首页|Medical University of Innsbruck Reports Findings in Prostate Cancer (Prediction of Clinically Significant Prostate Cancer by a Specific Collagen-related Transcr iptome, Proteome, and Urinome Signature)
Medical University of Innsbruck Reports Findings in Prostate Cancer (Prediction of Clinically Significant Prostate Cancer by a Specific Collagen-related Transcr iptome, Proteome, and Urinome Signature)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Oncology - Prostate Ca ncer is the subject of a report. According to news reporting out of Innsbruck, A ustria, by NewsRx editors, research stated, "While collagen density has been ass ociated with poor outcomes in various cancers, its role in prostate cancer (PCa) remains elusive. Our aim was to analyze collagen-related transcriptomic, proteo mic, and urinome alterations in the context of detection of clinically significa nt PCa (csPCa, International Society of Urological Pathology [ISUP] grade group 2)." Our news journalists obtained a quote from the research from the Medical Univers ity of Innsbruck, "Comprehensive analyses for PCa transcriptome (n = 1393), prot eome (n = 104), and urinome (n = 923) data sets focused on 55 collagen-related g enes. Investigation of the cellular source of collagen-related transcripts via s ingle-cell RNA sequencing was conducted. Statistical evaluations, clustering, an d machine learning models were used for data analysis to identify csPCa signatur es. Differential expression of 30 of 55 collagen-related genes and 34 proteins w as confirmed in csPCa in comparison to benign prostate tissue or ISUP 1 cancer. A collagen-high cancer cluster exhibited distinct cellular and molecular charact eristics, including fibroblast and endothelial cell infiltration, intense extrac ellular matrix turnover, and enhanced growth factor and inflammatory signaling. Robust collagen-based machine learning models were established to identify csPCa . The models outcompeted prostate-specific antigen (PSA) and age, showing compar able performance to multiparametric magnetic resonance imaging (mpMRI) in predic ting csPCa. Of note, the urinome-based collagen model identified four of five cs PCa cases among patients with Prostate Imaging- Reporting and Data System (PI-IRA DS) 3 lesions, for which the presence of csPCa is considered equivocal. The retr ospective character of the study is a limitation. Collagen-related transcriptome , proteome, and urinome signatures exhibited superior accuracy in detecting csPC a in comparison to PSA and age. The collagen signatures, especially in cases of ambiguous lesions on mpMRI, successfully identified csPCa and could potentially reduce unnecessary biopsies. The urinome-based collagen signature represents a p romising liquid biopsy tool that requires prospective evaluation to improve the potential of this collagenbased approach to enhance diagnostic precision in PCa for risk stratification and guiding personalized interventions."
InnsbruckAustriaEuropeCancerColl agenCyborgsEmerging TechnologiesExtracellular Matrix ProteinsGeneticsH ealth and MedicineMachine LearningOncologyPeptides and ProteinsProstate CancerProstatic NeoplasmsProteinsProteome
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