miRTarBase 2025: updates to the collection of experimentally validated microRNA–target interactions

Shidong Cui(Chinese University of Hong Kong, Shenzhen), Sicong Yu(Chinese University of Hong Kong, Shenzhen), Hsi-Yuan Huang(Chinese Academy of Medical Sciences & Peking Union Medical College), Yang-Chi-Dung Lin(Chinese University of Hong Kong, Shenzhen), Yixian Huang(Chinese University of Hong Kong, Shenzhen), Bojian Zhang(Chinese University of Hong Kong, Shenzhen), Junyao Xiao(Chinese University of Hong Kong, Shenzhen), Hua‐Li Zuo(Chinese University of Hong Kong, Shenzhen), Jiayi Wang(Dalian Medical University), Zhuoran Li(Chinese University of Hong Kong, Shenzhen), Guanghao Li(Chinese University of Hong Kong, Shenzhen), Jiajun Ma(Chinese University of Hong Kong, Shenzhen), Baiming Chen(Chinese University of Hong Kong, Shenzhen), Hao-Xuan Zhang(Chinese University of Hong Kong, Shenzhen), Jiayi Fu(Chinese University of Hong Kong, Shenzhen), Liang Wang(Dalian Medical University), Hsien‐Da Huang(Chinese Academy of Medical Sciences & Peking Union Medical College)
Nucleic Acids Research
November 23, 2024
10.1093/nar/gkae1072
Cited by 176Open Access
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Abstract

MicroRNAs (miRNAs) are small non-coding RNAs (18-26 nucleotides) that regulate gene expression by interacting with target mRNAs, affecting various physiological and pathological processes. miRTarBase, a database of experimentally validated miRNA-target interactions (MTIs), now features over 3 817 550 validated MTIs from 13 690 articles, significantly expanding its previous version. The updated database includes miRNA interactions with therapeutic agents, revealing roles in drug resistance and therapeutic strategies. It also highlights miRNAs as predictive, safety and monitoring biomarkers for toxicity assessment, clinical treatment guidance and therapeutic optimization. The expansion of miRNA-mRNA and miRNA-miRNA networks allows the identification of key regulatory genes and co-regulatory miRNAs, providing deeper insights into miRNA functions and critical target genes. Information on oxidized miRNA sequences has been added, shedding light on how oxidative modifications influence miRNA targeting and regulation. The integration of the LLAMA3 model into the NLP pipeline, alongside prompt engineering, enables the efficient identification of MTIs and miRNA-disease associations without large training datasets. An updated data integration and a redesigned user interface enhance accessibility, reinforcing miRTarBase as an essential resource for molecular oncology, drug development and related fields. The updated miRTarBase is available at https://mirtarbase.cuhk.edu.cn/∼miRTarBase/miRTarBase_2025.

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