Computational approaches and data analysis tools for comprehensive non-coding small RNA profiling in liquid biopsies
TL;DR
This thesis demonstrates that the integration of refined isomiR and tRF profiles within targeted liquid biopsy populations (TEPs and PD), supported by advanced bioinformatics tools like miRGalaxy, significantly enhances the accuracy and sensitivity of cancer diagnosis.
Abstract
This PhD thesis establishes a comprehensive framework for advancing non-invasive cancer diagnostics through the characterization and combinatorial analysis of small non-coding RNAs (sncRNAs), specifically microRNA isoforms (isomiRs) and tRNA-derived fragments (tRFs). Liquid biopsy offers a minimally invasive alternative to traditional tissue biopsies, allowing for real-time disease monitoring via biomolecules like circulating tumor cells, extracellular vesicles (EVs), and tumor-educated platelets (TEPs). While canonical miRNAs are established biomarkers, this work demonstrates that isomiRs (variants resulting from alternative processing) and tRFs arising from tRNA cleavage represent a richer, underutilized reservoir of disease-specific signals. To address the significant bioinformatics challenges and lack of standardized pipelines in the field, this thesis presents miRGalaxy. miRGalaxy is a novel, open-source, Galaxy-based framework designed for interactive and in-depth sequencing data analysis, enabling researchers without extensive computational backgrounds to identify and assess the differential expression of individual isomiR species. The clinical utility of these sncRNAs was explored through multiple omics studies. In pancreatic ductal adenocarcinoma (PDAC), multi-omics profiling of TEPs revealed profound changes in the biological repertoire, including significantly high activity in RNA splicing and mRNA processing. A key finding was the downregulation of SPARC transcripts in PDAC platelets, which was strongly correlated with negative regulation by specific isomiRs such as miR-29a-3p and miR-22-3p. Further characterization of the small RNA landscape in non-small-cell lung cancer (NSCLC) focused on "Platelet Dust" (PD) platelet-derived extracellular vesicles. PD was found to be significantly more enriched with miRNAs (~80–82%) compared to general EVs (~66%), which are relatively more enriched in tRNAs. This highlights PD as a more informative and distinct biomarker source for distinguishing cancer patients from healthy controls. The culmination of this research is a combinatorial analysis of miRNAs, isomiRs, and tRFs from plasma EVs in colorectal and prostate cancer. By leveraging the synergistic effects of these different RNA species, the study achieved a diagnostic accuracy and Area Under the Curve (AUC) of approximately 80%. This approach proves more effective than analyzing single RNA types in isolation, providing a robust statistical framework for improved cancer management. In conclusion, this thesis demonstrates that the integration of refined isomiR and tRF profiles within targeted liquid biopsy populations (TEPs and PD), supported by advanced bioinformatics tools like miRGalaxy, significantly enhances the accuracy and sensitivity of cancer diagnosis. These findings pave the way for more precise preventive screening and personalized oncology.