METHOD FOR ASSESSING THE RISK OF PROGRESSING INTRAEPITITAL CERVICAL NEOPLASIA AND THE DEVELOPMENT OF Cervical Cancer ON THE BASIS OF DETERMINING HUMAN GENE mRNA LEVELS

摘要

A method for assessing the risk of progression of intraepithelial cervical neoplasia and the development of cervical cancer based on the determination of human gene mRNA levels, characterized in that the mRNA levels of human genes MKI67, CDKN2A are measured or any other markers of cell cycle proliferation and regulation are used, changes in the expression level of which with the progression of intraepithelial cervical neoplasia and the development of cervical cancer correlates with the expression level of MKI67, CDKN2A, in particular CCNB1, BIRC5, AURKA; BCL2 or use any other markers of apoptosis, the change in the expression level of which with the progression of intraepithelial cervical neoplasia and the development of cervical cancer correlates with the expression level of BCL2, in particular BAX, BAG1, NDRG1; PGR or use any other markers of the receptor apparatus of cells, the change in the expression level of which with the progression of intraepithelial cervical neoplasia and the development of cervical cancer correlates with the expression level of PGR, in particular ESR1, CD68, in the samples from the lesion sites and the values of mRNA obtained during the reaction are substituted into the formula, where p is the risk of neoplastic transformation; z is the value of the regression function, is determined by the formula z = b1 * X1 + b2 * X2 + ... + bn * Xn + a, where X1 ... Xn is the level of expression of the gene mRNA or the index of the ratio of the level of ex gene pressures; b1 ... bn - regression coefficients; a is a constant; in this case, if the p value is less than or equal to the threshold value (p≤cut-off), conclude that there is no risk of malignancy; if the value p cut-off, make a conclusion about the progression of the disease and the high risk of neoplastic transformation; the threshold value is determined on the basis of experimental data using ROC analysis.