Evidence map›Paper›PMID 42107019›Full record

ArticleJournal of cancer research and clinical oncology2026

Enhanced detection of bladder cancer using combined circulating tumor cells, urine-derived epithelial cells, and molecular biomarkers.

Zhijie Jiang, Cheng Yuan, Honggang Yuan, Chengchen Qin, Yanqing Li, Yuanyuan Hu, Changsheng Sun, Jiangping Deng, Feng Deng, Chunyan Li and 6 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of cancer research and clinical oncology, 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

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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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

16 authors.

Zhijie Jiang *Department of Anesthesiology, Yichang Central People's Hospital, China Three Gorges University, Yichang, 443000, China.
Cheng Yuan *Department of Oncology, Tumor Prevention and Treatment Center, Clinical Medical Research Center for Precision Diagnosis and Treatment of Lung Cancer and Management of Advanced Cancer Pain of Hubei Province, The First College of Clinical Medical Science, Yichang Central People's Hospital and The First College of Clinical Medical Science, China Three Gorges University, Yichang, 443000, China.
Honggang Yuan *Department of Urology, The First College of Clinical Medical Science, Yichang Central People's Hospital, China Three Gorges University, Yichang, 443000, China.
Chengchen Qin *China Three Gorges Medical Laboratory (Hubei) Co., LTD., Yichang, 443000, China.
Yanqing Li *Advanced Gene Diagnostics Co., LTD., Yichang, 443000, China.
Yuanyuan HuChina Three Gorges Medical Laboratory (Hubei) Co., LTD., Yichang, 443000, China.
Changsheng SunAdvanced Gene Diagnostics Co., LTD., Yichang, 443000, China.
Jiangping DengAdvanced Gene Diagnostics Co., LTD., Yichang, 443000, China.
Feng DengAdvanced Gene Diagnostics Co., LTD., Yichang, 443000, China.
Chunyan LiChina Three Gorges Medical Laboratory (Hubei) Co., LTD., Yichang, 443000, China.
Xin LiResearch Center for Single-Cell Omics and Personalized Medicine, Ningbo No.2 Hospital, Wenzhou Medical University, Ningbo, 315000, China.
Jianjun ZhengResearch Center for Single-Cell Omics and Personalized Medicine, Ningbo No.2 Hospital, Wenzhou Medical University, Ningbo, 315000, China.
Xingguang LuoDivision of Human Genetics, Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA.
Xinghua PanPrecision Regenerative Medicine Research Centre, Medical Science Division, Macau University of Science and Technology, Macao, China.
Kejun YanDepartment of Urology, The First College of Clinical Medical Science, Yichang Central People's Hospital, China Three Gorges University, Yichang, 443000, China. 834199278@qq.com.
Glenn DengResearch Center for Single-Cell Omics and Personalized Medicine, Ningbo No.2 Hospital, Wenzhou Medical University, Ningbo, 315000, China. glenndeng@126.com.

Funding

National Natural Science Foundation of China 2023035, 202446
6 · The paper itself

Abstract

purposeThe sensitivity of bladder cancer detection using a single biomarker from single sample type is limited. This study aimed to investigate whether a combined approach utilizing multiple biomarkers from different clinical samples could improve detection sensitivity.

methodsA total of 85 patients with bladder cancer and 30 healthy individuals were enrolled in this study. Urine and blood samples were collected for the isolation of urine-derived epithelial cells (UDECs) and circulating tumor cells (CTCs). These cells were then analyzed via PD-L1 assay and fluorescence in situ hybridization (FISH) targeting chromosomes 7 and 8. In parallel, matched urine samples from patients underwent conventional urine exfoliation cytology testing (UEC). All data were analyzed in conjunction with pathological information using specialized statistical software.

resultsAnalysis of CTCs demonstrated a significantly higher bladder cancer detection rate (78.6%) compared to UEC (36.7%). The combination of UDEC-FISH and CTC analysis utilizing urine and blood samples achieved a higher detection rate (94.1%) than the combination of UDEC-FISH with UEC performed on the same urine sample (79.8%). Furthermore, combined analysis of three markers of CTC, UEC, and UDEC-FISH (96.5%) or CTC, UEC, and UDEC-PD-L1 (90.6%) yielded significantly higher detection rates than any single biomarker analysis alone.

conclusionIntegrating multiple biomarkers from distinct sample types significantly enhances the detection sensitivity for bladder cancer.

Indexed as

Biomarkers, TumorEpithelial CellsNeoplastic Cells, CirculatingUrinary Bladder NeoplasmsAdultAgedAged, 80 and overFemaleHumansIn Situ Hybridization, FluorescenceMaleMiddle AgedBiomarkers, TumorBladder Cancer diagnosisChromosome FISH (fluorescence in situ hybridization) analysisCirculating tumor cellsProgrammed cell death ligand 1 (PD-L1)Urinary exfoliative cytology (UEC)Urine-derived epithelial cells (UDECs)Urine-derived tumor cells (UDTCs)

Identifiers

PMID42107019
PMCPMC13341993

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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.