Evidence map›Paper›PMID 41624772›Full record

ReviewFrontiers in molecular biosciences2025

Regulatory QTLs affecting miRNA-mRNA interactions in cancer: mechanisms, methods, and clinical implications.

Vivek Kumar, Rohit Shukla, Amit Chaudhary, Mansi Gautam

Abstract readReview
In one paragraph

Review in Frontiers in molecular biosciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Vivek KumarUSF Center for Microbiome Research, Microbiomes Institute, University of South Florida, Tampa, FL, United States.
Rohit ShuklaUSF Center for Microbiome Research, Microbiomes Institute, University of South Florida, Tampa, FL, United States.
Amit ChaudharyHeersink School of Medicine, Department of Cell, Developmental and Integrative Biology, The University of Alabama at Birmingham, Birmingham, AL, United States.
Mansi GautamDepartment of Biomedical Engineering, Galgotias University, Noida, Uttar Pradesh, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

MicroRNAs (miRNAs) are post-transcriptional regulators that play essential roles in cancer initiation, progression, and therapy response. Single nucleotide polymorphisms (SNPs) that affect miRNA-mRNA interactions, termed regulatory quantitative trait loci (regQTLs), have emerged as critical modulators of gene expression landscapes in tumors. These regQTLs can disrupt or enhance miRNA binding to target sites, modulate transcript stability, and influence oncogenic or tumor-suppressive pathways, thus shaping individual cancer susceptibility and clinical outcomes. In this review, we comprehensively examine the biological, computational, and translational aspects of regQTLs in cancer. We summarize key computational approaches used to investigate germline influences on miRNA-mediated regulation, including interaction-based regQTL models, miR- and isomiR-eQTL analyses, and sequence-based prediction tools. We further discuss emerging miRNA-TWAS methods, which do not directly detect regQTLs but provide a valuable upstream strategy by identifying genetically regulated miRNAs that may participate in downstream regQTL interactions. We also summarize publicly available datasets and annotation platforms supporting large-scale discovery efforts. Through critical evaluation of recent experimental validations and clinical association studies, we highlight regQTLs that serve as biomarkers for prognosis and therapy response in diverse cancers such as breast, lung, prostate, and colorectal. Furthermore, we explore the therapeutic potential of targeting miRNA-SNP interactions, including emerging strategies in miRNA-tailored immunotherapies and mRNA vaccines. We propose a strategic roadmap for future research, emphasizing the need for population-specific analyses, single-cell regQTL mapping, and mechanistic dissection using multi-omic models. By connecting genetic variation, regulatory biology, and clinical translation, this review provides a foundational framework to harness miRNA-regulatory QTLs for precision oncology.

Indexed as

biomarkercancereQTLmiRNAregQTLsSNP

Identifiers

PMID41624772
PMCPMC12851987

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.