征。这些簇提供了对乳腺癌亚型内免疫反应的见解，揭示了不同的免疫功能，包括先天性和适应性免疫反应。我们的研究结果有助于更深入地了解乳腺癌亚型的分子机制，并突出其独特的特征。本研究中确定的免疫学特征对诊断和治疗策略具有潜在影响。此外，本文引入的基于网络的方法为理解其他疾病的复杂性并阐明其基础生物学提供了一个有价值的框架。版权所有 © 2024 Velazquez-Caldelas、Zamora-Fuentes 和 Hernandez-Lemus。

Breast cancer, characterized by its complexity and diversity, presents significant challenges in understanding its underlying biology. In this study, we employed gene co-expression network analysis to investigate the gene composition and functional patterns in breast cancer subtypes and normal breast tissue. Our objective was to elucidate the detailed immunological features distinguishing these tumors at the transcriptional level and to explore their implications for diagnosis and treatment. The analysis identified nine distinct gene module clusters, each representing unique transcriptional signatures within breast cancer subtypes and normal tissue. Interestingly, while some clusters exhibited high similarity in gene composition between normal tissue and certain subtypes, others showed lower similarity and shared traits. These clusters provided insights into the immune responses within breast cancer subtypes, revealing diverse immunological functions, including innate and adaptive immune responses. Our findings contribute to a deeper understanding of the molecular mechanisms underlying breast cancer subtypes and highlight their unique characteristics. The immunological signatures identified in this study hold potential implications for diagnostic and therapeutic strategies. Additionally, the network-based approach introduced herein presents a valuable framework for understanding the complexities of other diseases and elucidating their underlying biology.Copyright © 2024 Velazquez-Caldelas, Zamora-Fuentes and Hernandez-Lemus.