Biomarkers differentiating regression from progression among untreated cervical intraepithelial neoplasia grade 2 lesions
- Xiang Li 1, Yan Chen 2, Jing Xiong 3, Puxiang Chen 3, Dongdong Zhang 4, Qing Li 5, Peng Zhu 5
- Xiang Li 1, Yan Chen 2, Jing Xiong 3
- 1Department of Gynecology, The Third Xiangya Hospital, Central South University, 138 Tong Zipo Road, Changsha 410013, P. R. China.
- 2Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha 410008, P. R. China; Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, 110 Xiangya Road, Changsha 410078, P. R. China; Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, 110 Xiangya Road, Changsha 410078, P. R. China; National Clinical Research Center for Geriatric Disorders, 87 Xiangya Road, Changsha 410008, Hunan, P. R. China; Xiangya Medical Laboratory, Central South University, 110 Xiangya Road, Changsha 410078, P. R. China.
- 3Department of Gynecology and Obstetrics, The Second Xiangya Hospital, Central South University, 139 Renmin Road, Changsha 410011, P. R. China.
- 4Department of Gynecology, The Maternal and Child Health Hospital of Zibo City, Zibo City, Shandong 255029, P. R. China.
- 5Department of Clinical Pharmacology, Xiangya Hospital, Central South University, 87 Xiangya Road, Changsha 410008, P. R. China; Institute of Clinical Pharmacology, Central South University, Hunan Key Laboratory of Pharmacogenetics, 110 Xiangya Road, Changsha 410078, P. R. China; Engineering Research Center of Applied Technology of Pharmacogenomics, Ministry of Education, 110 Xiangya Road, Changsha 410078, P. R. China; National Clinical Research Center for Geriatric Disorders, 87 Xiangya Road, Changsha 410008, Hunan, P. R. China.
- 0Department of Gynecology, The Third Xiangya Hospital, Central South University, 138 Tong Zipo Road, Changsha 410013, P. R. China.
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View abstract on PubMed
Summary
This summary is machine-generated.Identifying biomarkers for cervical intraepithelial neoplasia grade 2 (CIN2) regression is crucial for personalized treatment. This review explores HPV, host genetics, and microenvironment factors to predict CIN2 outcomes, aiming to reduce over-treatment.
Area Of Science
- Gynecologic oncology
- Molecular pathology
- Infectious disease
Background
- Cervical intraepithelial neoplasia grade 2 (CIN2) has an unpredictable natural history, with lesions potentially progressing to cancer, regressing, or persisting.
- Current management often involves surgical treatment for most CIN2 cases, leading to potential over-treatment and complications, especially for fertility-sparing management.
- Identifying biomarkers for CIN2 regression is essential for individualized treatment strategies.
Purpose Of The Study
- To review and summarize biomarkers that predict the spontaneous regression of CIN2 lesions.
- To explore factors influencing CIN2 natural history, including human papillomavirus (HPV) and host-related elements.
- To highlight the need for further validation of potential biomarkers in prospective studies.
Main Methods
- Literature review of studies on biomarkers for CIN2 regression.
- Analysis of factors such as HPV genotype, HPV methylation, p16/Ki-67 status, host gene methylation, HLA subtypes, immune microenvironment, and vaginal microbiota.
- Synthesis of current knowledge on predictive markers for CIN2 outcomes.
Main Results
- Biomarkers associated with CIN2 regression include HPV infection characteristics, host gene (epi)genetic changes, and alterations in the tumor microenvironment.
- The interplay of viral, host, and microenvironmental factors influences the clinical course of CIN2.
- No single biomarker is currently sufficient; a combination of factors may be necessary.
Conclusions
- Biomarkers correlating with HPV infection, host gene (epi)genetic changes, and microenvironment shifts can predict CIN2 regression.
- Prospective cohort studies with larger enrollment, longer follow-up, and comprehensive patient tracking are required for biomarker validation.
- Accurate prediction of CIN2 behavior can guide personalized treatment, avoiding unnecessary interventions and complications.
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