{"title":"骨肉瘤中潜在细胞外囊泡蛋白标记物的鉴定","authors":"Jinhe Zhang, Huiyan Li","doi":"10.1002/prca.202200084","DOIUrl":null,"url":null,"abstract":"<p><strong>Purpose: </strong>Extracellular vesicles (EVs) have become promising biomarkers for cancer management. Particularly, the molecular cargo such as proteins carried by EVs are similar to their cells of origin, providing important information that can be used for cancer diagnostics, prognosis, and treatment monitoring. However, to date, molecular analysis on EVs is still challenging, limited by the availability of efficient analytical technologies, largely due to the small size of EVs. In this work, we developed a computational workflow for in silico identification of potential EV protein markers from genomic and proteomic databases, and applied it for the discovery of osteosarcoma (OS) EV protein markers.</p><p><strong>Experimental design: </strong>Both mRNA and protein data were computed and compared from publicly accessible databases, and top markers with high differential expression levels were selected.</p><p><strong>Results: </strong>Thirty nine markers were identified overexpressed and seven found to be downregulated. These identified markers have been found to be associated with OS on different aspects in literature, demonstrating the usability of this workflow.</p><p><strong>Conclusions and clinical relevance: </strong>This work provides a list of potential EV protein markers that are either overexpressed or downregulated in OS for further experimental validation for improved clinical management of OS.</p>","PeriodicalId":20571,"journal":{"name":"PROTEOMICS – Clinical Applications","volume":"17 4","pages":"e2200084"},"PeriodicalIF":2.1000,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Identification of potential extracellular vesicle protein markers altered in osteosarcoma from public databases.\",\"authors\":\"Jinhe Zhang, Huiyan Li\",\"doi\":\"10.1002/prca.202200084\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Purpose: </strong>Extracellular vesicles (EVs) have become promising biomarkers for cancer management. Particularly, the molecular cargo such as proteins carried by EVs are similar to their cells of origin, providing important information that can be used for cancer diagnostics, prognosis, and treatment monitoring. However, to date, molecular analysis on EVs is still challenging, limited by the availability of efficient analytical technologies, largely due to the small size of EVs. In this work, we developed a computational workflow for in silico identification of potential EV protein markers from genomic and proteomic databases, and applied it for the discovery of osteosarcoma (OS) EV protein markers.</p><p><strong>Experimental design: </strong>Both mRNA and protein data were computed and compared from publicly accessible databases, and top markers with high differential expression levels were selected.</p><p><strong>Results: </strong>Thirty nine markers were identified overexpressed and seven found to be downregulated. These identified markers have been found to be associated with OS on different aspects in literature, demonstrating the usability of this workflow.</p><p><strong>Conclusions and clinical relevance: </strong>This work provides a list of potential EV protein markers that are either overexpressed or downregulated in OS for further experimental validation for improved clinical management of OS.</p>\",\"PeriodicalId\":20571,\"journal\":{\"name\":\"PROTEOMICS – Clinical Applications\",\"volume\":\"17 4\",\"pages\":\"e2200084\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2023-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"PROTEOMICS – Clinical Applications\",\"FirstCategoryId\":\"99\",\"ListUrlMain\":\"https://doi.org/10.1002/prca.202200084\",\"RegionNum\":4,\"RegionCategory\":\"生物学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"BIOCHEMICAL RESEARCH METHODS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"PROTEOMICS – Clinical Applications","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1002/prca.202200084","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"BIOCHEMICAL RESEARCH METHODS","Score":null,"Total":0}
Identification of potential extracellular vesicle protein markers altered in osteosarcoma from public databases.
Purpose: Extracellular vesicles (EVs) have become promising biomarkers for cancer management. Particularly, the molecular cargo such as proteins carried by EVs are similar to their cells of origin, providing important information that can be used for cancer diagnostics, prognosis, and treatment monitoring. However, to date, molecular analysis on EVs is still challenging, limited by the availability of efficient analytical technologies, largely due to the small size of EVs. In this work, we developed a computational workflow for in silico identification of potential EV protein markers from genomic and proteomic databases, and applied it for the discovery of osteosarcoma (OS) EV protein markers.
Experimental design: Both mRNA and protein data were computed and compared from publicly accessible databases, and top markers with high differential expression levels were selected.
Results: Thirty nine markers were identified overexpressed and seven found to be downregulated. These identified markers have been found to be associated with OS on different aspects in literature, demonstrating the usability of this workflow.
Conclusions and clinical relevance: This work provides a list of potential EV protein markers that are either overexpressed or downregulated in OS for further experimental validation for improved clinical management of OS.
期刊介绍:
PROTEOMICS - Clinical Applications has developed into a key source of information in the field of applying proteomics to the study of human disease and translation to the clinic. With 12 issues per year, the journal will publish papers in all relevant areas including:
-basic proteomic research designed to further understand the molecular mechanisms underlying dysfunction in human disease
-the results of proteomic studies dedicated to the discovery and validation of diagnostic and prognostic disease biomarkers
-the use of proteomics for the discovery of novel drug targets
-the application of proteomics in the drug development pipeline
-the use of proteomics as a component of clinical trials.