QUANTITATIVE ASSESSMENT OF hsa-miR-20а-5p, hsa-miR-30d-5p, hsa-miR-99a-5p AND hsa-miR-10b-5p IN WOMEN'S PERIPHERAL BLOOD PLASMA AS MINIMALLY INVASIVE METHOD FOR DIAGNOSING HIGH-GRADE CERVICAL INTRAEPITHELIAL NEOPLASIA AND CERVICAL CANCER ASSOCIATED WITH HUMAN PAPILLOMA VIRUS

Abstract

FIELD: obstetrics-gynecology; oncogynecology; laboratory molecular diagnostics.SUBSTANCE: concerns a minimally invasive method for diagnosing high-grade cervical intraepithelial neoplasia and cervical cancer associated with the human papillomavirus, based on the content of hsa-miR-20a-5p, hsa-miR-30d-5p, hsa-miR-99a-5p and hsa-miR-10b-5p in the plasma of women's peripheral blood using quantitative RT-PCR (polymerase chain reaction coupled with reverse transcription) in real time.EFFECT: method can be used by an obstetrician-gynecologist and oncologist to stratify the risk in women with HPV-associated cervical pathology, select management tactics (dynamic observation or surgical treatment), and optimize the routing of patients in screening programs.1 cl, 1 dwg, 5 tbl, 1 ex

Description

[0001] Quantitative assessment of hsa-miR-20a-5p, hsa-miR-30d-5p, hsa-miR-99a-5p and hsa-miR-10b-5p in women's peripheral blood plasma as a minimally invasive method for diagnosing high-grade cervical intraepithelial neoplasia and cervical cancer associated with human papillomavirus.

[0002] The invention relates to the field of medicine, namely to obstetrics-gynecology, oncogynecology and laboratory molecular diagnostics, and concerns a minimally invasive method for diagnosing high-grade cervical intraepithelial neoplasia and cervical cancer associated with the human papillomavirus, based on the content of hsa-miR-20a-5p, hsa-miR-30d-5p, hsa-miR-99a-5p and hsa-miR-10b-5p in the plasma of women's peripheral blood using quantitative RT-PCR (polymerase chain reaction coupled with reverse transcription) in real time. The method can be used by an obstetrician-gynecologist and oncologist to stratify the risk in women with HPV-associated cervical pathology, select management tactics (dynamic observation or surgical treatment), and optimize the routing of patients in screening programs.

[0003] Cervical cancer (CC) remains one of the leading causes of morbidity and mortality from malignant neoplasms in women of reproductive age worldwide [1]. The vast majority of cervical cancer cases are associated with persistent infection with oncogenic types of human papillomavirus (HPV), which has determined the widespread introduction of HPV testing and cytological screening into clinical practice. The development of invasive cancer is usually preceded by a long stage of precancerous changes - cervical intraepithelial neoplasia (CIN). According to the latest classification of the World Health Organization, CIN is classified as low-grade squamous intraepithelial lesions - LSIL (CIN I) and high-grade squamous cell lesions - HSIL (CIN II, CIN III and carcinoma in situ) [2].

[0004] It is known that LSIL in most cases has a benign course with a high probability of spontaneous regression and the acquisition of a morphological picture close to NILM (absence of intraepithelial lesion or malignant neoplasm); while HSIL is associated with a significantly higher risk of progression to invasive cervical cancer [3]. In this regard, the key task of modern screening is not only the identification of HPV-positive women, but also the accurate distinction between CIN I- (LSIL, NILM) and CIN II + (HSIL and cervical cancer). Misinterpretation of the extent of the lesion leads either to overdiagnosis and unnecessary destructive interventions on the cervix, increasing the risk of reproductive complications (cervical insufficiency, premature birth, miscarriage), or to underdiagnosis and late detection of the invasive process.

[0005] Currently, cytological examination (PAP test or liquid-based cytology), HPV testing, colposcopy and histological examination of cervical biopsy specimens are used to diagnose HPV-associated cervical pathology. For decades, the Pap test has been the main screening method and has proven highly effective in reducing morbidity and mortality from cervical cancer [4], [5]. Published studies report high specificity (86-100%) of the oncocytological method, but its limited reproducibility and sensitivity (30-87%) allow for a serious risk of missing precancerous lesions [6], [7]. Thus, according to A. Gupta et al. (2020), in 27% of cases, a discrepancy between the cytological conclusion and the subsequent histological diagnosis is detected [8]. A more advanced cytological method is liquid-based cytology (LBC).It allows identification of not only the cellular composition of the exo- and endocervix, reducing the proportion of unsatisfactory smears, but also HPV testing and assessment of p16 / Ki67 expression. However, studies comparing the sensitivity of traditional cytology and LCC give conflicting results as to whether LCC is more [9],

[10] or equally

[11] ,

[12] ,

[13] sensitive than the Pap test. The identification of the etiologic relationship between HPV and the development of cervical cancer led to the introduction of DNA testing as a primary screening method for cervical cancer

[14] ,

[15] ,

[16] . A multinational cohort study demonstrated (n= 24-295) that the HCR HPV (HCR - high carcinogenic risk) test has high sensitivity (90%) for the detection of HSIL and cervical cancer

[17] .However, the HPV test has lower specificity and the positive predictive value of the HPV HCR test is 22% lower than that of cytological examination, which is explained by the transient nature of papillomavirus infection and leads to unjustified diagnostic and therapeutic interventions

[18] . Tissue biopsy followed by histological examination is the gold standard for diagnosing neoplasms, including squamous cell lesions of the cervix. However, traditional tissue biopsy is an invasive procedure associated with risks for patients, difficulties in determining the site of material collection, and difficulties in histological interpretation of some samples

[19] . Thus, the development of non-invasive or minimally invasive strategies for the diagnosis and prognosis of precancerous and cancerous lesions of the cervix is ​​promising.

[0006] Progression to intraepithelial precancerous lesions occurs due to multiple disruptions in signaling pathways that regulate the cell cycle, caused by overexpression of viral proteins E6 and E7, which inactivate tumor suppressors p53 and pRB1

[20] . This process is multistep and may involve a series of genetic and epigenetic changes, including alterations in the transcriptional activity of genes and microRNAs

[21] .

[0007] MicroRNAs are small non-coding RNAs approximately 19–25 nucleotides long that regulate post-transcriptional processes in all cells and can act as oncogenes or tumor suppressors in cancer. The miRNA database, miRBase (http: / / www.mirbase.org), indicates that more than 2,600 miRNAs have been identified in humans to date, and the number of identified miRNAs is constantly growing. Mature miRNAs are involved in the regulation of most fundamental biological processes, including cell division, cell cycle transitions, apoptosis, cell migration and invasion, angiogenesis, and the formation of the immune response

[22] ,

[23] . In the work of Ma et al.

[24] increased expression of four circulating (exosomal) microRNAs (miR-146a-5p, miR-151a-3p, miR-2110 and miR-21-5p) was detected in cervical cancer. In another study, Zheng et al.

[25] examined the profile of exosomal microRNAs in healthy women, patients with CIN I, CIN II-III, squamous cell carcinoma of the cervix and adenocarcinoma.Based on the results of the study, miR-30d-5p and let-7d-3p were identified as valuable diagnostic biomarkers for non-invasive screening of cervical cancer and precancerous cervical lesions.

[0008] The prototype of the proposed method is the method described in patent RU2688169C9 (https: / / new.fips.ru / registers-doc-view / fips_servlet?DB=RUPAT&DocNumber=2688169&TypeFile=html), according to which the quantitative real-time PCR method can determine the content of microRNA in tissue or biological fluid of the body through a cyclic amplification reaction of cDNA, which is a copy of RNA and is synthesized during the reverse transcription reaction.

[0009] Quantitative determination of microRNA by real-time RT-PCR includes three steps: 1) synthesis of cDNA (during coupled reactions of polyadenylation and reverse transcription of microRNA with oligo(dT) linked to a universal primer); 2) amplification of the cDNA sequence by a cyclic reaction of a) DNA denaturation, b) annealing of a microRNA-specific sense primer and an antisense primer identical to the universal primer sequence, c) extension of primers with DNA polymerase with simultaneous detection of fluorescence of SYBR green or Eva Green dye intercalating into double-stranded DNA after each reaction cycle; 3) analysis of the obtained results and determination of the relative amount of cDNA in the sample.The amount of synthesized and amplified cDNA in a sample depends on the concentration of microRNA in the biological material, the degree of degradation of the analyzed RNA, the efficiency of RNA isolation, the reverse transcription reaction, which is extremely sensitive to contaminants in the sample

[26] , and the efficiency of PCR. To take into account the influence of the above factors on the quantitative assessment of specific cDNA in a sample, the amount of normalizing endogenous RNA, the level of which does not change during the development of the pathological process under study, is analyzed.

[0010] Generally, the relative cDNA level is determined by equation (1): (M 0РНК1 / M 0РНКреф )=2Δ Ct , where M 0РНК1 and M 0РНКреф - initial amounts of cDNA in the sample, ΔСt=(Ct РНКреф -Ct РНК1), Ct is the value of the amplification cycle at the point of intersection of the kinetic curve of accumulation of the amplification product with the threshold fluorescence level line, which is determined automatically by the software of the amplifier; Ct РНК1 - the value of the threshold cycle of cDNA amplification of the analyzed microRNA in the sample; Ct РНКреф - the threshold cycle value for cDNA amplification of the reference endogenous RNA in the same sample. The ΔCt method is applicable provided that the amplification efficiencies of the analyzed microRNA and reference endogenous RNA cDNA are equal and close to 100%.

[0011] The problem solved by the proposed invention is to create a highly sensitive and highly specific minimally invasive method for the differential diagnosis of HPV-associated cervical intraepithelial neoplasia, which allows, based on the expression profile of a certain panel of extracellular microRNAs circulating in the plasma of peripheral blood, to reliably differentiate between CIN I and CIN II.+ , improve the accuracy of risk stratification in HPV-positive patients, reduce the number of unjustified diagnostic and therapeutic manipulations on the cervix and, as a result, reduce the incidence of reproductive complications.

[0012] The proposed method for the quantitative assessment of extracellular microRNAs in the patient's peripheral blood plasma using quantitative RT-PCR with real-time detection of results involves: 1) isolating RNA from peripheral blood plasma samples using the phenol-chloroform method (e.g., Serum Plasma kit Qiagen) or the phenol-free method (e.g., RIBO-prep kit, AmpliSens) for 1 hour; 2) reverse transcription of microRNA with the incorporation of a universal primer sequence at the 5' end of the synthesized cDNA chain (e.g., miRCURY LNA RT Kit by Qiagen or TransScript®miRNA First-Strand cDNA Synthesis SuperMix kit by TransGen) for 1 hour 30 minutes; 3) amplification of cDNA using real-time PCR (e.g., in a reaction mixture containing 1X qPCRmix-HS SYBR+HighROX (Eurogen, Russia), 1xUniversal primer (Qiagen) or 0.2 μM universal primer with a sequence identical to that ofintroduced at the stage of reverse transcription), 0.2 μM sense primer specific for the analyzed microRNA in the amplifier, allowing to carry out PCR in real time, for example, in the StepOnePlus™ thermal cycler (Applied Biosystems, USA) with a given cDNA amplification program, for example, preliminary denaturation of cDNA at 95°C for 15 minutes, carrying out 40 reaction cycles in the mode: 94°C - 15 sec, optimized primer annealing temperature in the range of 45-60°C - 30 sec, 70°C - 30 sec, which includes detection of the fluorescent signal from the SYBR Green dye intercalating into the double-stranded reaction product in real time,data processing and their presentation in graphical form using special software; 5) the Ct values ​​obtained during real-time PCR for each analyzed microRNA and endogenous reference microRNA, followed by calculation of the ΔCt values; 6) the ΔCt values ​​obtained in point 5 for each analyzed microRNA in various combinations are used in logistic regression models (linear regression with logit transformation), where the dependent variable (response variable) is the morphology of the squamous epithelium of the cervix (0 - CIN I-; 1 - CIN II, + ); if the obtained value of the logistic function is above the specified cutoff level, then the blood plasma sample under study was taken from a patient with CIN II + ; if the obtained value of the logistic function is below the specified cutoff level, then the blood plasma sample under study was taken from a patient with CIN I-.

[0013] Implementation of the Invention

[0014] Example 1

[0015] The study material is 43 samples of peripheral blood plasma of female patients (Table 1), forming the following groups: NILM without HPV (9 people), NILM with HPV (7 people), LSIL (8 people), HSIL (10 people), cervical cancer (9 people).

[0016] Table 1. Clinical characteristics of patients included in the study

[0017] Sample name Age Menstrual cycle phase at the time of plasma sampling Diagnosis based on histological examination WHO classification 3 36 Phase 1 RSM RSM 23 48 Phase 1 RSM RSM 44 50 Phase 1 RSM RSM 56 55 menopause RSM RSM 65 59 menopause RSM RSM 72 63 menopause RSM RSM 54699 54 menopause RSM RSM 89 69 menopause RSM RSM 111 24 Phase 1 RSM RSM 37 25 Phase 1 HSIL HSIL 10 34 Phase 1 HSIL HSIL 32 33 Phase 1 HSIL HSIL 20 27 Phase 1 HSIL HSIL 61 37 Phase 1 HSIL HSIL 62 24 Phase 1 HSIL HSIL 64 38 Phase 1 HSIL HSIL 81 39 Phase 1 HSIL HSIL 40 34 Phase 1 HSIL HSIL 122 31 Phase 1 HSIL HSIL 11 22 Phase 1 LSIL LSIL 25 33 Phase 1 LSIL LSIL 30 34 Phase 1 LSIL LSIL 70 30 Phase 1 LSIL LSIL 68 35 Phase 1 LSIL LSIL 80 22 Phase 1 LSIL LSIL 112 22 Phase 1 LSIL LSIL 48117 37 Phase 1 LSIL LSIL 5 34 Phase 1 HPV+ NILM HPV+ NILM 18 28 Phase 1 HPV+ NILM HPV+ NILM 17 28 Phase 1 HPV+ NILM HPV+ NILM 16 26 Phase 1 HPV+ NILM HPV+ NILM 93 26 Phase 1 HPV+ NILM HPV+ NILM 31 26 Phase 1 HPV+ NILM HPV+ NILM 43 28 Phase 1 HPV+ NILM HPV+ NILM 90 27 Phase 1 Norm Norm 100 26 Phase 1 Norm Norm 108 46 Phase 1 Norm Norm 115 30 Phase 1 Norm Norm 29135 24 Phase 1 Norm Norm 123 29 Phase 1 Norm Norm 113 53 menopause Norm Norm 114 67 menopause Norm Norm 116 49 menopause Norm Norm 120 65 menopause Norm Norm

[0018] 5 ml of peripheral venous blood samples are collected in VACUETTE® tubes containing 0.1 M EDTA (an anticoagulant). The peripheral blood samples are centrifuged for 20 minutes at 300 g (4°C), plasma is collected in an empty tube, and then centrifuged again for 10 minutes at 16,000 g (4°C). RNA is isolated from 100 µl of plasma using the RIBO-prep kit from AmpliSense (K2-9-Et-100, Moscow, Russia) according to the instructions. One and a half microliters of RNA sample was polyadenylated in a reaction mixture (final volume 10 µl) containing 1x E. coli polyA buffer (LA101-01, TransGen Biotech) and 1.25 units of E. coli Poly (A) Polymerase (LA101-01, TransGen Biotech) at 37°C for 20 minutes, followed by enzyme inactivation at 65°C for 20 minutes.Polyadenylated RNA (10 µl) was converted to cDNA in a reaction mixture (final volume 20 µl) containing buffer (1x TS RT buffer, AT101-02, TransGen Biotech), deoxynucleotide triphosphate mix (dNTP mix (0.5 mM each, PB006L, Eurogen), 250 units of reverse transcriptase (TransScript®Reverse Transcriptase, AT101-02, TransGen Biotech) and 4 µM antisense RT primer, at 42°C for 30 minutes, followed by enzyme inactivation at 85°C for 5 seconds; upon completion of the reaction, the sample volume was adjusted to 200 µl with deionized water.

[0019] The synthesized cDNA (2 μl) was used as a template for real-time PCR using 1x qPCRmix-HS SYBR ready-to-use PCR mix (PK147L, Evrogen, Moscow, Russia), 0.2 μM sense primer specific for either hsa-miR-20a-5p, hsa-miR-30d-5p, hsa-miR-99a-5p, or hsa-miR-10b-5p, and 0.2 μM antisense universal PCR primer.The amplification protocol was as follows: (1) DNA denaturation for 15 min at 95°C, (2) 40 amplification cycles: DNA denaturation at 94°C for 15 sec, annealing of sense and antisense primers at the optimized temperature (for hsa-miR-20a-5p - 52°C; for hsa-miR-30d-5p - 55°C; for hsa-miR-99a-5p - 53.2°C; for hsa-miR-10b-5p - 58.7°C) for 30 sec, primer extension at 70°C for 30 sec with fluorescent signal detection after each reaction cycle, (3) construction of PCR product melting curves (heating the reaction mixture from 65°C to 95°C in 0.1°C increments, (accompanied by fluorescent signal detection at each step) in a StepOnePlus™ thermocycler (Applied Biosystems, USA). The specificity of amplification was verified by analyzing the melting curves of the PCR product. The threshold value for all PCR curves was 1.2.The numbers of threshold cycles (Ct) at which the amplification curves of hsa-miR-20a-5p, hsa-miR-30d-5p, hsa-miR-99a-5p, hsa-miR-10b-5p cDNA cross the threshold line are shown in Table 2.

[0020] Table 2. Threshold amplification cycles of cDNA synthesized from microRNA in samples

[0021] St Sample number hsa-miR-20a-5p hsa-miR-30d-5p hsa-miR-99a-5p hsa-miR-10b-5p 3 37.63 40.01 42.00 33.91 5 38.20 39.55 38.92 34.38 10 37.39 40.23 42.00 33.27 11 38.17 38.41 37.28 34.09 16 36.76 38.05 36.24 34.37 17 36.16 38.29 42.00 34.54 18 36.74 38.49 42.00 33.94 20 36.62 40.13 42.00 34.32 23 36.27 38.23 42.00 33.96 25 36.12 42.00 37.30 34.76 30 36.50 37.85 40.35 33.95 31 37.44 38.32 35.63 35.34 32 35.65 38.77 42.00 33.96 37 37.01 40.51 41.93 33.81 40 37.25 42.00 42.00 33.77 43 38.48 38.36 39.55 33.51 44 35.75 37.62 42.00 34.57 56 37.29 36.82 42.00 33.89 61 38.23 38.53 42.00 34.21 62 36.09 38.44 42.00 33.41 64 36.02 38.12 37.28 33.88 65 35.90 38.91 36.32 35.88 68 39.05 39.61 42.00 34.27 70 38.00 38.78 39.79 33.91 72 36.16 38.34 40.14 33.99 80 36.57 37.84 40.21 34.93 81 36.10 37.29 42.00 34.18 89 35.77 38.74 42.00 34.18 90 35.26 36.90 42.00 34.57 93 36.24 37.07 37.20 33.92 100 36.78 37.93 39.03 34.40 108 36.82 38.50 38.55 34.97 111 37.79 39.00 42.00 34.24 112 39.08 39.38 38.71 33.96 113 36.51 37.80 38.29 34.81 114 36.76 39.29 32.68 34.00 116 36.60 39.18 39.76 33.66 120 36.52 38.74 40.58 34.09 122 36.98 37.91 37.28 33.83 123 35.88 36.64 42.00 34.62 29135 36.35 38.05 38.10 34.30 48117 37.14 37.60 37.36 34.09 54699 37.07 42.00 34.67 33.97

[0022] The relative cDNA expression level (ΔCt) is calculated based on the difference in the threshold cycles of cDNA amplification of the analyzed microRNA and the reference endogenous microRNA (Table 3).

[0023] Table 3. Relative expression level (ΔCt) of analyzed microRNAs in samples

[0024] ΔСt Sample number Сt(hsa-miR-30d-5p)-Ct(hsa-miR-20a-5p) Ct(hsa-miR-10b-5p)-Ct(hsa-miR-99a-5p) Ct(hsa-miR-99a-5p)-Ct(hsa-miR-20a-5p) 3 2.39 -8.09 4.37 5 1.35 -4.54 0.71 10 2.84 -8.73 4.61 11 0.24 -3.19 -0.89 16 1.29 -1.88 -0.52 17 2.13 -7.46 5.84 18 1.75 -8.06 5.26 20 3.51 -7.68 5.38 23 1.95 -8.04 5.73 25 5.88 -2.54 1.18 30 1.36 -6.40 3.85 31 0.87 -0.29 -1.81 32 3.13 -8.04 6.35 37 3.50 -8.12 4.91 40 4.75 -8.23 4.75 43 -0.12 -6.04 1.07 44 1.87 -7.43 6.25 56 -0.48 -8.11 4.71 61 0.30 -7.79 3.77 62 2.35 -8.59 5.91 64 2.10 -3.40 1.26 65 3.01 -0.44 0.42 68 0.56 -7.73 2.95 70 0.78 -5.88 1.79 72 2.17 -6.15 3.97 80 1.27 -5.28 3.64 81 1.19 -7.82 5.90 89 2.97 -7.82 6.23 90 1.63 -7.43 6.74 93 0.83 -3.28 0.96 100 1.15 -4.62 2.24 108 1.68 -3.58 1.73 111 1.21 -7.76 4.21 112 0.30 -4.75 -0.37 113 1.29 -3.49 1.78 114 2.53 1.33 -4.08 116 2.58 -6.10 3.16 120 2.22 -6.48 4.05 122 0.92 -3.45 0.30 123 0.77 -7.38 6.12 29135 1.70 -3.80 1.75 48117 0.46 -3.27 0.22 54699 4.93 -0.70 -2.40

[0025] The ΔСt values ​​presented in Table 3 are used to construct logistic regression models (Figure 1, Table 4), where the dependent variable (response variable) is the morphology of the squamous epithelium of the cervix (0 - CIN I-; 1 - CIN II + ).

[0026] Logistic Function Formula 1

[0027] ,

[0028] where Ct(hsa-miR-30d-5p)-Ct(hsa-miR-20a-5p) is the base two logarithm value of the hsa-miR-20a-5p level relative to the hsa-miR-30d-5p level in a specific peripheral blood plasma sample of the patient from Table 3.

[0029] Logistic Function Formula 2

[0030] ,

[0031] where Ct(hsa-miR-10b-5p)-Ct(hsa-miR-99a-5p) is the base two logarithm value of the hsa-miR-99a-5p level relative to the hsa-miR-10b-5p level in a specific peripheral blood plasma sample of the patient from Table 3.

[0032] Logistic Function Formula 3

[0033] ,

[0034] where Ct(hsa-miR-99a-5p)-Ct(hsa-miR-20a-5p) is the base two logarithm value of the hsa-miR-20a-5p level relative to the hsa-miR-99a-5p level in a specific peripheral blood plasma sample of the patient from Table 3.

[0035] Table 4. Parameters of logistic regression models 1, 2, 3 shown in Fig. 1

[0036] Independent variables of the model Coefficients statistical significance value, p Cutoff threshold level Sensitivity, Se Specificity, Sp Wald criterion Model 1 (Intercept) -1,3308 0.03 0,4364 0,7368 0,7917 -2.1 Сt(hsa-miR-30d-5p)-Ct(hsa-miR-20a-5p) 0,5948 0.04 2 Model 2 (Intercept) -2,2083 0.02 0,5939 0,6842 0,9167 -2.3 Ct(hsa-miR-10b-5p)-Ct(hsa-miR-99a-5p) -0,3420 0.02 -2.3 Model 3 (Intercept) -1,2729 0.02 0,4942 0,7895 0,75 -2.2 Ct(hsa-miR-99a-5p)-Ct(hsa-miR-20a-5p) 0,3375 0.01 2.4

[0037] The results of the three logistic regression models are presented in Table 5.

[0038] Table 5. Comparison of the results of using logistic regression models 1, 2, 3, presented in Fig. 1 and characterized in Table 4, with the results of histological analysis (Table 1).

[0039]

[0040]

[0041] The diagnosis of CIN I- in the analysis of the patient's peripheral blood plasma by quantitative real-time PCR coupled with reverse transcription can be made when the value of logistic function 1 is less than 0.4364 using the difference between the values ​​of Ct(hsa-miR-30d-5p) and Ct(hsa-miR-20a-5p), when the value of logistic function 2 is less than 0.5939 using the difference between the values ​​of Ct(hsa-miR-10b-5p) and Ct(hsa-miR-99a-5p), when the value of logistic function 3 is less than 0.4942 using the difference between the values ​​of Ct(hsa-miR-99a-5p) and Ct(hsa-miR-20a-5p), in case of coincidence of the results of at least two logistic functions.

[0042] Diagnosis CIN II +when analyzing the patient's peripheral blood plasma using quantitative real-time PCR coupled with reverse transcription, it can be diagnosed with a logistic function value of 1 greater than 0.4364 using the difference between the values ​​of Ct(hsa-miR-30d-5p) and Ct(hsa-miR-20a-5p), with a logistic function value of 2 greater than 0.5939 using the difference between the values ​​of Ct(hsa-miR-10b-5p) and Ct(hsa-miR-99a-5p), with a logistic function value of 3 greater than 0.4942 using the difference between the values ​​of Ct(hsa-miR-99a-5p) and Ct(hsa-miR-20a-5p), in the event of a match between the results of at least two logistic functions.

[0043] Fig. 1. Logistic regression models for the diagnosis of high-grade cervical intraepithelial neoplasia and cervical cancer associated with human papillomavirus. Se is sensitivity, Sp is specificity.

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[18] C. Pileggi, D. Flotta, A. Bianco, C. G. A. Nobile, and M. Pavia, “Is HPV DNA testing specificity comparable to that of cytological testing in primary cervical cancer screening? Results of a meta-analysis of randomized controlled trials.,” Int J Cancer, vol. 135, no. 1, pp. 166-77, Jul. 2014, doi: 10.1002 / ijc.28640.

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[20] A. Shai, T. Brake, C. Somoza, and P. F. Lambert, “The human papillomavirus E6 oncogene dysregulates the cell cycle and contributes to cervical carcinogenesis through two independent activities.,” Cancer Res, vol. 67, no. 4, pp. 1626-35, Feb. 2007, doi: 10.1158 / 0008-5472.CAN-06-3344.

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[0071] The work was carried out with the financial support of the Ministry of Health of the Russian Federation within the framework of the state assignment 1024022000105-1 on the topic: “Development of a test system for early and minimally invasive diagnosis of precancerous and oncological diseases of the anogenital region associated with papillomavirus infection in women of different ages using liquid biopsy” (supervisor - V.N. Prilepskaya).

[0072] Appendix 1. Sequence List

[0073] --->

[0074] <ST26SequenceListing dtdVersion="V1_3" fileName="ST26SequenceListing2025136916.xml

[0075] " softwareName="WIPO Sequence" softwareVersion="2.3.0" productionDate="2026-08-

[0076] 26">

[0077] <applicationidentification>

[0078] <ipofficecode> RU< / ipofficecode>

[0079] <applicationnumbertext> 2025136916< / applicationnumbertext>

[0080] <filingdate> 2025-12-19< / filingdate>

[0081] < / applicationidentification>

[0082] <earliestpriorityapplicationidentification>

[0083] <ipofficecode> RU< / ipofficecode>

[0084] <applicationnumbertext> 2025136916< / applicationnumbertext>

[0085] <filingdate> 2025-12-19< / filingdate>

[0086] < / earliestpriorityapplicationidentification>

[0087] <ApplicantName languageCode="ru"> Federal State Budgetary Institution National Medical Research

[0088] Center of Obstetrics, Gynecology and Perinatology named after Academician V.I. Kulakov

[0089] Ministry of Health of the Russian Federation

[0090] <applicantnamelatin>FSBI National Medical Research Center for Obstetrics,

[0091] Gynecology and Perinatology named after Academician V.I.Kulakov Ministry of

[0092] Health of the Russian Federation< / applicantnamelatin>

[0093] <InventionTitle languageCode="ru"> Quantification of hsa-miR-20a-5p, hsa-miR-

[0094] 30d-5p, hsa-miR-99a-5p and hsa-miR-10b-5p in the peripheral blood plasma of women as

[0095] Minimally invasive method for diagnosing cervical intraepithelial neoplasia

[0096] High-grade cervical cancer and cervical cancer associated with human papillomavirus

[0097] human.

[0098] <sequencetotalquantity> 6< / sequencetotalquantity>

[0099] <SequenceData sequenceIDNumber="1">

[0100] <insdseq>

[0101] <INSDSeq_length> 23< / INSDSeq_length>

[0102] <INSDSeq_moltype> DNA< / INSDSeq_moltype>

[0103] <INSDSeq_division> PAT< / INSDSeq_division>

[0104] <INSDSeq_feature-table>

[0105] <insdfeature>

[0106] <INSDFeature_key>source< / INSDFeature_key>

[0107] <INSDFeature_location>1..23< / INSDFeature_location>

[0108] <INSDFeature_quals>

[0109] <insdqualifier>

[0110] <INSDQualifier_name>mol_type< / INSDQualifier_name>

[0111] <INSDQualifier_value>other DNA< / INSDQualifier_value>

[0112] < / insdqualifier>

[0113] <INSDQualifier id="q2">

[0114] <INSDQualifier_name>organism< / INSDQualifier_name>

[0115] <INSDQualifier_value>synthetic construct< / INSDQualifier_value>

[0116]

[0117] < / INSDFeature_quals>

[0118] < / insdfeature>

[0119] < / INSDSeq_feature-table>

[0120] <INSDSeq_sequence> taaagtgcttatagtgcaggtag< / INSDSeq_sequence>

[0121] < / insdseq>

[0122]

[0123] <SequenceData sequenceIDNumber="2">

[0124] <insdseq>

[0125] <INSDSeq_length> 22< / INSDSeq_length>

[0126] <INSDSeq_moltype> DNA< / INSDSeq_moltype>

[0127] <INSDSeq_division> PAT< / INSDSeq_division>

[0128] <INSDSeq_feature-table>

[0129] <insdfeature>

[0130] <INSDFeature_key>source< / INSDFeature_key>

[0131] <INSDFeature_location>1..22< / INSDFeature_location>

[0132] <INSDFeature_quals>

[0133] <insdqualifier>

[0134] <INSDQualifier_name>mol_type< / INSDQualifier_name>

[0135] <INSDQualifier_value>other DNA< / INSDQualifier_value>

[0136] < / insdqualifier>

[0137] <INSDQualifier id="q4">

[0138] <INSDQualifier_name>organism< / INSDQualifier_name>

[0139] <INSDQualifier_value>synthetic construct< / INSDQualifier_value>

[0140]

[0141] < / INSDFeature_quals>

[0142] < / insdfeature>

[0143] < / INSDSeq_feature-table>

[0144] <INSDSeq_sequence> tgtaaacatccccgactggaag< / INSDSeq_sequence>

[0145] < / insdseq>

[0146]

[0147] <SequenceData sequenceIDNumber="3">

[0148] <insdseq>

[0149] <INSDSeq_length>22< / INSDSeq_length>

[0150] <INSDSeq_moltype>DNA< / INSDSeq_moltype>

[0151] <INSDSeq_division>PAT< / INSDSeq_division>

[0152] <INSDSeq_feature-table>

[0153] <insdfeature>

[0154] <INSDFeature_key>source< / INSDFeature_key>

[0155] <INSDFeature_location>1..22< / INSDFeature_location>

[0156] <INSDFeature_quals>

[0157] <insdqualifier>

[0158] <INSDQualifier_name>mol_type< / INSDQualifier_name>

[0159] <INSDQualifier_value>other DNA< / INSDQualifier_value>

[0160] < / insdqualifier>

[0161] <INSDQualifier id="q6">

[0162] <INSDQualifier_name>organism< / INSDQualifier_name>

[0163] <INSDQualifier_value>synthetic construct< / INSDQualifier_value>

[0164]

[0165] < / INSDFeature_quals>

[0166] < / insdfeature>

[0167] < / INSDSeq_feature-table>

[0168] <INSDSeq_sequence>aacccgtagatccgatcttgtg< / INSDSeq_sequence>

[0169] < / insdseq>

[0170]

[0171] <SequenceData sequenceIDNumber="4">

[0172] <insdseq>

[0173] <INSDSeq_length> 23< / INSDSeq_length>

[0174] <INSDSeq_moltype> DNA< / INSDSeq_moltype>

[0175] <INSDSeq_division> PAT< / INSDSeq_division>

[0176] <INSDSeq_feature-table>

[0177] <insdfeature>

[0178] <INSDFeature_key>source< / INSDFeature_key>

[0179] <INSDFeature_location>1..23< / INSDFeature_location>

[0180] <INSDFeature_quals>

[0181] <insdqualifier>

[0182] <INSDQualifier_name>mol_type< / INSDQualifier_name>

[0183] <INSDQualifier_value>other DNA< / INSDQualifier_value>

[0184] < / insdqualifier>

[0185] <INSDQualifier id="q8">

[0186] <INSDQualifier_name>organism< / INSDQualifier_name>

[0187] <INSDQualifier_value>synthetic construct< / INSDQualifier_value>

[0188]

[0189] < / INSDFeature_quals>

[0190] < / insdfeature>

[0191] < / INSDSeq_feature-table>

[0192] <INSDSeq_sequence> taccctgtagaaccgaatttgtg< / INSDSeq_sequence>

[0193] < / insdseq>

[0194]

[0195] <SequenceData sequenceIDNumber="5">

[0196] <insdseq>

[0197] <INSDSeq_length>44< / INSDSeq_length>

[0198] <INSDSeq_moltype>DNA< / INSDSeq_moltype>

[0199] <INSDSeq_division>PAT< / INSDSeq_division>

[0200] <INSDSeq_feature-table>

[0201] <insdfeature>

[0202] <INSDFeature_key>source< / INSDFeature_key>

[0203] <INSDFeature_location>1..44< / INSDFeature_location>

[0204] <INSDFeature_quals>

[0205] <insdqualifier>

[0206] <INSDQualifier_name>mol_type< / INSDQualifier_name>

[0207] <INSDQualifier_value>other DNA< / INSDQualifier_value>

[0208] < / insdqualifier>

[0209] <INSDQualifier id="q10">

[0210] <INSDQualifier_name>organism< / INSDQualifier_name>

[0211] <INSDQualifier_value>synthetic construct< / INSDQualifier_value>

[0212]

[0213] < / INSDFeature_quals>

[0214] < / insdfeature>

[0215] < / INSDSeq_feature-table>

[0216] <INSDSeq_sequence>gcgagcacagaattaatacgactcactataggtttttttttttt< / INSDSeq_sequence>

[0217] < / insdseq>

[0218]

[0219] <SequenceData sequenceIDNumber="6">

[0220] <insdseq>

[0221] <INSDSeq_length>27< / INSDSeq_length>

[0222] <INSDSeq_moltype>DNA< / INSDSeq_moltype>

[0223] <INSDSeq_division>PAT< / INSDSeq_division>

[0224] <INSDSeq_feature-table>

[0225] <insdfeature>

[0226] <INSDFeature_key>source< / INSDFeature_key>

[0227] <INSDFeature_location>1..27< / INSDFeature_location>

[0228] <INSDFeature_quals>

[0229] <insdqualifier>

[0230] <INSDQualifier_name>mol_type< / INSDQualifier_name>

[0231] <INSDQualifier_value>other DNA< / INSDQualifier_value>

[0232] < / insdqualifier>

[0233] <INSDQualifier id="q12">

[0234] <INSDQualifier_name>organism< / INSDQualifier_name>

[0235] <INSDQualifier_value>synthetic construct< / INSDQualifier_value>

[0236]

[0237] < / INSDFeature_quals>

[0238] < / insdfeature>

[0239] < / INSDSeq_feature-table>

[0240] <INSDSeq_sequence>gcgagcacagaattaatacgactcact< / INSDSeq_sequence>

[0241] < / insdseq>

[0242]

[0243]

[0244] <---

Claims

Quantitative assessment of hsa-miR-20a-5p, hsa-miR-30d-5p, hsa-miR-99a-5p and hsa-miR-10b-5p in the plasma of peripheral blood of women as a minimally invasive method for diagnosing high-grade cervical intraepithelial neoplasia and cervical cancer associated with the human papillomavirus, characterized in that the content of hsa-miR-20a-5p, hsa-miR-30d-5p, hsa-miR-99a-5p and hsa-miR-10b-5p is determined in peripheral blood plasma samples by quantitative real-time PCR based on Ct values ​​for the subsequent use of the obtained values ​​in logistic regression models, and with a logistic function value of 1 less than 0.4364 using the difference in the Ct values ​​of hsa-miR-30d-5p and Ct hsa-miR-20a-5p, with a logistic function value of 2 less than 0.5939 using the difference in the Ct values ​​of hsa-miR-10b-5p and Ct hsa-miR-99a-5p, with a logistic function value of 3 less than 0.4942 using the difference in the Ct values ​​of hsa-miR-99a-5p and Ct hsa-miR-20a-5p,in case of coincidence of the results of at least two logistic functions, a conclusion is made about a high probability of diagnosis of CIN I– in the patient, and if the value of logistic function 1 is greater than 0.4364 using the difference in the values ​​of Ct hsa-miR-30d-5p and Ct hsa-miR-20a-5p, if the value of logistic function 2 is greater than 0.5939 using the difference in the values ​​of Ct hsa-miR-10b-5p and Ct hsa-miR-99a-5p, if the value of logistic function 3 is greater than 0.4942 using the difference in the values ​​of Ct hsa-miR-99a-5p and Ct hsa-miR-20a-5p, in case of coincidence of the results of at least two logistic functions, a conclusion is made about a high probability of diagnosis of CIN II+ in the patient.,

Images