Non-invasive diagnostic evaluation of urinary exosomal let-7c cluster expression in bladder cancer using machine learning approaches

@article{kural2026noninvasive,
  title = {{Non-invasive diagnostic evaluation of urinary exosomal let-7c cluster expression in bladder cancer using machine learning approaches}},
  author = {Sukhad Kural and Lalit Kumar and Abhay Kumar Pathak and Shweta Singh and Garima Jain and Mahima Yadav and Yashasvi Singh and Ujwal Kumar and Sameer Trivedi and Manjari Gupta and Sakshi Agarwal and Vibhav Gautam and Parimal Das},
  journal = {BMC Cancer},
  year = {2026},
  month = jul,
  doi = {10.1186/s12885-026-16217-6},
  url = {https://doi.org/10.1186/s12885-026-16217-6},
  note = {CC BY},
}

TY  - JOUR
TI  - Non-invasive diagnostic evaluation of urinary exosomal let-7c cluster expression in bladder cancer using machine learning approaches
AU  - Sukhad Kural
AU  - Lalit Kumar
AU  - Abhay Kumar Pathak
AU  - Shweta Singh
AU  - Garima Jain
AU  - Mahima Yadav
AU  - Yashasvi Singh
AU  - Ujwal Kumar
AU  - Sameer Trivedi
AU  - Manjari Gupta
AU  - Sakshi Agarwal
AU  - Vibhav Gautam
AU  - Parimal Das
PY  - 2026
DA  - 2026/07/01
JO  - BMC Cancer
DO  - 10.1186/s12885-026-16217-6
UR  - https://doi.org/10.1186/s12885-026-16217-6
AB  - Background Bladder cancer (BCa) diagnosis typically relies on invasive cystoscopy, which is effective but costly and uncomfortable. Urinary microRNAs (miRNAs), especially exosomal ones, are promising non-invasive biomarkers due to their stability in biological fluids and disease specificity. The let-7c cluster, owing to its tumor-suppressive function and frequent dysregulation in BCa, has emerged as a promising candidate for diagnostic evaluation. This study assessed the diagnostic potential of the urinary exosomal let-7c cluster through Machine Learning (ML)-integrated miRNA profiling, offering early proof-of-concept for its role in BCa classification. Methods Urine samples were collected from 66 participants, including 50 BCa patients and 16 healthy controls (HC). Exosomal miRNAs were isolated and quantified using Quantitative Real-Time-Polymerase-Chain-Reaction (qRT-PCR). Statistical analysis and hypothesis tests were conducted to explore the nature and diagnostic relevance of individual biomarkers. A logistic regression classifier was applied to evaluate both the combined and differential diagnostic capabilities of the selected biomarkers. Accuracy, precision, recall and AU-ROC scores were used to assess model performance. Bioinformatics analysis was performed to identify pathways associated with the features prioritized by the ML models, ensuring their relevance to BCa. Results Statistically significant differentiation was observed between BCa patients and HC, with miR-99a-5p (p = 0.013, AU-ROC = 0.71) demonstrating preliminary diagnostic performance. In contrast, let-7c-5p (AU-ROC = 0.65, p > 0.1) and miR-125b-5p (p = 0.087, AU-ROC = 0.64) exhibited non-significant trends, showing weaker discrimination. The logistic regression ML model achieved 80.0% accuracy (AU-ROC = 0.86, recall = 90%) in distinguishing cancer from HC and 53.3% (AU-ROC = 0.63) for miRNA-only grade classification. With clinical variables added, accuracy increased to 73.3%, but the AU-ROC remained modest at 0.61, indicating limited discriminatory ability in high-grade versus low-grade. Across Ta–T2, miR-99a-5p showed clearer separation, whereas let-7c-5p and miR-125b-5p exhibited weak stage-related differences. Bioinformatics analysis confirmed the biological relevance of these miRNAs in BCa-related pathways, including PI3K–Akt, p53, NF-κB and RAS/MAPK signaling, with hub genes such as TP53, MYC, EGFR and CCND1 identified. Conclusion Urinary let-7c cluster miRNAs show preliminary diagnostic potential when evaluated using ML models and may serve as a non-invasive supportive tool to conventional diagnostic approaches. However, these results should be regarded as proof-of-concept evidence that has to be validated in larger, independent cohorts because of the small sample size, low subgroup power and class imbalance.
SN  - 1471-2407
ER  -