Analysis of precancerous lesion-related microRNAs for early diagnosis of cervical cancer in the Thai population

• Published in: Scientific reports Vol. 15; no. 1; pp. 142 - 9
• Main Authors: Suvanasuthi, Rooge, Therasakvichya, Suwanit, Kanchanapiboon, Potjanee, Promptmas, Chamras, Chimnaronk, Sarin
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 02.01.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 631/67/1517/1371; 631/67/2322; 692/4028/67/1517/1371; 692/53/2421; Adenocarcinoma; Adenocarcinoma in situ; Adult; Biomarkers; Biomarkers, Tumor - genetics; Cancer; Cervical cancer; Diagnosis; Early Detection of Cancer - methods; Female; Gene Expression Profiling; Gene Expression Regulation, Neoplastic; High-grade squamous intraepithelial lesion; Humanities and Social Sciences; Humans; Invasiveness; Lesions; Machine learning; microRNA; MicroRNAs; MicroRNAs - genetics; Middle Aged; miRNA; multidisciplinary; Precancerous Conditions - diagnosis; Precancerous Conditions - genetics; Prediction models; Science; Science (multidisciplinary); Southeast Asian People; Thailand - epidemiology; Uterine Cervical Neoplasms - diagnosis; Uterine Cervical Neoplasms - genetics; Thailand; Adenocarcinoma in situ; High-grade squamous intraepithelial lesion; microRNA; Cervical cancer; Machine learning
• ISSN: 2045-2322; 2045-2322
• DOI: 10.1038/s41598-024-84080-1

The incidence rate of cervical cancer (CC) is three times greater in Southeast Asia (SEA), where screening tests are less common than in Northern America, underlining a need for convenient self-diagnostic methods. The expression pattern of microRNAs (miRNAs) has been considered a molecular tool for non-invasive cancer diagnosis and prognosis. This study aimed at the development of the first miRNA biomarker panel for early detection of CC in Thai women. Genome-wide miRNA expression profiling was performed on cervical tissue and discharge samples from high-grade squamous intraepithelial lesion (HSIL) and adenocarcinoma in situ (AIS) subjects. Machine learning was used for handling imbalanced data and feature selection before differential expression analysis to identify significantly dysregulated miRNA panels. Pathway analysis was conducted to provide the cellular functions involved in CC progression. The study identified a shared 18-miRNA panel for both tissue and discharge, with which the prediction model distinguished HSIL and AIS from normal samples with an accuracy of 90.9%. Three dysregulated miRNAs comprised of miR-125b-1-3p, miR-487b-3p, and miR-1180-3p in CC were first described. Most of the miRNAs in the panel were down-regulated, whereas merely miR-142-3p was significantly up-regulated in HSIL and AIS, suggesting a convenient biomarker for detecting precancerous conditions. Moreover, our miRNA panel highlighted important roles played by the cell-cell interaction pathways in CC. Together, our miRNA panel hold promise as a biomarker for the early detection of cervical cancer with cervical discharge, offering the possibility for developing non-invasive diagnostic tools.