Abstract 24P
Background
Urothelial carcinoma of the bladder (UCC) is the ninth most common causes of cancer death worldwide. Urine, which directly contact with UCC tumor, offers a convenient way to identify tumor cells and serves as a novel diagnostic tool. Our study aimed to elucidate the molecular mechanisms and discover potential biomarkers by analyzing liquid biopsies from UCC patients.
Methods
We performed proteomic and transcriptomic analyses on urine samples from 41 UCC and 27 patients with non-cancerous hematuria. Cell-free urine and urinary cell pellets were obtained using an in-house differential centrifugation method. The pellets were utilized for total RNA isolation, while the supernatant served for total protein isolation. Additionally, we collected fresh tumor tissue from the UCC patients, which was then used for extracting tumor RNA. Subsequently, transcriptomic and proteomic profiling were performed. To validate our findings, we leveraged publicly available proteomic data from project ID ‘PXD010260’ in the ProteomeXchange Consortium database, thereby evaluating the diagnostic performance of the identified markers.
Results
Our assessment of urine tumor purity demonstrated that urine is a reliable source for tumor cell detection, with a mean tumor cell fraction of 0.605 (95% CI: 0.505 - 0.705), compared to tumor tissue, which exhibited a very high mean tumor cell proportion of 0.963 (95% CI: 0.913 - 1.000). Our proteogenomic analysis revealed 11 genes that were altered at both the urinary protein and mRNA levels. Notably, three of these genes-CYTB, SBP1, and ANXA4-showed upregulation with AUC values exceeding 0.750. CYTB and ANXA4 were significantly upregulated even in the early stages of UCC (Stage Cis, I, and II). Furthermore, we identified 2 proteins, including CATC and SPB10, that were markedly upregulated in recurrent UCC and correlated with poor overall survival.
Conclusions
In conclusion, our study provides compelling evidence supporting urine as an attractive source for detecting UCC tumor cells. Notably, we are able to identify potential diagnostic and prognostic urine-based markers with high diagnostic accuracy.
Editorial acknowledgement
During the preparation of this work the authors used ChatGPT 4.0 and Microsoft Copilot in order to identifying grammatical errors, suggesting alternative phrasing, and improving overall readability. After using this tool, the authors reviewed and edited the content as needed and take full responsibility for the content of the publication.
Clinical trial identification
Legal entity responsible for the study
The authors.
Funding
The National Science, Research and Innovation Fund (NSRF), and Talent Utilization Type 1 (Grant No. TU1-03/2564) of Prince of Songkla University.
Disclosure
All authors have declared no conflicts of interest.
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