Integration of bulk RNA-seq data with single-cell RNA-seq data to identify urinary biomarkers derived from prostate tumor tissues.
9 affiliations
- 1School of Gongli Hospital Medical Technology, University of Shanghai for Science and Technology, Shanghai, 200093, PR China.
- 2School of clinical medicine, Shanghai University of Medicine & Health Sciences, Shanghai, 200237, PR China.
- 3Department of Urology and andrology, Pudong Gongli Hospital, Shanghai University of Medicine & Health Sciences, Shanghai, 200135, PR China.
- 4School of Medicine, Southeast University, Nanjing, 210009, PR China.
- 5Department of Urology, Qingpu Branch of Zhongshan Hospital Affiliated to Fudan University, Shanghai, 201799, PR China.
- 6Department of Pathology, Qingpu Branch of Zhongshan Hospital Affiliated to Fudan University, Shanghai, 201799, PR China.
- 7Wuxi Maternity and Child Health Care Hospital, Affiliated Women's Hospital of Jiangnan University, Jiangsu, 214002, PR China. [email protected].
- 8Department of Urology and andrology, Pudong Gongli Hospital, Shanghai University of Medicine & Health Sciences, Shanghai, 200135, PR China. [email protected].
- 9Department of Urology, Qingpu Branch of Zhongshan Hospital Affiliated to Fudan University, Shanghai, 201799, PR China. [email protected].
Abstract
Background
Prostate cancer (PCa) tissue biopsy has potential complications, and various methods guide biopsy decision. However, these methods have limitations. Urine-derived RNA shows promise for predicting and distinguishing PCa or clinically significant PCa (csPCa). The study aims to screen for prostate tumor cell-derived mRNAs in urine samples.Methods
Strand-specific capture RNA sequencing (RNA-seq) was performed on five paired tumor and adjacent normal prostate tissues from PCa patients, as well as ten urine samples derived from PCa patients and age-matched cancer-free healthy men. By integrating these transcriptomic data with single-cell RNA sequencing (scRNA-seq) datasets of three normal prostate tissues and 13 PCa tissues, we systematically screened and identified potential urinary biomarkers for PCa diagnosis. To further explore the diagnostic efficacy of the screened urinary biomarkers for csPCa, quantitative real-time polymerase chain reaction (qPCR) was conducted on 63 urine samples, including 33 samples from PCa patients and 30 samples from age-matched cancer-free healthy men. Additionally, in vitro gene knockdown experiments were implemented to preliminarily explore the biological functions of the identified urinary biomarkers.Results
Integrated analysis of strand-specific capture bulk RNA-seq and single-cell RNA-seq datasets identified urine-derived MARCKSL1 as a highly specific candidate biomarker for PCa diagnosis. In vitro functional assays demonstrated that genetic knockdown of MARCKSL1 robustly suppresses the migratory and invasive capacities of PCa cells. Furthermore, qPCR profiling verified that urine-derived MARCKSL1 exhibits favorable diagnostic performance, allowing accurate stratification of patients with PCa and csPCa.Conclusions
Urine-derived MARCKSL1 has the potential to facilitate pre-biopsy diagnosis and risk stratification of PCa and csPCa. Our findings may advance the development of non-invasive tools for pre-biopsy PCa risk evaluation and csPCa discrimination.
Related
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BibTeX
@article{shen2026integration,
title = {{Integration of bulk RNA-seq data with single-cell RNA-seq data to identify urinary biomarkers derived from prostate tumor tissues.}},
author = {Yanting Shen and Yuhan Guo and You Wu and Liyang Dai and Jican Liu and Ting Zhu and Junyan Xu and Huifeng Li and Rui Yang and Zhikang Cai and Xiawei Fei},
journal = {BMC urology},
year = {2026},
month = aug,
doi = {10.1186/s12894-026-02295-y},
url = {https://europepmc.org/article/MED/42958227},
note = {CC BY},
}
RIS
TY - JOUR
TI - Integration of bulk RNA-seq data with single-cell RNA-seq data to identify urinary biomarkers derived from prostate tumor tissues.
AU - Yanting Shen
AU - Yuhan Guo
AU - You Wu
AU - Liyang Dai
AU - Jican Liu
AU - Ting Zhu
AU - Junyan Xu
AU - Huifeng Li
AU - Rui Yang
AU - Zhikang Cai
AU - Xiawei Fei
PY - 2026
DA - 2026/08/03
JO - BMC urology
DO - 10.1186/s12894-026-02295-y
UR - https://europepmc.org/article/MED/42958227
AB - Background Prostate cancer (PCa) tissue biopsy has potential complications, and various methods guide biopsy decision. However, these methods have limitations. Urine-derived RNA shows promise for predicting and distinguishing PCa or clinically significant PCa (csPCa). The study aims to screen for prostate tumor cell-derived mRNAs in urine samples.Methods Strand-specific capture RNA sequencing (RNA-seq) was performed on five paired tumor and adjacent normal prostate tissues from PCa patients, as well as ten urine samples derived from PCa patients and age-matched cancer-free healthy men. By integrating these transcriptomic data with single-cell RNA sequencing (scRNA-seq) datasets of three normal prostate tissues and 13 PCa tissues, we systematically screened and identified potential urinary biomarkers for PCa diagnosis. To further explore the diagnostic efficacy of the screened urinary biomarkers for csPCa, quantitative real-time polymerase chain reaction (qPCR) was conducted on 63 urine samples, including 33 samples from PCa patients and 30 samples from age-matched cancer-free healthy men. Additionally, in vitro gene knockdown experiments were implemented to preliminarily explore the biological functions of the identified urinary biomarkers.Results Integrated analysis of strand-specific capture bulk RNA-seq and single-cell RNA-seq datasets identified urine-derived MARCKSL1 as a highly specific candidate biomarker for PCa diagnosis. In vitro functional assays demonstrated that genetic knockdown of MARCKSL1 robustly suppresses the migratory and invasive capacities of PCa cells. Furthermore, qPCR profiling verified that urine-derived MARCKSL1 exhibits favorable diagnostic performance, allowing accurate stratification of patients with PCa and csPCa.Conclusions Urine-derived MARCKSL1 has the potential to facilitate pre-biopsy diagnosis and risk stratification of PCa and csPCa. Our findings may advance the development of non-invasive tools for pre-biopsy PCa risk evaluation and csPCa discrimination.
SN - 1471-2490
ER -