Evidence map›Paper›PMID 41658469›Full record

ArticleTranslational andrology and urology2026

Application of serum miRNA panel in bladder cancer detection.

Huimei Zhou, Zhenjian Ge, Shengjie Lin, Yutong Wu, Pengwu Zhang, Xutai Li, Zhengping Zhao, Yongqing Lai, Lingzhi Tao

Abstract read
In one paragraph

Article in Translational andrology and urology, 2026. 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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0cells of the map it votes in
0citing papers in PubMed
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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Huimei ZhouDepartment of Urology, Peking University Shenzhen Hospital, Shenzhen, China.
Zhenjian GeDepartment of Urology, Peking University Shenzhen Hospital, Shenzhen, China.
Shengjie LinDepartment of Urology, Peking University Shenzhen Hospital, Shenzhen, China.
Yutong WuDepartment of Urology, Peking University Shenzhen Hospital, Shenzhen, China.
Pengwu ZhangDepartment of Urology, Peking University Shenzhen Hospital, Shenzhen, China.
Xutai LiDepartment of Urology, Peking University Shenzhen Hospital, Shenzhen, China.
Zhengping ZhaoDepartment of Urology, Peking University Shenzhen Hospital, Shenzhen, China.
Yongqing LaiDepartment of Urology, Peking University Shenzhen Hospital, Shenzhen, China.
Lingzhi TaoDepartment of Urology, Peking University Shenzhen Hospital, Shenzhen, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Bladder cancer (BC) screening contributes to better patient outcomes. However, existing diagnostic methods for BC are not suitable for large-scale screening. Serum microRNAs (miRNAs) are expected to be simple, cost-effective, and non-invasive screening tools for BC. This study aims to identify whether relevant serum miRNAs as diagnostic markers for BC. Methods: This research covered 112 BCs and 112 healthy controls (HCs), utilizing quantitative reverse transcription polymerase chain reaction (RT-qPCR) to pinpoint miRNAs viable for diagnosing BC in serum across three phases. In addition, bioinformatics analyses were conducted to predict target genes and provide functional annotations. Results: Six serum miRNAs showed significantly abnormal expression levels compared with the HC group. A highly efficient screening model, comprising four serum miRNAs (hsa-miR-107, hsa-miR-146a-5p, hsa-miR-15b-5p, and hsa-miR-218-5p) was developed, with an area under the receiver operating characteristic curve (AUC) value of 0.924, 95.24% sensitivity, and 78.57% specificity. The predicted target genes of these serum miRNAs, including Conclusions: A panel of four serum miRNAs (hsa-miR-107, hsa-miR-146a-5p, hsa-miR-15b-5p, and hsa-miR-218-5p) exhibits high specificity and sensitivity. It shows potential as an efficient, non-invasive, and cost-effective biomarker for BC screening.

Indexed as

biomarkerBladder cancer (BC)microRNA (miRNA)

Identifiers

PMID41658469
PMCPMC12877890

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.