Integrating AI and RNA biomarkers in cancer: advances in diagnostics and targeted therapies

Early detection and personalized treatment strategies are essential for enhancing patient outcomes, as cancer continues to be a significant cause of mortality on a global basis. In clinical practice, the identification and validation of reliable biomarkers for cancer diagnosis, prognosis, and therap...

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Published inCell communication and signaling Vol. 23; no. 1; pp. 430 - 19
Main Authors Hussen, Bashdar Mahmud, Abdullah, Snur Rasool, Hidayat, Hazha Jamal, Samsami, Majid, Taheri, Mohammad
Format Journal Article
LanguageEnglish
Published London BioMed Central 10.10.2025
BioMed Central Ltd
BMC
Subjects
Algorithms
Artificial Intelligence
Artificial intelligence (AI)
Biological markers
Biomarkers, Tumor - genetics
Biomarkers, Tumor - metabolism
Biomedical and Life Sciences
Cancer
Care and treatment
Cell Biology
Cytokines and Growth Factors
Diagnosis
Humans
Identification and classification
Life Sciences
Machine learning
Molecular Targeted Therapy
Neoplasms - diagnosis
Neoplasms - genetics
Neoplasms - therapy
Protein-Ligand Interactions
Receptors
Review
RNA
RNA - genetics
RNA - metabolism
RNA biomarkers
Therapeutic target
Iran
RNA biomarkers
Artificial intelligence (AI)
Therapeutic target
Cancer
Online AccessGet full text
ISSN1478-811X
1478-811X
DOI10.1186/s12964-025-02434-2

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Abstract Early detection and personalized treatment strategies are essential for enhancing patient outcomes, as cancer continues to be a significant cause of mortality on a global basis. In clinical practice, the identification and validation of reliable biomarkers for cancer diagnosis, prognosis, and therapeutic monitoring continue to present significant challenges. The present study explores the current state and applications of artificial intelligence-driven approaches in the identification and usage of RNA biomarkers for cancer diagnostics and therapeutics. In various aspects of cancer management, we explore the integration of machine learning and deep learning algorithms with a variety of RNA biomarker classes, such as circRNAs, miRNAs, and lncRNAs. Improved detection, subtype categorization, prognosis prediction, and treatment response monitoring are all possible due to AI-powered approaches that can efficiently analyse complex RNA expression patterns, discover novel biomarkers, and explain their functions in cancer biology. There are still many obstacles to overcome in the biomarker development, validation, and clinical application processes, despite the fact that RNA biomarkers hold great potential to transform cancer treatment by improving early detection and individualized therapy methods. Integrating AI with RNA biomarker research is a crucial strategy with enormous promise for precision oncology and better patient care all the way through the cancer spectrum, from risk prediction to recurrence management.
AbstractList Early detection and personalized treatment strategies are essential for enhancing patient outcomes, as cancer continues to be a significant cause of mortality on a global basis. In clinical practice, the identification and validation of reliable biomarkers for cancer diagnosis, prognosis, and therapeutic monitoring continue to present significant challenges. The present study explores the current state and applications of artificial intelligence-driven approaches in the identification and usage of RNA biomarkers for cancer diagnostics and therapeutics. In various aspects of cancer management, we explore the integration of machine learning and deep learning algorithms with a variety of RNA biomarker classes, such as circRNAs, miRNAs, and lncRNAs. Improved detection, subtype categorization, prognosis prediction, and treatment response monitoring are all possible due to AI-powered approaches that can efficiently analyse complex RNA expression patterns, discover novel biomarkers, and explain their functions in cancer biology. There are still many obstacles to overcome in the biomarker development, validation, and clinical application processes, despite the fact that RNA biomarkers hold great potential to transform cancer treatment by improving early detection and individualized therapy methods. Integrating AI with RNA biomarker research is a crucial strategy with enormous promise for precision oncology and better patient care all the way through the cancer spectrum, from risk prediction to recurrence management.
Early detection and personalized treatment strategies are essential for enhancing patient outcomes, as cancer continues to be a significant cause of mortality on a global basis. In clinical practice, the identification and validation of reliable biomarkers for cancer diagnosis, prognosis, and therapeutic monitoring continue to present significant challenges. The present study explores the current state and applications of artificial intelligence-driven approaches in the identification and usage of RNA biomarkers for cancer diagnostics and therapeutics. In various aspects of cancer management, we explore the integration of machine learning and deep learning algorithms with a variety of RNA biomarker classes, such as circRNAs, miRNAs, and lncRNAs. Improved detection, subtype categorization, prognosis prediction, and treatment response monitoring are all possible due to AI-powered approaches that can efficiently analyse complex RNA expression patterns, discover novel biomarkers, and explain their functions in cancer biology. There are still many obstacles to overcome in the biomarker development, validation, and clinical application processes, despite the fact that RNA biomarkers hold great potential to transform cancer treatment by improving early detection and individualized therapy methods. Integrating AI with RNA biomarker research is a crucial strategy with enormous promise for precision oncology and better patient care all the way through the cancer spectrum, from risk prediction to recurrence management. Keywords: Artificial intelligence (AI), RNA biomarkers, Cancer, Therapeutic target
Abstract Early detection and personalized treatment strategies are essential for enhancing patient outcomes, as cancer continues to be a significant cause of mortality on a global basis. In clinical practice, the identification and validation of reliable biomarkers for cancer diagnosis, prognosis, and therapeutic monitoring continue to present significant challenges. The present study explores the current state and applications of artificial intelligence-driven approaches in the identification and usage of RNA biomarkers for cancer diagnostics and therapeutics. In various aspects of cancer management, we explore the integration of machine learning and deep learning algorithms with a variety of RNA biomarker classes, such as circRNAs, miRNAs, and lncRNAs. Improved detection, subtype categorization, prognosis prediction, and treatment response monitoring are all possible due to AI-powered approaches that can efficiently analyse complex RNA expression patterns, discover novel biomarkers, and explain their functions in cancer biology. There are still many obstacles to overcome in the biomarker development, validation, and clinical application processes, despite the fact that RNA biomarkers hold great potential to transform cancer treatment by improving early detection and individualized therapy methods. Integrating AI with RNA biomarker research is a crucial strategy with enormous promise for precision oncology and better patient care all the way through the cancer spectrum, from risk prediction to recurrence management.
Early detection and personalized treatment strategies are essential for enhancing patient outcomes, as cancer continues to be a significant cause of mortality on a global basis. In clinical practice, the identification and validation of reliable biomarkers for cancer diagnosis, prognosis, and therapeutic monitoring continue to present significant challenges. The present study explores the current state and applications of artificial intelligence-driven approaches in the identification and usage of RNA biomarkers for cancer diagnostics and therapeutics. In various aspects of cancer management, we explore the integration of machine learning and deep learning algorithms with a variety of RNA biomarker classes, such as circRNAs, miRNAs, and lncRNAs. Improved detection, subtype categorization, prognosis prediction, and treatment response monitoring are all possible due to AI-powered approaches that can efficiently analyse complex RNA expression patterns, discover novel biomarkers, and explain their functions in cancer biology. There are still many obstacles to overcome in the biomarker development, validation, and clinical application processes, despite the fact that RNA biomarkers hold great potential to transform cancer treatment by improving early detection and individualized therapy methods. Integrating AI with RNA biomarker research is a crucial strategy with enormous promise for precision oncology and better patient care all the way through the cancer spectrum, from risk prediction to recurrence management.Early detection and personalized treatment strategies are essential for enhancing patient outcomes, as cancer continues to be a significant cause of mortality on a global basis. In clinical practice, the identification and validation of reliable biomarkers for cancer diagnosis, prognosis, and therapeutic monitoring continue to present significant challenges. The present study explores the current state and applications of artificial intelligence-driven approaches in the identification and usage of RNA biomarkers for cancer diagnostics and therapeutics. In various aspects of cancer management, we explore the integration of machine learning and deep learning algorithms with a variety of RNA biomarker classes, such as circRNAs, miRNAs, and lncRNAs. Improved detection, subtype categorization, prognosis prediction, and treatment response monitoring are all possible due to AI-powered approaches that can efficiently analyse complex RNA expression patterns, discover novel biomarkers, and explain their functions in cancer biology. There are still many obstacles to overcome in the biomarker development, validation, and clinical application processes, despite the fact that RNA biomarkers hold great potential to transform cancer treatment by improving early detection and individualized therapy methods. Integrating AI with RNA biomarker research is a crucial strategy with enormous promise for precision oncology and better patient care all the way through the cancer spectrum, from risk prediction to recurrence management.
ArticleNumber 430
Audience Academic
Author Abdullah, Snur Rasool
Taheri, Mohammad
Hidayat, Hazha Jamal
Samsami, Majid
Hussen, Bashdar Mahmud
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Snippet Early detection and personalized treatment strategies are essential for enhancing patient outcomes, as cancer continues to be a significant cause of mortality...
Abstract Early detection and personalized treatment strategies are essential for enhancing patient outcomes, as cancer continues to be a significant cause of...
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SubjectTerms Algorithms
Artificial Intelligence
Artificial intelligence (AI)
Biological markers
Biomarkers, Tumor - genetics
Biomarkers, Tumor - metabolism
Biomedical and Life Sciences
Cancer
Care and treatment
Cell Biology
Cytokines and Growth Factors
Diagnosis
Humans
Identification and classification
Life Sciences
Machine learning
Molecular Targeted Therapy
Neoplasms - diagnosis
Neoplasms - genetics
Neoplasms - therapy
Protein-Ligand Interactions
Receptors
Review
RNA
RNA - genetics
RNA - metabolism
RNA biomarkers
Therapeutic target
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Title Integrating AI and RNA biomarkers in cancer: advances in diagnostics and targeted therapies
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