Same Fragments, Different Diseases: Analysis of Identical tRNA Fragments Across Diseases Utilizing Functional and Abundance-Based Databases

<b>Background/Objectives:</b> Transfer RNA-derived fragments (tRFs) are small non-coding RNAs increasingly implicated in gene regulation and disease, yet their target specificity and disease relevance remain poorly understood. This is an exploratory study that investigates the phenomenon of identical tRF sequences reported in distinct disease contexts and evaluates the consistency between experimental findings and predictions from both target-based and abundance-based tRF databases. <b>Methods:</b> Five tRFs with identical sequences across at least two peer-reviewed disease studies were selected from a recent systematic review. Their validated targets and disease associations were extracted from the literature. Motifs and predicted targets were cross-referenced using three target-oriented databases: tatDB, tRFTar, and tsRFun. In parallel, the abundance enrichment of cancer-associated tRFs was assessed in OncotRF and MINTbase using TCGA-based abundance data. <b>Results:</b> Among the five tRFs, only LeuAAG-001-N-3p-68-85 showed complete alignment between experimental data and both tatDB and tRFTar predictions. Most of the other four displayed at least partial overlaps in motif/binding regions with some of validated targets. tRF abundance data from MINTbase and OncotRF showed inconsistent enrichment, with only AlaAGC-002-N-3p-58-75 exhibiting concordance with its experimentally validated cancer type. Most functionally relevant tRFs were not strongly represented in abundance-only databases. <b>Conclusions:</b> Given the limited number of tRFs analyzed, this study serves primarily as a pilot analysis designed to generate hypotheses and guide future in-depth research, rather than offering comprehensive conclusions. We did, however, illustrate how the analysis of tRFs can benefit from utilizing currently available databases. Target-based databases more closely reflected experimental evidence for mechanistic details when a tRF or a motif match is found. Yet all database types are incomplete, including the abundance-focused tools, which often fail to capture disease-specific regulatory roles of tRFs. These findings underscore the importance of using integrated data sources for tRF annotation. As a pilot analysis, the study provides insights into how identical tRF sequences might function differently across disease contexts, highlighting areas for further investigation while pointing out the limitations of relying on expression data alone to infer functional relevance.