K-core decomposition of a protein domain co-occurrence network reveals lower cancer mutation rates for interior cores

Identification of critical autophagy-related proteins in diabetic retinopathy: A multi-dimensional computational study

Diabetic retinopathy (DR) is a common consequence of diabetes mellitus and a primary cause of visual impairment in middle-aged and elderly individuals. DR is susceptible to cellular degradation facilitated by autophagy. In this study, we have employed a multi-layer relatedness (MLR) approach to uncover novel autophagy-related proteins involved in DR. The objective of MLR is to determine the relatedness of autophagic and DR proteins by incorporating both expression and prior-knowledge-based similarities. We constructed a prior knowledge-based network and identified the topologically significant novel disease-related candidate autophagic proteins (CAPs). Then, we evaluated their significance in a gene co-expression and a differentially-expressed gene (DEG) network. Finally, we investigated the proximity of CAPs to the known disease-related proteins. Leveraging this methodology, we identified three crucial autophagy-related proteins, TP53, HSAP90AA1, and PIK3R1, which can influence the DR interactome in various layers of heterogeneity of clinical manifestations. They are strongly related to multiple detrimental characteristics of DR, such as pericyte loss, angiogenesis, apoptosis, and endothelial cell migration, and hence may be used to prevent or delay the progression and development of DR. We evaluated one of the identified targets, TP53, in a cell-based model and found that its inhibition resulted in reduced angiogenesis in high glucose condition required to control DR.

Investigating Prescriptions and Mechanisms of Acupuncture for Chronic Stable Angina Pectoris: An Association Rule Mining and Network Analysis Study

Chronic stable angina pectoris (CSAP) is a worldwide cardiovascular disease that severely affects people's quality of life and causes serious cardiovascular accidents. Although acupuncture had been confirmed as a potential adjunctive treatment for CSAP, the basic rules and mechanisms of acupoints were little understood. We conducted a systematic search of the China Biology Medicine (CBM), VIP database, Wangfang database, China National Knowledge Infrastructure (CNKI), PubMed, Cochrane Library, Embase, and Web of Science to identify eligible clinical controlled trials (CCTs) and randomized controlled trials (RCTs), from their inception to 18th February 2020. The acupoint prescriptions in the treatment of CSAP were extracted and analyzed based on the association rule mining (ARM) and network analysis. In addition, potential mechanisms of acupuncture for treating CSAP were summarized by data mining. A total of 27 eligible trials were included. Analysis of acupoint prescriptions covered 36 conventional acupoints and 1 experience acupoint, distributing in 10 meridians. The top three frequently used acupoints were PC6, LU9, and ST36. The top three frequently used meridians were the pericardial meridian, lung meridian, and heart meridian. The most frequently used acupoint combinations were LU9 combined with PC6. Besides, network analysis indicated that the core acupoints included PC6, BL15, ST40, and RN17. Moreover, potential mechanisms of acupuncture for treating CSAP involved the regulation of autonomic nerve function, the content of matrix metalloproteinase-9 (MMP-9), volume and the equivalent block of coronary artery calcified plaque (CACP), endothelin (ET), and nitric oxide (NO), neutrophil-lymphocyte ratio (NLR), the content of C-reactive protein (CRP), and tumor necrosis factor-α (TNF-α). In conclusion, our findings concerning acupoint prescriptions and potential mechanisms in the acupuncture treatment of CSAP could provide an optimized acupuncture treatment plan for clinical treatment of CSAP and promote further mechanism research and network research of CSAP.

Hierarchical closeness-based properties reveal cancer survivability and biomarker genes in molecular signaling networks

Specific molecular signaling networks underlie different cancer types and quantitative analyses on those cancer networks can provide useful information about cancer treatments. Their structural metrics can reveal survivability of cancer patients and be used to identify biomarker genes for early cancer detection. In this study, we devised a novel structural metric called hierarchical closeness (HC) entropy and found that it was negatively correlated with 5-year survival rates. We also made an interesting observation that a network of higher HC entropy was likely to be more robust against mutations. This finding suggested that cancers of high HC entropy tend to be incurable because their signaling networks are robust to perturbations caused by treatment. We also proposed a novel core identification method based on the reachability factor in the HC measure. The cores were permitted to decompose such that the negative relationship between HC entropy and cancer survival rate was consistently conserved in every core level. Interestingly, we observed that many promising biomarker genes for early cancer detection reside in the innermost core of a signaling network. Taken together, the proposed analyses of the hierarchical structure of cancer signaling networks may be useful in developing future novel cancer treatments.

Computer-Aided Clinical Trial Recruitment Based on Domain-Specific Language Translation: A Case Study of Retinopathy of Prematurity

Reusing the data from healthcare information systems can effectively facilitate clinical trials (CTs). How to select candidate patients eligible for CT recruitment criteria is a central task. Related work either depends on DBA (database administrator) to convert the recruitment criteria to native SQL queries or involves the data mapping between a standard ontology/information model and individual data source schema. This paper proposes an alternative computer-aided CT recruitment paradigm, based on syntax translation between different DSLs (domain-specific languages). In this paradigm, the CT recruitment criteria are first formally represented as production rules. The referenced rule variables are all from the underlying database schema. Then the production rule is translated to an intermediate query-oriented DSL (e.g., LINQ). Finally, the intermediate DSL is directly mapped to native database queries (e.g., SQL) automated by ORM (object-relational mapping).