Low-coverage whole-genome sequencing for differentiating cervical cancer from cervical intraepithelial neoplasia and benign diseases.

OBJECTIVES

This study aimed to analyze chromosomal arm-level copy number variations (CNVs) in benign diseases, cervical intraepithelial neoplasia (CIN), and cervical cancer (CC) using low-coverage whole genome sequencing (LC-WGS) and evaluate the efficacy of the ultrasensitive chromosomal aneuploidy detector (UCAD) model in distinguishing CC from CIN and benign diseases.

METHODS

Cervical exfoliated cell specimens from 50 patients were collected for high-risk human papillomavirus(hr-HPV) testing, ThinPrep Cytologic Test (TCT), and CNV detection via LC-WGS. UCAD was employed to analyze chromosomal changes, with validation using WGS data from the National Center for Biotechnology Information(NCBI) database.

RESULTS

Among 50 patients, 8 had benign disease, 3 CIN1, 15 CIN2, 6 CIN2-3, 13 CIN3, and 5 CC. Chromosomal instability was detected in 9 patients (18%): all 5 CC cases, 3 CIN3 cases, and 1 CIN1 case. Gains in 3q were observed in all CC and CIN3 cases with CNVs. UCAD achieved 100% sensitivity and 91.11% specificity in differentiating CC from CIN and benign diseases, outperforming hr-HPV and TCT. The UCAD model was also applied to 5 CC and 1 high-grade squamous intraepithelial lesion (HSIL) cases obtained from the NCBI database, and the findings validated its ability to detect chromosomal aberrations in all cases.

CONCLUSIONS

CNV analysis of cervical exfoliated cells shows promise for CC detection, with UCAD demonstrating high accuracy. Further validation in larger cohorts is needed to confirm its clinical utility.