Network Science Institute, Northeastern University, Boston, MA 02115.
Department of Physics, Northeastern University, Boston, MA 02115.
Proc Natl Acad Sci U S A. 2023 Nov 7;120(45):e2301342120. doi: 10.1073/pnas.2301342120. Epub 2023 Oct 31.
Network medicine has improved the mechanistic understanding of disease, offering quantitative insights into disease mechanisms, comorbidities, and novel diagnostic tools and therapeutic treatments. Yet, most network-based approaches rely on a comprehensive map of protein-protein interactions (PPI), ignoring interactions mediated by noncoding RNAs (ncRNAs). Here, we systematically combine experimentally confirmed binding interactions mediated by ncRNA with PPI, constructing a comprehensive network of all physical interactions in the human cell. We find that the inclusion of ncRNA expands the number of genes in the interactome by 46% and the number of interactions by 107%, significantly enhancing our ability to identify disease modules. Indeed, we find that 132 diseases lacked a statistically significant disease module in the protein-based interactome but have a statistically significant disease module after inclusion of ncRNA-mediated interactions, making these diseases accessible to the tools of network medicine. We show that the inclusion of ncRNAs helps unveil disease-disease relationships that were not detectable before and expands our ability to predict comorbidity patterns between diseases. Taken together, we find that including noncoding interactions improves both the breath and the predictive accuracy of network medicine.
网络医学提高了对疾病的机制理解，为疾病机制、合并症和新的诊断工具和治疗方法提供了定量见解。然而，大多数基于网络的方法依赖于蛋白质-蛋白质相互作用（PPI）的综合图谱，忽略了非编码 RNA（ncRNA）介导的相互作用。在这里，我们系统地将 ncRNA 介导的实验证实的结合相互作用与 PPI 相结合，构建了人类细胞中所有物理相互作用的综合网络。我们发现，包含 ncRNA 将互作组中的基因数量增加了 46%，相互作用数量增加了 107%，显著提高了我们识别疾病模块的能力。事实上，我们发现 132 种疾病在基于蛋白质的互作组中缺乏统计学上显著的疾病模块，但在包含 ncRNA 介导的相互作用后，这些疾病具有统计学上显著的疾病模块，使这些疾病能够应用网络医学的工具。我们表明，包含 ncRNAs 有助于揭示以前无法检测到的疾病-疾病关系，并扩展了我们预测疾病之间共病模式的能力。总之，我们发现包含非编码相互作用可以提高网络医学的广度和预测准确性。
