Prediction of disease-related genes based on weighted tissue-specific networks by using DNA methylation

Aberrantly hypermethylated ARID1B is a novel biomarker and potential therapeutic target of colon adenocarcinoma

Background and Objective: Understanding the tumor microenvironment (TME) and immune cell infiltration (ICI) may help guide immunotherapy efforts for colon cancer (COAD). However, whether ARID1B is truly regulated by hypermethylation or linked to immune infiltration remains unknown. The current work focused on the ARID1B gene expression and methylation in COAD, as well as its relation with ICI.

Methods and Results: Multiple tools based on TCGA were used to analyze the differences in the expression of the ARID1B gene, DNA methylation, and its association with various clinicopathological features, somatic mutations, copy number variation, and the prognosis of patients with COAD. According to the analysis results, patients with high mRNA, low methylation levels showed better overall survival than patients with low mRNA, high methylation levels. The correlation analysis of immune cell infiltration and immune checkpoint gene expression showed that the infiltration rates of the main ICI subtypes, cancer-associated fibroblast, and myeloid cells were significantly enriched and correlated with ARID1B in COAD. An association between ARID1B expression and immune infiltration in COAD was found by correlating ICI indicators with ARID1B expression in the immune cell composition of the COAD microenvironment. Notably, M2 chemokines were related to ARID1B expression, while M1 chemokines were not.

Conclusion: This study provided evidence that ARID1B may have a role in the pathogenesis of COAD. The specific underlying mechanisms that could be responsible for ARID1B’s downregulation in COAD will need to be investigated in the future.

Differential Co-Expression Analyses Allow the Identification of Critical Signalling Pathways Altered during Tumour Transformation and Progression

Biological systems respond to perturbations through the rewiring of molecular interactions, organised in gene regulatory networks (GRNs). Among these, the increasingly high availability of transcriptomic data makes gene co-expression networks the most exploited ones. Differential co-expression networks are useful tools to identify changes in response to an external perturbation, such as mutations predisposing to cancer development, and leading to changes in the activity of gene expression regulators or signalling. They can help explain the robustness of cancer cells to perturbations and identify promising candidates for targeted therapy, moreover providing higher specificity with respect to standard co-expression methods. Here, we comprehensively review the literature about the methods developed to assess differential co-expression and their applications to cancer biology. Via the comparison of normal and diseased conditions and of different tumour stages, studies based on these methods led to the definition of pathways involved in gene network reorganisation upon oncogenes' mutations and tumour progression, often converging on immune system signalling. A relevant implementation still lagging behind is the integration of different data types, which would greatly improve network interpretability. Most importantly, performance and predictivity evaluation of the large variety of mathematical models proposed would urgently require experimental validations and systematic comparisons. We believe that future work on differential gene co-expression networks, complemented with additional omics data and experimentally tested, will considerably improve our insights into the biology of tumours.

Systems Biology and Machine Learning in Plant-Pathogen Interactions

Systems biology is an inclusive approach to study the static and dynamic emergent properties on a global scale by integrating multiomics datasets to establish qualitative and quantitative associations among multiple biological components. With an abundance of improved high throughput -omics datasets, network-based analyses and machine learning technologies are playing a pivotal role in comprehensive understanding of biological systems. Network topological features reveal most important nodes within a network as well as prioritize significant molecular components for diverse biological networks, including coexpression, protein-protein interaction, and gene regulatory networks. Machine learning techniques provide enormous predictive power through specific feature extraction from biological data. Deep learning, a subtype of machine learning, has plausible future applications because a domain expert for feature extraction is not needed in this algorithm. Inspired by diverse domains of biology, we here review classic systems biology techniques applied in plant immunity thus far. We also discuss additional advanced approaches in both graph theory and machine learning, which may provide new insights for understanding plant-microbe interactions. Finally, we propose a hybrid approach in plant immune systems that harnesses the power of both network biology and machine learning, with a potential to be applicable to both model systems and agronomically important crop plants.

inFRank: a ranking-based identification of influential genes in biological networks

Capturing the predominant driver genes is critical in the analysis of high-throughput experimental data; however, existing methods scarcely include the unique characters of biological networks. Herein we introduced a ranking-based computational framework (inFRank) to rank the proteins by their influence. Using inFRank, we identified the top 20 influential genes in hepatocellular carcinoma (HCC). Network analysis revealed a prominent community composed of 7 influential genes. Intriguingly, five genes among the community were critical for mitotic spindle assembly checkpoint (SAC), suggesting that dysregulation of SAC could be a distinct feature of HCC and targeting SAC-associated genes might be a promising therapeutic strategy. Cox regression analysis revealed that CDC20 exerted as an independent risk factor for patient survival, indicating that CDC20 could be a novel biomarker for HCC prognosis. inFRank was then used for pan-cancer study, and all of the most influential genes in 18 cancers were achieved. We identified altogether 19 genes that were important in multiple cancers, and observed that cancers originating from the same organ or function-related organs tended to share more influential genes. Collectively, our results demonstrated that the inFRank was a powerful approach for deep interpretation of high-throughput data and better understanding of complex diseases.

Revealing Alzheimer's disease genes spectrum in the whole-genome by machine learning

Background

Alzheimer’s disease (AD) is an important, progressive neurodegenerative disease, with a complex genetic architecture. A key goal of biomedical research is to seek out disease risk genes, and to elucidate the function of these risk genes in the development of disease. For this purpose, expanding the AD-associated gene set is necessary. In past research, the prediction methods for AD related genes has been limited in their exploration of the target genome regions. We here present a genome-wide method for AD candidate genes predictions.

Methods

We present a machine learning approach (SVM), based upon integrating gene expression data with human brain-specific gene network data, to discover the full spectrum of AD genes across the whole genome.

Results

We classified AD candidate genes with an accuracy and the area under the receiver operating characteristic (ROC) curve of 84.56% and 94%. Our approach provides a supplement for the spectrum of AD-associated genes extracted from more than 20,000 genes in a genome wide scale.

Conclusions

In this study, we have elucidated the whole-genome spectrum of AD, using a machine learning approach. Through this method, we expect for the candidate gene catalogue to provide a more comprehensive annotation of AD for researchers.

Electronic supplementary material

The online version of this article (10.1186/s12883-017-1010-3) contains supplementary material, which is available to authorized users.

LINE1 CpG-DNA Hypomethylation in Granulosa Cells and Blood Leukocytes Is Associated With PCOS and Related Traits

CONTEXT

Altered global DNA methylation is indicative of epigenomic instability concerning chronic diseases. Investigating its incidence and association with polycystic ovary syndrome (PCOS) is essential to understand the etiopathogenesis of this disorder.

OBJECTIVES

We assessed global DNA methylation differences in peripheral blood leukocytes (PBLs) and cumulus granulosa cells (CGCs) of controls and women with PCOS; and their association with PCOS and its traits.

DESIGN, SETTING, PARTICIPANTS, MAIN OUTCOME MEASURE

This study included a total of 102 controls and women with PCOS. Forty-one women undergoing controlled ovarian hyperstimulation (COH) and 61 women not undergoing COH were recruited from in vitro fertilization (IVF) and infertility clinics. DNA methylation was measured by ELISA for 5'-methyl-cytosine content and bisulfite sequencing of 5'-untranslated region (5'-UTR) of long interspersed nucleotide element-1 (LINE1/L1).

RESULTS

Total 5'-methyl-cytosine and L1 methylation levels in PBLs and CGCs were similar between controls and women with PCOS. Methylation assessed at CpG sites of L1 5'-UTR revealed a single CpG-site (CpG-4) to be consistently hypomethylated in PBLs of both PCOS groups and CGCs of stimulated PCOS group. In unstimulated women, hypomethylation at CpG-4 was strongly associated with PCOS susceptibility, whereas in stimulated group it showed strong associations with PCOS and its hormonal traits. Furthermore, CGCs demonstrated consistent global and CpG-DNA hypomethylation relative to PBLs, irrespective of normal or disease states.

CONCLUSION

Our study revealed strong association of single hypomethylated CpG-site with PCOS. Identification and characterization of more such methyl-CpG signatures in repetitive elements in larger study populations would provide valuable epigenetic insights into PCOS.

A fast and high performance multiple data integration algorithm for identifying human disease genes

BACKGROUND

Integrating multiple data sources is indispensable in improving disease gene identification. It is not only due to the fact that disease genes associated with similar genetic diseases tend to lie close with each other in various biological networks, but also due to the fact that gene-disease associations are complex. Although various algorithms have been proposed to identify disease genes, their prediction performances and the computational time still should be further improved.

RESULTS

In this study, we propose a fast and high performance multiple data integration algorithm for identifying human disease genes. A posterior probability of each candidate gene associated with individual diseases is calculated by using a Bayesian analysis method and a binary logistic regression model. Two prior probability estimation strategies and two feature vector construction methods are developed to test the performance of the proposed algorithm.

CONCLUSIONS

The proposed algorithm is not only generated predictions with high AUC scores, but also runs very fast. When only a single PPI network is employed, the AUC score is 0.769 by using F2 as feature vectors. The average running time for each leave-one-out experiment is only around 1.5 seconds. When three biological networks are integrated, the AUC score using F3 as feature vectors increases to 0.830, and the average running time for each leave-one-out experiment takes only about 12.54 seconds. It is better than many existing algorithms.

Human protein interaction networks across tissues and diseases

Protein interaction networks are an important framework for studying protein function, cellular processes, and genotype-to-phenotype relationships. While our view of the human interaction network is constantly expanding, less is known about networks that form in biologically important contexts such as within distinct tissues or in disease conditions. Here we review efforts to characterize these networks and to harness them to gain insights into the molecular mechanisms underlying human disease.

ProSim: A Method for Prioritizing Disease Genes Based on Protein Proximity and Disease Similarity

Predicting disease genes for a particular genetic disease is very challenging in bioinformatics. Based on current research studies, this challenge can be tackled via network-based approaches. Furthermore, it has been highlighted that it is necessary to consider disease similarity along with the protein's proximity to disease genes in a protein-protein interaction (PPI) network in order to improve the accuracy of disease gene prioritization. In this study we propose a new algorithm called proximity disease similarity algorithm (ProSim), which takes both of the aforementioned properties into consideration, to prioritize disease genes. To illustrate the proposed algorithm, we have conducted six case studies, namely, prostate cancer, Alzheimer's disease, diabetes mellitus type 2, breast cancer, colorectal cancer, and lung cancer. We employed leave-one-out cross validation, mean enrichment, tenfold cross validation, and ROC curves to evaluate our proposed method and other existing methods. The results show that our proposed method outperforms existing methods such as PRINCE, RWR, and DADA.