{"title":"通过综合生物信息学分析确定银屑病中与外泌体相关的基因特征并构建诊断模型。","authors":"Lifen Chen, Shuangmei Zhu, Lu Zhao, Wenxia Ye","doi":"10.1080/10255842.2024.2410224","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Psoriasis, a chronic inflammatory dermatosis, profoundly affects patients' well-being. Although exosomes are key in disease etiology, diagnostic potentials of associated genes are unclear. Our research targeted bioinformatics-based characterization of exosome-related genes and the development of a diagnostic model for psoriasis.</p><p><strong>Methods: </strong>Within GSE30999 dataset, an exosome-centric diagnostic model was formulated. Its diagnostic capability was appraised in GSE30999 and GSE14905 cohorts. Human keratinocytes (HaCaT) were used to construct psoriasis cell model. qRT-PCR was used to detect expression of diagnostic genes in the model. Construction of a protein-protein interaction network was undertaken, complemented by enrichment analyses. Comparative evaluation of immunological microenvironments between healthy controls and disease cohort was executed. Prospective miRNAs and transcription factors (TFs) were prognosticated using online prediction tools.</p><p><strong>Results: </strong>A distinctive diagnostic model with superior diagnostic performance, evidenced by an AUC value greater than 0.88, was unveiled. The model featured seven exosome-related biomarker genes (CCNA2, NDC80, CCNB1, CDCA8, KIF11, CENPF, and ASPM) interwoven in a complex network and chiefly linked in the regulation of Cell Cycle and Cellular Senescence. These genes were significantly overexpressed in psoriasis cell models. Immune infiltration analysis distinguished profound discrepancies (<i>p</i> < 0.05) in immunological microenvironment between disease and control groups with enrichment of T cells CD4 memory activated, Macrophages M1, and Neutrophils in the disease group. 11 miRNAs and 27 TFs were identified.</p><p><strong>Conclusion: </strong>The study introduces a new and potent diagnostic model for psoriasis, with selection of credible exosome-associated biomarker genes. These discoveries aid in clinical diagnostics and research on exosome involvement in psoriasis.</p>","PeriodicalId":50640,"journal":{"name":"Computer Methods in Biomechanics and Biomedical Engineering","volume":null,"pages":null},"PeriodicalIF":1.7000,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identification of exosome-related gene features in psoriasis and construction of a diagnostic model <i>via</i> integrated bioinformatics analysis.\",\"authors\":\"Lifen Chen, Shuangmei Zhu, Lu Zhao, Wenxia Ye\",\"doi\":\"10.1080/10255842.2024.2410224\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Psoriasis, a chronic inflammatory dermatosis, profoundly affects patients' well-being. Although exosomes are key in disease etiology, diagnostic potentials of associated genes are unclear. Our research targeted bioinformatics-based characterization of exosome-related genes and the development of a diagnostic model for psoriasis.</p><p><strong>Methods: </strong>Within GSE30999 dataset, an exosome-centric diagnostic model was formulated. Its diagnostic capability was appraised in GSE30999 and GSE14905 cohorts. Human keratinocytes (HaCaT) were used to construct psoriasis cell model. qRT-PCR was used to detect expression of diagnostic genes in the model. Construction of a protein-protein interaction network was undertaken, complemented by enrichment analyses. Comparative evaluation of immunological microenvironments between healthy controls and disease cohort was executed. Prospective miRNAs and transcription factors (TFs) were prognosticated using online prediction tools.</p><p><strong>Results: </strong>A distinctive diagnostic model with superior diagnostic performance, evidenced by an AUC value greater than 0.88, was unveiled. The model featured seven exosome-related biomarker genes (CCNA2, NDC80, CCNB1, CDCA8, KIF11, CENPF, and ASPM) interwoven in a complex network and chiefly linked in the regulation of Cell Cycle and Cellular Senescence. These genes were significantly overexpressed in psoriasis cell models. Immune infiltration analysis distinguished profound discrepancies (<i>p</i> < 0.05) in immunological microenvironment between disease and control groups with enrichment of T cells CD4 memory activated, Macrophages M1, and Neutrophils in the disease group. 11 miRNAs and 27 TFs were identified.</p><p><strong>Conclusion: </strong>The study introduces a new and potent diagnostic model for psoriasis, with selection of credible exosome-associated biomarker genes. These discoveries aid in clinical diagnostics and research on exosome involvement in psoriasis.</p>\",\"PeriodicalId\":50640,\"journal\":{\"name\":\"Computer Methods in Biomechanics and Biomedical Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.7000,\"publicationDate\":\"2024-10-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Methods in Biomechanics and Biomedical Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1080/10255842.2024.2410224\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Methods in Biomechanics and Biomedical Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1080/10255842.2024.2410224","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Identification of exosome-related gene features in psoriasis and construction of a diagnostic model via integrated bioinformatics analysis.
Background: Psoriasis, a chronic inflammatory dermatosis, profoundly affects patients' well-being. Although exosomes are key in disease etiology, diagnostic potentials of associated genes are unclear. Our research targeted bioinformatics-based characterization of exosome-related genes and the development of a diagnostic model for psoriasis.
Methods: Within GSE30999 dataset, an exosome-centric diagnostic model was formulated. Its diagnostic capability was appraised in GSE30999 and GSE14905 cohorts. Human keratinocytes (HaCaT) were used to construct psoriasis cell model. qRT-PCR was used to detect expression of diagnostic genes in the model. Construction of a protein-protein interaction network was undertaken, complemented by enrichment analyses. Comparative evaluation of immunological microenvironments between healthy controls and disease cohort was executed. Prospective miRNAs and transcription factors (TFs) were prognosticated using online prediction tools.
Results: A distinctive diagnostic model with superior diagnostic performance, evidenced by an AUC value greater than 0.88, was unveiled. The model featured seven exosome-related biomarker genes (CCNA2, NDC80, CCNB1, CDCA8, KIF11, CENPF, and ASPM) interwoven in a complex network and chiefly linked in the regulation of Cell Cycle and Cellular Senescence. These genes were significantly overexpressed in psoriasis cell models. Immune infiltration analysis distinguished profound discrepancies (p < 0.05) in immunological microenvironment between disease and control groups with enrichment of T cells CD4 memory activated, Macrophages M1, and Neutrophils in the disease group. 11 miRNAs and 27 TFs were identified.
Conclusion: The study introduces a new and potent diagnostic model for psoriasis, with selection of credible exosome-associated biomarker genes. These discoveries aid in clinical diagnostics and research on exosome involvement in psoriasis.
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The primary aims of Computer Methods in Biomechanics and Biomedical Engineering are to provide a means of communicating the advances being made in the areas of biomechanics and biomedical engineering and to stimulate interest in the continually emerging computer based technologies which are being applied in these multidisciplinary subjects. Computer Methods in Biomechanics and Biomedical Engineering will also provide a focus for the importance of integrating the disciplines of engineering with medical technology and clinical expertise. Such integration will have a major impact on health care in the future.
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