Aberrant metabolic regulation has been observed in human cancers, but the corresponding regulation in human papillomavirus (HPV) infection-associated cervical cancer is not well understood. Here, we explored potential biomarkers for the early prediction of cervical carcinoma based on the metabolic profile of uterine cervical tissue specimens that were positive for HPV16 infection. Fifty-two fresh cervical tissues were collected from women confirmed to have cervical squamous cell carcinoma (SCC; n = 21) or cervical intraepithelial neoplasia (CIN) stages II-III (n = 20). Eleven healthy women constituted the controls (negative controls [NCs]). Real-time polymerase chain reaction (PCR) was performed to detect HPV infection in the tissues. High-resolution magic angle spinning nuclear magnetic resonance was utilized for the analysis of the metabolic profile in the tissues. The expression of rate-limiting enzymes involved in key metabolic pathways was detected by reverse-transcription quantitative PCR. An independent immunohistochemical analysis was performed using 123 cases of paraffin-embedded cervical specimens. A profile of 17 small molecular metabolites that showed differential expression in HPV16-positive cervical SCC or CIN II-III compared with HPV-negative NC group was identified. According to the profile, the levels of α-and β-glucose decreased, those of lactate and low-density lipoproteins increased, and the expression of multiple amino acids was altered. Significantly increased transcript and protein levels of glycogen synthase kinase 3 beta (GSK3β) and glutamate decarboxylase 1 (GAD1) and decreased transcript and protein levels of pyruvate kinase muscle isozyme 2 (PKM2) and carnitine palmitoyltransferase 1A (CPT1A) were observed in the patient group (p < 0.05). HPV infection and cervical carcinogenesis drive metabolic modifications that might be associated with the aberrant regulation of enzymes related to metabolic pathways.
BackgroundCervical cancer is the most common genital malignant tumor in women worldwide. However, the reliability of different detection methods may vary according to populations and epidemics. This study analyzed factors relevant to high-risk human papillomavirus (hrHPV) infection among rural Uyghur women aged > 30 years and evaluated the value of different screening methods for cervical precancerous lesions.MethodsFrom July 2015 to May 2016, 225 rural Uyghur women aged > 30 years were recruited from local health clinics throughout Pishan, Xinjiang, China. HrHPV DNA testing, colposcopy, biopsy of cervical precancerous lesions, and surveys were conducted. The results of different screening methods were compared, and factors associated with hrHPV infection were analyzed.ResultsThe rates of hrHPV infection and cervical epithelial lesions were 9.3 and 1.8%, respectively. The area under the ROC curve was 0.538 (95% CI: 0.292, 0.784; P = 0.753) for the HPV test and 0.995 (95% CI: 0.988, 1.003; P < 0.001) for colposcopy. Factors associated with HPV infection included widowhood (OR = 13.601 (2.170, 85.263), P = 0.005) and ≥ 3 sexual partners in the past 5 years (OR = 16.808 (4.148, 68.101), P < 0.001). .ConclusionsAmong rural Uyghur women aged > 30 years, the main factors for HPV infection include marriage and frequent sexual intercourse. Colposcopy has a higher screening value for cervical epithelial lesions than hrHPV testing.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.