Institute of Medical Intelligence, School of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100063, China.
The First Affiliated Hospital of Henan University of Chinese Medicine (Co-construction Collaborative Innovation Center for Chinese Medicine and Respiratory Diseases by Henan, Henan University of Chinese Medicine), Zhengzhou, 450046, China.
NPJ Syst Biol Appl. 2021 Nov 30;7(1):41. doi: 10.1038/s41540-021-00206-5.
Symptom phenotypes have continuously been an important clinical entity for clinical diagnosis and management. However, non-specificity of symptom phenotypes for clinical diagnosis is one of the major challenges that need be addressed to advance symptom science and precision health. Network medicine has delivered a successful approach for understanding the underlying mechanisms of complex disease phenotypes, which will also be a useful tool for symptom science. Here, we extracted symptom co-occurrences from clinical textbooks to construct phenotype network of symptoms with clinical co-occurrence and incorporated high-quality symptom-gene associations and protein-protein interactions to explore the molecular network patterns of symptom phenotypes. Furthermore, we adopted established network diversity measure in network medicine to quantify both the phenotypic diversity (i.e., non-specificity) and molecular diversity of symptom phenotypes. The results showed that the clinical diversity of symptom phenotypes could partially be explained by their underlying molecular network diversity (PCC = 0.49, P-value = 2.14E-08). For example, non-specific symptoms, such as chill, vomiting, and amnesia, have both high phenotypic and molecular network diversities. Moreover, we further validated and confirmed the approach of symptom clusters to reduce the non-specificity of symptom phenotypes. Network diversity proposes a useful approach to evaluate the non-specificity of symptom phenotypes and would help elucidate the underlying molecular network mechanisms of symptom phenotypes and thus promotes the advance of symptom science for precision health.
症状表型一直是临床诊断和管理的重要临床实体。然而，症状表型对临床诊断的非特异性是需要解决的主要挑战之一，以推进症状科学和精准健康。网络医学为理解复杂疾病表型的潜在机制提供了一种成功的方法，这也将是症状科学的有用工具。在这里，我们从临床教科书中提取症状共同出现，构建具有临床共同出现的症状表型网络，并纳入高质量的症状-基因关联和蛋白质-蛋白质相互作用，以探索症状表型的分子网络模式。此外，我们采用网络医学中已建立的网络多样性度量来量化症状表型的表型多样性（即非特异性）和分子多样性。结果表明，症状表型的临床多样性可以部分解释为其潜在的分子网络多样性（PCC=0.49，P 值=2.14E-08）。例如，非特异性症状，如寒战、呕吐和遗忘，具有高表型和分子网络多样性。此外，我们进一步验证和证实了症状群的方法来降低症状表型的非特异性。网络多样性提出了一种评估症状表型非特异性的有用方法，并有助于阐明症状表型的潜在分子网络机制，从而促进精准健康的症状科学的发展。
