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Relation between kallikrein (hK7) and superoxide dismutase 2 (SOD2) proteins expression with papilomavirus (HPV) types, histological origin and severity of cervical neoplasia

Grant number: 10/07739-3
Support type:Regular Research Grants
Duration: August 01, 2010 - July 31, 2012
Field of knowledge:Health Sciences - Medicine
Principal Investigator:Luiz Carlos Zeferino
Grantee:Luiz Carlos Zeferino
Home Institution: Centro de Atenção Integral à Saúde da Mulher (CAISM). Hospital da Mulher Professor Doutor José Aristodemo Pinotti. Universidade Estadual de Campinas (UNICAMP). Campinas , SP, Brazil
Assoc. researchers:Adhemar Longatto Filho ; Lara Termini ; Liliana Aparecida Lucci de Angelo Andrade ; Luisa Lina Villa ; Maria Cristina Do Amaral Westin ; Silvia Helena Rabelo dos Santos ; Sophie Françoise Mauricette Derchain

Abstract

Biomarkers predictive of severity cervical lesions may be useful in monitoring response to treatment and early detection of recurrent or persistent cervical cancer. Differential expressions of transcripts of kallikrein (hK7) and superoxide dismutase (SOD2) were observed in HPV16 and HPV 18 immortalized lines cells. There are indications that the levels of these proteins increased with the severity of cervical lesion, however, there is no information regarding to the relation with HPV specific types. The aim of this study is to analyze the correlation between the hK7 and SOD2 proteins expression with the HPV types, histological origin and severity of cervical neoplasia. This case-control study will be consisted by two groups of cases. The first group corresponds to women with histopathological diagnosis of squamous cervical neoplasia and the second group will be composed by women with invasive adenocarcinomas and its precursor lesions. The third group, composed by women with non-neoplastic histopathological diagnosis, including cervicitis and normal cervices will be considered the control. The proteins expression will be assessed by immunohistochemical technique The analysis will be performed using chi-square and p-value to test the associations between the variables and odds ratios (OR) with their respective 95% confidence intervals (95%CI) to estimate the magnitude of association. Logistic regression models will be used for multivariate analysis. (AU)