摘要
一位新闻记者-机器人和机器学习的工作人员新闻编辑每日新闻-肿瘤学的新研究-前列腺癌是一篇报道的主题。根据NewsRx编辑对Ustria Innsbruck的新闻报道，研究表明：“虽然胶原密度与各种癌症的不良预后有关，但它在前列腺癌(PCa)中的作用仍然难以捉摸。我们的目的是在检测临床意义的前列腺癌（csPCa，国际泌尿病理学学会[ISUP]2级）的背景下分析胶原相关转录组、蛋白组和尿组的改变。”我们的新闻记者从因斯布鲁克医学院的研究中获得了一句话，“PCa转录组(n=1393)，PROT EOME(n=104)和URINOME(n=923)数据集的综合分析，重点关注55个胶原相关基因。通过单细胞RNA测序调查胶原相关转录物的细胞来源。csPCa中55个胶原相关基因中的30个和34个蛋白与前列腺良性病变和ISUP-1癌组织相比有差异表达,一个胶原高表达的肿瘤簇具有明显的细胞和分子特征,包括成纤维细胞和内皮细胞浸润,细胞基质转化强烈,细胞周期延长。建立了稳健的基于胶原的机器学习模型来识别csPCa。该模型优于前列腺特异性抗原(PSA)和年龄，显示出在预测csPCa方面优于多参数磁共振成像(mpMRI)。值得注意的是，基于尿液的胶原模型在前列腺图像报告和数据系统(PI-IRA DS)3个病灶的患者中识别了5个csPCa病例中的4个。csPCa的存在被认为是不确定的。本研究的反应性特征是一个局限性。与PSA和AGE相比，胶原相关转录组、蛋白组和尿组标记在检测csPCa方面显示出更高的准确性。胶原标记，特别是在mpMRI上不明确的病变情况下，成功识别csPCa，并可能减少不必要的活检。尿基胶原标志代表了一种实用的液体活检工具，需要前瞻性评估，以提高这种基于胶原的方法在PCa风险分层和指导个性化干预中的诊断精度。
Abstract
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."
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