Development of a robust and generalizable algorithm "gQuant" for accurate normalizer gene selection in qRT-PCR analysis.

The emergent role of nucleic acid-based biomarkers-microRNAs(miRNAs), long non-coding RNAs(lncRNAs), and messenger RNAs(mRNAs), is becoming increasingly prominent in disease diagnostics and risk assessment. qRT-PCR is the primary analytical method for quantitative measurement of biomarkers. Yet, the relative infancy of non-coding RNAs recognition as biomarkers poses a challenge due to the absence of a consensus on a universally accepted normalizer gene, an absolute requirement for accurate quantification. Current tools normalizer selection are fraught with statistical limitations and suboptimal graphical user interface for data visualisation. These deficiencies underscore the necessity for a balanced tool tailored to handle qRT-PCR datasets. Addressing the identified challenges, we have developed 'gQuant' tool crafted to address these limitations. We employed voting classifiers that combine predictions from multiple statistical methods. Tool's efficacy was validated through different available and in house data derived from urinary exosomal miRNAs datasets. Comparative analysis with existing tools revealed that their integrated methodologies could skew the ranking of normalizer genes, whereas 'gQuant' consistently yielded rankings characterised by lower standard-deviation, reduced covariance, and enhanced kernel density estimation values. Given 'gQuant's' promising performance, normalizer gene identification will be greatly improved, improving precision of gene expression quantification in a variety of research scenarios. The gQuant tool developed for this study is available for public use and can be accessed at [ https://github.com/ABHAYHBB/gQuant-Tool ]."

Urinary extracellular vesicles-encapsulated miRNA signatures: A new paradigm for urinary bladder cancer diagnosis and classification.

Bladder cancer (BCa) stands as prevalent malignancy of the urinary system globally, especially among men. The clinical classification of BCa into non-muscle invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC) is crucial for prognosis and treatment decisions. However, challenges persist in current diagnostic methods like Urine cytopathology that shows poor sensitivity therefore compromising on accurately diagnosing and monitoring BCa. In recent years, research has emphasized the importance of identifying urine and blood-based specific biomarkers for BCa that can enable early and precise diagnosis, effective tumor classification, and monitoring. The convenient proximity of urine with the urinary bladder epithelium makes urine a good source of noninvasive biomarkers, in particular urinary EVs because of the packaged existence of tumor-associated molecules. Therefore, the review assesses the potential of urinary extracellular vesicles (uEVs) as noninvasive biomarkers for BCa. We have elaborately reviewed and discussed the research that delves into the role of urinary EVs in the context of BCa diagnosis and classification. Extensive research has been dedicated to investigating differential microRNA (miRNA) expressions, with the goal of establishing distinct, noninvasive biomarkers for BCa. The identification of such biomarkers has the potential to revolutionize early detection, risk stratification, therapeutic interventions, and ultimately, the long-term prognosis of BCa patients. Despite notable advancements, inconsistencies persist in the biomarkers identified, methodologies employed, and study populations. This review meticulously compiles reported miRNA biomarkers, critically assessing the variability and discrepancies observed in existing research. By synthesizing these findings, the article aims to direct future studies toward a more cohesive and dependable approach in BCa biomarker identification, fostering progress in patient care and management.