Background: MicroRNAs (miRNAs) are stable, small non-coding RNAs involved in asthma-related pathways and are promising diagnostic biomarkers and therapeutic targets in childhood asthma. Objective: To identify miRNAs differentially expressed in preschool wheezing and childhood asthma, evaluate their association with asthma diagnosis and severity-related phenotypes, and explore their potential translational relevance through exploratory bioinformatic analyses. Methods: A systematic search of Medline, Embase, SCOPUS, PubMed, CINAHL, and Web of Science was conducted for English-language articles published up to March 19, 2025. Eligible human studies reported that miRNAs were differentially expressed in children with wheeze or asthma versus healthy controls (p < 0.05, fold change ≥ 1.5). Bioinformatic analysis identified hub genes, constructed protein–protein interaction networks, and predicted drug–gene interactions. Results: Forty-seven studies met the inclusion criteria, yielding 58 differentially expressed miRNAs (31 up, 27 down). Recurrently reported miRNAs included miR-497, let-7e, miR-98, miR-21, miR-126a, miR-196a2, miR-1, miR-146a-5p, miR-210-3p, miR-145-5p, and miR-200c-3p across blood, nasal swabs, BALF, and exhaled breath condensate. miR-26a showed strong diagnostic performance (sensitivity 83%, specificity 93%; p < 0.002, 95% CI 0.831–0.987). Functional enrichment implicated 56 differentially expressed genes in metabolic and immune processes. Ten hub genes (including TNF, IL5, IL13, TLR4) were linked to 339 potential therapeutic agents; the exploratory network analysis highlighted overlap between predicted miRNA-regulated hub genes and existing asthma-relevant drug targets, including approved biologics. Conclusions: Our review findings suggest that several miRNAs are promising candidate biomarkers for childhood asthma phenotyping and severity assessment; however, their diagnostic utility remains exploratory and requires rigorous external validation and standardisation before clinical application.

Human rhinovirus (RV) is the most frequent cause of the common cold, as well as severe exacerbations of chronic obstructive pulmonary disease (COPD) and asthma. Currently, there are no effective and accurate diagnostic tools or antiviral therapies. MicroRNAs (miRNAs) are small, non-coding sections of RNA involved in the regulation of gene expression and have been shown to be associated with different pathologies. However, the precise role of miRNAs in RV infection is not yet well established. Also, no unified computational framework exists to specifically link miRNA expression with functional gene targets during RV infection. This study aimed to first analyse the impact of RV16 on miRNA expression across the viral life cycle to identify a small panel with altered expression. We then developed a novel bioinformatics pipeline that integrated time-resolved miRNA profiling with multi-database gene-phenotype mapping to identify diagnostic biomarkers and their regulatory networks. Our in-house Python-based tool, combining mirDIP, miRDB and VarElect APIs, predicted seven genes (EZH2, RARG, PTPN13, OLFML3, STAG2, SMARCA2 and CD40LG) implicated in antiviral responses and specifically targeted by RV16 and regulated by our miRNAs. This method therefore offers a scalable approach to interrogate miRNA-gene interactions for viral infections, with potential applications in rapid diagnostics and therapeutic target discovery.