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Des-Arg(9)bradykinin as a causal metabolite for autism spectrum disorder

World Journal of Psychiatry 2024年第1期14卷 88-101页

作者： Zhong-Yu Huang Zi-Pan Lyu Hong-Gui Li Hua-Zhi You Xiang-Na Yang Cai-Hui Cha Center for Medical Research on Innovation and Translation Institute of Clinical MedicineGuangzhou First People’s HospitalSchool of MedicineSouth China University of TechnologyGuangzhou 510000Guangdong ProvinceChina School of Biological Sciences Nanyang Technological UniversityNanyang Ave 639798Singapore Department of Pediatrics First Affiliated Hospital of Guangzhou University of Chinese MedicineGuangzhou 510405Guangdong ProvinceChina Department of Nutrition First Affiliated Hospital of Guangzhou University of Chinese MedicineGuangzhou 510405Guangdong ProvinceChina Department of Pediatric Traditional Chinese Medicine Clinic Guangzhou Women and Children's Medical CenterGuangzhou 510632Guangdong ProvinceChina Department of Psychology Guangzhou Women and Children's Medical CenterGuangzhou 510632Guangdong ProvinceChina

BACKGROUND Early diagnosis and therapeutic interventions can greatly enhance the developmental trajectory of children with autism spectrum disorder(ASD).However,the etiology of ASD is not completely understood.The presence of confounding factors from environment and genetics has increased the difficulty of the identification of diagnostic biomarkers for ASD.AIM To estimate and interpret the causal relationship between ASD and metabolite profile,taking into consideration both genetic and environmental influences.METHODS A two-sample Mendelian randomization(MR)analysis was conducted using summarized data from large-scale genome-wide association studies(GWAS)including a metabolite GWAS dataset covering 453 metabolites from 7824 European and an ASD GWAS dataset comprising 18381 ASD cases and 27969 healthy controls.Metabolites in plasma were set as exposures with ASD as the main outcome.The causal relationships were estimated using the inverse variant weight(IVW)algorithm.We also performed leave-one-out sensitivity tests to validate the robustness of the results.Based on the drafted metabolites,enrichment analysis was conducted to interpret the association via constructing a protein-protein interaction network with multi-scale evidence from databases including Infinome,SwissTargetPrediction,STRING,and Metascape.RESULTS Des-Arg(9)-bradykinin was identified as a causal metabolite that increases the risk of ASD(β=0.262,SE=0.064,P_(IVW)=4.64×10^(-5)).The association was robust,with no significant heterogeneity among instrument variables(P_(MR Egger)=0.663,P_(IVW)=0.906)and no evidence of pleiotropy(P=0.949).Neuroinflammation and the response to stimulus were suggested as potential biological processes mediating the association between Des-Arg(9)bradykinin and ASD.CONCLUSION Through the application of MR,this study provides practical insights into the potential causal association between plasma metabolites and ASD.These findings offer perspectives for the discovery of diagnostic or predictive biomarkers to support clinical

关键词： Des-Arg(9)bradykinin Autism spectrum disorder Mendelian randomization Metabolite enrichment analysis

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Mechanism of pachymic acid in the treatment of gastric cancer based on network pharmacology and experimental verification

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World Journal of Gastrointestinal Oncology 2024年第1期16卷 30-50页

作者： Yu-Hua Du Jian-Jun Zhao Xia Li Shi-Cong Huang Na Ning Guo-Qing Chen Yi Yang Yi Nan Ling Yuan College of Pharmacy Ningxia Medical UniversityYinchuan 750004Ningxia Hui Autonomous RegionChina Ningxia Chinese Medicine Research Center Manufacturing LaboratoryYinchuan 750004Ningxia Hui Autonomous RegionChina College of Foundation Ningxia Medical UniversityYinchuan 750004Ningxia Hui Autonomous RegionChina Key Laboratory of Ningxia Minority Medicine Modernization Ministry of Education Ningxia Medical UniversityYinchuan 750004Ningxia Hui Autonomous RegionChina

BACKGROUND Pachymic acid(PA)is derived from Poria cocos.PA has a variety of pharmacological and inhibitory effects on various tumors.However,the mechanism of action of PA in gastric cancer(GC)remains unclear.AIM To investigate the mechanism of PA in treating GC via the combination of network pharmacology and experimental verification.METHODS The GeneCards and OMIM databases were used to derive the GC targets,while the Pharm Mapper database provided the PA targets.Utilizing the STRING database,a protein-protein interaction network was constructed and core targets were screened.The analyses of Gene Ontology,Kyoto Encyclopedia of Genes and Genomes(KEGG),and gene set enrichment analysis were conducted,and molecular docking and clinical correlation analyses were performed on the core targets.Ultimately,the network pharmacology findings were validated through in vitro cell assays,encompassing assessments of cell viability,apoptosis,cell cycle,cloning,and western blot analysis.RESULTS According to network pharmacology analysis,the core targets were screened,and the PI3K/AKT signaling pathway is likely to be the mechanism by which PA effectively treats GC,according to KEGG enrichment analysis.The experimental findings showed that PA could control PI3K/AKT signaling to prevent GC cell proliferation,induce apoptosis,and pause the cell cycle.CONCLUSION Network pharmacology demonstrated that PA could treat GC by controlling a variety of signaling pathways and acting on a variety of targets.This has also been supported by in vitro cell studies,which serve as benchmarks for further research.

关键词： Pachymic acid Gastric cancer Network pharmacology enrichment analysis Cell proliferation

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Identification of Proteins and Genes Associated with Hedgehog Signaling Pathway Involved in Neoplasm Formation Using Text-Mining Approach

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Big Data Mining and Analytics 2024年第1期7卷 107-130页

作者： Nadezhda Yu.Biziukova Sergey M.Ivanov Oga A.Tarasova Department of Bioinformatics Institute of Biomedical ChemistryMoscow 119121Russia Department of Bioinformatics Pirogov Russian National Research Medical UniversityMoscow 117997Russia

analysis of molecular mechanisms that lead to the development of various types of tumors is essential for biology and medicine,because it may help to find new therapeutic opportunities for cancer treatment and cure including personalized treatment approaches.One of the pathways known to be important for the development of neoplastic diseases and pathological processes is the Hedgehog signaling pathway that normally controls human embryonic development.Systematic accumulation of various types of biological data,including interactions between proteins,regulation of genes transcription,proteomics,and metabolomics experiments results,allows the application of computational analysis of these big data for identification of key molecular mechanisms of certain diseases and pathologies and promising therapeutic targets.The aim of this study is to develop a computational approach for revealing associations between human proteins and genes interacting with the Hedgehog pathway components,as well as for identifying their roles in the development of various types of tumors.We automatically collect sets of abstract texts from the NCBI PubMed bibliographic database.For recognition of the Hedgehog pathway proteins and genes and neoplastic diseases we use a dictionary-based named entity recognition approach,while for all other proteins and genes machine learning method is used.For association extraction,we develop a set of semantic rules.We complete the results of the text analysis with the gene set enrichment analysis.The identified key pathways that may influence the Hedgehog pathway and their roles in tumor development are then verified using the information in the literature.

关键词： text-mining data mining Hedgehog pathway neoplastic processes enrichment analysis pathology molecularmechanisms

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The DrugPattern tool for drug set enrichment analysis and its prediction for beneficial effects of oxLDL on type 2 diabetes

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Journal of Genetics and Genomics 2018年第7期45卷 389-397页

作者： Chuanbo Huang Weili Yang Junpei wang Yuan Zhou Bin Geng Georgios Kararigas Jichun Yang Qinghua Cui Department of Biomedical Informatics Department of Physiology and Pathophysiology MOE Key Lab of Cardiovascular Sciences School of Basic Medical Sciences Center for Non-Coding RNA Medicine Peking UniversityBeijing 100191China School of Mathematics Sciences Huaqiao University Quanzhou 362021 China Hypertension Center Fuwai Hospital Chinese Academy of Medical Sciences and Peking Union Medical CollegeState Key Laboratory of Cardiovascular Disease National Center for Cardiovascular Diseases Beijing 100037 China Charit-Universitatsmedizin Berlin. Corporate Member of Freie Universitat Berlin Humboldt-Universitat zu Berlin Berlin Institute of Health Institute of Gender in Medicine and Center for Cardiovascular Research. DZHK (German Centre for Cardiovascular Research) 10115 Berlin Germany Center of Bioinformatics Key Laboratory for Neuro-lnformation of Ministry of Education School of Life Science and Technology University of Electronic Science and Technology of China Chengdu 610054 China

enrichment analysis methods, e.g., gene set enrichment analysis, represent one class of important bio- informatical resources for mining patterns in biomedical datasets. However, tools for inferring patterns and rules of a list of drugs are limited. In this study, we developed a web-based tool, DrugPattern, for drug set enrichment analysis. We first collected and curated 7019 drug sets, including indications, adverse reactions, targets, pathways, etc. from public databases. For a list of interested drugs, DrugPat- tern then evaluates the significance of the enrichment of these drugs in each of the 7019 drug sets. To validate DrugPattern, we employed it for the prediction of the effects of oxidized low-density lipoprotein （oxLDL）, a factor expected to be deleterious. We predicted that oxLDL has beneficial effects on some diseases, most of which were supported by evidence in the literature. Because DrugPattern predicted the potential beneficial effects of oxLDL in type 2 diabetes （T2D）, animal experiments were then performed to further verify this prediction. As a result, the experimental evidences validated the DrugPattern prediction that oxLDL indeed has beneficial effects on T2D in the case of energy restriction. These data confirmed the prediction accuracy of our approach and revealed unexpected protective roles for oxLDL in various diseases. This study provides a tool to infer patterns and rules in biomedical datasets based on drug set enrichment analysis.

关键词： enrichment analysis Drug Data mining oxLDL Type 2 diabetes

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Exploring the potential mechanism of WuFuYin against hypertrophic scar using network pharmacology and molecular docking

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World Journal of Clinical Cases 2024年第18期12卷 3505-3514页

作者： Shu-Yang Zhang Song-Xue Guo Lei-Lei Chen Jia-Yan Zhu Ming-Sheng Hou Jia-Ke Lu Xue-Xiang Shen Department of General Surgery Shaoxing Hospital of Traditional Chinese MedicineShaoxing 312000Zhejiang ProvinceChina Department of Plastic Surgery The Second Affiliated Hospital of Zhejiang University School of MedicineHangzhou 310009Zhejiang ProvinceChina Hand and Plastic Surgery The first People’s Hospital of Linping DistrictHangzhou 311013Zhejiang ProvinceChina Department of Pathology Shaoxing Hospital of Traditional Chinese MedicineShaoxing 312000Zhejiang ProvinceChina Department of Traumatology Yuyao Hospital of Traditional Chinese MedicineNingbo 315400Zhejiang ProvinceChina

BACKGROUND Hypertrophic scar(HTS)is dermal fibroproliferative disorder,which may cause physiological and psychological problems.Currently,the potential mechanism of WuFuYin(WFY)in the treatment of HTS remained to be elucidated.AIM To explore the potential mechanism of WFY in treating HTS.METHODS Active components and corresponding targets were retrieved from the Traditional Chinese Medicine Systems Pharmacology Database and analysis Platform.HTSrelated genes were obtained from the GeneCards,DisGeNET,and National Center for Biotechnology Information.The function of targets was analyzed by performing Gene Ontology and Kyoto Encyclopaedia of Genes and Genome(KEGG)enrichment analysis.A protein+IBM-protein interaction(PPI)network was developed using STRING database and Cytoscape.To confirm the high affinity between compounds and targets,molecular docking was performed.RESULTS A total of 65 core genes,which were both related to compounds and HTS,were selected from multiple databases.PPI analysis showed that CKD2,ABCC1,MMP2,MMP9,glycogen synthase kinase 3 beta(GSK3B),PRARG,MMP3,and phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit gamma(PIK3CG)were the hub targets and MOL004941,MOL004935,MOL004866,MOL004993,and MOL004989 were the key compounds of WFY against HTS.The results of KEGG enrichment analysis demonstrated that the function of most genes were enriched in the PI3K-Akt pathway.Moreover,by performing molecular docking,we confirmed that GSK3B and 8-prenylated eriodictyol shared the highest affinity.CONCLUSION The current findings showed that the GSK3B and cyclin dependent kinase 2 were the potential targets and MOL004941,MOL004989,and MOL004993 were the main compounds of WFY in HTS treatment.

关键词： WuFuYin Hypertrophic scar Network pharmacology Molecular docking enrichment analysis

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A knowledge-based approach for KEGG pathway expansion by enrichment analysis of GO terms

A knowledge-based approach for KEGG pathway expansion by enr...

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第五届全国生物信息学与系统生物学学术大会暨国际生物信息...

作者： Xiaolei Zhao Shaoqi Rao Institute of Medical Systems Biology and School of Public Health Guangdong Medical College

Background:Pathway databases,especially KEGG,have been widely used as a reference knowledge base for biomedical scientists to interpret their experimental findings.Nevertheless, our knowledge about the existing biological pathways is incomplete and a large number of pathways have to be further expanded.Computational pathway expansion offers us a cheap and reliable way to take the challenging task.Several developed approaches,which rely on analysis of large-scale datasets generated by genome sequencing and other high-throughput experiments, are limited by across-studies variations and information provided by single experiments.In this study,we proposed a pathway expansion algorithm by systematic learning of functional knowledge bases(PPI and GO) for genes(their products) and their relations with others. Methods:In essence,pathway expansion is equivalent to test whether a target gene belongs to a specific pathway(s) or not.First,we identified all the interacting genes with the target gene,by using two large protein-protein interaction databases,HPRD and BioGRID. Then,we identified all the candidate KEGG pathways that these interacting genes belong to. Finally,for each candidate pathway,all the contained genes and the target gene were subject to enrichment analysis at each GO term of the target gene.We claimed that the target gene belongs to the candidate pathway if all GO terms of the target gene were enriched with the genes of this pathway. Results:The proposed knowledge-based approach achieved excellent performance in predicting a gene’s pathway,based on either of two PPI databases.The average consistent rate (defined as the proportion of the right predicted pathways in the total annotated pathways) was increased with the number of interacting genes,and reached to the highest value of 0.95 when the number of interacting genes was 22.However,the relative precision rates(RP,defined as the proportion of genes which all the annotated pathway(s) of them are right predicted in the total target genes) based on HPRD or B

关键词： protein-protein interaction pathway prediction gene ontology enrichment analysis

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Identification of potential diagnostic and prognostic biomarkers for breast cancer based on gene expression omnibus

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世界临床病例杂志 2023年第27期11卷 6344-6362页

作者： Xiong Zhang Zhi-Hui Mi Department of Pathology HuLunBuir Peoples’s HospitalHuLunBuir 010018Nei Monggol Autonomous RegionChina Department of Research and Marketing Inner Mongolia Di An Feng Xin Medical Technology Co.LTDHuhhot 010010Nei Monggol Autonomous RegionChina

BACKGROUND Breast cancer is regarded as a highly malignant neoplasm in the female population,posing a significant risk to women’s overall well-being.The prevalence of breast cancer has been observed to rise in China,accompanied by an earlier age of onset when compared to Western countries.Breast cancer continues to be a prominent contributor to cancer-related mortality and morbidity among women,primarily due to its limited responsiveness to conventional treatment modalities.The diagnostic process is challenging due to the presence of non-specific clinical manifestations and the suboptimal precision of conventional diagnostic tests.There is a prevailing uncertainty regarding the most effective screening method and target populations,as well as the specificities and execution of screening programs.AIM To identify diagnostic and prognostic biomarkers for breast cancer.METHODS Overlapping differentially expressed genes were screened based on Gene Expression Omnibus(GSE36765,GSE10810,and GSE20086)and The Cancer Genome Atlas datasets.A protein-protein interaction network was applied to excavate the hub genes among these differentially expressed genes.Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses,as well as gene set enrichment analyses,were conducted to examine the functions of these genes and their potential mechanisms in the development of breast cancer.For clarification of the diagnostic and prognostic roles of these genes,Kaplan–Mei-er and Cox proportional hazards analyses were conducted.RESULTS This study demonstrated that calreticulin,heat shock protein family B member 1,insulin-like growth Factor 1,interleukin-1 receptor 1,Krüppel-like factor 4,suppressor of cytokine signaling 3,and triosephosphate isomerase 1 are potential diagnostic biomarkers of breast cancer as well as potential treatment targets with clinical implications.CONCLUSION The screening of biomarkers is of guiding significance for the diagnosis and prognosis of the diseases.

关键词： Breast cancer Diagnostic biomarker The Cancer Genome Atlas datasets Gene expression omnibus enrichment analysis

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analysis of Significant Genes and Pathways in Esophageal Cancer Based on Gene Expression Omnibus Database

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Chinese Medical Sciences Journal 2023年第1期38卷 20-28页

作者： An-Yi Song Lan Mu Xiao-Yong Dai Li-Jun Wang Lai-Qiang Huang The Shenzhen Key Laboratory of Gene and Antibody Therapy State Key Laboratory of Chemical OncogenomicsTsinghua-Berkeley Shenzhen Institute(TBSI)Tsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhen 518055Guangdong ProvinceChina Department of Chemistry Tsinghua UniversityBeijing 100084China

Objective To screen antigen targets for immunotherapy by analyzing over-expressed genes,and to identify significant pathways and molecular mechanisms in esophageal cancer by using bioinformatic methods such as enrichment analysis,protein-protein interaction(PPI)network,and survival analysis based on the Gene Expression Omnibus(GEO)database.Methods By screening with highly expressed genes,we mainly analyzed proteins MUC13 and EPCAM with transmembrane domain and antigen epitope from TMHMM and IEDB websites.Significant genes and pathways associated with the pathogenesis of esophageal cancer were identified using enrichment analysis,PPI network,and survival analysis.Several software and platforms including Prism 8,R language,Cytoscape,DAVID,STRING,and GEPIA platform were used in the search and/or figure creation.Results Genes MUC13 and EPCAM were over-expressed with several antigen epitopes in esophageal squamous cell carcinoma(ESCC)tissue.enrichment analysis revealed that the process of keratinization was focused and a series of genes were related with the development of esophageal cancer.Four genes including ALDH3A1,C2,SLC6A1,and ZBTB7C were screened with significant P value of survival curve.Conclusions Genes MUC13 and EPCAM may be promising antigen targets or biomarkers for esophageal cancer.Keratinization may greatly impact the pathogenesis of esophageal cancer.Genes ALDH3A1,C2,SLC6A1,and ZBTB7C may play important roles in the development of esophageal cancer.

关键词： GEO esophageal cancer antigen enrichment analysis survival curve signaling pathway

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Molecular targets and mechanisms of Jiawei Jiaotai Pill on diabetic cardiomyopathy based on network pharmacology

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World Journal of Diabetes 2023年第11期14卷 1659-1671页

作者： Yu-Juan Wang Yan-Li Wang Xiao-Fan Jiang Juan-E Li Department of Otolaryngology Shaanxi Provincial People’s HospitalXi’an 710068Shaaxi ProvinceChina Department of Pediatrics Affiliated Hospital of Traditional Chinese MedicineXinjiang Medical UniversityUrumqi 830000Xinjiang Uygur Autonomous RegionChina Department of Chinese Medicine Shaanxi Provincial People’s HospitalXi’an 710068Shaaxi ProvinceChina

BACKGROUND Jiawei Jiaotai Pill is commonly used in clinical practice to reduce apoptosis,increase insulin secretion,and improve blood glucose tolerance.However,its mechanism of action in the treatment of diabetic cardiomyopathy(DCM)remains unclear,hindering research efforts aimed at developing drugs specifically for the treatment of DCM.AIM To explore the pharmacodynamic basis and molecular mechanism of Jiawei Jiaotai Pill in DCM treatment.METHODS We explored various databases and software,including the Traditional Chinese Medicine Systems Pharmacology Database,Uniport,PubChem,GenCards,String,and Cytoscape,to identify the active components and targets of Jiawei Jiaotai Pill,and the disease targets in DCM.Protein-protein interaction network,gene ontology,and Kyoto Encyclopedia of Genes and Genomes analyses were used to determine the mechanism of action of Jiawei Jiaotai Pill in treating DCM.Molecular docking of key active components and core targets was verified using AutoDock software.RESULTS Total 42 active ingredients and 142 potential targets of Jiawei Jiaotai Pill were identified.There were 100 common targets between the DCM and Jiawei Jiaotai Pills.Through this screening process,TNF,IL6,TP53,EGFR,INS,and other important targets were identified.These targets are mainly involved in the positive regulation of the mitogen-activated protein kinase(MAPK)MAPK cascade,response to xenobiotic stimuli,response to hypoxia,positive regulation of gene expression,positive regulation of cell proliferation,negative regulation of the apoptotic process,and other biological processes.It was mainly enriched in the AGE-RAGE signaling pathway in diabetic complications,DCM,PI3K-Akt,interleukin-17,and MAPK signaling pathways.Molecular docking results showed that Jiawei Jiaotai Pill's active ingredients had good docking activity with DCM's core target.CONCLUSION The active components of Jiawei Jiaotai Pill may play a role in the treatment of DCM by reducing oxidative stress,cardiomyocyte apoptosis and fibrosis,and maintaining metabol

关键词： Jiawei Jiaotai pill Diabetic cardiomyopathy Mechanism of action enrichment analysis Network pharmacology Molecular docking

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What should be the future direction of development in the field of prostate cancer with lung metastasis?

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World Journal of Clinical Oncology 2023年第10期14卷 420-439页

作者： Zhi-Guang Huang Yi Chen Tong Wu Bin-Tong Yin Xiao Feng Sheng-Hua Li Dong-Ming Li Gang Chen Ji-Wen Cheng Juan He Department of Pathology First Affiliated Hospital of Guangxi Medical UniversityNanning 530021Guangxi Zhuang Autonomous RegionChina Department of Radiology First Affiliated Hospital of Guangxi Medical UniversityNanning 530021Guangxi Zhuang Autonomous RegionChina Department of Urology First Affiliated Hospital of Guangxi Medical UniversityNanning 530021Guangxi Zhuang Autonomous RegionChina

BACKGROUND Since the start of the 21st century,prostate cancer with lung metastasis(PCLM)has accumulated significant scientific research output.However,a systematic knowledge framework for PCLM is still lacking.AIM To reconstruct the global knowledge system in the field of PCLM,sort out hot research directions,and provide reference for the clinical and mechanism research of PCLM.METHODS We retrieved 280 high-quality papers from the Web of Science Core Collection and conducted a bibliometric analysis of keywords,publication volume,and citation frequency.Additionally,we selected differentially expressed genes from global high-throughput datasets and performed enrichment analysis and proteinprotein interaction analysis to further summarize and explore the mechanisms of PCLM.RESULTS PCLM has received extensive attention over the past 22 years,but there is an uneven spatial distribution in PCLM research.In the clinical aspect,the treatment of PCLM is mainly based on chemotherapy and immunotherapy,while diagnosis relies on methods such as prostate-specific membrane antigen positron emission tomography/computed tomography.In the basic research aspect,the focus is on cell adhesion molecules and signal transducer and activator of transcription 3,among others.Traditional treatments,such as chemotherapy,remain the mainstay of PCLM treatment,while novel approaches such as immunotherapy have limited effectiveness in PCLM.This study reveals for the first time that pathways related to coronavirus disease 2019,cytokinecytokine receptor interaction,and ribosome are closely associated with PCLM.CONCLUSION Future research should focus on exploring and enhancing mechanisms such as cytokine-cytokine receptor interaction and ribosome and improve existing mechanisms like cadherin binding and cell adhesion molecules.

关键词： Prostate cancer Lung metastasis Chemotherapy Immunotherapy Bibliometric analysis enrichment analysis

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