Abstract
BackgroundTo improve the efficiency of early diagnosis systems for cervical cancer, the use of cellular and viral markers for identifying precancerous lesions with a greater probability to progress to cancer has been proposed. Several cellular proteins and markers of oxidative DNA damage have been suggested as possible biomarkers of cervical carcinogenesis; however, they have not been evaluated together. In this study, we analyzed the expression of the cellular markers p16INK4a, Ki-67, CyclinE1, TOP2A/MCM2, and telomerase, as well as the DNA oxidative damage markers ROS and 8-OHdG. The analyses were performed in liquid-based cervical cytology samples or biopsies with premalignant lesions or cervical cancer diagnosis, with the purpose of selecting a panel of biomarkers that allow the identification of precursor lesions with greater risk of progression to cervical cancer.MethodsWe analyzed 1485 liquid-based cytology samples, including 239 non-squamous intraepithelial lesions (NSIL), 901 low-grade squamous intraepithelial lesions (LSIL), 54 high-grade squamous intraepithelial lesions (HSIL), and 291 cervical cancers (CC). The biomarkers were analyzed by immunocytochemistry and Human Papilloma Virus (HPV) genotyping with the INNO-LiPA genotyping Extra kit.ResultsWe found that all tested cellular biomarkers were overexpressed in samples with high risk-HPV infection, and the expression levels increased with the severity of the lesion. TOP2A/MCM2 was the best biomarker for discriminating between LSIL and HSIL, followed by p16INK4a and cyclinE1. Statistical analysis showed that TOP2A/MCM2 provided the largest explanation of HSIL and CC cases (93.8%), followed by p16INK4a (91%), cyclin E1 (91%), Ki-67 (89.3%), and telomerase (88.9%).ConclusionsWe propose that the detection of TOP2A/MCM2, p16INK4a and cyclin E1 expression levels is useful as a panel of biomarkers that allow identification of cervical lesions with a higher risk for progression to CC with high sensitivity and precision; this can be done inexpensively, in a single and non-invasive liquid-based cytology sample.
Highlights
To improve the efficiency of early diagnosis systems for cervical cancer, the use of cellular and viral markers for identifying precancerous lesions with a greater probability to progress to cancer has been proposed
We propose that the detection of DNA Topoisomerase II α (TOP2A)/minichromosome maintenance protein-2 (MCM2), p16INK4a and cyclin E1 expression levels is useful as a panel of biomarkers that allow identification of cervical lesions with a higher risk for progression to CC with high sensitivity and precision; this can be done inexpensively, in a single and non-invasive liquid-based cytology sample
The results indicate that an increase in TOP2A/MCM2, p16INK4a, cyclin-E, Ki-67, and telomerase expression confers a greater joint risk to develop CC (OR = 8290, CI: 1309-∞), high-grade squamous intraepithelial lesions (HSIL) (OR = 4012, CI: 755–21, 323), and low-grade squamous intraepithelial lesions (LSIL) (OR = 58, CI: 18.5–186.1) compared to the non-squamous intraepithelial lesions (NSIL) group (Table 5, Additional file 3: Table S3)
Summary
To improve the efficiency of early diagnosis systems for cervical cancer, the use of cellular and viral markers for identifying precancerous lesions with a greater probability to progress to cancer has been proposed. Several cellular proteins and markers of oxidative DNA damage have been suggested as possible biomarkers of cervical carcinogenesis; they have not been evaluated together. The analyses were performed in liquid-based cervical cytology samples or biopsies with premalignant lesions or cervical cancer diagnosis, with the purpose of selecting a panel of biomarkers that allow the identification of precursor lesions with greater risk of progression to cervical cancer. Cervical cancer (CC) is the fourth leading cause of cancer-related death in women worldwide, with an estimated 528,000 new cases and 266,000 deaths in 2012. The possible reasons include factors inherent to the host, such as immune response, genetic risk factors, and lifestyle, and virus-related factors, such as differences in virus genomes and viral load [3, 4]
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