Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/114730
Title: An effective algorithm to detect the possibility of being MSI phenotype in endometrial cancer given the BMI status and histological subtype: a statistical study
Authors: González Villa, Isabel 
González Dávila, Enrique Francisco
Expósito Afonso, Idaira Jael 
Martínez Blanco, Leynis Isabel
Loro Ferrer, Juan Francisco 
Cabrera Galván, Juan José 
UNESCO Clasification: 32 Ciencias médicas
320101 Oncología
Keywords: Body Mass Index
Dna Mismatch Repair Deficiency
Endometrial Neoplasms
Immunohistochemistry
Msi Phenotype
Issue Date: 2022
Journal: Clinical and Translational Oncology 
Abstract: Purpose: In endometrial cancer, the incidence of mutations in mismatch repair genes (MMR) is estimated at 17–30%. Patients with alterations at this level (MSI) are known to have different clinical and anatomopathological characteristics than those without this genetic alteration (MSS). In this study, we aim to identify the MSI phenotype in patients who underwent hysterectomy for endometrial cancer. We assessed the correlation of this phenotype with anatomoclinical parameters such as obesity and histological subtype. Methods/patients: Clinical and anatomopathological data were collected from 147 patients diagnosed with endometrial cancer and an immunohistochemical study of MMR system proteins was performed. PMS2 and MSH6 proteins were evaluated as primary screening and subsequent evaluation of MLH1 and MSH6, respectively, if the former were negative. Statistical association between the anatomopathological data and the immunohistochemical result was analyzed. Results and conclusions: 22.4% of our patients were MSI phenotype. We obtained statistically significant differences by multivariate analysis between endometrioid subtype and higher FIGO classification grade with MSI phenotype and obesity with MSS phenotype. Given these statistical results, we propose a function for predicting the probability of being MSI phenotype taking into account the histological subtype (endometrioid/non-endometrioid carcinoma) and FIGO grade as well as obesity. This prediction may be useful prior to hysterectomy, for genetic study of the MLH1 promoter and subsequent genetic counseling.
URI: http://hdl.handle.net/10553/114730
ISSN: 1699-048X
DOI: 10.1007/s12094-022-02837-4
Source: Clinical and Translational Oncology[ISSN 1699-048X], (Mayo 2022)
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