Biomedical Data Science Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 6 avenue du Swing, L-4367 Belvaux, Luxembourg.
Brief Bioinform. 2024 Sep 23;25(6). doi: 10.1093/bib/bbae499.
Many complex diseases exhibit pronounced sex differences that can affect both the initial risk of developing the disease, as well as clinical disease symptoms, molecular manifestations, disease progression, and the risk of developing comorbidities. Despite this, computational studies of molecular data for complex diseases often treat sex as a confounding variable, aiming to filter out sex-specific effects rather than attempting to interpret them. A more systematic, in-depth exploration of sex-specific disease mechanisms could significantly improve our understanding of pathological and protective processes with sex-dependent profiles. This survey discusses dedicated bioinformatics approaches for the study of molecular sex differences in complex diseases. It highlights that, beyond classical statistical methods, approaches are needed that integrate prior knowledge of relevant hormone signaling interactions, gene regulatory networks, and sex linkage of genes to provide a mechanistic interpretation of sex-dependent alterations in disease. The review examines and compares the advantages, pitfalls and limitations of various conventional statistical and systems-level mechanistic analyses for this purpose, including tailored pathway and network analysis techniques. Overall, this survey highlights the potential of specialized bioinformatics techniques to systematically investigate molecular sex differences in complex diseases, to inform biomarker signature modeling, and to guide more personalized treatment approaches.
许多复杂疾病表现出明显的性别差异，这些差异既会影响疾病的初始发病风险，也会影响临床疾病症状、分子表现、疾病进展以及合并症的发病风险。尽管如此，复杂疾病的分子数据的计算研究通常将性别视为混杂变量，旨在滤除性别特异性影响，而不是试图对其进行解释。更系统、更深入地探索性别特异性疾病机制，可以显著提高我们对具有性别依赖性特征的病理和保护过程的理解。本综述讨论了专门用于研究复杂疾病中分子性别差异的生物信息学方法。它强调，除了经典的统计方法外，还需要整合相关激素信号转导相互作用、基因调控网络和基因性别连锁的先验知识，以提供对疾病中性别依赖性改变的机制解释。该综述检查并比较了各种传统统计和系统水平的机制分析方法在这方面的优缺点和局限性，包括针对特定的途径和网络分析技术。总的来说，本综述强调了专门的生物信息学技术在系统研究复杂疾病中的分子性别差异、为生物标志物特征建模提供信息以及指导更个性化的治疗方法方面的潜力。
