Application of biomarker in preparation of product for diagnosing cervical intraepithelial neoplasia patient and detection kit

Abstract

The invention discloses an application of a biomarker in preparation of a product for diagnosing a patient suffering from cervical intraepithelial neoplasia and a detection kit for diagnosing the patient suffering from cervical intraepithelial neoplasia. The biomarker is selected from miR-21-5p, miR-143-3p, miR-375-3p, STAT2, TNFSF10 and PRKCD. According to the application, the specific combination of miRNA and mRNA is taken as the biomarker, and the accuracy of cervical cancer screening can be improved through a dual verification mechanism on a molecular level. Early screening and early diagnosis of cervical cancer can be facilitated, so that prognosis of a patient is improved, and the survival rate of the patient is increased.

Description

Technical Field

[0001] The present application relates to the fields of medicine and diagnostic technology, and in particular to an application of a biomarker in the preparation of a product for diagnosing patients with cervical intraepithelial neoplasia and a detection kit. Background Art

[0002] Cervical cancer is one of the most common malignant tumors in women worldwide, and early diagnosis is crucial to improving patient survival rates. The grading of cervical intraepithelial neoplasia mainly includes CIN1 (mild dysplasia), CIN2 (moderate dysplasia), and CIN3 (severe dysplasia / carcinoma in situ). CIN1 refers to abnormal changes in approximately one-third of the cell layer below the surface of the cervix; in most cases, immediate treatment is not required, but regular follow-up observation is required. CIN2 means that approximately one-third to two-thirds of the cell layer below the surface of the cervix is ​​affected; compared with CIN1, CIN2 has a higher chance of progressing to more serious lesions or cancer, but a certain proportion of cases can still resolve naturally. Subsequent treatment is required based on the patient's specific circumstances (such as age, fertility plan, etc.), and closer monitoring or treatment measures may be required. CIN3 covers severe cellular abnormalities ranging from nearly full-thickness to the entire epithelial layer, including the so-called "carcinoma in situ," that is, precancerous lesions are limited to the epithelial layer and do not penetrate the basement membrane. Without intervention, CIN3 carries a high risk of developing into invasive cervical cancer, and aggressive treatment is generally recommended to prevent further progression. In the field of cervical cancer screening, in vitro diagnostic technologies have become the cornerstone for identifying cervical precancerous lesions and early-stage cancers. These methods provide important evidence for clinical decision-making by analyzing the morphological characteristics of cervical cells or detecting biomarkers associated with cervical cancer development.

[0003] Currently, commonly used in vitro diagnostic methods include liquid-based cytology (TCT) and human papillomavirus (HPV) molecular testing. Each method has its own technical characteristics and clinical application value, and they are often used in combination to improve screening sensitivity and specificity. However, both methods have limitations. The accuracy of TCT depends largely on the quality of the specimen and the pathologist's interpretation skills. Improper specimen collection may lead to sampling errors and failure to obtain sufficient diseased cells. It is estimated that the false-negative rate of liquid-based cytology can reach 10%-30%, meaning that a significant number of cervical cancers or precancerous lesions may be missed. HPV is a known major risk factor for cervical cancer, with approximately 99% of cervical cancer cases linked to HPV infection. However, HPV infection is very common, and most HPV infections are transient and do not lead to cervical cancer. Therefore, the positive predictive value of HPV testing is low. Furthermore, the interpretation of HPV test results must be considered in the context of the patient's age, previous screening history, and other risk factors; HPV test results alone should not be relied upon for diagnosis. In addition to the traditional methods mentioned above, recent advances in molecular biology have led to the gradual application of novel in vitro diagnostic methods for cervical cancer screening, including DNA methylation and microRNA (microRNA) testing. DNA methylation testing is a novel molecular diagnostic technique that assesses cervical cancer risk by measuring the methylation status of specific gene promoter regions in cervical cell samples. While DNA methylation testing has shown promise in cervical cancer screening, it also has several limitations. First, based on current cervical cancer screening strategies and the World Health Organization's cervical cancer screening guidelines, the specific clinical application value of DNA methylation testing for cervical cancer remains under discussion. Furthermore, the potential high cost of DNA methylation testing may limit its application in resource-limited settings. MicroRNAs (miRNAs) are a class of small, non-coding RNA molecules involved in regulating gene expression and exhibit abnormal expression patterns in a variety of diseases, including cervical cancer. Measuring the levels of specific miRNAs in cervical cell samples can aid in the diagnosis of cervical cancer lesions. Although microRNA testing has shown promise in cervical cancer research, its clinical application remains at an investigational stage and faces several challenges. Although some miRNAs display expression patterns associated with cervical cancer, their specificity and sensitivity in clinical practice require further validation. Furthermore, miRNA expression may be affected by multiple factors, including treatment response and disease progression, complicating its application in dynamic monitoring. As key molecules in post-transcriptional gene regulation, miRNAs may exhibit specific abnormal expression patterns during cervical cancer progression. Changes in miRNAs often precede morphological changes, effectively compensating for the lack of sensitivity of traditional liquid-based cytology (TCT) for early-stage lesions.At the same time, messenger RNA (mRNA) level detection is helpful in tracking the regulatory effects of HPV oncogenic proteins on host genes, thereby distinguishing between transient high-risk HPV infection and persistent carcinogenic state, and reducing the false positive problem caused by high infection rates in HPV testing.

[0004] In summary, early screening and diagnosis of cervical cancer rely on in vitro diagnostic techniques. Traditional methods include TCT and HPV testing, but these methods lack specificity. Emerging technologies such as DNA methylation and miRNA testing can assess cancer risk or aid in diagnosis. However, the clinical value of DNA methylation testing remains to be verified and the cost is high. MicroRNA testing has been less studied, may lack specificity, and is susceptible to interference, requiring further optimization and validation. However, molecular diagnostic strategies based on the combined detection of miRNA and mRNA hold great promise. Summary of the Invention

[0005] The present application provides an application of a biomarker in the preparation of a product for diagnosing patients with cervical intraepithelial neoplasia, and a detection kit, which are used to solve the problem of poor accuracy of existing auxiliary diagnosis technologies for cervical cancer.

[0006] The present application discloses the use of a biomarker in the preparation of a product for diagnosing cervical intraepithelial neoplasia, wherein the biomarker comprises any one of the following combinations 1 to 13:

[0007]

[0008]

[0009] In one implementation of the present application, the biomarkers include any one group from combination 7 to combination 13; preferably, the biomarkers include any one group from combination 10 to combination 13; preferably, the biomarkers include combination 13.

[0010] In one implementation of the present application, the product includes a reagent for detecting the expression level of the biomarker.

[0011] In one implementation of the present application, the reagent for detecting the expression level of the biomarker includes primers and probes for detecting the biomarker and the internal reference, the sequence of the primer is selected from the sequences shown in SEQ ID NO.1 to SEQ ID NO.16, and the sequence of the probe is selected from the sequences shown in SEQ ID NO.17 to SEQ ID NO.24.

[0012] In one implementation of the present application, the product further includes an mRNA reverse transcription reagent and / or a miRNA reverse transcription reagent; preferably, the miRNA reverse transcription reagent includes a stem-loop primer sequence of at least one of miR-21-5p, miR-143-3p, miR-375-3p and an internal reference miRNA, and the stem-loop primer sequence is selected from the sequences shown in SEQ ID NO.25 to SEQ ID NO.28.

[0013] In one implementation of the present application, the sample types detected by the product include exfoliated cells of the cervix and / or vagina.

[0014] In one implementation of the present application, the biomarker is used to distinguish the following groups of people: healthy people, patients with mild atypical hyperplasia, patients with moderate atypical hyperplasia, and patients with severe atypical hyperplasia or above.

[0015] The present application also provides a detection kit for diagnosing patients with cervical intraepithelial neoplasia, wherein the detection kit comprises a reagent for detecting the expression level of a biomarker, wherein the biomarker comprises any one of the following combinations 1 to 13:

[0016]

[0017]

[0018] In one implementation of the present application, the reagent for detecting the expression level of the biomarker includes primers and probes for detecting the biomarker and the internal reference gene, the sequence of the primer is selected from the sequences shown in SEQ ID NO.1 to SEQ ID NO.16, and the sequence of the probe is selected from the sequences shown in SEQ ID NO.17 to SEQ ID NO.24.

[0019] In one implementation of the present application, the product further includes an mRNA reverse transcription reagent and / or a miRNA reverse transcription reagent; preferably, the miRNA reverse transcription reagent includes a stem-loop primer sequence of at least one of miR-21-5p, miR-143-3p, miR-375-3p and an internal reference miRNA, and the stem-loop primer sequence is selected from the sequences shown in SEQ ID NO.25 to SEQ ID NO.28.

[0020] The beneficial effects of this application are:

[0021] This application uses a specific combination of miRNA and mRNA as biomarkers, which can improve the accuracy of cervical cancer screening through a dual verification mechanism at the molecular level. It can facilitate early screening and diagnosis of cervical cancer, thereby improving patient prognosis and increasing patient survival rates. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 The graph shows the detection results of the ΔCt values ​​of STAT2 in various samples involved in the examples of the present application.

[0023] Figure 2 The graph shows the detection results of the ΔCt values ​​of TNFSF10 in various samples involved in the examples of the present application.

[0024] Figure 3 The figure shows the detection results of the ΔCt values ​​of PRKCD in various samples involved in the embodiments of the present application.

[0025] Figure 4 The figure shows the detection results of the ΔCt value of miR-21-5p in various samples involved in the examples of the present application.

[0026] Figure 5 The figure shows the detection results of the ΔCt value of miR-143-3p in various samples involved in the examples of the present application.

[0027] Figure 6 The figure shows the detection results of the ΔCt value of miR-375-3p in various samples involved in the examples of the present application.

[0028] Figure 7 A decision tree model diagram involved in an embodiment of the present application is shown. DETAILED DESCRIPTION

[0029] The present invention is further described in detail below by specific embodiments in conjunction with the accompanying drawings. In the following embodiments, many detailed descriptions are intended to enable the present application to be better understood. However, those skilled in the art can readily appreciate that some of the features may be omitted in different circumstances, or may be replaced by other materials or methods. In some cases, some operations related to the present application are not shown or described in the specification. This is to avoid the core of the present application being overwhelmed by excessive descriptions, and for those skilled in the art, it is not necessary to describe these related operations in detail. They can fully understand the related operations based on the description in the specification and the general technical knowledge in the art.

[0030] In addition, the features, operations, or characteristics described in the specification may be combined in any appropriate manner to form various embodiments. Furthermore, the steps or actions in the method description may be reordered or adjusted in a manner readily apparent to those skilled in the art. Therefore, the various sequences in the specification and drawings are provided solely for the purpose of clearly describing a particular embodiment and are not intended to be mandatory, unless otherwise specified.

[0031] The serial numbers assigned to the components in this document, such as "first", "second", etc., are only used to distinguish the objects described and do not have any order or technical meaning.

[0032] As a key molecule in post-transcriptional regulation of genes, miRNA may show specific abnormal expression patterns in the progression of cervical cancer, and the changes in miRNA are usually earlier than cell morphological changes, which can effectively make up for the lack of sensitivity of traditional liquid-based cytology (TCT) to early lesions. At the same time, combined with mRNA detection, it can track the regulatory effects of HPV oncogenic proteins on host genes, thereby distinguishing between transient infection of high-risk HPV and persistent carcinogenic state, and reducing the false positive problem caused by high infection rate of HPV detection. Therefore, the molecular diagnostic strategy based on the combined detection of miRNA and mRNA has excellent prospects. This technology achieves accurate identification of cervical precancerous lesions by analyzing the changes in the expression profiles of miRNA and its regulatory genes in diseased cells.

[0033] Compared with traditional methods, molecular diagnostic strategies based on the combined detection of miRNA and mRNA have two major advantages: (1) Through a dual verification mechanism at the molecular level, it can detect carcinogenesis caused by non-HPV factors (such as gene methylation or chronic inflammation), thereby increasing the sensitivity to non-HPV-related cervical cancer. (2) The detection of miRNA and mRNA can achieve numerical output based on quantitative PCR, avoiding the high dependence of cytological examination on the experience of pathologists. In the future, the integration and analysis of multi-omics data (miRNA, mRNA, protein) combined with artificial intelligence algorithms may become a key direction to break through the current screening bottleneck.

[0034] The present application provides an application of a biomarker in the preparation of a product for diagnosing patients with cervical intraepithelial neoplasia and a detection kit for diagnosing patients with cervical intraepithelial neoplasia.

[0035] In one embodiment, the biomarkers may include both mRNA and miRNA.

[0036] In a specific embodiment, the mRNA may include at least one of the following: STAT2, TNFSF10, and PRKCD. The miRNA may include at least one of the following: miR-21-5p, miR-143-3p, and miR-375-3p. It should be noted that this application, starting from the mRNA and miRNA omics levels, has screened a group of candidate biomarkers through extensive research, experiments, and studies. These candidate biomarkers are differentially expressed in patients with different grades of cervical intraepithelial neoplasia and can assist in the diagnosis of patients with cervical intraepithelial neoplasia.

[0037] In a specific embodiment, the biomarkers may include any one of the following combinations 1 to 13:

[0038] Combination 1 miR-143-3p,miR-375-3p,STAT2 Combination 2 miR-21-5p,miR-143-3p,miR-375-3p,STAT2 Combination 3 miR-21-5p, miR-143-3p, TNFSF10, PRKCD Combination 4 miR-21-5p,miR-143-3p,miR-375-3p,STAT2,TNFSF10 Combination 5 miR-21-5p, miR-143-3p, STAT2, TNFSF10, PRKCD Combination 6 miR-143-3p, miR-375-3p, STAT2, TNFSF10, PRKCD Combination 7 miR-143-3p, miR-375-3p, TNFSF10, PRKCD Combination 8 miR-21-5p, miR-375-3p, STAT2, TNFSF10, PRKCD Combination 9 miR-21-5p,miR-143-3p,miR-375-3p,STAT2,TNFSF10,PRKCD Combination 10 miR-143-3p, TNFSF10, and PRKCD Combination 11 miR-21-5p, miR-143-3p, STAT2, TNFSF10 Combination 12 miR-143-3p, STAT2, TNFSF10, PRKCD Combination 13 miR-21-5p, miR-375-3p, TNFSF10, PRKCD

[0039] It should be noted that, in a specific embodiment, combinations 1 to 13 are used to diagnose patients with cervical intraepithelial neoplasia of different grades with an accuracy of 90% or above.

[0040] In a specific embodiment, the biomarkers may include any one of combination 7 to combination 13:

[0041] Combination 7 miR-143-3p, miR-375-3p, TNFSF10, PRKCD Combination 8 miR-21-5p, miR-375-3p, STAT2, TNFSF10, PRKCD Combination 9 miR-21-5p,miR-143-3p,miR-375-3p,STAT2,TNFSF10,PRKCD Combination 10 miR-143-3p, TNFSF10, and PRKCD Combination 11 miR-21-5p, miR-143-3p, STAT2, TNFSF10 Combination 12 miR-143-3p, STAT2, TNFSF10, PRKCD Combination 13 miR-21-5p, miR-375-3p, TNFSF10, PRKCD

[0042] It should be noted that, in a specific embodiment, combinations 7 to 13 are used to diagnose patients with cervical intraepithelial neoplasia of different grades with an accuracy of 95% or above.

[0043] In a specific embodiment, the biomarkers may include any one of combination 10 to combination 13:

[0044]

[0045]

[0046] It should be noted that, in a specific embodiment, combinations 10 to 13 are used to diagnose patients with cervical intraepithelial neoplasia of different grades with an accuracy of more than 97%.

[0047] In one embodiment, the biomarkers may include combination 13. Specifically, the biomarkers include miR-21-5p, miR-375-3p, TNFSF10, and PRKCD. It should be noted that, in one embodiment, combination 13 was used to diagnose patients with different grades of cervical intraepithelial neoplasia with 100% accuracy.

[0048] In a specific embodiment, the product for diagnosing patients with cervical intraepithelial neoplasia (hereinafter referred to as the "product") or the detection kit for diagnosing patients with cervical intraepithelial neoplasia (hereinafter referred to as the "detection kit" or "kit") can be the same.

[0049] In a specific embodiment, the biomarker can be used to distinguish the following groups of people: healthy people and patients with cervical intraepithelial neoplasia.

[0050] In one embodiment, the biomarker can be used to distinguish the following groups of people: healthy people, patients with mild dysplasia (CIN1), patients with moderate dysplasia (CIN2), and patients with severe dysplasia or above (CIN3 and CIN3+).

[0051] In a specific embodiment, the product or kit may include reagents for detecting the expression levels of biomarkers, including miRNA levels and mRNA levels.

[0052] In one embodiment, the expression level of a biomarker can be detected by qPCR (quantitative polymerase chain reaction) (sometimes also referred to as real-time quantitative PCR). In one embodiment, the product or kit can include qPCR reagents. qPCR reagents can include DNA polymerase, dNTPs, primers, and probes.

[0053] In a specific embodiment, the reagents for detecting the expression level of a biomarker include primers and probes for detecting the biomarker and an internal control.

[0054] In a specific embodiment, the internal reference of miRNA may include U6 snRNA. The internal reference of mRNA may include GAPDH.

[0055] In a specific embodiment, the sequences of the primers and probes can be selected from the sequences shown in the following table:

[0056]

[0057]

[0058] In a specific embodiment, the expression level of miR-21-5p, miR-143-3p, miR-375-3p, STAT2, TNFSF10, or PRKCD can be expressed as the difference between the Ct values ​​of the corresponding target and the internal reference (i.e., ΔCt value). For miR-21-5p, miR-143-3p, or miR-375-3p, U6 snRNA can be used as an internal reference to calculate the ΔCt value. For STAT2, TNFSF10, or PRKCD, GAPDH can be used as an internal reference to calculate the ΔCt value.

[0059] In a specific embodiment, different samples can be distinguished based on the ΔCt values ​​of targets such as miR-21-5p, miR-143-3p, miR-375-3p, STAT2, TNFSF10, and PRKCD.

[0060] In one embodiment, a PRKCD ΔCt value greater than 2.8 is used to distinguish HC samples from other samples (herein, PRKCD ΔCt > 2.8 indicates healthy samples, and PRKCD ΔCt ≤ 2.8 indicates CIN1 samples, CIN2 samples, or CIN3 or higher samples; the same applies to other targets). A PRKCD ΔCt value greater than 0 is used to distinguish CIN3 or higher samples from other samples. A miR-21-5p ΔCt value greater than 5.5 is used to distinguish HC and CIN1 samples from CIN2 and CIN3 samples (herein, miR-21-5p ΔCt > 5.5 indicates healthy samples or CIN1 samples, and miR-21-5p ΔCt ≤ 5.5 indicates CIN2 samples or CIN3 or higher samples; the same applies to other targets). A TNFSF10 ΔCt value greater than 2.0 is used to distinguish CIN3 or higher samples from other samples. A miR-375-3p ΔCt value greater than 3.4 distinguishes HC and CIN1 samples from CIN2, CIN3, and higher samples. A miR-375-3p ΔCt value greater than 4.9 distinguishes CIN3 and higher samples from other samples. A miR-143-3p ΔCt value greater than 7.5 distinguishes HC and CIN1 samples from CIN2, CIN3, and higher samples. A miR-143-3p ΔCt value greater than 2.4 distinguishes CIN3 and higher samples from other samples. A STAT2 ΔCt value greater than 6.7 distinguishes HC samples from other samples. A STAT2 ΔCt value greater than 4.4 distinguishes HC and CIN1 samples from CIN2, CIN3, and higher samples. By combining the above targets and thresholds, it is possible to distinguish HC, CIN1, CIN2, and CIN3 and higher samples. The desired discrimination performance can be achieved by using different combinations and adjusting the order of target thresholds.

[0061] In a specific embodiment, the product or kit may further include an mRNA reverse transcription reagent and / or a miRNA reverse transcription reagent.

[0062] In a specific embodiment, the mRNA reverse transcription reagent may include mRNA reverse transcriptase, reverse transcription primer, dNTP and Mg 2+ The reverse transcription primer can be used to reverse transcribe at least one of STAT2, TNFSF10, PRKCD, and an internal control mRNA into cDNA. The internal control can include GAPDH.

[0063] In one embodiment, the miRNA reverse transcription reagent can be miRNA reverse transcriptase, stem-loop primer, dNTP and Mg 2+ .

[0064] In a specific embodiment, the stem-loop primer may include a stem-loop primer sequence of at least one of miR-21-5p, miR-143-3p, miR-375-3p, and an internal reference miRNA, and the stem-loop primer sequence may be selected from the sequences shown in the following table:

[0065]

[0066] In a specific embodiment, the product or kit may further include an RNA extraction kit.

[0067] In a specific embodiment, the sample type detected by the product or kit may include exfoliated cells from the cervix and / or vagina. For example, exfoliated cells can be sampled from a vaginal brush.

[0068] In one implementation of the present application, patients with cervical intraepithelial neoplasia can be diagnosed based on biomarkers. In other words, patients with cervical intraepithelial neoplasia can be diagnosed based on the test results of the biomarkers of the present application.

[0069] In one implementation of the present application, biomarkers can be used to assist in the diagnosis of patients with cervical intraepithelial neoplasia. In one implementation of the present application, patients with cervical intraepithelial neoplasia can be diagnosed based on the biomarkers combined with other detection technologies or clinical parameters. In other words, patients with cervical intraepithelial neoplasia can be diagnosed by combining the test results of the biomarkers of the present application with other detection technologies (such as TCT testing, HPV molecular testing, etc.) or clinical parameters (such as clinical symptoms, fertility status, etc.).

[0070] In one implementation of the present application, the biomarker can effectively distinguish between healthy subjects and patients with cervical intraepithelial neoplasia. In one implementation of the present application, the biomarker can effectively distinguish between healthy subjects, patients with mild dysplasia (CIN1), patients with moderate dysplasia (CIN2), and patients with severe dysplasia or above (CIN3 and CIN3+).

[0071] The present invention is further described in detail below by specific experimental process and experimental data examples. The following examples are only used to further illustrate the present invention and should not be construed as limiting the present invention. In the present embodiment, unless otherwise specified, the reagents and instruments used are all commercially available, and the experimental operations are all carried out in accordance with the product specifications and conventional experimental specifications.

[0072] 1. Sample preparation

[0073] The test sample is exfoliated cells collected from a vaginal brush and placed in sample preservation solution. After collection, the sample should be sent to the laboratory for processing at room temperature as soon as possible (no more than 4 hours). If immediate processing is not possible, the sample should be stored in a freezer at -20°C or -80°C. Avoid repeated freezing and thawing to avoid affecting cell structure and RNA integrity.

[0074] 2. RNA Extraction

[0075] Remove the collected cell sample from the preservation solution. If the sample has been frozen, slowly thaw it on ice. Ensure that all extraction instruments and reagents are RNase-free. RNA is extracted using the TRIzol method, which includes the following steps:

[0076] Cell lysis: Mix the cell sample with TRIzol solution in a 1:1 ratio, vortex thoroughly to mix, and let stand at room temperature for 5 minutes. (Purpose: To lyse cell membranes, release RNA, and inhibit RNase activity)

[0077] Phase separation: Add 1 / 5 volume of chloroform, vortex mix for 15 seconds, let stand at room temperature for 15 minutes, and centrifuge at 12000 rpm for 15 minutes. (Purpose: to form an aqueous phase layer, an organic phase layer, and an intermediate layer. RNA is mainly present in the aqueous phase)

[0078] RNA precipitation: Pipette the aqueous phase from the supernatant into a new centrifuge tube, add an equal volume of isopropanol, mix well, let stand at room temperature for 10 minutes, and centrifuge at 12000 rpm for 10 minutes. (RNA precipitates to the bottom of the tube)

[0079] Washing: Discard the supernatant, add 75% ethanol to wash the precipitate, gently vortex to mix, centrifuge at 12000 rpm for 5 minutes, and discard the supernatant. (To remove contaminants and salt ions in RNA)

[0080] Dissolve: Add 10 μL of RNase-free water to dissolve the RNA pellet, ensuring the final concentration is suitable for subsequent experiments. (The RNA is completely dissolved and ready for reverse transcription.)

[0081] 3. RNA quality testing:

[0082] Use a spectrophotometer to measure the absorbance of the RNA solution at 260 nm and 280 nm. Calculate the A260 / A280 ratio. A normal range of 1.8-2.0 indicates good RNA purity. If the ratio is not within the normal range, repurify the RNA and perform DNase I treatment. To remove residual DNA contamination, add an appropriate amount of DNase I and incubate at 37°C for 30 minutes to allow DNase I to completely degrade the DNA. Terminate the reaction by adding EDTA to inactivate DNase I.

[0083] IV. cDNA Synthesis

[0084] (1) mRNA reverse transcription: Prepare a reverse transcription mixture, including mRNA reverse transcriptase, reverse transcription primer, dNTP mixture, and an appropriate amount of template RNA. Set the reaction conditions according to the instructions of the reverse transcription kit (manufacturer: Novizan; catalog number: R433-01), which usually includes incubation at 50°C for 10 minutes. After the reaction is completed, inactivate the reverse transcriptase by incubation at 85°C for 5 seconds. The prepared cDNA can be directly used for subsequent qPCR detection.

[0085] (2) miRNA reverse transcription: Design specific stem-loop primers based on the target miRNA sequence. Prepare a miRNA reverse transcription mixture, including miRNA reverse transcriptase, stem-loop primers, dNTP mixture, and an appropriate amount of template RNA. Set the reaction conditions according to the instructions of the miRNA reverse transcription kit (manufacturer: Novizan; catalog number: MR101-01), which usually include incubation at 25°C for 5 minutes, incubation at 50°C for 15 minutes, and incubation at 85°C for 5 minutes. After the reaction is completed, the prepared cDNA can be directly used for subsequent qPCR detection.

[0086] The miRNA targets of this example include hsa-miR-21-5p, hsa-miR-143-3p, and hsa-miR-375-3p, as well as U6 snRNA as an internal reference. The corresponding stem-loop primer sequences are shown in the following table:

[0087]

[0088] 5. Fluorescence qPCR detection

[0089] Primer and probe design: The detection targets of this example are STAT2, TNFSF10, PRKCD, hsa-miR-21, hsa-miR-143-3p, and hsa-miR-375, and GAPDH and U6 snRNA are used as mRNA and miRNA internal references, respectively. The relevant primer and probe sequences are shown in the following table:

[0090]

[0091] qPCR reaction system: Prepare a qPCR mixture including 10 μL of qPCR premix (Manufacturer: Novezan; Catalog No.: Q513-02), 0.2 μL of TaqMan probe (10 μM), 0.5 μL of specific primers (10 μM), 0.5 μL of cDNA template, and 8.8 μL of water. The TaqMan probe and specific primer sequences are as shown in the table above. Perform a single-tube reaction for each target (one reaction well per target). Set up control groups, including a negative control (no template control) and a positive control (standard of known concentration). Add the qPCR mixture to the wells of the qPCR plate and prepare for fluorescent qPCR detection.

[0092] qPCR Amplification and Data Analysis: Amplification was performed using a fluorescence qPCR instrument according to the protocol in the table below. The amplification process was monitored by fluorescence signal, and a curve was plotted showing the relationship between fluorescence intensity and cycle number. This method uses GAPDH and U6 snRNA as internal standards for mRNA and miRNA, respectively, to calculate ΔCt, which represents the relative expression of the target mRNA and miRNA. Expression differences of the target mRNA and miRNA were assessed by comparing intergroup differences in Ct values ​​(the number of cycles required for fluorescence intensity to reach the set threshold).

[0093]

[0094] VI. Biomarker Performance Analysis

[0095] The above method was used to test 39 samples, of which the disease status of these 39 samples was confirmed by other methods (normal samples were confirmed by TCT and HPV testing, and CIN1-3 samples were confirmed by colposcopy and histopathology). The disease status results of these 39 samples confirmed by other methods were as follows: 10 normal (HC) samples, 10 CIN1 samples, 10 CIN2 samples, and 9 CIN3 or higher samples. GAPDH and U6 snRNA were used as internal standards for mRNA and miRNA, respectively, and the ΔCt values ​​of the six targets STAT2, TNFSF10, PRKCD, hsa-miR-21, hsa-miR-143-3p, and hsa-miR-375 were calculated. Figures 1 to 6 The ΔCt values ​​of STAT2, TNFSF10, PRKCD, hsa-miR-21, hsa-miR-143-3p and hsa-miR-375 in different types of samples are shown in the figure. The vertical axis is the ΔCt value, and the horizontal axis corresponds to different groups (HC sample group, CIN1 sample group, CIN2 sample group, CIN3 or above sample group). Figure 1 The graph shows the detection results of the ΔCt values ​​of STAT2 in various samples involved in the examples of this application, Figure 2The graph shows the detection results of the ΔCt values ​​of TNFSF10 in various samples involved in the examples of the present application, Figure 3 The figure shows the detection results of the ΔCt values ​​of PRKCD in various samples involved in the embodiments of the present application, Figure 4 The figure shows the detection results of the ΔCt values ​​of miR-21-5p in various samples involved in the examples of the present application, Figure 5 The figure shows the detection results of the ΔCt values ​​of miR-143-3p in various samples involved in the examples of this application, Figure 6 The figure shows the detection results of the ΔCt value of miR-375-3p in various samples involved in the examples of the present application.

[0096] To further enhance the usability of the judgment process, a decision tree model is used to develop detailed judgment criteria such as Figure 7 As shown, Figure 7 A decision tree model diagram involved in an embodiment of the present application is shown. In the decision tree model diagram, the results are obtained by conditional screening from top to bottom according to the direction of the arrow, and each box has 3 or 4 rows. If there are 4 rows, the first row is the judgment condition, the second row is the proportion of the number of samples contained in this step to the total (39 samples), the 4 numbers in the third row correspond to the proportion of the 4 categories of CIN1, CIN2, CIN3 or above and HC in the samples contained, and the fourth row is the name of the category with the highest proportion (the first one is displayed if the proportion is the same). The 3 rows of boxes all output the decision results at the end of the decision tree, so there is no judgment condition in the first row. When making a result judgment, if the judgment condition result is "yes", follow the arrow on the left to reach the next box, and if the result is "no", follow the arrow on the right to reach the next box until reaching the end of the decision tree. Here, the decision path and the instance are shown when passing through the decision path. In the actual application scenario, you only need to pay attention to the judgment condition and the classification of the end to complete the judgment.

[0097] The results were analyzed using a linear SVM (support vector machine) model. The linear SVM model uses a three-fold cross-validation method to calculate the accuracy that can be achieved for all possible combinations between targets. The results are shown in the following table. The three-fold cross-validation here means that the 39 samples are evenly divided into three groups (13 in each group), and the proportion of each type of sample in the group is kept basically the same as the proportion before grouping; then two of the groups are used as training groups to train the model, and the remaining group is used as a validation group to calculate the accuracy of the model, that is, the proportion of correctly classified samples to the 13 samples in the group; then the validation group is rotated in turn, and the training and validation are re-performed to calculate the accuracy. A total of three rounds of training and validation are performed to ensure that each group contributes to the accuracy result as a validation group; finally, the three accuracy results obtained in the three rounds are averaged to obtain the accuracy results in the following table. This multiple cross-validation method can not only avoid overfitting caused by using all data for training, but also ensure that the final accuracy result includes an evaluation of all data:

[0098]

[0099]

[0100]

[0101] As can be seen from the table above, the following 13 target combinations have an accuracy of over 90%, and the following 7 target combinations have an accuracy of approximately 95% or above. The optimal detection combination is miR-21-5p, miR-375-3p, TNFSF10, and PRKCD, which can completely correctly distinguish all samples in this example.

[0102] The above content is a further detailed description of the present application in conjunction with specific implementation methods, and the specific implementation of the present application cannot be considered to be limited to these descriptions. For ordinary technicians in the technical field to which the present application belongs, several simple deductions or substitutions can be made without departing from the concept of the present application.

Claims

1. Use of a biomarker in the preparation of a product for diagnosing cervical intraepithelial neoplasia, characterized in that: The biomarkers include any one of the following combinations 1 to 13:

2. The use according to claim 1, characterized in that The biomarkers include any one of combination 7 to combination 13; Preferably, the biomarkers include any one of combination 10 to combination 13; Preferably, the biomarkers include combination 13.

3. The use according to claim 1, wherein The product includes a reagent for detecting the expression level of the biomarker.

4. The use according to claim 3, characterized in that The reagent for detecting the expression level of the biomarker includes primers and probes for detecting the biomarker and the internal reference, the sequence of the primer is selected from the sequences shown in SEQ ID NO.1 to SEQ ID NO.16, and the sequence of the probe is selected from the sequences shown in SEQ ID NO.17 to SEQ ID NO.

24.

5. The use according to claim 3 or 4, characterized in that The product also includes mRNA reverse transcription reagents and / or miRNA reverse transcription reagents; Preferably, the miRNA reverse transcription reagent comprises a stem-loop primer sequence of at least one of miR-21-5p, miR-143-3p, miR-375-3p and an internal reference miRNA, and the stem-loop primer sequence is selected from the sequences shown in SEQ ID NO.25 to SEQ ID NO.

28.

6. The use according to claim 1, wherein The sample types tested by the product include exfoliated cells from the cervix and / or vagina.

7. The use according to any one of claims 1 to 6, characterized in that The biomarkers are used to distinguish the following groups of people: healthy people, patients with mild atypical hyperplasia, patients with moderate atypical hyperplasia, and patients with severe atypical hyperplasia or above.

8. A detection kit for diagnosing cervical intraepithelial neoplasia, the detection kit comprising a reagent for detecting the expression level of a biomarker, wherein the biomarker comprises any one of the following combinations 1 to 13:

9. The detection kit according to claim 8, characterized in that The reagent for detecting the expression level of the biomarker includes primers and probes for detecting the biomarker and the internal reference gene, the sequence of the primer is selected from the sequences shown in SEQ ID NO.1 to SEQ ID NO.16, and the sequence of the probe is selected from the sequences shown in SEQ ID NO.17 to SEQ ID NO.

24.

10. The detection kit according to claim 8, characterized in that The product also includes mRNA reverse transcription reagents and / or miRNA reverse transcription reagents; Preferably, the miRNA reverse transcription reagent comprises a stem-loop primer sequence of at least one of miR-21-5p, miR-143-3p, miR-375-3p and an internal reference miRNA, and the stem-loop primer sequence is selected from the sequences shown in SEQ ID NO.25 to SEQ ID NO.28.

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