Protective effect of paeoniflorin in diabetic nephropathy: A preclinical systematic review revealing the mechanism of action.

BACKGROUND: Paeoniflorin (PF), the main active glucoside of Paeonia Lactiflora, has many pharmacological activities, such as inhibition of vasodilation, hypoglycemia, and immunomodulation. Although the current evidence has suggested the therapeutic effects of PF on diabetic nephropathy (DN), its potential mechanism of action is still unclear. PURPOSE: A systematic review and meta-analysis of the existing literature on paeoniflorin treatment in DN animal models was performed to evaluate the efficacy and mechanism of PF in DN animal models. METHODS: The risk of bias in each study was judged using the CAMARADES 10-item quality checklist with the number of criteria met varying from 4 / 10 to 7 / 10, with an average of 5.44. From inception to July 2022, We searched eight databases. We used the Cochrane Collaboration's 10-item checklist and RevMan 5.3 software to assess the risk of bias and analyze the data. Three-dimensional dose/time-effect analyses were conducted to examine the dosage/time-response relations between PF and DN. RESULTS: Nine animal studies were systematically reviewed to evaluate the effectiveness of PF in improving animal models of DN. Meta-analysis data and intergroup comparisons indicated that PF slowed the index of mesangial expansion and tubulointerstitial injury, 24-h urinary protein excretion rate, expression of anti-inflammatory mediators (mRNA of MCP-1, TNF-α, iNOS, and IL-1 ß), and expression of immune downstream factors (P-IRAK1, TIRF, P-IRF3, MyD88, and NF-κBp-p65). Furthermore, modeling methods, animal species, treatment duration, thickness of tissue sections during the experiment, and experimental procedures were subjected to subgroup analyses. CONCLUSION: The present study demonstrated that the reno-protective effects of PF were associated with its inhibition on macrophage infiltration, reduction of inflammatory mediators, and immunomodulatory effects. In conclusion, PF can effectively slow down the progression of DN and hold promise as a protective drug for the treatment of DN. Due to the low bioavailability of PF, further studies on renal histology in animals are urgently needed. We therefore recommend an active exploration of the dose and therapeutic time frame of PF in the clinic and in animals. Moreover, it is suggested to actively explore methods to improve the bioavailability of PF to expand the application of PF in the clinic.

lncRNA and circRNA expression profiles in the hippocampus of Aß25­35­induced AD mice treated with Tripterygium glycoside.

Tripterygium glycosides (TG) have been reported to ameliorate Alzheimer's disease (AD), although the mechanism involved remains to be determined. In the present study, the lncRNA and circRNA expression profiles of an AD mouse model treated with TG were assessed using microarrays. lncRNAs, mRNAs, and circRNAs in the hippocampi of 3 AD+normal saline (NS) mice and 3 AD+TG mice were detected using microarrays. The most differentially expressed lncRNAs, mRNAs, and circRNAs were screened between the AD+NS and AD+TG groups. The differentially expressed lncRNAs and circRNAs were analyzed using GO enrichment and KEGG analyses. Co-expression analysis of lncRNAs, circRNAs, and mRNAs was performed by calculating the correlation coefficients. Protein-protein interaction (PPI) network analysis was performed on mRNAs using STRING. The lncRNA-target-transcription factor (TF) network was analyzed using the Network software. In total, 661 lncRNAs, 64 circRNAs, and 503 mRNAs were found to be differentially expressed in AD mice treated with TG. Pou4f1, Egr2, Mag, and Nr4a1 were the hub genes in the PPI network. The KEGG results showed that the mRNAs that were co-expressed with lncRNAs were enriched in the TNF, PI3K-Akt, and Wnt signaling pathways. LncRNA-target-TF network analysis indicated that TFs, including Cebpa, Zic2, and Rxra, were the most likely to regulate the detected lncRNAs. The circRNA-miRNA interaction network indicated that 275 miRNAs may bind to the 64 circRNAs. In conclusion, these findings provide a novel perspective on AD pathogenesis, and the detected lncRNAs, mRNAs, and circRNAs may serve as novel therapeutic targets for the management of AD.

CACO: A Core-Attachment Method With Cross-Species Functional Ortholog Information to Detect Human Protein Complexes.

Protein complexes play an essential role in living cells. Detecting protein complexes is crucial to understand protein functions and treat complex diseases. Due to high time and resource consumption of experiment approaches, many computational approaches have been proposed to detect protein complexes. However, most of them are only based on protein-protein interaction (PPI) networks, which heavily suffer from the noise in PPI networks. Therefore, we propose a novel core-attachment method, named CACO, to detect human protein complexes, by integrating the functional information from other species via protein ortholog relations. First, CACO constructs a cross-species ortholog relation matrix and transfers GO terms from other species as a reference to evaluate the confidence of PPIs. Then, a PPI filter strategy is adopted to clean the PPI network and thus a weighted clean PPI network is constructed. Finally, a new effective core-attachment algorithm is proposed to detect protein complexes from the weighted PPI network. Compared to other thirteen state-of-the-art methods, CACO outperforms all of them in terms of F-measure and Composite Score, showing that integrating ortholog information and the proposed core-attachment algorithm are effective in detecting protein complexes.

Integrated transcriptome and metabolome analysis to investigate the mechanism of intranasal insulin treatment in a rat model of vascular dementia.

Introduction: Insulin has an effect on neurodegenerative diseases. However, the role and mechanism of insulin in vascular dementia (VD) and its underlying mechanism are unknown. In this study, we aimed to investigate the effects and mechanism of insulin on VD. Methods: Experimental rats were randomly assigned to control (CK), Sham, VD, and insulin (INS) + VD groups. Insulin was administered by intranasal spray. Cognitive function was evaluated using the Morris's water maze. Nissl's staining and immunohistochemical staining were used to assess morphological alterations. Apoptosis was evaluated using TUNEL-staining. Transcriptome and metabolome analyses were performed to identify differentially expressed genes (DEGs) and differentially expressed metabolites (DEMs), respectively. Results: Insulin significantly improved cognitive and memory functions in VD model rats (p < 0.05). Compared with the VD group, the insulin + VD group exhibited significantly reduced the number of Nissl's bodies numbers, apoptosis level, GFAP-positive cell numbers, apoptosis rates, and p-tau and tau levels in the hippocampal CA1 region (p < 0.05). Transcriptomic analysis found 1,257 and 938 DEGs in the VD vs. CK and insulin + VD vs. VD comparisons, respectively. The DEGs were mainly enriched in calcium signaling, cAMP signaling, axon guidance, and glutamatergic synapse signaling pathways. In addition, metabolomic analysis identified 1 and 14 DEMs between groups in negative and positive modes, respectively. KEGG pathway analysis indicated that DEGs and DEMs were mostly enriched in metabolic pathway. Conclusion: Insulin could effectively improve cognitive function in VD model rats by downregulating tau and p-tau expression, inhibiting astrocyte inflammation and neuron apoptosis, and regulating genes involved in calcium signaling, cAMP signaling, axon guidance, and glutamatergic synapse pathways, as well as metabolites involved in metabolic pathway.

Disease-gene prediction based on preserving structure network embedding.

Many diseases, such as Alzheimer's disease (AD) and Parkinson's disease (PD), are caused by abnormalities or mutations of related genes. Many computational methods based on the network relationship between diseases and genes have been proposed to predict potential pathogenic genes. However, how to effectively mine the disease-gene relationship network to predict disease genes better is still an open problem. In this paper, a disease-gene-prediction method based on preserving structure network embedding (PSNE) is introduced. In order to predict pathogenic genes more effectively, a heterogeneous network with multiple types of bio-entities was constructed by integrating disease-gene associations, human protein network, and disease-disease associations. Furthermore, the low-dimension features of nodes extracted from the network were used to reconstruct a new disease-gene heterogeneous network. Compared with other advanced methods, the performance of PSNE has been confirmed more effective in disease-gene prediction. Finally, we applied the PSNE method to predict potential pathogenic genes for age-associated diseases such as AD and PD. We verified the effectiveness of these predicted potential genes by literature verification. Overall, this work provides an effective method for disease-gene prediction, and a series of high-confidence potential pathogenic genes of AD and PD which may be helpful for the experimental discovery of disease genes.

Analysis of Potential Mechanism of Herbal Formula Taohong Siwu Decoction against Vascular Dementia Based on Network Pharmacology and Molecular Docking.

Vascular dementia (VaD) is the second most prevalent dementia, which is attributable to neurovascular dysfunction. Currently, no approved pharmaceuticals are available. Taohong Siwu decoction (TSD) is a traditional Chinese medicine prescription with powerful antiapoptosis and anti-inflammatory properties. In this study, a network pharmacology approach together with molecular docking validation was used to explore the probable mechanism of action of TSD against VaD. A total of 44 active components, 202 potential targets of components, and 3,613 VaD-related targets including 161 intersecting were obtained. The potential chemical components including kaempferol, baicalein, beta-carotene, luteolin, quercetin, and beta-sitosterol involved in the inflammatory response, oxidative stress, and apoptosis might have potential therapeutic effects on the treatment of VaD. The potential core targets including AKT1, CASP3, IL1ß, JUN, and TP53 associated with cell apoptosis and inflammatory might account for the essential therapeutic effects of TSD in VaD. The results indicated that TSD protected against VaD through multicomponent and multitarget modes. Though the detailed mechanism of action of various active ingredients needs to be further illustrated, TSD still showed a promising therapeutic agent for VaD due to its biological activity.

Application of Nanomaterials in Stem Cell-Based Therapeutics for Cardiac Repair and Regeneration.

Cardiovascular disease is a leading cause of disability and death worldwide. Although the survival rate of patients with heart diseases can be improved with contemporary pharmacological treatments and surgical procedures, none of these therapies provide a significant improvement in cardiac repair and regeneration. Stem cell-based therapies are a promising approach for functional recovery of damaged myocardium. However, the available stem cells are difficult to differentiate into cardiomyocytes, which result in the extremely low transplantation efficiency. Nanomaterials are widely used to regulate the myocardial differentiation of stem cells, and play a very important role in cardiac tissue engineering. This study discusses the current status and limitations of stem cells and cell-derived exosomes/micro RNAs based cardiac therapy, describes the cardiac repair mechanism of nanomaterials, summarizes the recent advances in nanomaterials used in cardiac repair and regeneration, and evaluates the advantages and disadvantages of the relevant nanomaterials. Besides discussing the potential clinical applications of nanomaterials in cardiac therapy, the perspectives and challenges of nanomaterials used in stem cell-based cardiac repair and regeneration are also considered. Finally, new research directions in this field are proposed, and future research trends are highlighted.

Mechanism Study on Chinese Medicine in Treatment of Nodular Goiter.

Nodular goiter has become increasingly prevalent in recent years. Clinically, there has been a burgeoning interest in nodular goiter due to the risk of progression to thyroid cancer. This review aims to provide a comprehensive summary of the mechanisms underlying the therapeutic effect of Chinese medicine (CM) in nodular goiter. Articles were systematically retrieved from databases, including PubMed, Web of Science and China National Knowledge Infrastructure. New evidence showed that CM exhibited multi-pathway and multi-target characteristics in the treatment of nodular goiter, involving hypothalamus-pituitary-thyroid axis, oxidative stress, blood rheology, cell proliferation, apoptosis, and autophagy, especially inhibition of cell proliferation and promotion of cell apoptosis, involving multiple signal pathways and a variety of cytokines. This review provides a scientific basis for the therapeutic use of CM against nodular goiter. Nonetheless, future studies are warranted to identify more regulatory genes and pathways to provide new approaches for the treatment of nodular goiter.

Mechanism Study on Chinese Medicine in Treatment of Nodular Goiter / 中国结合医学杂志

Nodular goiter has become increasingly prevalent in recent years. Clinically, there has been a burgeoning interest in nodular goiter due to the risk of progression to thyroid cancer. This review aims to provide a comprehensive summary of the mechanisms underlying the therapeutic effect of Chinese medicine (CM) in nodular goiter. Articles were systematically retrieved from databases, including PubMed, Web of Science and China National Knowledge Infrastructure. New evidence showed that CM exhibited multi-pathway and multi-target characteristics in the treatment of nodular goiter, involving hypothalamus-pituitary-thyroid axis, oxidative stress, blood rheology, cell proliferation, apoptosis, and autophagy, especially inhibition of cell proliferation and promotion of cell apoptosis, involving multiple signal pathways and a variety of cytokines. This review provides a scientific basis for the therapeutic use of CM against nodular goiter. Nonetheless, future studies are warranted to identify more regulatory genes and pathways to provide new approaches for the treatment of nodular goiter.

Drug repositioning for SARS-CoV-2 by Gaussian kernel similarity bilinear matrix factorization.

Coronavirus disease 2019 (COVID-19), a disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is currently spreading rapidly around the world. Since SARS-CoV-2 seriously threatens human life and health as well as the development of the world economy, it is very urgent to identify effective drugs against this virus. However, traditional methods to develop new drugs are costly and time-consuming, which makes drug repositioning a promising exploration direction for this purpose. In this study, we collected known antiviral drugs to form five virus-drug association datasets, and then explored drug repositioning for SARS-CoV-2 by Gaussian kernel similarity bilinear matrix factorization (VDA-GKSBMF). By the 5-fold cross-validation, we found that VDA-GKSBMF has an area under curve (AUC) value of 0.8851, 0.8594, 0.8807, 0.8824, and 0.8804, respectively, on the five datasets, which are higher than those of other state-of-art algorithms in four datasets. Based on known virus-drug association data, we used VDA-GKSBMF to prioritize the top-k candidate antiviral drugs that are most likely to be effective against SARS-CoV-2. We confirmed that the top-10 drugs can be molecularly docked with virus spikes protein/human ACE2 by AutoDock on five datasets. Among them, four antiviral drugs ribavirin, remdesivir, oseltamivir, and zidovudine have been under clinical trials or supported in recent literatures. The results suggest that VDA-GKSBMF is an effective algorithm for identifying potential antiviral drugs against SARS-CoV-2.

Evidence-based core information for health communication of tobacco control: The effect of smoking on risks of female disease.

Objective: Cigarettes have become the the biggest killer of contemporary female's health and beauty. What kind of health information is suitable for the general public is an important issue to be discussed globally. The purpose of this study is to generate systematic, rigorous, public-demand-oriented and appropriate core information relevant to tobacco control based on the best available evidence, combined with audience preferences and pre-dissemination content review from multidisciplinary expertise in order to improve the effectiveness of health communication of tobacco control. Methods: Relevant systematic reviews meta-analysis that reported smoking on risks of female disease were identified by searching PubMed, Embase, the Cochrane Library, Web of Science, Clinical Trials.gov, and the International Clinical Trial Registry Platform. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) process was applied to assess the evidence in order to make rigorous core information. The audience prevalence survey was conducted to ensure that core information was targeted and tailored. Finally, the expert assessment was used for a pre-dissemination content review and to evaluate whether the core information was appropriate or not. Results: The final core information consisted of eight parts concerning the effects of smoking and female cardiovascular disease, diabetes, rheumatoid arthritis, respiratory disease, digestive system disease, mental disease, non-pregnant female reproductive system disease, as well as pregnant women and their fetuses. A total of 35 items of core information suitable for dissemination was included and the quality of evidence, the degree of public demand and the outcome of pre-dissemination content review were reported. Conclusion: The core information related to female cardiovascular system diseases, as well as liver cancer and upper gastrointestinal cancer is the preferred content for health communication of tobacco control. The quality of evidence for core information related to pregnant women and their infants, as well as diseases of reproductive system, respiratory system, and diabetes needs to be improved to meet high public demand. The core information related to mental disease is more suitable for dissemination to patients with mental illness than to the general public. Besides, dissemination of core information should be individualized. Evidence-based Core Information for Health Communication of Tobacco Control would be helpful to provide evidence support for health communication related to tobacco control and enhance public health literacy for international communities that have high smoking prevalence and related disease burden.

Occurrence, source tracking and removal of antibiotics in recirculating aquaculture systems (RAS) in southern China.

The recirculating aquaculture system (RAS) has attracted much attention in China as a way to rapidly transform and upgrade aquaculture ponds to realize zero-emissions of pollutants in aquaculture tail water. Tail water purification ponds (TWPPs) play an important role in the treatment of aquaculture wastewater. However, until now, there have been few reports on the occurrence of antibiotics in RAS and the removal of antibiotics from the TWPPs of RAS. Therefore, this study focused on the occurrence of antibiotics in a typical ecological RAS. For comparison, the same measurements were simultaneously carried out in nearby open aquaculture ponds and rivers. The pollution level and spatial distribution of antibiotics in the RAS and the removal of antibiotics in the TWPPs were explored. The results showed that (1) eleven and twelve antibiotics were detected in water and sediment samples in the RAS, respectively, but no antibiotics were found in fish muscles and feed. Erythromycin (ERY), lincomycin (LIN), and ciprofloxacin (CFX) were the three main types of antibiotics found in water and sediment samples. (2) The TWPPs of the RAS can effectively remove antibiotics in aquaculture water. The antibiotic concentration in recirculating aquaculture ponds of the RAS was as high as 180 ng/L. After treatments in the TWPPs, the antibiotic concentration of aquaculture water decreased to 81.6 ng/L (3) The antibiotic concentrations in recirculating aquaculture ponds (25.2-180 ng/L) were lower than those in the nearby open aquaculture ponds (126-267.3 ng/L), and the concentration of antibiotics in the sediments of recirculating aquaculture ponds was up to 22.9 ng/g, while that in TWPPs was as high as 56.1 ng/g. In conclusion, the antibiotic residues in the RAS were low after antibiotics were banned in feed in China, and the removal of antibiotics in the TWPPs was more pronounced. Furthermore, cross-contamination was found between the RAS, surrounding open aquaculture ponds and the river, and the water supply of the RAS was likely to be the main contributor of antibiotics in the aquaculture environments. This study can help the government formulate discharge standards for antibiotics in aquaculture and also provide a reference for the transformation and upgrading of aquaculture ponds to achieve a zero-emission aquaculture mode.

DGHNE: network enhancement-based method in identifying disease-causing genes through a heterogeneous biomedical network.

The identification of disease-causing genes is critical for mechanistic understanding of disease etiology and clinical manipulation in disease prevention and treatment. Yet the existing approaches in tackling this question are inadequate in accuracy and efficiency, demanding computational methods with higher identification power. Here, we proposed a new method called DGHNE to identify disease-causing genes through a heterogeneous biomedical network empowered by network enhancement. First, a disease-disease association network was constructed by the cosine similarity scores between phenotype annotation vectors of diseases, and a new heterogeneous biomedical network was constructed by using disease-gene associations to connect the disease-disease network and gene-gene network. Then, the heterogeneous biomedical network was further enhanced by using network embedding based on the Gaussian random projection. Finally, network propagation was used to identify candidate genes in the enhanced network. We applied DGHNE together with five other methods into the most updated disease-gene association database termed DisGeNet. Compared with all other methods, DGHNE displayed the highest area under the receiver operating characteristic curve and the precision-recall curve, as well as the highest precision and recall, in both the global 5-fold cross-validation and predicting new disease-gene associations. We further performed DGHNE in identifying the candidate causal genes of Parkinson's disease and diabetes mellitus, and the genes connecting hyperglycemia and diabetes mellitus. In all cases, the predicted causing genes were enriched in disease-associated gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways, and the gene-disease associations were highly evidenced by independent experimental studies.

Tubeimoside-1: A review of its antitumor effects, pharmacokinetics, toxicity, and targeting preparations.

Tubeimoside-1 (TBMS-1), a natural triterpenoid saponin found in traditional Chinese herbal medicine Bolbostemmatis Rhizoma, is present in numerous Chinese medicine preparations. This review aims to comprehensively describe the pharmacology, pharmacokinetics, toxicity and targeting preparations of TBMS-1, as well the therapeutic potential for cancer treatement. Information concerning TBMS-1 was systematically collected from the authoritative internet database of PubMed, Web of Science, and China National Knowledge Infrastructure applying a combination of keywords involving "tumor," "pharmacokinetics," "toxicology," and targeting preparations. New evidence shows that TBMS-1 possesses a remarkable inhibitory effect on the tumors of the respiratory system, digestive system, nervous system, genital system as well as other systems in vivo and in vitro. Pharmacokinetic studies reveal that TBMS-1 is extensively distributed in various tissues and prone to degradation by the gastrointestinal tract after oral administration, causing a decrease in bioavailability. Meanwhile, several lines of evidence have shown that TBMS-1 may cause adverse and toxic effects at high doses. The development of liver-targeting and lung-targeting preparations can reduce the toxic effect of TBMS-1 and increase its efficacy. In summary, TBMS-1 can effectively control tumor treatment. However, additional research is necessary to investigate in vivo antitumor effects and the pharmacokinetics of TBMS-1. In addition, to reduce the toxicity of TBMS-1, future research should aim to modify its structure, formulate targeting preparations or combinations with other drugs.

The haplotypes GCA and ACA in ESR1 gene are associated with the susceptibility of recurrent spontaneous abortion (RSA) in Chinese Han: A case-control study and meta-analysis.

ABSTRACT: The nonstructured abstract were supplied as following: Estrogen receptor is involved in the pathogenesis of recurrent spontaneous abortion (RSA). The ESR1 and ESR2 genes can mediate nongenomic estrogen responses. This study aimed to assess the genetic association between the ESR1 and ESR2 genes polymorphisms and RSA susceptibility in a Chinese Han population. A total of 258 women who had experienced RSA and 264 unrelated healthy women were recruited. Genotypes of the 6 polymorphisms in the ESR1 (rs9340799, rs2234693, and rs3798759) and ESR2 genes (rs207764, rs4986938, and rs1256049) were analyzed using Snapshot technology. No association was detected between the alleles and genotypes of ESR1 rs9340799, rs2234693, and rs3798759 polymorphims and RSA risk (P > .05). Subjects carrying the haplotype of rs9340799A-rs2234693C-rs3798759A had a significantly increased RSA risk in the case group compared with the control group (P = .0005, Padj = .003, odds ratios [95% CI] = 0.35 [0.19-0.65]). However, subjects carrying the haplotype of rs9340799G-rs2234693C-rs3798759A had a significantly decreased RSA risk in the case group compared with the control group (P = .0005, Padj = .003, odds ratios [95% CI] = 2.99 [1.57-5.70]). In addition, no association was found between the alleles, genotypes, and haplotypes of ESR2 rs207764, rs4986938, rs1256049 polymorphisms and RSA risk (P > .05). In conclusion, the haplotype rs9340799A-rs2234693C-rs3798759A of ESR1 might be a risk factor. And the haplotype rs9340799G-rs2234693C-rs3798759A of ESR1 might be a protective factor for RSA in a Chinese Han population.

HyMM: hybrid method for disease-gene prediction by integrating multiscale module structure.

MOTIVATION: Identifying disease-related genes is an important issue in computational biology. Module structure widely exists in biomolecule networks, and complex diseases are usually thought to be caused by perturbations of local neighborhoods in the networks, which can provide useful insights for the study of disease-related genes. However, the mining and effective utilization of the module structure is still challenging in such issues as a disease gene prediction. RESULTS: We propose a hybrid disease-gene prediction method integrating multiscale module structure (HyMM), which can utilize multiscale information from local to global structure to more effectively predict disease-related genes. HyMM extracts module partitions from local to global scales by multiscale modularity optimization with exponential sampling, and estimates the disease relatedness of genes in partitions by the abundance of disease-related genes within modules. Then, a probabilistic model for integration of gene rankings is designed in order to integrate multiple predictions derived from multiscale module partitions and network propagation, and a parameter estimation strategy based on functional information is proposed to further enhance HyMM's predictive power. By a series of experiments, we reveal the importance of module partitions at different scales, and verify the stable and good performance of HyMM compared with eight other state-of-the-arts and its further performance improvement derived from the parameter estimation. CONCLUSIONS: The results confirm that HyMM is an effective framework for integrating multiscale module structure to enhance the ability to predict disease-related genes, which may provide useful insights for the study of the multiscale module structure and its application in such issues as a disease-gene prediction.

SEPA: signaling entropy-based algorithm to evaluate personalized pathway activation for survival analysis on pan-cancer data.

MOTIVATION: Biomarkers with prognostic ability and biological interpretability can be used to support decision-making in the survival analysis. Genes usually form functional modules to play synergistic roles, such as pathways. Predicting significant features from the functional level can effectively reduce the adverse effects of heterogeneity and obtain more reproducible and interpretable biomarkers. Personalized pathway activation inference can quantify the dysregulation of essential pathways involved in the initiation and progression of cancers, and can contribute to the development of personalized medical treatments. RESULTS: In this study, we propose a novel method to evaluate personalized pathway activation based on signaling entropy for survival analysis (SEPA), which is a new attempt to introduce the information-theoretic entropy in generating pathway representation for each patient. SEPA effectively integrates pathway-level information into gene expression data, converting the high-dimensional gene expression data into the low-dimensional biological pathway activation scores. SEPA shows its classification power on the prognostic pan-cancer genomic data, and the potential pathway markers identified based on SEPA have statistical significance in the discrimination of high- and low-risk cohorts and are likely to be associated with the initiation and progress of cancers. The results show that SEPA scores can be used as an indicator to precisely distinguish cancer patients with different clinical outcomes, and identify important pathway features with strong discriminative power and biological interpretability. AVAILABILITY AND IMPLEMENTATION: The MATLAB-package for SEPA is freely available from https://github.com/xingyili/SEPA. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Biomedical data, computational methods and tools for evaluating disease-disease associations.

In recent decades, exploring potential relationships between diseases has been an active research field. With the rapid accumulation of disease-related biomedical data, a lot of computational methods and tools/platforms have been developed to reveal intrinsic relationship between diseases, which can provide useful insights to the study of complex diseases, e.g. understanding molecular mechanisms of diseases and discovering new treatment of diseases. Human complex diseases involve both external phenotypic abnormalities and complex internal molecular mechanisms in organisms. Computational methods with different types of biomedical data from phenotype to genotype can evaluate disease-disease associations at different levels, providing a comprehensive perspective for understanding diseases. In this review, available biomedical data and databases for evaluating disease-disease associations are first summarized. Then, existing computational methods for disease-disease associations are reviewed and classified into five groups in terms of the usages of biomedical data, including disease semantic-based, phenotype-based, function-based, representation learning-based and text mining-based methods. Further, we summarize software tools/platforms for computation and analysis of disease-disease associations. Finally, we give a discussion and summary on the research of disease-disease associations. This review provides a systematic overview for current disease association research, which could promote the development and applications of computational methods and tools/platforms for disease-disease associations.

Inferring Latent MicroRNA-Disease Associations on a Gene-Mediated Tripartite Heterogeneous Multiplexing Network.

MicroRNA (miRNA) is a class of non-coding single-stranded RNA molecules encoded by endogenous genes with a length of about 22 nucleotides. MiRNAs have been successfully identified as differentially expressed in various cancers. There is evidence that disorders of miRNAs are associated with a variety of complex diseases. Therefore, inferring potential miRNA-disease associations (MDAs) is very important for understanding the aetiology and pathogenesis of many diseases and is useful to disease diagnosis, prognosis and treatment. First, We creatively fused multiple similarity subnetworks from multi-sources for miRNAs, genes and diseases by multiplexing technology, respectively. Then, three multiplexed biological subnetworks are connected through the extended binary association to form a tripartite complete heterogeneous multiplexed network (Tri-HM). Finally, because the constructed Tri-HM network can retain subnetworks' original topology and biological functions and expands the binary association and dependence between the three biological entities, rich neighbourhood information is obtained iteratively from neighbours by a non-equilibrium random walk. Through cross-validation, our tri-HM-RWR model obtained an AUC value of 0.8657, and an AUPR value of 0.2139 in the global 5-fold cross-validation, which shows that our model can more fully speculate disease-related miRNAs.

A Dual Ranking Algorithm Based on the Multiplex Network for Heterogeneous Complex Disease Analysis.

Identifying biomarkers of heterogeneous complex diseases has always been one of the focuses in medical research. In previous studies, the powerful network propagation methods have been applied to finding marker genes related to specific diseases, but existing methods are mostly based on a single network, which may be greatly affected by the incompleteness of the network and the ignorance of a large amount of information about physical and functional interactions between biological components. Other methods that directly integrate multiple types of interactions into an aggregate network have the risks that different types of data may conflict with each other and the characteristics and topologies of each individual network are lost. Meanwhile, biomarkers used in clinical trials should have the characteristics of small quantity and strong discriminate ability. In this study, we developed a multiplex network-based dual ranking framework (DualRank) for heterogeneous complex disease analysis. We applied the proposed method to heterogeneous complex diseases for diagnosis, prognosis, and classification. The results showed that DualRank outperformed competing methods and could identify biomarkers with the small quantity, great prediction performance (average AUC = 0.818) and biological interpretability.