SynthesisWorld journal of urology2025
Diagnostic efficacy of serum microRNAs in predicting pathology of retroperitoneal lymph node dissection in patients with testicular germ cell tumors: a systematic review and meta-analysis.
Synthesis in World journal of urology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeTo evaluate the diagnostic efficacy of serum microRNAs in predicting pathologic findings of retroperitoneal lymph node dissection (RPLND) in patients with testicular germ cell tumors (TGCT).
methodsPUBMED, SCOPUS, and Cochrane Library were searched in August 2024 to identify eligible studies according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA 2020) guidelines.
resultsNine studies comprising 603 patients were selected in this review. The pooled sensitivity, specificity, and diagnostic odds ratio (DOR) of microRNA-371a-3p for predicting viable tumor other than pure teratoma in RPLND specimen were 0.76 (95% CI 0.49-0.90), 0.97 (95% CI 0.81-0.99) and 31.75 (95% CI 9.24-109.10), respectively. The pooled sensitivity for primary and post-chemotherapy RPLND (PC-RPLND), were 0.77 (95% CI 0.47-0.93) and 0.73 (95% CI 0.28-0.95), respectively. The pooled specificity for primary and PC-RPLND were 0.92 (95% CI 0.72-0.98) and 0.99 (95% CI 0.62-1.00), respectively. The pooled DOR for primary and PC-RPLND were 13.86 (95% CI 2.97-64.79) and 64.11 (95% CI 13.09-313.98), respectively. The major limitation is the lack of standardization of miR371 testing.
conclusionmiR-371a-3p is a relatively sensitive and highly specific marker for predicting viable tumors in RPLND pathologic findings. The DOR was particularly significant for patients who underwent PC-RPLND. While serum microRNAs may be useful in distinguishing viable germ cell tumors from necrosis, fibrosis, and teratomas, their ability to differentiate teratomas from necrosis is limited. Well-designed prospective studies are essential to enhance our understanding of the predictive performance of microRNAs.
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