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Next Research 2025年第1期2卷

作者： Prasanna Kumar Selvam Santhosh Mudipalli Elavarasu T Dhanushkumar Supraja Mohan George Priya Doss C Karthick Vasudevan Manipal Academy of Higher Education (MAHE) Manipal 576104 India Institute of Bioinformatics International Technology Park Bangalore 560066 India Laboratory of Integrative Genomics Department of Integrative Biology School of BioSciences and Technology Vellore Institute of Technology (VIT) Vellore Tamil Nadu India Department of Biotechnology School of Applied Sciences REVA University Bengaluru 560064 India

Type 1 Diabetes (T1D) and Celiac Disease (CD) are chronic autoimmune disorders with rising global prevalence, sharing genetic susceptibility and immune-mediated mechanisms. CD is characterized by an immune response to dietary gluten causing enteropathy, while T1D involves the progressive loss of insulin-producing pancreatic beta cells. The coexistence of these diseases necessitates deeper insight into their shared pathogenesis and potential diagnostic biomarkers. In this study, transcriptomic datasets for T1D (39 patients, 43 controls) and CD (6 patients, 5 controls) were retrieved from public repositories and analyzed using bioinformatics and machine learning approaches. Differential expression analysis identified 568 significantly associated genes (361 upregulated, 207 downregulated). Functional enrichment analysis highlighted pathways linked to immune regulation and cellular metabolism. Machine learning models, including Random Forest and LASSO regression, refined the selection of candidate genes, identifying three key diagnostic biomarkers NAA15, RPL21, and HCLS1 with high diagnostic performance (AUC values: 0.848, 0.831, and 0.805). A Protein-Protein Interaction (PPI) network revealed these genes’ involvement in shared autoimmune mechanisms. NAA15, RPL21 , and HCLS1 play crucial roles in immune regulation and cellular metabolism, providing insights into the shared genetic basis of T1D and CD. These findings open avenues for the development of diagnostic tools, combination therapies, and personalized treatment strategies for patients with both diseases. This study underscores the potential of integrating bioinformatics and machine learning to uncover biomarkers in complex autoimmune disorders.

关键词： Type 1 diabetes Celiac disease Autoimmune disorder Transcriptomics Diagnostic biomarkers AUC Area Under the Curve BP Biological Process CD Celiac Disease CC Cellular Component CI Confidence Interval DEG Differentially Expressed Gene ENA European Nucleotide Archive FASTQC Fast Quality Control FDR False Discovery Rate GO Gene Ontology HCLS1 Hematopoietic Cell-Specific Lyn Substrate 1 KEGG Kyoto Encyclopedia of Genes and Genomes LASSO Least Absolute Shrinkage and Selection Operator MCODE molecular complex detection MF molecular Function NGS Next-Generation Sequencing PPI Protein-Protein Interaction RNA-seq RNA Sequencing ROC Receiver Operating Characteristic T1D Type 1 Diabetes

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Prediction of the anti-inflammatory mechanisms of curcumin by module-based protein interaction network analysis

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Acta Pharmaceutica Sinica B 2015年第6期5卷 590-595页

作者： Yanxiong Gan Shichao Zheng Jan P.A.Baak Silei Zhao Yongfeng Zheng Nini Luo Wan Liao Chaomei Fu State Key Laboratory Breeding Base of Systematic Research Development and Utilization of Chinese Medicine ResourcesSchool of PharmacyChengdu University of Traditional Chinese Medicine School of Chinese Pharmacy Beijing University of Chinese Medicine Faculty of Sports Sciences and Medicine Technical University Munich

Curcumin, the medically active component from Curcuma Tonga (Turmeric), is widely used to treat inflammatory diseases. Protein interaction network (PIN) analysis was used to predict its mechanisms of molecular action. Targets of curcumin were obtained based on ChEMBL and STITCH databases. Protein protein interactions (PPIs) were extracted from the String database. The PIN of curcumin was constructed by Cytoscape and the function modules identified by gene ontology (GO) enrichment analysis based on molecular complex detection (MCODE). A PIN of curcumin with 482 nodes and 1688 interactions was constructed, which has scale-free, small world and modular properties. Based on analysis of these function modules, the mechanism of curcumin is proposed. Two modules were found to be intimately associated with inflammation. With function modules analysis, the anti-inflammatory effects of curcumin were related to SMAD, ERG and mediation by the TLR family. TLR9 may be a potential target of curcumin to treat inflammation. (C) 2015 Chinese Pharmaceutical Association and Institute of Materia Medica, Chinese Academy of Medical Sciences. Production and hosting by Elsevier B.V.

关键词： Curcumin Protein interaction network Module Anti-inflamatory molecular mechanism Gene ontology enrichment analysis molecular complex detection Cytoseape

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Bioinformatics Analysis and Identification of Genes and molecular Pathways Involved in Venous Thromboembolism (VTE)

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ANNALS OF VASCULAR SURGERY 2021年 74卷 389-399页

作者： Lu, Shun Ren Lijuan Tang, Qian-Hui Liu, Qi-Li Zhang Xian-Lan Sun Yat Sen Univ Affiliated Hosp 1 Dept Intervent Radiol Guangzhou Guangdong Peoples R China Guilin Med Univ Dept Vasc Surg Affiliated Hosp Guilin Guangxi Zhuang Peoples R China Sun Yat Sen Univ Affiliated Hosp 1 Dept Pathol Guangzhou Guangdong Peoples R China

Objectives: To explore the key genes, and correlated pathways in venous thromboembolism (VTE) via bioinformatic analysis, and expected our findings could contribute to the development of new biomarkers and therapeutic target for VTE. Methods: Two VTE-related microarray expression profiles (GSE48000 and GSE19151) were downloaded from the Gene Expression Ominibus (GEO) database. Differentially expressed genes (DEGs) were analyzed using limma package, and overlapping DEGs were identified form the above two expression profiles. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEEG) pathway enrichment analyses were performed by DAVID. Protein-protein interaction (PPI) network was constructed by using STRING and visualized with Cytoscape. Furthermore, module analysis plus centrality analysis of the PPI network were executed to identify the potential key genes. Finally, the pathway analysis was performed using GenCLiP 3.0. Results: A total of 173 DEGs (125 upregulated and 48 downregulated) were identified. GO analysis demonstrated that DEGs were mainly enriched in viral life cycle, ribosome and structural constituent of ribosome. Meanwhile, KEGG pathway analysis showed that these genes were enriched in ribosome, Parkinson's disease and cell cycle. Additionally, one most significant module and 12 hub genes were found. Finally, 6 key genes, namely ISG15, RPS15A, MRPL13, ICT1, MRPL15 and RPLP0, with high centrality features were identified. These key genes were mainly involved in translation, metabolism of proteins and ribosome pathway. Conclusions: In summary, these 6 identified genes and correlated pathways should play an important role in VTE, which can provide new insight into the molecular mechanism, potential biomarkers and therapeutic targets associated with

关键词： VTE venous thromboembolism DVT deep vein thrombosis DEGs differently expressed genes BP biological processes MF molecular functions CC cell components GO Gene Ontology KEEG Kyoto Encyclopedia of Genes and Genomes PPI protein-protein interaction DAVID Database for Annotation, Visualization, and Integrated Discovery MCODE molecular complex detection NCBI-GEO National Center Biotechnology Information Gene Expression Omnibus database

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Unravelling the genomic maze: Bioinformatics unleashes insights into Sotos syndrome (Cerebral Gigantism)

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Health Sciences Review 2024年 12卷

作者： Ravinder Sharma Simarjeet Kaur Vikas Gupta Harpreet Grover Kiran Yadav Viney Chawla Pooja A Chawla Faculty of Pharmaceutical Sciences The ICFAI University Baddi India University Institute of Pharmaceutical Sciences and Research Baba Farid University of Health Sciences Faridkot Punjab 151203 India University Centre of Excellence in Research BFUHS Faridkot India Parexel International India Pvt Ltd Chandigarh India

The overgrowth condition known as Sotos syndrome is distinguished by its characteristic facial gestalt, macrocephaly, excessive development during childhood, varying degrees of learning problems, and a variety of other abnormalities. Due to abnormally high height, occipitofrontal circumference (OFC), advanced bone age, neonatal problems such as hypotonia and feeding issues, and facial gestalt, the diagnosis is typically recognized after birth. The current work aims to identify potential therapeutic treatments through bioinformatics analysis, focusing on key genes and pathways implicated in the disease. Text mining techniques were employed to identify 41 genes associated with Sotos syndrome, 37 of which were enriched with Gene Ontology (GO) terms and 24 with Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Using protein-protein interaction (PPI) network analysis, two gene modules were extracted using the molecular complex detection (MCODE) algorithm, highlighting 15 hub genes as central candidates. Furthermore, leveraging drug-gene interaction databases and network pharmacology tools, 23 FDA-approved drugs were identified that target 11 of these core hub genes, suggesting potential therapeutic avenues for Sotos syndrome. Only bioinformatics tools were used in this study further in-vitro and in-vivo studies are required because phenotypic differences will vary from person to person depending on the expressivity of the gene. In future this approach may help to collaborate with clinical researchers to integrate bioinformatics findings with real-world clinical data. This will enhance understanding of clinical relevance of the identified genes and pathways and validate bioinformatics predictions with patient-derived samples and clinical histories.

关键词： Sotos syndrome Drug discovery Text mining Hub genes String Cytoscape SS Sotos syndrome GO Gene Ontology DNA Deoxyribonucleic Acid BP Biological Processes KEGG Kyoto Encyclopedia of Genes and Genomes PPI Protein-Protein Interaction DGIdb Drug-Gene Interaction Database FDA Food and Drug Administration STRING Search Tool for the Retrieval of Interacting Genes/Proteins MCODE molecular complex detection PubMed Public/Publisher MEDLINE Ensembl Ensemble Genome Database Project TMGs Target Mining Genes Cytoscape Cytoscape Network Visualization Software ClueGO ClueGO Plugin for Cytoscape DAVID Database for Annotation, Visualization and Integrated Discovery

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