#AI reads Urine# Dynamic urinary proteomics integrates single-cell and spatial transcriptomics to reveal tumour microenvironment and predict immunotherapy response in biliary tract cancer

To address the lack of effective biomarkers for predicting the response of patients with biliary tract cancer (BTC) to immune checkpoint inhibitor (ICI) therapy, this study employed a mass spectrometry-based staged discovery-validation proteomics workflow to analyze 211 urine samples from 97 treatment-naive BTC patients. A 4-urinary protein prediction panel (4-UP) containing PTPN13, SUB1, MICAL-L1, and VARS1 was developed. Through machine learning modeling and external validation, this panel could reliably predict patients' durable clinical benefit (DCB) and early treatment response. Meanwhile, by integrating single-cell transcriptomics and spatial transcriptomics of pretreatment tumor biopsy samples from 11 patients, the study revealed the association between the urinary proteome and the tumor microenvironment (TME), and found that PTPN13+ malignant cells could regulate proapoptotic TME states to sustain ICI response. These results confirm that urinary proteomics can serve as a non-invasive tool for predicting and monitoring ICI treatment response in BTC patients and analyzing TME dynamic mechanisms, providing a new direction for biomarker research and clinical application in BTC immunotherapy🔶.

Gut. 2025 Oct 28:gutjnl-2025-335513. doi: 10.1136/gutjnl-2025-335513.

Youhe Gao

Statement: During the preparation of this work the author(s) used Doubao / AI reading for summarizing the content. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.

For earlier AI Reads Urine articles：

https://www.keaipublishing.com/en/journals/advances-in-biomarker-sciences-and-technology/ai-reads-urine/