Cervical cancer diagnostics: non-coding RNAs and biosensors to AI-derived methods

IF 2.9 3区医学 Q2 MEDICAL LABORATORY TECHNOLOGY

Clinica Chimica Acta Pub Date : 2025-10-08 DOI:10.1016/j.cca.2025.120641

Seyyed Navid Mousavinejad , Rania Lachouri , Felora Ferdosi , Seyyed Hossein Khatami

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引用次数: 0

Abstract

Cervical cancer ranks fourth in terms of cancer mortality among women. The most important risk factor for cervical cancer is infection with HPV 16 and HPV 18. The prevalence and mortality rates of this cancer are much higher in countries with low and medium development indices than in developed countries. Improving health, access to vaccination, and screening tests are highly helpful in preventing this type of cancer. Recent advances have revealed novel biomarkers, particularly noncoding RNAs, including microRNAs, long noncoding RNAs, and circular RNAs, which are promising biomarkers for early detection and disease monitoring. Concurrently, artificial intelligence (AI)-derived methods, which leverage machine learning and deep learning algorithms, have revolutionized diagnostic accuracy by enhancing image analysis and pattern recognition in cytology and histopathology. This review focused on the latest developments in cervical cancer diagnostic technologies, with a focus on the role of noncoding RNAs, biosensors, and AI-derived methods (machine learning and deep learning approaches) in clinical diagnosis. By evaluating the strengths, challenges, and future potential of these innovations, we aim to provide a deeper understanding of noncoding RNAs and AI-derived methods as a future for the laboratory diagnosis of cervical cancer.

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子宫颈癌诊断：非编码rna和人工智能衍生方法的生物传感器

宫颈癌在妇女癌症死亡率中排名第四。宫颈癌最重要的危险因素是感染HPV 16和HPV 18。这种癌症的发病率和死亡率在发展指数中低的国家比发达国家高得多。改善健康、获得疫苗接种和筛查测试对预防这类癌症非常有帮助。近年来，新的生物标志物，特别是非编码rna，包括微rna、长链非编码rna和环状rna，被认为是早期检测和疾病监测的有前途的生物标志物。同时，人工智能（AI）衍生的方法利用机器学习和深度学习算法，通过增强细胞学和组织病理学中的图像分析和模式识别，彻底改变了诊断准确性。本文综述了宫颈癌诊断技术的最新进展，重点介绍了非编码rna、生物传感器和人工智能衍生方法（机器学习和深度学习方法）在临床诊断中的作用。通过评估这些创新的优势、挑战和未来潜力，我们的目标是更深入地了解非编码rna和人工智能衍生方法，作为宫颈癌实验室诊断的未来。

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来源期刊

Clinica Chimica Acta 医学-医学实验技术

CiteScore

10.10

自引率

2.00%

发文量

1268

审稿时长

23 days

期刊介绍： The Official Journal of the International Federation of Clinical Chemistry and Laboratory Medicine (IFCC) Clinica Chimica Acta is a high-quality journal which publishes original Research Communications in the field of clinical chemistry and laboratory medicine, defined as the diagnostic application of chemistry, biochemistry, immunochemistry, biochemical aspects of hematology, toxicology, and molecular biology to the study of human disease in body fluids and cells. The objective of the journal is to publish novel information leading to a better understanding of biological mechanisms of human diseases, their prevention, diagnosis, and patient management. Reports of an applied clinical character are also welcome. Papers concerned with normal metabolic processes or with constituents of normal cells or body fluids, such as reports of experimental or clinical studies in animals, are only considered when they are clearly and directly relevant to human disease. Evaluation of commercial products have a low priority for publication, unless they are novel or represent a technological breakthrough. Studies dealing with effects of drugs and natural products and studies dealing with the redox status in various diseases are not within the journal''s scope. Development and evaluation of novel analytical methodologies where applicable to diagnostic clinical chemistry and laboratory medicine, including point-of-care testing, and topics on laboratory management and informatics will also be considered. Studies focused on emerging diagnostic technologies and (big) data analysis procedures including digitalization, mobile Health, and artificial Intelligence applied to Laboratory Medicine are also of interest.