Subpathway-CorSP: Identification of metabolic subpathways via integrating expression correlations and topological features between metabolites and genes of interest within pathways

GREAP: a comprehensive enrichment analysis software for human genomic regions

The rapid development of genomic high-throughput sequencing has identified a large number of DNA regulatory elements with abundant epigenetics markers, which promotes the rapid accumulation of functional genomic region data. The comprehensively understanding and research of human functional genomic regions is still a relatively urgent work at present. However, the existing analysis tools lack extensive annotation and enrichment analytical abilities for these regions. Here, we designed a novel software, Genomic Region sets Enrichment Analysis Platform (GREAP), which provides comprehensive region annotation and enrichment analysis capabilities. Currently, GREAP supports 85 370 genomic region reference sets, which cover 634 681 107 regions across 11 different data types, including super enhancers, transcription factors, accessible chromatins, etc. GREAP provides widespread annotation and enrichment analysis of genomic regions. To reflect the significance of enrichment analysis, we used the hypergeometric test and also provided a Locus Overlap Analysis. In summary, GREAP is a powerful platform that provides many types of genomic region sets for users and supports genomic region annotations and enrichment analyses. In addition, we developed a customizable genome browser containing >400 000 000 customizable tracks for visualization. The platform is freely available at http://www.liclab.net/Greap/view/index.

TRmir: A Comprehensive Resource for Human Transcriptional Regulatory Information of MiRNAs

MicroRNAs (miRNAs) are small non-coding RNAs, which play important roles in regulating various biological functions. Many available miRNA databases have provided a large number of valuable resources for miRNA investigation. However, not all existing databases provide comprehensive information regarding the transcriptional regulatory regions of miRNAs, especially typical enhancer, super-enhancer (SE), and chromatin accessibility regions. An increasing number of studies have shown that the transcriptional regulatory regions of miRNAs, as well as related single-nucleotide polymorphisms (SNPs) and transcription factors (TFs) have a strong influence on human diseases and biological processes. Here, we developed a comprehensive database for the human transcriptional regulation of miRNAs (TRmir), which is focused on providing a wealth of available resources regarding the transcriptional regulatory regions of miRNAs and annotating their potential roles in the regulation of miRNAs. TRmir contained a total of 5,754,414 typical enhancers/SEs and 1,733,966 chromatin accessibility regions associated with 1,684 human miRNAs. These regions were identified from over 900 human H3K27ac ChIP-seq, ATAC-seq, and DNase-seq samples. Furthermore, TRmir provided detailed (epi)genetic information about the transcriptional regulatory regions of miRNAs, including TFs, common SNPs, risk SNPs, linkage disequilibrium (LD) SNPs, expression quantitative trait loci (eQTLs), 3D chromatin interactions, and methylation sites, especially supporting the display of TF binding sites in the regulatory regions of over 7,000 TF ChIP-seq samples. In addition, TRmir integrated miRNA expression and related disease information, supporting extensive pathway analysis. TRmir is a powerful platform that offers comprehensive information about the transcriptional regulation of miRNAs for users and provides detailed annotations of regulatory regions. TRmir is free for academic users and can be accessed at http://bio.liclab.net/trmir/index.html.

MiRNA-Mediated Subpathway Identification and Network Module Analysis to Reveal Prognostic Markers in Human Pancreatic Cancer

Background

Pancreatic cancer (PC) remains one of the most lethal cancers. In contrast to the steady increase in survival for most cancers, the 5-year survival remains low for PC patients.

Methods

We describe a new pipeline that can be used to identify prognostic molecular biomarkers by identifying miRNA-mediated subpathways associated with PC. These modules were then further extracted from a comprehensive miRNA-gene network (CMGN). An exhaustive survival analysis was performed to estimate the prognostic value of these modules.

Results

We identified 105 miRNA-mediated subpathways associated with PC. Two subpathways within the MAPK signaling and cell cycle pathways were found to be highly related to PC. Of the miRNA-mRNA modules extracted from CMGN, six modules showed good prognostic performance in both independent validated datasets.

Conclusions

Our study provides novel insight into the mechanisms of PC. We inferred that six miRNA-mRNA modules could serve as potential prognostic molecular biomarkers in PC based on the pipeline we proposed.

Inference of Subpathway Activity Profiles Reveals Metabolism Abnormal Subpathway Regions in Glioblastoma Multiforme

Glioblastoma, also known as glioblastoma multiforme (GBM), is the most malignant form of glioma and represents 81% of malignant brain and central nervous system (CNS) tumors. Like most cancers, GBM causes metabolic recombination to promote cell survival, proliferation, and invasion of cancer cells. In this study, we propose a method for constructing the metabolic subpathway activity score matrix to accurately identify abnormal targets of GBM metabolism. By integrating gene expression data from different sequencing methods, our method identified 25 metabolic subpathways that were significantly abnormal in the GBM patient population, and most of these subpathways have been reported to have an effect on GBM. Through the analysis of 25 GBM-related metabolic subpathways, we found that (S)-2,3-Epoxysqualene, which was at the central region of the sterol biosynthesis subpathway, may have a greater impact on the entire pathway, suggesting a potential high association with GBM. Analysis of CCK8 cell activity indicated that (S)-2,3-Epoxysqualene can indeed inhibit the activity of U87-MG cells. By flow cytometry, we demonstrated that (S)-2,3-Epoxysqualene not only arrested the U87-MG cell cycle in the G0/G1 phase but also induced cell apoptosis. These results confirm the reliability of our proposed metabolic subpathway identification method and suggest that (S)-2,3-Epoxysqualene has potential therapeutic value for GBM. In order to make the method more broadly applicable, we have developed an R system package crmSubpathway to perform disease-related metabolic subpathway identification and it is freely available on the GitHub (https://github.com/hanjunwei-lab/crmSubpathway).

HiFreSP: A novel high-frequency sub-pathway mining approach to identify robust prognostic gene signatures

With the increasing awareness of heterogeneity in cancers, better prediction of cancer prognosis is much needed for more personalized treatment. Recently, extensive efforts have been made to explore the variations in gene expression for better prognosis. However, the prognostic gene signatures predicted by most existing methods have little robustness among different datasets of the same cancer. To improve the robustness of the gene signatures, we propose a novel high-frequency sub-pathways mining approach (HiFreSP), integrating a randomization strategy with gene interaction pathways. We identified a six-gene signature (CCND1, CSF3R, E2F2, JUP, RARA and TCF7) in esophageal squamous cell carcinoma (ESCC) by HiFreSP. This signature displayed a strong ability to predict the clinical outcome of ESCC patients in two independent datasets (log-rank test, P = 0.0045 and 0.0087). To further show the predictive performance of HiFreSP, we applied it to two other cancers: pancreatic adenocarcinoma and breast cancer. The identified signatures show high predictive power in all testing datasets of the two cancers. Furthermore, compared with the two popular prognosis signature predicting methods, the least absolute shrinkage and selection operator penalized Cox proportional hazards model and the random survival forest, HiFreSP showed better predictive accuracy and generalization across all testing datasets of the above three cancers. Lastly, we applied HiFreSP to 8137 patients involving 20 cancer types in the TCGA database and found high-frequency prognosis-associated pathways in many cancers. Taken together, HiFreSP shows higher prognostic capability and greater robustness, and the identified signatures provide clinical guidance for cancer prognosis. HiFreSP is freely available via GitHub: https://github.com/chunquanlipathway/HiFreSP.

TRlnc: a comprehensive database for human transcriptional regulatory information of lncRNAs

Long noncoding RNAs (lncRNAs) have been proven to play important roles in transcriptional processes and biological functions. With the increasing study of human diseases and biological processes, information in human H3K27ac ChIP-seq, ATAC-seq and DNase-seq datasets is accumulating rapidly, resulting in an urgent need to collect and process data to identify transcriptional regulatory regions of lncRNAs. We therefore developed a comprehensive database for human regulatory information of lncRNAs (TRlnc, http://bio.licpathway.net/TRlnc), which aimed to collect available resources of transcriptional regulatory regions of lncRNAs and to annotate and illustrate their potential roles in the regulation of lncRNAs in a cell type-specific manner. The current version of TRlnc contains 8 683 028 typical enhancers/super-enhancers and 32 348 244 chromatin accessibility regions associated with 91 906 human lncRNAs. These regions are identified from over 900 human H3K27ac ChIP-seq, ATAC-seq and DNase-seq samples. Furthermore, TRlnc provides the detailed genetic and epigenetic annotation information within transcriptional regulatory regions (promoter, enhancer/super-enhancer and chromatin accessibility regions) of lncRNAs, including common SNPs, risk SNPs, eQTLs, linkage disequilibrium SNPs, transcription factors, methylation sites, histone modifications and 3D chromatin interactions. It is anticipated that the use of TRlnc will help users to gain in-depth and useful insights into the transcriptional regulatory mechanisms of lncRNAs.

Topologically inferring active miRNA-mediated subpathways toward precise cancer classification by directed random walk

Accurate predictions of classification biomarkers and disease status are indispensable for clinical cancer diagnosis and research. However, the robustness of conventional gene biomarkers is limited by issues with reproducibility across different measurement platforms and cohorts of patients. In this study, we collected 4775 samples from 12 different cancer datasets, which contained 4636 TCGA samples and 139 GEO samples. A new method was developed to detect miRNA‐mediated subpathway activities by using directed random walk (miDRW). To calculate the activity of each miRNA‐mediated subpathway, we constructed a global directed pathway network (GDPN) with genes as nodes. We then identified miRNAs with expression levels which were strongly inversely correlated with differentially expressed target genes in the GDPN. Finally, each miRNA‐mediated subpathway activity was integrated with the topological information, differential levels of miRNAs and genes, expression levels of genes, and target relationships between miRNAs and genes. The results showed that the proposed method yielded a more robust and accurate overall performance compared with other existing pathway‐based, miRNA‐based, and gene‐based classification methods. The high‐frequency miRNA‐mediated subpathways are more reliable in classifying samples and for selecting therapeutic strategies.

ce-Subpathway: Identification of ceRNA-mediated subpathways via joint power of ceRNAs and pathway topologies

Competing endogenous RNAs (ceRNAs) represent a novel mechanism of gene regulation that may mediate key subpathway regions and contribute to the altered activities of pathways. However, the classical methods used to identify pathways fail to specifically consider ceRNAs within the pathways and key regions impacted by them. We proposed a powerful strategy named ce-Subpathway for the identification of ceRNA-mediated functional subpathways. It provided an effective level of pathway analysis via integrating ceRNAs, differentially expressed (DE) genes and their key regions within the given pathways. We respectively analysed one pulmonary arterial hypertension (PAH) and one myocardial infarction (MI) data sets and demonstrated that ce-Subpathway could identify many subpathways whose corresponding entire pathways were ignored by those non-ceRNA-mediated pathway identification methods. And these pathways have been well reported to be associated with PAH/MI-related cardiovascular diseases. Further evidence showed reliability of ceRNA interactions and robustness/reproducibility of the ce-Subpathway strategy by several data sets of different cancers, including breast cancer, oesophageal cancer and colon cancer. Survival analysis was finally applied to illustrate the clinical application value of the ceRNA-mediated functional subpathways using another data sets of pancreatic cancer. Comprehensive analyses have shown the power of a joint ceRNAs/DE genes and subpathway strategy based on their topologies.

Inference of patient-specific subpathway activities reveals a functional signature associated with the prognosis of patients with breast cancer

Breast cancer is one of the most deadly forms of cancer in women worldwide. Better prediction of breast cancer prognosis is essential for more personalized treatment. In this study, we aimed to infer patient-specific subpathway activities to reveal a functional signature associated with the prognosis of patients with breast cancer. We integrated pathway structure with gene expression data to construct patient-specific subpathway activity profiles using a greedy search algorithm. A four-subpathway prognostic signature was developed in the training set using a random forest supervised classification algorithm and a prognostic score model with the activity profiles. According to the signature, patients were classified into high-risk and low-risk groups with significantly different overall survival in the training set (median survival of 65 vs 106 months, P = 1.82e-13) and test set (median survival of 75 vs 101 months, P = 4.17e-5). Our signature was then applied to five independent breast cancer data sets and showed similar prognostic values, confirming the accuracy and robustness of the subpathway signature. Stratified analysis suggested that the four-subpathway signature had prognostic value within subtypes of breast cancer. Our results suggest that the four-subpathway signature may be a useful biomarker for breast cancer prognosis.

Comprehensive functional enrichment analysis of male infertility

Spermatogenesis is a multifactorial process that forms differentiated sperm cells in a complex microenvironment. This process involves the genome, epigenome, transcriptome, and proteome to ensure the stability of the spermatogonia and supporting cells. The identification of signaling pathways linked to infertility has been hampered by the inherent complexity and multifactorial aspects of spermatogenesis. Systems biology is a promising approach to unveil underlying signaling pathways and genes and identify putative biomarkers. In this study, we analyzed thirteen microarray libraries of infertile humans and mice, and different classes of male infertility were compared using differentially expressed genes and functional enrichment analysis. We found regulatory processes, immune response, glutathione transferase and muscle tissue development to be among the most common biological processes in up-regulated genes, and genes involved in spermatogenesis were down-regulated in maturation arrest (MArrest) and oligospermia cases. We also observed the overexpression of genes involved in steroid metabolism in post-meiotic and meiotic arrest. Furthermore, we found that the infertile mouse model most similar to human MArrest was the Dazap1 mutant mouse. The results of this study could help elucidate features of infertility etiology and provide the basis for diagnostic markers.

Metabolomics and Integrative Omics for the Development of Thai Traditional Medicine

In recent years, interest in studies of traditional medicine in Asian and African countries has gradually increased due to its potential to complement modern medicine. In this review, we provide an overview of Thai traditional medicine (TTM) current development, and ongoing research activities of TTM related to metabolomics. This review will also focus on three important elements of systems biology analysis of TTM including analytical techniques, statistical approaches and bioinformatics tools for handling and analyzing untargeted metabolomics data. The main objective of this data analysis is to gain a comprehensive understanding of the system wide effects that TTM has on individuals. Furthermore, potential applications of metabolomics and systems medicine in TTM will also be discussed.