[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127891-en":3,"doc-seo-127891-105":31,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127891,2336474459895,"Aria","https://ap-avatar.wpscdn.com/avatar/22000baeef7a5ed0655?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786071322749376916",8,"Research & Report","Identifying and Validating Extracellular Matrix-Related Gene CTSH in Diabetic Foot Ulcer Using Bioinformatics and Machine Learning","Diabetic foot ulcer (DFU) remains a serious clinical challenge with high amputation and mortality rates, while extracellular matrix (ECM) biomarkers specific to DFU are not well established. This study identifies ECM-related biomarkers by integrating bioinformatics screening and machine-learning feature selection from GEO microarray datasets, followed by functional and immune-context analyses. CTSH emerges as the key ECM-related biomarker, linked to altered immune cell populations and regulatory activity in hedgehog, IL-17 and TNF signaling pathways. Animal model validation supports its diagnostic and prognostic potential and suggests therapeutic value.","Journal of Inflammation Research downloaded from [https://www.dovepress.com/](https://www.dovepress.com/)  \nFor personal use only.  \nJournal of Inflammation Research Dovepress  \nopen access to scientific and medical research  \n Open Access Full Text Article ORIGINAL RESEARCH  \nIdentifying and Validating Extracellular  \nMatrix-Related Gene CTSH in Diabetic Foot Ulcer Using Bioinformatics and Machine Learning  \nPei-Yu Wu 1 , 2 , Yan-Lin Yu 1 , Wen-Rui Zhao 1 , Bo Zhou 1  \n1Department of Endocrinology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China; 2Department of VIP, Chongqing General Hospital, Chongqing University, Chongqing, People’s Republic of China  \nCorrespondence: Bo Zhou, Department of Endocrinology, The First Affiliated Hospital of Chongqing Medical University, No. 1 Friendship Road, Yuzhong District, Chongqing, 400042, People’s Republic of China, Email [zhoubo915@126.com](zhoubo915@126.com)  \n\n| Background: Diabetic foot ulcer (DFU) is a serious clinical problem with high amputation and mortality rates, yet there is a lack of desirable therapy. While the extracellular matrix (ECM) contributes significantly to wound healing, ECM-related biomarker for DFU is still unknown. The study was designed to identify ECM-related biomarker in DFU using bioinformatics and machine learning and validate it in STZ-induced mice models.\u003Cbr>Methods: GSE80178 and GSE134431 microarray datasets were fetched from the GEO database, and differentially expressed genes (DEGs) analysis was performed, respectively. By analyzing DEGs and ECM genes, we identified ECM-related DEGs, and functional enrichment analysis was conducted. Subsequently, three machine learning algorithms (LASSO, RF and SVM-RFE) were applied to filter ECM-related DEGs to identify key ECM-related biomarkers. Next, we conducted immune infiltration analysis, GSEA, and correlation analysis to explore the hub gene underlying mechanism. A lncRNA-miRNA-mRNA and drug regulatory network were constructed. Finally, we validated the key ECM-related biomarker in STZ-induced mice models.\u003Cbr>Results: One hundred and forty-five common DEGs in adult DFU between the two datasets were identified. Taking the intersection of 145 common DEGs and 964 ECM genes, we identified 13 ECM-related DEGs. Thirteen ECM-related DEGs were mainly enriched in pathways associated with tissue remodeling, inflammation and defense against infectious agents. Ultimately, CTSH was identified asthe key ECM-related biomarker. CTSH was associated with difference immune cells during the occurrence and development of DFU, and it influenced hedgehog, IL-17 and TNF signaling pathway. Additionally, CTSH expression is correlated with many ECM- and immune-related genes. A lncRNA-miRNA-mRNA and drug regulatory network were constructed with 10 lncRNAs, 2 miRNAs, CTSH and 1 drug. Finally, CTSH was validated as a key biomarker for DFU in animal models.\u003Cbr>Conclusion: Our study found that CTSH can be used for both diagnostic and prognostic purposes and might be a potential therapeutic target.\u003Cbr>Keywords: diabetic wound healing, extracellular matrix, bioinformatics, machine learning, CTSH |\n| --- |\n| Introduction\u003Cbr>Diabetic foot ulcer (DFU) is defined as a break of the foot skin in diabetic patients that affects at least the epidermis and part of dermis and is commonly accompanied by peripheral neuropathy and/or peripheral artery disease in the lower extremity.1 DFU, a severe complication of diabetes, is linked to higher rates of amputation and mortality. DFUs area significant health concern, currently affecting 6.3% of the global population with diabetes, with annual recurrence rates of 40%, and a 5-year mortality rate of 49 . 1% .2,3 The standard care/conventional treatment for patients with DFU includes good glycemic control, wound debridement, offloading, infection control and the use of medical dressing. Furthermore, several other approaches like intralesional epidermal growth ","cbCaijhZNtTlutOd","https://ap.wps.com/l/cbCaijhZNtTlutOd","pdf",11990820,3,1,17,"English","en",105,"# Background\n## Methods\n## Results\n## Conclusion","[{\"question\":\"What was the main goal of this study on DFU?\",\"answer\":\"To identify and validate an extracellular matrix (ECM)-related biomarker for diabetic foot ulcer using bioinformatics, machine learning, and animal model verification.\"},{\"question\":\"How were the candidate ECM-related genes selected?\",\"answer\":\"Differentially expressed genes were determined from two GEO microarray datasets, intersected with known ECM genes, and then filtered using three machine-learning methods: LASSO, RF, and SVM-RFE.\"},{\"question\":\"Why is CTSH considered important for DFU?\",\"answer\":\"CTSH was identified as the key ECM-related biomarker, associated with differences in immune cells during DFU progression and correlated with activity in hedgehog, IL-17, and TNF signaling pathways, with expression also linked to ECM- and immune-related genes.\"}]","Identifying and Validating Extracellular Matrix-Related Gene CTSH in Diabetic Foot Ulcer Using Bioinformatics and Machine Learning | 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was the main goal of this study on DFU?","Question",{"text":76,"@type":77},"To identify and validate an extracellular matrix (ECM)-related biomarker for diabetic foot ulcer using bioinformatics, machine learning, and animal model verification.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were the candidate ECM-related genes selected?",{"text":81,"@type":77},"Differentially expressed genes were determined from two GEO microarray datasets, intersected with known ECM genes, and then filtered using three machine-learning methods: LASSO, RF, and SVM-RFE.",{"name":83,"@type":74,"acceptedAnswer":84},"Why is CTSH considered important for DFU?",{"text":85,"@type":77},"CTSH was identified as the key ECM-related biomarker, associated with differences in immune cells during DFU progression and correlated with activity in hedgehog, IL-17, and TNF signaling pathways, with expression also linked to ECM- and immune-related 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