Translational potential of RNA biomarkers in mental health: A narrative review

Lim, ECN; Lim, CED

Translational potential of RNA biomarkers in mental health: A narrative review

Lim, ECN Lim, CED

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: NeuroMarkers, 2025, pp. 100127
Issue Date:: 2025-09

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Field	Value	Language
dc.contributor.author	Lim, ECN
dc.contributor.author	Lim, CED
dc.date.accessioned	2026-01-13T05:53:30Z
dc.date.available	2026-01-13T05:53:30Z
dc.date.issued	2025-09
dc.identifier.citation	NeuroMarkers, 2025, pp. 100127
dc.identifier.issn	2950-5887
dc.identifier.uri	http://hdl.handle.net/10453/191746
dc.description.abstract	Schizophrenia, major depressive disorder, and bipolar disorder lack established objective diagnostic methods. Non-coding RNAs, particularly microRNAs and long non-coding RNAs, are emerging as promising biomarkers due to their involvement in gene regulation and their detectability in bodily fluids. This narrative review summarizes the translational potential of RNA biomarkers in mental health by examining the expression patterns of selected microRNAs and long non-coding RNAs associated with schizophrenia, major depressive disorder, and bipolar disorder. Particular focus is given to frequently altered species such as miR-34a, miR-181b, nuclear paraspeckle assembly transcript 1, and myocardial infarction associated transcript. These non-coding RNAs influence critical neural processes including synaptic plasticity, neuroinflammation, and stress-response pathways—domains central to neuropsychiatric pathophysiology. Circulating RNA profiles demonstrate diagnostic potential; for example, miR-4743 is elevated in schizophrenia and reduced in major depressive disorder. Diagnostic panels containing sex-specific long non-coding RNA signatures have achieved accuracies exceeding 90 %, and some RNAs, such as miR-1202, have been linked to treatment response. Therefore, RNA biomarkers show promise for non-invasive assessment and monitoring of patients with mental disorders. While clinical implementation requires further validation and standardization, continued research—supported by artificial intelligence and machine learning—offers promising new directions for integrating RNA-based biomarkers into psychiatric neuroscience.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	NeuroMarkers
dc.relation.isbasedon	10.1016/j.neumar.2025.100127
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Translational potential of RNA biomarkers in mental health: A narrative review
dc.type	Journal Article
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Science
pubs.organisational-group	University of Technology Sydney/Faculty of Science/School of Life Sciences
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.date.updated	2026-01-13T05:53:28Z
pubs.publication-status	Published

Abstract:

Schizophrenia, major depressive disorder, and bipolar disorder lack established objective diagnostic methods. Non-coding RNAs, particularly microRNAs and long non-coding RNAs, are emerging as promising biomarkers due to their involvement in gene regulation and their detectability in bodily fluids. This narrative review summarizes the translational potential of RNA biomarkers in mental health by examining the expression patterns of selected microRNAs and long non-coding RNAs associated with schizophrenia, major depressive disorder, and bipolar disorder. Particular focus is given to frequently altered species such as miR-34a, miR-181b, nuclear paraspeckle assembly transcript 1, and myocardial infarction associated transcript. These non-coding RNAs influence critical neural processes including synaptic plasticity, neuroinflammation, and stress-response pathways—domains central to neuropsychiatric pathophysiology. Circulating RNA profiles demonstrate diagnostic potential; for example, miR-4743 is elevated in schizophrenia and reduced in major depressive disorder. Diagnostic panels containing sex-specific long non-coding RNA signatures have achieved accuracies exceeding 90 %, and some RNAs, such as miR-1202, have been linked to treatment response. Therefore, RNA biomarkers show promise for non-invasive assessment and monitoring of patients with mental disorders. While clinical implementation requires further validation and standardization, continued research—supported by artificial intelligence and machine learning—offers promising new directions for integrating RNA-based biomarkers into psychiatric neuroscience.

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http://hdl.handle.net/10453/191746