Validation of tRNA-derived fragments as diagnostic biomarkers in suspected acute stroke; limitations in analysis and quantification methods
In acute stroke, timely diagnosis is essential to prevent extensive neuronal damage and improve patient outcomes. However, differentiating between ischemic stroke, intracerebral hemorrhage, and stroke mimics remains challenging. Transfer RNA-derived fragments (tRFs) have emerged as potential biomark...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-06-01
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| Series: | Molecular Therapy: Nucleic Acids |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2162253125001076 |
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| Summary: | In acute stroke, timely diagnosis is essential to prevent extensive neuronal damage and improve patient outcomes. However, differentiating between ischemic stroke, intracerebral hemorrhage, and stroke mimics remains challenging. Transfer RNA-derived fragments (tRFs) have emerged as potential biomarkers for distinguishing between these stroke subtypes. Here we used reverse transcription-quantitative PCR (RT-qPCR) to investigate the expression of specific tRFs that we previously identified in small RNA sequencing data as potential biomarkers. Out of 12 measured tRFs, only the fragments ArgTCG53–67 and TyrGTA1–19 showed a trend for differential expression between stroke subtypes, but with insufficient predictive value to be of use in clinical practice. Combining expression data of specific tRFs into a joint model did not improve the predictive ability. Technical and computational challenges may compromise the reliability of tRF expression data from RNA sequencing, possibly explaining our inability to validate the specific tRFs as potential stroke biomarkers. Moreover, the limitations of RT-qPCR challenge a reliable quantification of these fragments even further. Our findings highlight the need for improved quantitative methods for tRF analysis to fully exploit their potential as clinically meaningful biomarkers. Addressing these technical barriers could unlock the diagnostic potential of tRFs, facilitating faster, more accurate stroke subtype identification in an acute setting. |
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| ISSN: | 2162-2531 |