Combined miRNA and SERS urine liquid biopsy for the point-of-care diagnosis and molecular stratification of bladder cancer

Abstract

Background Bladder cancer (BC) has the highest per-patient cost of all cancer types. Hence, we aim to develop a non-invasive, point-of-care tool for the diagnostic and molecular stratification of patients with BC based on combined microRNAs (miRNAs) and surface-enhanced Raman spectroscopy (SERS) profiling of urine. Methods Next-generation sequencing of the whole miRNome and SERS profiling were performed on urine samples collected from 15 patients with BC and 16 control subjects (CTRLs). A retrospective cohort (BC = 66 and CTRL = 50) and RT-qPCR were used to confirm the selected differently expressed miRNAs. Diagnostic accuracy was assessed using machine learning algorithms (logistic regression, naïve Bayes, and random forest), which were trained to discriminate between BC and CTRL, using as input either miRNAs, SERS, or both. The molecular stratification of BC based on miRNA and SERS profiling was performed to discriminate between high-grade and low-grade tumors and between luminal and basal types. Results Combining SERS data with three differentially expressed miRNAs (miR-34a-5p, miR-205-3p, miR-210-3p) yielded an Area Under the Curve (AUC) of 0.92 ± 0.06 in discriminating between BC and CTRL, an accuracy which was superior either to miRNAs (AUC = 0.84 ± 0.03) or SERS data (AUC = 0.84 ± 0.05) individually. When evaluating the classification accuracy for luminal and basal BC, the combination of miRNAs and SERS profiling averaged an AUC of 0.95 ± 0.03 across the three machine learning algorithms, again better than miRNA (AUC = 0.89 ± 0.04) or SERS (AUC = 0.92 ± 0.05) individually, although SERS alone performed better in terms of classification accuracy. Conclusion miRNA profiling synergizes with SERS profiling for point-of-care diagnostic and molecular stratification of BC. By combining the two liquid biopsy methods, a clinically relevant tool that can aid BC patients is envisaged.

Diagnostic Potential of Circulating Tumor Cells, Urinary MicroRNA, and Urinary Cell-Free DNA for Bladder Cancer: A Review

“…In BCa, a panel of the top three differentially expressed umiRNAs (miR-34a-5p, -205-3p, and -210-3p) found by NGS was combined with the surface-enhanced Raman spectroscopy data, revealing superior diagnostic accuracy for the combination. In the NGS-based studies described earlier, the umiRNAs with altered expression in BCa were not consistent; however, three of the four studies found an increased umiRNA in common: miR-205-5p [ 43 , 44 , 46 ]. Braicu et al performed Ingenuity Pathway Analysis and found that epithelial–mesenchymal transition appears to be associated with the miR-205-5p function.…”

“…A unique method in addition to NGS data for the diagnosis of BCa was reported by Moisoiu et al in 2022 [ 46 ]. Using surface-enhanced Raman spectroscopy, which amplifies the Raman signal of molecules adsorbed from urine, they discriminated patients with BCa from healthy volunteers.…”

“…Braicu et al performed Ingenuity Pathway Analysis and found that epithelial–mesenchymal transition appears to be associated with the miR-205-5p function. However, those studies analyzed only 20–116 samples; further large studies will be of great importance in identifying genetic markers in BCa, including those for stage and grade, helping to facilitate precision medicine in the disease [ 43 , 44 , 45 , 46 ].…”

A Liquid Biopsy in Bladder Cancer—The Current Landscape in Urinary Biomarkers

“…A study investigated miRNA profiles and surface-enhanced Raman spectroscopy (SERS) of urine to develop diagnosis and stratification biomarkers [ 60 ]. Evaluation using machine learning algorithms was applied to distinguish the BCa group from the healthy group.…”

How Surface-Enhanced Raman Spectroscopy Could Contribute to Medical Diagnoses

“…Unless there are specific peaks in SERS spectra making it possible to identify particular molecules, the SERS spectra of, e.g., samples from healthy and ill patients with Alzheimer's disease [21], Dengue virus [22] or cancer, from blood samples [23,24], urine [25,26], plasma [27], tears [28], circulating exosomes [29] or healthy and cancer cells [30] and tissues [31] can be very similar and require advanced chemometrics to distinguish between them. Label-free SERS can be also used to detect some morphological changes, e.g., a decrease in fluidity in the erythrocyte plasma membrane, or a decrease in the mobility of the heme of membranebound hemoglobin caused by hypertension [32], as well as platelet activity [33].…”