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Updated: Jan 12, 2026
MicroRNA Amplification and Recognition through Locked-nucleic-acid In situ Hybridization as A Novel Detection and Quantification Method
Published on: October 7, 2025
MicroRNA bioinformatics in precision oncology: an integrated pipeline from NGS to AI-based target discovery
Mritunjoy Dey1, Piotr Remiszewski2,3, Jakub Piątkowski4
1Department of Soft Tissue/Bone Sarcoma and Melanoma, Maria Sklodowska-Curie National Research Institute of Oncology, Warsaw, 02-781, Poland. mritunjoy.dey@nio.gov.pl.
Abstract:
Despite the growing recognition of microRNAs (miRNAs) as critical biomarkers in cancer, current approaches to their analysis remain fragmented, disjointed, and poorly integrated with emerging computational advances. This lack of cohesion limits progress toward reproducible and clinically actionable biomarker discovery. To address this unmet need, we present a review that unifies the latest findings and tools in bioinformatics, machine learning (ML), and large language models (LLMs) for miRNA analysis in oncology, thereby bridging a significant methodological gap in the field. We begin by critically synthesizing, benchmarking, and evaluating algorithms, including miRDeep2 and DIANA-miRPath, within a functional pipeline that spans next-generation sequencing (NGS) data processing to multi-omics integration. Building on this foundation, we review ML-augmented layers incorporating supervised and deep learning (DL) algorithms, specifically support vector machines (SVMs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), to enable robust miRNA signature identification, classification, and target prediction. Furthermore, we explore the integration of generative models and LLMs to support hypothesis generation and enhance reproducibility in biomarker discovery workflows. This comprehensive framework enhanced with artificial intelligence (AI) is contextualized through cancer-specific datasets, with particular emphasis on translational applications for early detection, prognosis, and therapy selection. By systematically organizing fragmented methodologies into a scalable and reproducible pipeline, our work provides a strategic roadmap to accelerate the development of miRNA-based precision cancer.
Insights
This review integrates bioinformatics, machine learning (ML), and large language models (LLMs) to create a unified pipeline for microRNA (miRNA) analysis in cancer. It aims to accelerate the discovery of reproducible and clinically actionable miRNA biomarkers for precision oncology.
Area of Science:
- Oncology
- Bioinformatics
- Computational Biology
Background:
- MicroRNA (miRNA) analysis in cancer is fragmented and lacks integration with computational tools.
- This disjointed approach hinders reproducible and clinically actionable biomarker discovery.
- A unified methodology is needed to advance miRNA-based cancer research.
Purpose of the Study:
- To present a comprehensive review unifying bioinformatics, machine learning (ML), and large language models (LLMs) for miRNA analysis in oncology.
- To bridge the methodological gap in current miRNA biomarker discovery.
- To provide a strategic roadmap for developing miRNA-based precision cancer diagnostics and therapeutics.
Main Methods:
- Synthesizing and benchmarking algorithms like miRDeep2 and DIANA-miRPath for next-generation sequencing (NGS) data processing and multi-omics integration.
- Reviewing ML-augmented layers, including support vector machines (SVMs), convolutional neural networks (CNNs), and recurrent neural networks (RNNs), for miRNA signature identification and target prediction.
- Exploring generative models and LLMs for hypothesis generation and enhanced reproducibility in biomarker discovery workflows.
Main Results:
- A unified pipeline integrating diverse computational tools for miRNA analysis in cancer.
- Demonstration of ML and LLM applications for robust miRNA signature identification, classification, and target prediction.
- Contextualization of the framework with cancer-specific datasets for translational applications.
Conclusions:
- The proposed AI-enhanced framework provides a scalable and reproducible pipeline for miRNA biomarker discovery.
- This systematic organization of fragmented methodologies accelerates the development of miRNA-based precision cancer medicine.
- The review offers a strategic roadmap to translate miRNA findings into clinical applications for early detection, prognosis, and therapy selection.
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