Risk Categorization with Different Grades of Cervical Pre-Neoplastic Lesions - High Risk HPV Associations and Expression of p53 and RARβ

Document Type : Research Articles

Authors

1 Department of Pathology and Cancer Screening, Chittaranjan National Cancer Institute, Kolkata, India.

2 Department of Signal Transduction and Biogenic Amines, Chittaranjan National Cancer Institute, Kolkata, India.

3 Department of Epidemiology and Biostatistics, Chittaranjan National Cancer Institute, Kolkata, India.

4 Institute of Medicine and Sagar Dutta Hospital, Kolkata, India.

Abstract

Objective: To identify high risk HPV associations by evaluating linked p16 overexpression and also the expression
of p53 and RARβ together with histopathology for risk categorization of cervical pre-neoplastic lesions. Materials and
Methods: Immunohistochemical staining was performed on 100 cases of cervical pre- neoplastic lesions for expression
of biomarkers like p16, p53 and RARβ for comparison with haematoxylin/eosin (HE) findings. All the experimentally
generated data were statistically analyzed. Results: In this study 70% cases showed overexpression of p16INK4A
increasing progressively from CIN I to CIN II but reduced in CIN III (p <0.01). p53 oncoprotein expression was seen
in 51% cases, again with increments from CIN I to CIN II with slight reduction in CIN III (p<0.01). Some 24% cases
showed negative immunoreactivity for the putative tumor suppressor gene RARβ (p>0.05). Conclusion: Our study
provides support for the idea that p16 can be used to identify associations with HPV , as well as having potential
along with p53 and RARβ for categorizing cervical pre-neoplastic cases having a higher risk of neoplastic conversion.
Thus it may be concluded that accurate risk categorization can be achieved with the help of genetic markers as well
as histopathology.

Keywords

Main Subjects

Asian Pacific Journal of Cancer Prevention
Volume 20, Issue 2
February 2019
Pages 549-555

Files

History

  • Receive Date: 16 July 2018
  • Revise Date: 11 October 2018
  • Accept Date: 03 January 2019

Share

How to cite

Statistics