Symptom-based network classification identifies distinct clinical subgroups of liver diseases with common molecular pathways. (June 2019)

• Record Type: Journal Article
• Authors: Shu, Zixin; Liu, Wenwen; Wu, Huikun; Xiao, Mingzhong; Wu, Deng; Cao, Ting; Ren, Meng; Tao, Junxiu; Zhang, Chuhua; He, Tangqing; Li, Xiaodong; Zhang, Runshun; Zhou, Xuezhong
• Is Part Of: Computer methods and programs in biomedicine. Volume 174(2019)
• Page Start: 41
• Page End: 50
• Publication Date: 2019-06
• Subjects: Disease subtypes -- Liver diseases -- Electronic medical records -- Network medicine -- Traditional Chinese medicine -- Community detection; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2018.02.014
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: This is the first study to classify the subgroups of liver diseases using TCM symptom phenotype similarity. The liver disease subgroups with both distinct clinical and molecular characteristics are identified. The mechanisms of liver disease and their comorbidities occur simultaneously is explored from the point of molecular pathways. Abstract: Background and objective: Liver disease is a multifactorial complex disease with high global prevalence and poor long-term clinical efficacy and liver disease patients with different comorbidities often incorporate multiple phenotypes in the clinic. Thus, there is a pressing need to improve understanding of the complexity of clinical liver population to help gain more accurate disease subtypes for personalized treatment. Methods: Individualized treatment of the traditional Chinese medicine (TCM) provides a theoretical basis to the study of personalized classification of complex diseases. Utilizing the TCM clinical electronic medical records (EMRs) of 6475 liver inpatient cases, we built a liver disease comorbidity network (LDCN) to show the complicated associations between liver diseases and their comorbidities, and then constructed a patient similarity network with shared symptoms (PSN). Finally, we identified liver patient subgroups using community detection methods and performed enrichment analyses to find both distinct clinical and molecular characteristics (with the phenotype-genotype associations and interactome networks) of these patient subgroups. Results: From the comorbidity network, we found that clinical liver patients have a wide range of disease comorbidities, in which the basic liver diseases (e.g. hepatitis b, decompensated liver cirrhosis), and the common chronic diseases (e.g. hypertension, type 2 diabetes), have high degree of disease comorbidities. In addition, we identified 303 patient modules (representing the liver patient subgroups) from the PSN, in which the top 6 modules with large number of cases include 51.68% of the whole cases and 251 modules contain only 10 or fewer cases, which indicates the manifestation diversity of liver diseases. Finally, we found that the patient subgroups actually have distinct symptom phenotypes, disease comorbidity characteristics and their underlying molecular pathways, which could be used for understanding the novel disease subtypes of liver conditions. For example, three patient subgroups, namely Module 6 (M6, n = 638), M2 (n = 623) and M1 (n = 488) were associated to common chronic liver disease conditions (hepatitis, cirrhosis, hepatocellular carcinoma). Meanwhile, patient subgroups of M30 (n = 36) and M36 (n = 37) were mostly related to acute gastroenteritis and upper respiratory infection, respectively, which reflected the individual comorbidity characteristics of liver subgroups. Furthermore, we identified the distinct genes and pathways of patient subgroups and the basic liver diseases (hepatitis b and cirrhosis), respectively. The high degree of overlapping pathways between them (e.g. M36 with 93.33% shared enriched pathways) indicates the underlying molecular network mechanisms of each patient subgroup. Conclusions: Our results demonstrate the utility and comprehensiveness of disease classification study based on community detection of patient network using shared TCM symptom phenotypes and it can be used to other more complex diseases.