Publications by authors named "Bairong Shen"

150 Publications

A Ferroptosis-Related Gene Signature Identified as a Novel Prognostic Biomarker for Colon Cancer.

Front Genet 2021 1;12:692426. Epub 2021 Jul 1.

Institute for Systems Genetics, West China Hospital, Sichuan University, Chengdu, China.

Background: Colon cancer (CC) is a common gastrointestinal malignant tumor with high heterogeneity in clinical behavior and response to treatment, making individualized survival prediction challenging. Ferroptosis is a newly discovered iron-dependent cell death that plays a critical role in cancer biology. Therefore, identifying a prognostic biomarker with ferroptosis-related genes provides a new strategy to guide precise clinical decision-making in CC patients.

Methods: Alteration in the expression profile of ferroptosis-related genes was initially screened in GSE39582 dataset involving 585 CC patients. Univariate Cox regression analysis and LASSO-penalized Cox regression analysis were combined to further identify a novel ferroptosis-related gene signature for overall survival prediction. The prognostic performance of the signature was validated in the GSE17536 dataset by Kaplan-Meier survival curve and time-dependent ROC curve analyses. Functional annotation of the signature was explored by integrating GO and KEGG enrichment analysis, GSEA analysis and ssGSEA analysis. Furthermore, an outcome risk nomogram was constructed considering both the gene signature and the clinicopathological features.

Results: The prognostic signature biomarker composed of 9 ferroptosis-related genes accurately discriminated high-risk and low-risk patients with CC in both the training and validation datasets. The signature was tightly linked to clinicopathological features and possessed powerful predictive ability for distinct clinical subgroups. Furthermore, the risk score was confirmed to be an independent prognostic factor for CC patients by multivariate Cox regression analysis ( < 0.05). Functional annotation analyses showed that the prognostic signature was closely correlated with pivotal cancer hallmarks, particularly cell cycle, transcriptional regulation, and immune-related functions. Moreover, a nomogram with the signature was also built to quantify outcome risk for each patient.

Conclusion: The novel ferroptosis-related gene signature biomarker can be utilized for predicting individualized prognosis, optimizing survival risk assessment and facilitating personalized management of CC patients.
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http://dx.doi.org/10.3389/fgene.2021.692426DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8280527PMC
July 2021

QSCR Analysis of Cytotoxicity of 6-Fluoro-3-(4H-1,2,4-triazol-3-yl)quinolin-4(1H)-ones on Chinese Hamster Ovary Cell Line: Design of REPUBLIC1986.

Curr Med Chem 2021 Jun 23. Epub 2021 Jun 23.

West China School of Nursing / Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China.

Background: 6-Fluoro-3-(4H-1,2,4-triazol-3-yl)quinolin-4(1H)-ones are promising antitumor agents with enormous data on their profound cytotoxic effects on the human cancer cell lines.

Objectives: We sought to perform a Quantitative structure cytotoxicity relationship (QSCR) analysis of a series of previously reported fluoroquinolone analogues using computer-assisted multiple regression analysis and investigate the cytotoxicity-inducing structural parameters among these congeners.

Methods: The dataset was segregated into training and test sets of 6-Fluoro-3-(4H-1,2,4-triazol-3-yl)quinolin-4(1H)-ones by using a random selection method embedded in Vlife MDS 4.6 software and subjected to QSCR analysis. Next, cross-validation of the generated QSCR models was performed along with the external test set prediction. Finally, the data was analyzed, and contour plots were developed to deduce the cytotoxicity-inducing structural parameters among these congeners using Minitab® software.

Results: The validated QSCR model exhibited a statistically significant predictive value of 92.27 percent. Our QSCR model revealed a direct proportionality between hydrogen counts and cytotoxicity and exclusion of sulphur and nitrogen with lesser crowding of cyclopropyl rings in future potential 6-Fluoro-3-(4H-1,2,4-triazol-3-yl)quinolin-4(1H)-one analogues. Based on the QSCR model predictions and contour plot analysis, the de novo REPUBLIC1986 molecule provided the best hit with predicted IC50 (µM) of 0.45 against CHO cell line and is amenable to salt formation crucial for anti-ovarian cancer activity.

Conclusion: These findings suggest the relevancy of the developed QSCR model in designing novel, potent, and safer anti-cancer drugs with 6-Fluoro-3-(4H-1,2,4-triazol-3-yl)quinolin-4(1H)-ones as seed compounds.
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http://dx.doi.org/10.2174/0929867328666210623150552DOI Listing
June 2021

HFBD: a biomarker knowledge database for heart failure heterogeneity and personalized applications.

Bioinformatics 2021 Jun 23. Epub 2021 Jun 23.

Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610212, Sichuan, China.

Motivation: Heart failure (HF) is a cardiovascular disease with a high incidence around the world. Accumulating studies have focused on the identification of biomarkers for HF precision medicine. To understand the HF heterogeneity and provide biomarker information for the personalized diagnosis and treatment of HF, a knowledge database collecting the distributed and multiple-level biomarker information is necessary.

Results: In this study, the HF biomarker knowledge database (HFBD) was established by manually collecting the data and knowledge from literature in PubMed. HFBD contains 2618 records and 868 HF biomarkers (731 single and 137 combined) extracted from 1237 original articles. The biomarkers were classified into proteins, RNAs, DNAs, and the others at molecular, image, cellular and physiological levels. The biomarkers were annotated with biological, clinical and article information as well as the experimental methods used for the biomarker discovery. With its user-friendly interface, this knowledge database provides a unique resource for the systematic understanding of HF heterogeneity and personalized diagnosis and treatment of HF in the era of precision medicine.

Availability: The platform is openly available at http://sysbio.org.cn/HFBD/.
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http://dx.doi.org/10.1093/bioinformatics/btab470DOI Listing
June 2021

CMBD: a manually curated cancer metabolic biomarker knowledge database.

Database (Oxford) 2021 Mar;2021

Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.

The pathogenesis of cancer is influenced by interactions among genes, proteins, metabolites and other small molecules. Understanding cancer progression at the metabolic level is propitious to the visual decoding of changes in living organisms. To date, a large number of metabolic biomarkers in cancer have been measured and reported, which provide an alternative method for cancer precision diagnosis, treatment and prognosis. To systematically understand the heterogeneity of cancers, we developed the database CMBD to integrate the cancer metabolic biomarkers scattered over literatures in PubMed. At present, CMBD contains 438 manually curated relationships between 282 biomarkers and 76 cancer subtypes of 18 tissues reported in 248 literatures. Users can access the comprehensive metabolic biomarker information about cancers, references, clinical samples and their relationships from our online database. As case studies, pathway analysis was performed on the metabolic biomarkers of breast and prostate cancers, respectively. 'Phenylalanine, tyrosine and tryptophan biosynthesis', 'phenylalanine metabolism' and 'primary bile acid biosynthesis' were identified as playing key roles in breast cancer. 'Glyoxylate and dicarboxylate metabolism', 'citrate cycle (TCA cycle)', and 'alanine, aspartate and glutamate metabolism' have important functions in prostate cancer. These findings provide us with an understanding of the metabolic pathway of cancer initiation and progression. Database URL: http://www.sysbio.org.cn/CMBD/.
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http://dx.doi.org/10.1093/database/baaa094DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7947571PMC
March 2021

Altered nitric oxide induced by gut microbiota reveals the connection between central precocious puberty and obesity.

Clin Transl Med 2021 02;11(2):e299

Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Sichuan, China.

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http://dx.doi.org/10.1002/ctm2.299DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7842634PMC
February 2021

MiR-378a-3p as a putative biomarker for hepatocellular carcinoma diagnosis and prognosis: Computational screening with experimental validation.

Clin Transl Med 2021 02;11(2):e307

Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.

Background: Hepatocellular carcinoma (HCC) is a malignant disease with high morbidity and mortality, and the molecular mechanism for the genesis and progression is complex and heterogeneous. Biomarker discovery is crucial for the personalized and precision treatment of HCC. The accumulation of reported microRNA biomarkers makes it possible to combine computational identification with experimental validation to accelerate the discovery of novel biomarker.

Results: In the present work, we applied a rational computer-aided biomarker discovery model to screen for the HCC diagnosis biomarker. Two HCC-associated networks were constructed based on the microRNA and mRNA expression profiles, and the potential microRNA biomarkers were identified based on their unique regulatory and influential power in the network. These putative biomarkers were then experimentally validated. One prominent example among these identified biomarkers is MiR-378a-3p: It was shown to independently regulate several important transcription factors such as PLAGL2 and β-catenin, affecting the β-catenin signaling. Such mechanism may indicate a potential tumor suppressor role of MiR-378a-3p and the impact of its abnormal expression on the cell growth and invasion of HCC.

Conclusions: A bioinformatics model with network topological and functional characterization was successfully applied to the identification of HCC biomarkers. The predicted microRNA biomarkers were than validated with experiments using human HCC cell lines, model animal, and clinical specimens. The results confirmed the prediction by our proposed model that miR-378a-3p was a putative biomarker for diagnosis and prognosis of HCC.
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http://dx.doi.org/10.1002/ctm2.307DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7882078PMC
February 2021

Identification of Key MicroRNAs and Mechanisms in Prostate Cancer Evolution Based on Biomarker Prioritization Model and Carcinogenic Survey.

Front Genet 2020 15;11:596826. Epub 2021 Jan 15.

Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.

Prostate cancer (PCa) is occurred with increasing incidence and heterogeneous pathogenesis. Although clinical strategies are accumulated for PCa prevention, there is still a lack of sensitive biomarkers for the holistic management in PCa occurrence and progression. Based on systems biology and artificial intelligence, translational informatics provides new perspectives for PCa biomarker prioritization and carcinogenic survey. In this study, gene expression and miRNA-mRNA association data were integrated to construct conditional networks specific to PCa occurrence and progression, respectively. Based on network modeling, hub miRNAs with significantly strong single-line regulatory power were topologically identified and those shared by the condition-specific network systems were chosen as candidate biomarkers for computational validation and functional enrichment analysis. Nine miRNAs, i.e., , and , were prioritized as key players for PCa management. Most of these miRNAs achieved high AUC values (AUC > 0.70) in differentiating different prostate samples. Among them, seven of the miRNAs have been previously reported as PCa biomarkers, which indicated the performance of the proposed model. The remaining and could serve as novel candidates for PCa predicting and monitoring. In particular, key miRNA-mRNA regulations were extracted for pathogenetic understanding. Here was selected as the case and and axis were found to be putative mechanisms during PCa evolution. In addition, signaling, prostate cancer, microRNAs in cancer etc. were significantly enriched by the identified miRNAs-mRNAs, demonstrating the functional role of the identified miRNAs in PCa genesis. Biomarker miRNAs together with the associated miRNA-mRNA relations were computationally identified and analyzed for PCa management and carcinogenic deciphering. Further experimental and clinical validations using low-throughput techniques and human samples are expected for future translational studies.
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http://dx.doi.org/10.3389/fgene.2020.596826DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7844321PMC
January 2021

Secondary Metabolites as Treatment of Choice for Metabolic Disorders and Infectious Diseases & their Metabolic Profiling-Part 2.

Curr Drug Metab 2020;21(14):1070-1071

Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China.

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http://dx.doi.org/10.2174/138920022114201230142204DOI Listing
January 2020

ANCA: A Web Server for Amino Acid Networks Construction and Analysis.

Front Mol Biosci 2020 19;7:582702. Epub 2020 Nov 19.

Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, China.

Amino acid network (AAN) models empower us to gain insights into protein structures and functions by describing a protein 3D structure as a graph, where nodes represent residues and edges as amino acid interactions. Here, we present the ANCA, an interactive Web server for Amino Acids Network Construction and Analysis based on a single structure or a set of structures from the Protein Data Bank. The main purpose of ANCA is to provide a portal for three types of an environment-dependent residue contact energy (ERCE)-based network model, including amino acid contact energy network (AACEN), node-weighted amino acid contact energy network (NACEN), and edge-weighted amino acid contact energy network (EACEN). For comparison, the C-alpha distance-based network model is also included, which can be extended to protein-DNA/RNA complexes. Then, the analyses of different types of AANs were performed and compared from node, edge, and network levels. The network and corresponding structure can be visualized directly in the browser. The ANCA enables researchers to investigate diverse concerns in the framework of AAN, such as the interpretation of allosteric regulation and functional residues. The ANCA portal, together with an extensive help, is available at http://sysbio.suda.edu.cn/anca/.
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http://dx.doi.org/10.3389/fmolb.2020.582702DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7711068PMC
November 2020

Topical Capsaicin for the Treatment of Neuropathic Pain.

Curr Drug Metab 2021 ;22(3):198-207

Center for Systems Biology, Soochow University, Suzhou, 215006, China.

Background: Neuropathic pain (NP) is an egregious problem worldwide. Due to the side-effects of oral drugs, drugs delivered directly to the affected area of pain are preferred.

Objective: Capsaicin, a chemical compound isolated from chili peppers, is used as an analgesic in topical ointments and dermal patches to alleviate pain. Objective of the study is to review the application and functionality of topical capsaicin in treatment of neuropathic pain.

Data Sources: To systematically review capsaicin's functions on NP, we retrieved articles from the PubMed database published in the last ten years.

Study Eligibility Criteria: The inclusion criteria were capsaicin and the use of capsaicin for the treatment of NP; on the other hand, articles were excluded according to the mentioned criteria such as abstracts, articles written in any language other than English, incomplete articles, and conference papers.

Participants And Interventions: Out of 265 articles, 108 articles were selected after filtering through the inclusion and exclusion criteria. The data and knowledge currently existing for capsaicin treatment in NP are summarized.

Results: This review indicates that capsaicin effectively improves NP treatment without affecting the motor and large nerve fibres involved in sensory function. Transient receptor potential channel vanilloid type 1 (TRPV1) is the capsaicin receptor expressed in central and peripheral terminals of a sensitive primary nerve cell. Conclusions and implications of key findings: Topical capsaicin has a sensible safety profile and is effective in reducing NP. Therefore, studies over the last decade suggest that capsaicin might be a potential drug for NP treatment.
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http://dx.doi.org/10.2174/1389200221999201116143701DOI Listing
January 2021

Biomarker Discovery for the Carcinogenic Heterogeneity Between Colon and Rectal Cancers Based on lncRNA-Associated ceRNA Network Analysis.

Front Oncol 2020 30;10:535985. Epub 2020 Oct 30.

Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, China.

Background: Colorectal cancer (CRC) is one of the leading causes of cancer death worldwide. Emerging evidence has revealed that risk factors and metastatic patterns differ greatly between colon and rectal cancers. However, the molecular mechanism underlying their pathogenic differences remains unclear. Therefore, we here aimed to identify non-coding RNA biomarkers based on lncRNA-associated ceRNA network (LceNET) to elucidate the carcinogenic heterogeneity between colon and rectal cancers.

Methods: A global LceNET in human was constructed by employing experimental evidence-based miRNA-mRNA and miRNA-lncRNA interactions. Then, four context-specific ceRNA networks related to cancer initiation and metastasis were extracted by mapping differentially expressed lncRNAs, miRNAs and mRNAs to the global LceNET. Notably, a novel network-based bioinformatics model was proposed and applied to identify lncRNA/miRNA biomarkers and critical ceRNA triplets for understanding the carcinogenic heterogeneity between colon and rectal cancers. Moreover, the identified biomarkers were further validated by their diagnostic/prognostic performance, expression pattern and correlation analysis.

Results: Based on network modeling, lncRNA KCNQ1OT1 (AUC>0.85) and SNHG1 (AUC>0.94) were unveiled as common diagnostic biomarkers for the initiation and metastasis of colon and rectal cancers. qRT-PCR analysis uncovered that these lncRNAs had significantly higher expression level in CRC cell lines with high metastatic potential. In particular, KCNQ1OT1 and SNHG1 function in colon and rectal cancers different ceRNA mechanisms. For example, KCNQ1OT1/miR-484/ANKRD36 axis was involved in the initiation of colon cancer, while KCNQ1OT1/miR-181a-5p/PCGF2 axis was implicated in the metastasis of rectal cancer; the SNHG1/miR-484/ORC6 axis played a role in colon cancer, while SNHG1/miR-423-5p/EZH2 and SNHG1/let-7b-5p/ATP6V1F axes participated in the initiation and metastasis of rectal cancer, respectively. In these ceRNA triplets, miR-484, miR-181a-5p, miR-423-5p and let-7b-5p were identified as miRNA biomarkers with excellent distinguishing ability between normal and tumor tissues, and ANKRD36, PCGF2, EZH2 and ATP6V1F were closely related to the prognosis of corresponding cancer.

Conclusion: The landscape of lncRNA-associated ceRNA network not only facilitates the exploration of non-coding RNA biomarkers, but also provides deep insights into the oncogenetic heterogeneity between colon and rectal cancers, thereby contributing to the optimization of diagnostic and therapeutic strategies of CRC.
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http://dx.doi.org/10.3389/fonc.2020.535985DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7662689PMC
October 2020

Regulation of Pain Genes-Capsaicin vs Resiniferatoxin: Reassessment of Transcriptomic Data.

Front Pharmacol 2020 29;11:551786. Epub 2020 Oct 29.

Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.

Emerging evidence has shown a strong association between neuropathic pain and chronic diseases. In recent years, the treatment of neuropathic pain has attracted more attention. Natural products, such as capsaicin and resiniferatoxin, have been well utilized to treat this disease. In this study, we aim to compare the regulatory effects of capsaicin and resiniferatoxin on pain-related genes as well as on genes with no direct association with pain. Public transcriptomic and microarray data on gene expression in the dorsal root ganglia and genes associated with TRPV1 (+) neurons were obtained from the GEO database and then analyzed. Differentially expressed genes were selected for further functional analysis, including pathway enrichment, protein-protein interaction, and regulatory network analysis. Pain-associated genes were extracted with the reference of two pain gene databases and the effects of these two natural drugs on the pain-associated genes were measured. The results of our research indicate that as compared to capsaicin, resiniferatoxin (RTX) regulates more non pain-associated genes and has a negative impact on beneficial genes (off-targets) which are supposed to alleviate nociception and hypersensitivity by themselves. So, based on this study, we may conclude that capsaicin may be less potent when compared to RTX, but it will elicit considerably less adverse effects too. Thereby confirming that capsaicin could be used for the efficient alleviation of neuropathic pain with possibly fewer side effects.
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http://dx.doi.org/10.3389/fphar.2020.551786DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7658921PMC
October 2020

PCLiON: An Ontology for Data Standardization and Sharing of Prostate Cancer Associated Lifestyles.

Int J Med Inform 2021 01 7;145:104332. Epub 2020 Nov 7.

Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, 610041, China. Electronic address:

Background: Researches on Lifestyle medicine (LM) have emerged in recent years to garner wide attention. Prostate cancer (PCa) could be prevented and treated by positive lifestyles, but the association between lifestyles and PCa is always personalized.

Objectives: In order to solve the heterogeneity and diversity of different data types related to PCa, establish a standardized lifestyle ontology, promote the exchange and sharing of disease lifestyle knowledge, and support text mining and knowledge discovery.

Methods: The overall construction of PCLiON was created in accordance with the principles and methodology of ontology construction. Following the principles of evidence-based medicine, we screened and integrated the lifestyles and their related attributes. Protégé was used to construct and validate the semantic framework. All annotations in PCLiON were based on SNOMED CT, NCI Thesaurus, the Cochrane Library and FooDB, etc. HTML5 and ASP.NET was used to develop the independent Web page platform and corresponding intelligent terminal application. The PCLiON also uploaded to the National Center for Biomedical Ontology BioPortal.

Results: PCLiON integrates 397 lifestyles and lifestyle-related factors associated with PCa, and is the first of its kind for a specific disease. It contains 320 attribute annotations and 11 object attributes. The logical relationship and completeness meet the ontology requirements. Qualitative analysis was carried out for 329 terms in PCLiON, including factors which are protective, risk or associated but functional unclear, etc. PCLiON is publicly available both at http://pcaontology.net/PCaLifeStyleDefault.aspx and https://bioportal.bioontology.org/ontologies/PCALION.

Conclusions: Through the bilingual online platforms, complex lifestyle research data can be transformed into standardized, reliable and responsive knowledge, which can promote the shared-decision making (SDM) on lifestyle intervention and assist patients in lifestyle self-management toward the goal of PCa targeted prevention.
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http://dx.doi.org/10.1016/j.ijmedinf.2020.104332DOI Listing
January 2021

CRC-EBD: Epigenetic Biomarker Database for Colorectal Cancer.

Front Genet 2020 6;11:907. Epub 2020 Oct 6.

Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, China.

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http://dx.doi.org/10.3389/fgene.2020.00907DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7573234PMC
October 2020

iODA: An integrated tool for analysis of cancer pathway consistency from heterogeneous multi-omics data.

J Biomed Inform 2020 12 20;112:103605. Epub 2020 Oct 20.

Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, China. Electronic address:

The latest advances in the next generation sequencing technology have greatly facilitated the extensive research of genomics and transcriptomics, thereby promoting the decoding of carcinogenesis with unprecedented resolution. Considering the contribution of analyzing high-throughput multi-omics data to the exploration of cancer molecular mechanisms, an integrated tool for heterogeneous multi-omics data analysis (iODA) is proposed for the systems-level interpretation of multi-omics data, i.e., transcriptomic profiles (mRNA or miRNA expression data) and protein-DNA interactions (ChIP-Seq data). Considering the data heterogeneity, iODA can compare six statistical algorithms in differential analysis for the selected sample data and assist users in choosing the globally optimal one for dysfunctional mRNA or miRNA identification. Since molecular signatures are more consistent at the pathway level than at the gene level, the tool is able to enrich the identified dysfunctional molecules onto the KEGG pathways and extracted the consistent items as key components for further pathogenesis investigation. Compared with other tools, iODA is multi-functional for the systematic analysis of different level of omics data, and its analytical power was demonstrated through case studies of single and cross-level prostate cancer omics data. iODA is open source under GNU GPL and can be downloaded from http://www.sysbio.org.cn/iODA.
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http://dx.doi.org/10.1016/j.jbi.2020.103605DOI Listing
December 2020

Herbal Resources to Combat a Progressive & Degenerative Nervous System Disorder- Parkinson's Disease.

Curr Drug Targets 2021 ;22(6):609-630

Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Xinchuan Road 2222, Chengdu, Sichuan, China.

Parkinson's disease is one of the most common adult-onset, a chronic disorder involving neurodegeneration, which progressively leads to deprivation of dopaminergic neurons in substantia nigra, causing a subsequent reduction of dopamine levels in the striatum resulting in tremor, myotonia, and dyskinesia. Genetics and environmental factors are believed to be responsible for the onset of Parkinson's disease. The exact pathogenesis of Parkinson's disease is quite complicated and the present anti-Parkinson's disease treatments appear to be clinically insufficient. Comprehensive researches have demonstrated the use of natural products such as ginseng, curcumin, ashwagandha, baicalein, etc. for the symptomatic treatment of this disease. The neuroprotective effects exhibited by these natural products are mainly due to their ability to increase dopamine levels in the striatum, manage oxidative stress, mitochondrial dysfunction, glutathione levels, clear the aggregation of α- synuclein, induce autophagy and decrease the pro-inflammatory cytokines and lipid peroxidation. This paper reviews various natural product studies conducted by scientists to establish the role of natural products (both metabolite extracts as well as pure metabolites) as adjunctive neuroprotective agents.
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http://dx.doi.org/10.2174/1389450121999201013155202DOI Listing
January 2021

Data-driven microbiota biomarker discovery for personalized drug therapy of cardiovascular disease.

Pharmacol Res 2020 11 29;161:105225. Epub 2020 Sep 29.

Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu 610041, Sichuan, China. Electronic address:

Cardiovascular disease (CVD) is the most wide-spread disorder all over the world. The personalized and precision diagnosis, treatment and prevention of CVD is still a challenge. With the developing of metagenome sequencing technologies and the paradigm shifting to data-driven discovery in life science, the computer aided microbiota biomarker discovery for CVD is becoming reality. We here summarize the data resources, knowledgebases and computational models available for CVD microbiota biomarker discovery, and review the present status of the findings about the microbiota patterns associated with the therapeutic effects on CVD. The future challenges and opportunities of the translational informatics on the personalized drug usages in CVD diagnosis, prognosis and treatment are also discussed.
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http://dx.doi.org/10.1016/j.phrs.2020.105225DOI Listing
November 2020

The landscape of emerging ceRNA crosstalks in colorectal cancer: Systems biological perspectives and translational applications.

Clin Transl Med 2020 Aug;10(4):e153

Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, 610041, China.

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http://dx.doi.org/10.1002/ctm2.153DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7426901PMC
August 2020

ADMET Evaluation of Natural DPP-IV Inhibitors for Rational Drug Design against Diabetes.

Curr Drug Metab 2020 ;21(10):768-777

Institutes for Systems Genetics, Frontiers Science Center for Disease-Related Molecular Network, West China Hospital, Sichuan University, Xinchuan Road 2222, Chengdu, Sichuan, China.

Background: As a metabolic and lifestyle disorder, diabetes mellitus poses a prodigious health risk. Out of the many key targets, DPP-IV is one of the very imperative therapeutic targets for the treatment of diabetic patients.

Methods: In our current study, we have done the in silico simulations of ADME-T properties for naturally originated potent DPP-IV inhibitors like quinovic acid, stigmasterol, quinovic acid-3-beta-D-glycopyranoside, zygophyloside E, and lupeol. Structural topographies associated with different pharmacokinetic properties have been systematically assessed.

Results: Glycosylation on quinovic acid is found to be noteworthy for the improvement of pharmacokinetic and toxicological properties, which leads to the prediction that zygophyloside E can be further tailored down to get the lead DPP-IV inhibitor.

Conclusion: This assessment provides useful insight into the future development of novel drugs for the treatment of diabetes mellitus.
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http://dx.doi.org/10.2174/1389200221999200901202945DOI Listing
January 2020

Identification of Intrinsic Disorder in Complexes from the Protein Data Bank.

ACS Omega 2020 Jul 14;5(29):17883-17891. Epub 2020 Jul 14.

Center for Computational Biology and Bioinformatics, Indiana University School of Medicine, Indianapolis, Indiana 46202, United States.

Background: Intrinsically disordered proteins or regions (IDPs or IDRs) lack stable structures in solution, yet often fold upon binding with partners. IDPs or IDRs are highly abundant in all proteomes and represent a significant modification of sequence → structure → function paradigm. The Protein Data Bank (PDB) includes complexes containing disordered segments bound to globular proteins, but the molecular mechanisms of such binding interactions remain largely unknown.

Results: In this study, we present the results of various disorder predictions on a nonredundant set of PDB complexes. In contrast to their structural appearances, many PDB proteins were predicted to be disordered when separated from their binding partners. These predicted-to-be-disordered proteins were observed to form structures depending upon various factors, including heterogroup binding, protein/DNA/RNA binding, disulfide bonds, and ion binding.

Conclusions: This study collects many examples of disorder-to-order transition in IDP complex formation, thus revealing the unusual structure-function relationships of IDPs and providing an additional support for the newly proposed paradigm of the sequence → IDP/IDR ensemble → function.
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http://dx.doi.org/10.1021/acsomega.9b03927DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7391252PMC
July 2020

CHDGKB: a knowledgebase for systematic understanding of genetic variations associated with non-syndromic congenital heart disease.

Database (Oxford) 2020 01;2020

Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu 610041, China.

Congenital heart disease (CHD) is one of the most common birth defects, with complex genetic and environmental etiologies. The reports of genetic variation associated with CHD have increased dramatically in recent years due to the revolutionary development of molecular technology. However, CHD is a heterogeneous disease, and its genetic origins remain inconclusive in most patients. Here we present a database of genetic variations for non-syndromic CHD (NS-CHD). By manually literature extraction and analyses, 5345 NS-CHD-associated genetic variations were collected, curated and stored in the public online database. The objective of our database is to provide the most comprehensive updates on NS-CHD genetic research and to aid systematic analyses of pathogenesis of NS-CHD in molecular level and the correlation between NS-CHD genotypes and phenotypes. Database URL: http://www.sysbio.org.cn/CHDGKB/.
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http://dx.doi.org/10.1093/database/baaa048DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7327432PMC
January 2020

Heart Rate Variability Based Prediction of Personalized Drug Therapeutic Response: The Present Status and the Perspectives.

Curr Top Med Chem 2020 ;20(18):1640-1650

Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, China.

Heart rate variability (HRV) signals are reported to be associated with the personalized drug response in many diseases such as major depressive disorder, epilepsy, chronic pain, hypertension, etc. But the relationships between HRV signals and the personalized drug response in different diseases and patients are complex and remain unclear. With the fast development of modern smart sensor technologies and the popularization of big data paradigm, more and more data on the HRV and drug response will be available, it then provides great opportunities to build models for predicting the association of the HRV with personalized drug response precisely. We here review the present status of the HRV data resources and models for predicting and evaluating of personalized drug responses in different diseases. The future perspectives on the integration of knowledge and personalized data at different levels such as, genomics, physiological signals, etc. for the application of HRV signals to the precision prediction of drug therapy and their response will be provided.
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http://dx.doi.org/10.2174/1568026620666200603105002DOI Listing
May 2021

MicroRNA Alterations for Diagnosis, Prognosis, and Treatment of Osteoporosis: A Comprehensive Review and Computational Functional Survey.

Front Genet 2020 3;11:181. Epub 2020 Mar 3.

Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, China.

Osteoporosis (OP) is a systemic bone disease with a series of clinical symptoms. The use of screening biomarkers in OP management is therefore of clinical significance, especially in the era of precision medicine and intelligent healthcare. MicroRNAs (miRNAs) are small, non-coding RNAs with the potential to regulate gene expression at the post-transcriptional level. Accumulating evidence indicates that miRNAs may serve as biomarkers for OP prediction and prevention. However, few studies have emphasized the role of miRNAs in systems-level pathogenesis during OP development. In this article, literature-reported OP miRNAs were manually collected and analyzed based on a systems biology paradigm. Functional enrichment studies were performed to decode the underlying mechanisms of miRNAs in OP etiology and therapeutics in three-dimensional space, i.e., integrated miRNA-gene-pathway analysis. In particular, interactions between miRNAs and three well-known OP pathways, i.e., estrogen-endocrine, WNT/β-catenin signaling, and RANKL/RANK/OPG, were systematically investigated, and the effects of non-genetic factors on personalized OP prevention and therapy were discussed. This article is a comprehensive review of OP miRNAs, and bridges the gap between an understanding of OP pathogenesis and clinical translation.
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http://dx.doi.org/10.3389/fgene.2020.00181DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7063117PMC
March 2020

Early Detection of Sudden Cardiac Death by Using Ensemble Empirical Mode Decomposition-Based Entropy and Classical Linear Features From Heart Rate Variability Signals.

Front Physiol 2020 25;11:118. Epub 2020 Feb 25.

Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, China.

Sudden cardiac death (SCD), which can deprive a person of life within minutes, is a destructive heart abnormality. Thus, providing early warning information for patients at risk of SCD, especially those outside hospitals, is essential. In this study, we investigated the performances of ensemble empirical mode decomposition (EEMD)-based entropy features on SCD identification. EEMD-based entropy features were obtained by using the following technology: (1) EEMD was performed on HRV beats to decompose them into intrinsic mode functions (IMFs), (2) five entropy parameters, namely Rényi entropy (RenEn), fuzzy entropy (FuEn), dispersion Entropy (DisEn), improved multiscale permutation entropy (IMPE), and Renyi distribution entropy(RdisEn), were computed from the first four IMFs obtained, which were named EEMD-based entropy features. Additionally, an automated scheme combining EEMD-based entropy and classical linear (time and frequency domains) features was proposed with the intention of detecting SCD early by analyzing 14 min (at seven successive intervals of 2 min) heart rate variability (HRV) in signals from a normal population and subjects at risk of SCD. Firstly, EEMD-based entropy and classical linear measurements were extracted from HRV beats, and then the integrated measurements were ranked by various methodologies, i.e., -test, entropy, receiver-operating characteristics (ROC), Wilcoxon, and Bhattacharyya. Finally, these ranked features were fed into a k-Nearest Neighbor algorithm for classification. Compared with several state-of-the-art methods, the proposed scheme firstly predicted subjects at risk of SCD up to 14 min earlier with an accuracy of 96.1%, a sensitivity of 97.5%, and a specificity of 94.4% 14 min before SCD onset. The simulation results exhibited that EEMD-based entropy estimators showed significant difference between SCD patients and normal individuals and outperformed the classical linear estimators in SCD detection, the EEMD-based FuEn and IMPE indexes were particularly useful assessments for identification of patients at risk of SCD and can be used as novel indices to reveal the disorders of rhythm variations of the autonomic nervous system when affected by SCD.
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http://dx.doi.org/10.3389/fphys.2020.00118DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7052183PMC
February 2020

Data-driven translational prostate cancer research: from biomarker discovery to clinical decision.

J Transl Med 2020 03 7;18(1):119. Epub 2020 Mar 7.

Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu, 610041, China.

Prostate cancer (PCa) is a common malignant tumor with increasing incidence and high heterogeneity among males worldwide. In the era of big data and artificial intelligence, the paradigm of biomarker discovery is shifting from traditional experimental and small data-based identification toward big data-driven and systems-level screening. Complex interactions between genetic factors and environmental effects provide opportunities for systems modeling of PCa genesis and evolution. We hereby review the current research frontiers in informatics for PCa clinical translation. First, the heterogeneity and complexity in PCa development and clinical theranostics are introduced to raise the concern for PCa systems biology studies. Then biomarkers and risk factors ranging from molecular alternations to clinical phenotype and lifestyle changes are explicated for PCa personalized management. Methodologies and applications for multi-dimensional data integration and computational modeling are discussed. The future perspectives and challenges for PCa systems medicine and holistic healthcare are finally provided.
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http://dx.doi.org/10.1186/s12967-020-02281-4DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7060655PMC
March 2020

Novel MicroRNA Biomarkers for Colorectal Cancer Early Diagnosis and 5-Fluorouracil Chemotherapy Resistance but Not Prognosis: A Study from Databases to AI-Assisted Verifications.

Cancers (Basel) 2020 Feb 3;12(2). Epub 2020 Feb 3.

Department of Oncology and Department of Biomedical and Clinical Sciences, Linköping University, SE-58183 Linköping, Sweden.

Colorectal cancer (CRC) is one of the major causes of cancer death worldwide. In general, early diagnosis for CRC and individual therapy have led to better survival for the cancer patients. Accumulating studies concerning biomarkers have provided positive evidence to improve cancer early diagnosis and better therapy. It is, however, still necessary to further investigate the precise biomarkers for cancer early diagnosis and precision therapy and predicting prognosis. In this study, AI-assisted systems with bioinformatics algorithm integrated with microarray and RNA sequencing (RNA-seq) gene expression (GE) data has been approached to predict microRNA (miRNA) biomarkers for early diagnosis of CRC based on the miRNA-messenger RNA (mRNA) interaction network. The relationships between the predicted miRNA biomarkers and other biological components were further analyzed on biological networks. Bayesian meta-analysis of diagnostic test was utilized to verify the diagnostic value of the miRNA candidate biomarkers and the combined multiple biomarkers. Biological function analysis was performed to detect the relationship of candidate miRNA biomarkers and identified biomarkers in pathways. Text mining was used to analyze the relationships of predicted miRNAs and their target genes with 5-fluorouracil (5-FU). Survival analyses were conducted to evaluate the prognostic values of these miRNAs in CRC. According to the number of miRNAs single regulated mRNAs (NSR) and the number of their regulated transcription factor gene percentage (TFP) on the miRNA-mRNA network, there were 12 promising miRNA biomarkers were selected. There were five potential candidate miRNAs (miRNA-186-5p, miRNA-10b-5, miRNA-30e-5p, miRNA-21 and miRNA-30e) were confirmed as CRC diagnostic biomarkers, and two of them (miRNA-21 and miRNA-30e) were previously reported. Furthermore, the combinations of the five candidate miRNAs biomarkers showed better prediction accuracy for CRC early diagnosis than the single miRNA biomarkers. miRNA-10b-5p and miRNA-30e-5p were associated with the 5-FU therapy resistance by targeting the related genes. These miRNAs biomarkers were not statistically associated with CRC prognosis.
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http://dx.doi.org/10.3390/cancers12020341DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7073235PMC
February 2020

PCaLiStDB: a lifestyle database for precision prevention of prostate cancer.

Database (Oxford) 2020 01;2020

Institutes for Systems Genetics, West China Hospital, Sichuan University, No.17 Gaopeng Avenue, Chengdu 610041, China.

The interaction between genes, lifestyles and environmental factors makes the genesis and progress of prostate cancer (PCa) very heterogeneous. Positive lifestyle is important to the prevention and controlling of PCa. To investigate the relationship between PCa and lifestyle at systems level, we established a PCa related lifestyle database (PCaLiStDB) and collected the PCa-related lifestyles including foods, nutrients, life habits and social and environmental factors as well as associated genes and physiological and biochemical indexes together with the disease phenotypes and drugs. Data format standardization was implemented for the future Lifestyle-Wide Association Studies of PCa (PCa_LWAS). Currently, 2290 single-factor lifestyles and 856 joint effects of two or more lifestyles were collected. Among these, 394 are protective factors, 556 are risk factors, 45 are no-influencing factors, 52 are factors with contradictory views and 1977 factors are lacking effective literatures support. PCaLiStDB is expected to facilitate the prevention and control of PCa, as well as the promotion of mechanistic study of lifestyles on PCa. Database URL: http://www.sysbio.org.cn/pcalistdb/.
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http://dx.doi.org/10.1093/database/baz154DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6966110PMC
January 2020

Phenotype-genotype network construction and characterization: a case study of cardiovascular diseases and associated non-coding RNAs.

Database (Oxford) 2020 01;2020

Institutes for Systems Genetics, West China Hospital, Sichuan University, No. 17 Gaopeng Avenue, Ji Tai'an Center, Chengdu, Sichuan 610041, China.

The phenotype-genotype relationship is a key for personalized and precision medicine for complex diseases. To unravel the complexity of the clinical phenotype-genotype network, we used cardiovascular diseases (CVDs) and associated non-coding RNAs (ncRNAs) (i.e. miRNAs, long ncRNAs, etc.) as the case for the study of CVDs at a systems or network level. We first integrated a database of CVDs and ncRNAs (CVDncR, http://sysbio.org.cn/cvdncr/) to construct CVD-ncRNA networks and annotate their clinical associations. To characterize the networks, we then separated the miRNAs into two groups, i.e. universal miRNAs associated with at least two types of CVDs and specific miRNAs related only to one type of CVD. Our analyses indicated two interesting patterns in these CVD-ncRNA networks. First, scale-free features were present within both CVD-miRNA and CVD-lncRNA networks; second, universal miRNAs were more likely to be CVDs biomarkers. These results were confirmed by computational functional analyses. The findings offer theoretical guidance for decoding CVD-ncRNA associations and will facilitate the screening of CVD ncRNA biomarkers. Database URL: http://sysbio.org.cn/cvdncr/.
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http://dx.doi.org/10.1093/database/baz147DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6964217PMC
January 2020

Translational Informatics for Parkinson's Disease: from Big Biomedical Data to Small Actionable Alterations.

Genomics Proteomics Bioinformatics 2019 08 28;17(4):415-429. Epub 2019 Nov 28.

Center for Translational Biomedical Informatics, Guizhou University School of Medicine, Guiyang 550025, China.

Parkinson's disease (PD) is a common neurological disease in elderly people, and its morbidity and mortality are increasing with the advent of global ageing. The traditional paradigm of moving from small data to big data in biomedical research is shifting toward big data-based identification of small actionable alterations. To highlight the use of big data for precision PD medicine, we review PD big data and informatics for the translation of basic PD research to clinical applications. We emphasize some key findings in clinically actionable changes, such as susceptibility genetic variations for PD risk population screening, biomarkers for the diagnosis and stratification of PD patients, risk factors for PD, and lifestyles for the prevention of PD. The challenges associated with the collection, storage, and modelling of diverse big data for PD precision medicine and healthcare are also summarized. Future perspectives on systems modelling and intelligent medicine for PD monitoring, diagnosis, treatment, and healthcare are discussed in the end.
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http://dx.doi.org/10.1016/j.gpb.2018.10.007DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6943761PMC
August 2019

MIRKB: a myocardial infarction risk knowledge base.

Database (Oxford) 2019 01;2019

Institutes for Systems Genetics, West China Hospital, Sichuan University, Chengdu 610041, China.

Myocardial infarction (MI) is a common cardiovascular disease and a leading cause of death worldwide. The etiology of MI is complicated and not completely understood. Many risk factors are reported important for the development of MI, including lifestyle factors, environmental factors, psychosocial factors, genetic factors, etc. Identifying individuals with an increased risk of MI is urgent and a major challenge for improving prevention. The MI risk knowledge base (MIRKB) is developed for facilitating MI research and prevention. The goal of MIRKB is to collect risk factors and models related to MI to increase the efficiency of systems biological level understanding of the disease. MIRKB contains 8436 entries collected from 4366 articles in PubMed before 5 July 2019 with 7902 entries for 1847 single factors, 195 entries for 157 combined factors and 339 entries for 174 risk models. The single factors are classified into the following five categories based on their characteristics: molecular factor (2356 entries, 649 factors), imaging (821 entries, 252 factors), physiological factor (1566 entries, 219 factors), clinical factor (2523 entries, 561 factors), environmental factor (46 entries, 26 factors), lifestyle factor (306 entries, 65 factors) and psychosocial factor (284 entries, 75 factors). MIRKB will be helpful to the future systems level unraveling of the complex mechanism of MI genesis and progression.
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http://dx.doi.org/10.1093/database/baz125DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6830040PMC
January 2019
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