Renal cell carcinoma detection: a systematic review in diagnostic urinary biomarkers

肾细胞癌检测:尿液诊断生物标志物的系统评价

阅读:1

Abstract

BACKGROUND: Renal cell carcinoma (RCC) accounts for 90% of all renal neoplasms and is often incidentally detected through unrelated imaging procedures. Differentiating between benign and malignant renal masses remains challenging using imaging alone. Urinary biomarkers may aid in this distinction, yet none are currently implemented in the clinic. Moreover, a comprehensive overview of urinary diagnostic biomarkers for RCC is lacking. Therefore, we aimed to systematically review and summarize existing literature on potential urinary biomarkers with diagnostic properties for RCC. METHODS: PubMed, Scopus and Web of Science were used for the identification of eligible studies evaluating urinary biomarkers in adults with sporadic RCC which reported diagnostic properties compared to controls groups. Standardized data extraction was performed. Risk of bias of was assessed by using a modified STROBE 22-items checklist for observational studies. RESULTS: In total 136 articles were identified through database search, four via a previous review and 19 through cross-referencing. After screening, 46 articles were included, identifying 105 individual biomarkers: metabolites (n = 40), proteins (n = 29), miRNAs (n = 12), DNA methylation markers (n = 13) and others (n = 11). Additionally, 29 multi-biomarker panels were described. Promising diagnostic markers (AUC ≥ 0.80) included dysregulated energy metabolism markers, proteins AQP1 and PLIN2, and miRNAs; miR-122-5p, miR-15a and miR-30c, however validation is severely lacking. CONCLUSIONS: Various urinary biomarkers for RCC show promising diagnostic potential. The diagnostic ability of multi-biomarker panels often exceeded those of individual markers. However, individual markers and panels require external validation before clinical implementation. TRIAL REGISTRATION: This systematic review was registered on PROSPERO (CRD42023474582), and was designed and written based on the PRISMA guidelines.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。