The translational network for metabolic disease - from protein interaction to disease co-occurrence

Clinicopathological and Genetic Characteristics of Patients of Different Ages with Diffuse Sclerosing Variant Papillary Thyroid Carcinoma

Diffuse sclerosing variant papillary thyroid carcinoma (DSVPTC) is commonly observed in young patients, with a median age at diagnosis in the third decade of life. Further, the risk of recurrence is higher for DSVPTC than for classical PTC. Therefore, this study aimed to describe the clinicopathological and genetic characteristics of patients of different ages with DSVPTC. We retrospectively reviewed 397 patients who underwent thyroidectomy for DSVPTC at Gangnam Severance Hospital, Yonsei University, from January 2005 to December 2017. The mean age at diagnosis was 36.7 ± 11.6 years, with most patients (163, 41.1%) aged 31-40 years. DSVPTC was predominant in women (276, 69.5%). We observed recurrence in 46 (11.6%) patients, with regional nodal recurrence being the most common type of recurrence (32 patients, 69.6%). The mean tumour size was larger in younger patients than in older patients. DSVPTC was more aggressive in paediatric patients with a larger-sized tumour, more common multiplicity, and lateral neck metastasis. Through random sampling, we selected 41 patients by age group and examined the mutations in 119 genes using next-generation sequencing. BRAF, KRAS, and TERT displayed relatively higher mutation rates than other genes. DSVPTC displays different clinical, pathological, and molecular profiles than classical PTC. The BRAF, KRAS, and TERT mutations are the most important, with age-specific differences.

Discovering comorbid diseases using an inter-disease interactivity network based on biobank-scale PheWAS data

MOTIVATION

Understanding comorbidity is essential for disease prevention, treatment and prognosis. In particular, insight into which pairs of diseases are likely or unlikely to co-occur may help elucidate the potential relationships between complex diseases. Here, we introduce the use of an inter-disease interactivity network to discover/prioritize comorbidities. Specifically, we determine disease associations by accounting for the direction of effects of genetic components shared between diseases, and categorize those associations as synergistic or antagonistic. We further develop a comorbidity scoring algorithm to predict whether diseases are more or less likely to co-occur in the presence of a given index disease. This algorithm can handle networks that incorporate relationships with opposite signs.

RESULTS

We finally investigate inter-disease associations among 427 phenotypes in UK Biobank PheWAS data and predict the priority of comorbid diseases. The predicted comorbidities were verified using the UK Biobank inpatient electronic health records. Our findings demonstrate that considering the interaction of phenotype associations might be helpful in better predicting comorbidity.

AVAILABILITY AND IMPLEMENTATION

The source code and data of this study are available at https://github.com/dokyoonkimlab/DiseaseInteractiveNetwork.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

netCRS: Network-based comorbidity risk score for prediction of myocardial infarction using biobank-scaled PheWAS data

The polygenic risk score (PRS) can help to identify individuals' genetic susceptibility for various diseases by combining patient genetic profiles and identified single-nucleotide polymorphisms (SNPs) from genome-wide association studies. Although multiple diseases will usually afflict patients at once or in succession, conventional PRSs fail to consider genetic relationships across multiple diseases. Even multi-trait PRSs, which take into account genetic effects for more than one disease at a time, fail to consider a sufficient number of phenotypes to accurately reflect the state of disease comorbidity in a patient, or are biased in terms of the traits that are selected. Thus, we developed novel network-based comorbidity risk scores to quantify associations among multiple phenotypes from phenome-wide association studies (PheWAS). We first constructed a disease-SNP heterogeneous multi-layered network (DS-Net), which consists of a disease network (disease-layer) and SNP network (SNP-layer). The disease-layer describes the population-level interactome from PheWAS data. The SNP-layer was constructed according to linkage disequilibrium. Both layers were attached to transform the information from a population-level interactome to individual-level inferences. Then, graph-based semi-supervised learning was applied to predict possible comorbidity scores on disease-layer for each subject. The SNP-layer serves as receiving individual genotyping data in the scoring process, and the disease-layer serves as the propagated output for an individual's multiple disease comorbidity scores. The possible comorbidity scores were combined by logistic regression, and it is denoted as netCRS. The DS-Net was constructed from UK Biobank PheWAS data, and the individual genetic profiles were collected from the Penn Medicine Biobank. As a proof-of-concept study, myocardial infarction (MI) was selected to compare netCRS with the PRS with pruning and thresholding (PRS-PT). The combined model (netCRS + PRS-PT + covariates) achieved an AUC improvement of 6.26% compared to the (PRS-PT + covariates) model. In terms of risk stratification, the combined model was able to capture the risk of MI up to approximately eight-fold higher than that of the low-risk group. The netCRS and PRS-PT complement each other in predicting high-risk groups of patients with MI. We expect that using these risk prediction models will allow for the development of prevention strategies and reduction of MI morbidity and mortality.

Dementia key gene identification with multi-layered SNP-gene-disease network

MOTIVATION

Recently, various approaches for diagnosing and treating dementia have received significant attention, especially in identifying key genes that are crucial for dementia. If the mutations of such key genes could be tracked, it would be possible to predict the time of onset of dementia and significantly aid in developing drugs to treat dementia. However, gene finding involves tremendous cost, time and effort. To alleviate these problems, research on utilizing computational biology to decrease the search space of candidate genes is actively conducted.

In this study, we propose a framework in which diseases, genes and single-nucleotide polymorphisms are represented by a layered network, and key genes are predicted by a machine learning algorithm. The algorithm utilizes a network-based semi-supervised learning model that can be applied to layered data structures.

RESULTS

The proposed method was applied to a dataset extracted from public databases related to diseases and genes with data collected from 186 patients. A portion of key genes obtained using the proposed method was verified in silico through PubMed literature, and the remaining genes were left as possible candidate genes.

AVAILABILITY AND IMPLEMENTATION

The code for the framework will be available at http://www.alphaminers.net/.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.