Simone Buraschi, Gabriel Pascal, Federico Liberatore, Renato V Iozzo
{"title":"乳腺癌中蛋白聚糖基因表达的综合研究:发现与恶性表型相关的独特蛋白聚糖基因特征。","authors":"Simone Buraschi, Gabriel Pascal, Federico Liberatore, Renato V Iozzo","doi":"10.1002/pgr2.70014","DOIUrl":null,"url":null,"abstract":"<p><p>Solid tumors present a formidable challenge in oncology, necessitating innovative approaches to improve therapeutic outcomes. Proteoglycans, multifaceted molecules within the tumor microenvironment, have garnered attention due to their diverse roles in cancer progression. Their unique ability to interact with specific membrane receptors, growth factors, and cytokines provides a promising avenue for the development of recombinant proteoglycan-based therapies that could enhance the precision and efficacy of cancer treatment. In this study, we performed a comprehensive analysis of the proteoglycan gene landscape in human breast carcinomas. Leveraging the available wealth of genomic and clinical data regarding gene expression in breast carcinoma and using a machine learning model, we identified a unique gene expression signature composed of five proteoglycans differentially modulated in the tumor tissue: Syndecan-1 and asporin (upregulated) and decorin, PRELP and podocan (downregulated). Additional query of the breast carcinoma data revealed that serglycin, previously shown to be increased in breast carcinoma patients and mouse models and to correlate with a poor prognosis, was indeed decreased in the vast majority of breast cancer patients and its levels inversely correlated with tumor progression and invasion. This proteoglycan gene signature could provide novel diagnostic capabilities in breast cancer biology and highlights the need for further utilization of publicly available datasets for the clinical validation of preclinical experimental results.</p>","PeriodicalId":74585,"journal":{"name":"Proteoglycan research","volume":"3 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11893098/pdf/","citationCount":"0","resultStr":"{\"title\":\"Comprehensive investigation of proteoglycan gene expression in breast cancer: Discovery of a unique proteoglycan gene signature linked to the malignant phenotype.\",\"authors\":\"Simone Buraschi, Gabriel Pascal, Federico Liberatore, Renato V Iozzo\",\"doi\":\"10.1002/pgr2.70014\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Solid tumors present a formidable challenge in oncology, necessitating innovative approaches to improve therapeutic outcomes. Proteoglycans, multifaceted molecules within the tumor microenvironment, have garnered attention due to their diverse roles in cancer progression. Their unique ability to interact with specific membrane receptors, growth factors, and cytokines provides a promising avenue for the development of recombinant proteoglycan-based therapies that could enhance the precision and efficacy of cancer treatment. In this study, we performed a comprehensive analysis of the proteoglycan gene landscape in human breast carcinomas. Leveraging the available wealth of genomic and clinical data regarding gene expression in breast carcinoma and using a machine learning model, we identified a unique gene expression signature composed of five proteoglycans differentially modulated in the tumor tissue: Syndecan-1 and asporin (upregulated) and decorin, PRELP and podocan (downregulated). Additional query of the breast carcinoma data revealed that serglycin, previously shown to be increased in breast carcinoma patients and mouse models and to correlate with a poor prognosis, was indeed decreased in the vast majority of breast cancer patients and its levels inversely correlated with tumor progression and invasion. This proteoglycan gene signature could provide novel diagnostic capabilities in breast cancer biology and highlights the need for further utilization of publicly available datasets for the clinical validation of preclinical experimental results.</p>\",\"PeriodicalId\":74585,\"journal\":{\"name\":\"Proteoglycan research\",\"volume\":\"3 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11893098/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proteoglycan research\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1002/pgr2.70014\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2025/1/8 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proteoglycan research","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1002/pgr2.70014","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2025/1/8 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
Comprehensive investigation of proteoglycan gene expression in breast cancer: Discovery of a unique proteoglycan gene signature linked to the malignant phenotype.
Solid tumors present a formidable challenge in oncology, necessitating innovative approaches to improve therapeutic outcomes. Proteoglycans, multifaceted molecules within the tumor microenvironment, have garnered attention due to their diverse roles in cancer progression. Their unique ability to interact with specific membrane receptors, growth factors, and cytokines provides a promising avenue for the development of recombinant proteoglycan-based therapies that could enhance the precision and efficacy of cancer treatment. In this study, we performed a comprehensive analysis of the proteoglycan gene landscape in human breast carcinomas. Leveraging the available wealth of genomic and clinical data regarding gene expression in breast carcinoma and using a machine learning model, we identified a unique gene expression signature composed of five proteoglycans differentially modulated in the tumor tissue: Syndecan-1 and asporin (upregulated) and decorin, PRELP and podocan (downregulated). Additional query of the breast carcinoma data revealed that serglycin, previously shown to be increased in breast carcinoma patients and mouse models and to correlate with a poor prognosis, was indeed decreased in the vast majority of breast cancer patients and its levels inversely correlated with tumor progression and invasion. This proteoglycan gene signature could provide novel diagnostic capabilities in breast cancer biology and highlights the need for further utilization of publicly available datasets for the clinical validation of preclinical experimental results.