Biomarker and diagnostic system for diagnosing cervical squamous carcinoma and cervical adenocarcinoma

Abstract

The invention discloses a biomarker for diagnosis of cervical squamous cell carcinoma and cervical adenocarcinoma and a diagnosis system. MRNA of SMC1B and CELSR3 genes in a cervical sample or a peripheral blood sample is combined with long-chain non-coding RNA FEZF1-AS1 and LINC01305 to serve as the biomarker for diagnosis of cervical cancer (especially cervical squamous cell carcinoma and cervical adenocarcinoma). And a diagnosis system capable of simultaneously covering the cervical squamous cell carcinoma and the cervical adenocarcinoma is established based on the biomarker. Compared with an incorporated single molecule, the diagnosis efficiency of the combined diagnosis model is remarkably enhanced in cervical tissues and serum, the quality and efficiency of cervical cancer screening are effectively improved, and a research basis is provided for non-invasive diagnosis of cervical squamous cell carcinoma and adenocarcinoma.

Description

Technical Field

[0001] The present invention belongs to the field of medicine, and in particular relates to biomarkers and diagnostic systems for diagnosing cervical squamous cell carcinoma and cervical adenocarcinoma. Background Art

[0002] Cervical cancer is the fourth most common malignant tumor in women worldwide and one of the female malignant tumors that endangers the health and safety of Chinese women. The two most common histological subtypes of cervical cancer are cervical squamous cell carcinoma (SCC) and cervical adenocarcinoma (ADC). Among them, cervical squamous cell carcinoma can currently be effectively screened and discovered early through human papillomavirus (HPV) detection combined with cervical liquid-based cytology screening; however, the proportion of HPV infection in people with cervical adenocarcinoma is not high, and the site of adenocarcinoma is more hidden, so it is difficult to be sampled in cytological screening and thus missed. The existing cervical cancer screening strategy in my country cannot fully cover the early diagnosis of cervical squamous cell carcinoma and adenocarcinoma. In addition, the existing cervical cancer screening is mainly based on cervical cell brushing, HPV testing or cervical tissue biopsy. This screening method requires professional gynecologists, and some patients have poor acceptance of this screening method, which reduces the screening rate. Therefore, it is of great clinical significance to develop a molecular-based detection method that can simultaneously cover cervical squamous cell carcinoma and adenocarcinoma and improve screening methods. Summary of the invention

[0003] In view of the problems existing in the prior art, the object of the present invention is to provide a biomarker and a diagnostic system for diagnosing cervical squamous cell carcinoma and cervical adenocarcinoma.

[0004] According to a first aspect of an embodiment of the present application, a biomarker for diagnosing cervical squamous cell carcinoma and cervical adenocarcinoma is provided, wherein the biomarker comprises mRNA of SMC1B and CELSR3 genes and long non-coding RNA FEZF1-AS1 and LINC01305.

[0005] Further:

[0006] SMC1B forward primer: 5′-AGCTGGCTGATTTAGATGCTG-3′;

[0007] SMC1B reverse primer: 5′-TCCAGTTGTTGAGTCATTGTAGC-3′;

[0008] CELSR3 forward primer: 5′-CCCTCCCTTCAGACTTTTTGATT-3′;

[0009] CELSR3 reverse primer: 5′-CTGTTGCCCATAATTCCCCAC-3′;

[0010] RNA FEZF1-AS1 forward primer: 5′-CACACCACAAGCAACCTTGACTC-3;

[0011] RNA FEZF1-AS1 reverse primer: 5′-CTTCTCCGCTCGGCATTCAGTAT-3′;

[0012] LINC01305 forward primer: 5′-GGTGTCACCTCCACGAAGTC-3′;

[0013] LINC01305 5'-TCGGTCCAGCTCAATTCCAC-3'.

[0014] According to a second aspect of an embodiment of the present application, a diagnostic system for cervical squamous cell carcinoma and cervical adenocarcinoma is provided, the system comprising:

[0015] Reagents and / or instruments for detecting the mRNA expression levels of SMC1B and CELSR3 in a sample;

[0016] Reagents and / or instruments for detecting the expression levels of LncRNA FEZF1-AS1 and LINC01305 in samples;

[0017] A diagnosis unit, the diagnosis unit includes a data acquisition module, a calculation module and a diagnosis module, the data acquisition module is used to obtain the relative expression levels of SMC1B and CELSR3 mRNA and the relative expression levels of LncRNA FEZF1-AS1 and LINC01305 in the sample; the calculation module is used to calculate the cervical squamous cell carcinoma prediction value and the cervical adenocarcinoma prediction value according to the relative expression levels of SMC1B and CELSR3 mRNA and the relative expression levels of LncRNA FEZF1-AS1 and LINC01305; the diagnosis module is used to compare the cervical squamous cell carcinoma prediction value with the cervical squamous cell carcinoma threshold value to determine whether the subject to be tested is or is a candidate for a cervical squamous cell carcinoma patient, and compare the cervical adenocarcinoma prediction value with the cervical adenocarcinoma threshold value to determine whether the subject to be tested is or is a candidate for a cervical adenocarcinoma patient.

[0018] Furthermore, the sample is a cervical tissue sample or a peripheral blood sample of the subject.

[0019] Further,

[0020] Predictive value of cervical squamous cell carcinoma

[0021] =-1.980+0.070×SMC1B+0.157×CELSR3+0.137×FEZF1-AS1+0.066×LINC01305;

[0022] Predictive value of cervical adenocarcinoma

[0023] =-3.495+0.158×SMC1B+0.114×CELSR3+0.176×FEZF1-AS1+0.138×LINC01305;

[0024] Wherein, SMC1B and CELSR3 represent the relative expression levels of SMC1B and CELSR3 gene mRNA in the sample, respectively; FEZF1-AS1 and LINC01305 represent the relative expression levels of non-coding RNA FEZF1-AS1 and LINC01305 in the sample, respectively.

[0025] Furthermore: the relative expression levels of SMC1B and CELSR3 mRNA and LncRNA FEZF1-AS1 and LINC01305 in the sample are reflected by the Ct value of SMC1B and CELSR3 mRNA and LncRNA FEZF1-AS and LINC01305 in the sample when performing Real-time PCR reaction.

[0026] Further, the cervical squamous cell carcinoma threshold is 0.743. When the cervical squamous cell carcinoma prediction value is ≤0.743, it is judged that the subject to be tested is not or is not a candidate for a cervical squamous cell carcinoma patient; when the cervical squamous cell carcinoma prediction value is greater than 0.743, it is judged that the subject to be tested is or is a candidate for a cervical squamous cell carcinoma patient;

[0027] The cervical adenocarcinoma threshold is 0.675. When the cervical adenocarcinoma prediction value is ≤0.675, it is judged that the subject is not or is not a candidate for a cervical adenocarcinoma patient; when the cervical adenocarcinoma prediction value is greater than 0.675, it is judged that the subject is or is a candidate for a cervical adenocarcinoma patient.

[0028] The technical solution provided by the embodiments of the present application may have the following beneficial effects:

[0029] It can be seen from the above embodiments that the present application combines the mRNA of the SMC1B and CELSR3 genes in cervical samples or peripheral blood samples with the long non-coding RNA FEZF1-AS1 and LINC01305 as biomarkers for the diagnosis of cervical cancer (especially cervical squamous cell carcinoma and cervical adenocarcinoma), and establishes a diagnostic system based on the biomarker that can simultaneously cover cervical squamous cell carcinoma and cervical adenocarcinoma, aiming to improve the quality and efficiency of cervical cancer screening. At the same time, the present invention further verifies the cervical cancer screening model based on cervical tissue in a validation set based on peripheral blood samples, and finds that the present invention can also be used for the diagnosis of cervical cancer based on peripheral blood samples. Related experiments confirm that the present invention provides a combined biomarker that can cover the two most important histological subtypes of cervical cancer. Compared with the single factor included, the diagnostic efficacy of the combined diagnostic model of the present invention is significantly enhanced.

[0030] It should be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0032] Figure 1 Relative expression levels of SMC1B and CELSR3 gene mRNA and non-coding RNA FEZF1-AS1 and LINC01305 in normal cervical tissue, cervical squamous cell carcinoma tissue and cervical adenocarcinoma tissue in the training group.

[0033] Figure 2 The mRNA of SMC1B and CELSR3 genes and the non-coding RNA FEZF1-AS1 and LINC01305 in the training group were used as single molecular markers, and their combined molecular diagnostic model was used in cervical squamous cell carcinoma ( Figure 2 .A) and cervical adenocarcinoma ( Figure 2 .B) ROC comparison among tissues.

[0034] Figure 3 The relative expression levels of SMC1B and CELSR3 gene mRNA and non-coding RNA FEZF1-AS1 and LINC01305 in normal cervical tissue, cervical squamous cell carcinoma tissue and cervical adenocarcinoma tissue in the validation group based on cervical tissue.

[0035] Figure 4 To explore the potential of SMC1B and CELSR3 gene mRNA and non-coding RNA FEZF1-AS1 and LINC01305 as single molecular markers and their combined molecular diagnostic model in cervical squamous cell carcinoma ( Figure 4 .A) and cervical adenocarcinoma ( Figure 4 .B) ROC comparison among tissues.

[0036] Figure 5 The relative expression levels of SMC1B and CELSR3 gene mRNA and non-coding RNA FEZF1-AS1 and LINC01305 in the serum of non-cervical disease control population and cervical cancer population in the peripheral blood sample validation group.

[0037] Figure 6 To compare the ROC of SMC1B and CELSR3 gene mRNA and non-coding RNA FEZF1-AS1 and LINC01305 as single molecular markers and their combined molecular diagnostic model in cervical cancer serum based on the peripheral blood sample validation group. DETAILED DESCRIPTION

[0038] Here, exemplary embodiments are described in detail, and examples thereof are shown in the accompanying drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application.

[0039] The terms used in this application are for the purpose of describing specific embodiments only and are not intended to limit this application. The singular forms of "a", "said" and "the" used in this application and the appended claims are also intended to include plural forms unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used herein refers to and includes any or all possible combinations of one or more associated listed items.

[0040] It should be understood that although the terms first, second, third, etc. may be used in the present application to describe various information, these information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other. For example, without departing from the scope of the present application, the first information may also be referred to as the second information, and similarly, the second information may also be referred to as the first information. Depending on the context, the word "if" as used herein may be interpreted as "at the time of" or "when" or "in response to determining".

[0041] Messenger RNA (mRNA) and long noncoding RNA (LncRNA) as molecular markers have shown clinical application value in the clinical diagnosis of various tumors. Moreover, LncRNA may form a regulatory network with mRNA, and the combined molecular model of the two has the potential to be superior to a single molecular model in diagnosis. Based on our whole transcriptome analysis data for normal cervix, cervical squamous cell carcinoma and cervical adenocarcinoma tissues, we jointly screened and verified mRNA and LncRNA with differential expression in cervical squamous cell carcinoma and cervical adenocarcinoma tissues, among which chromosome structure maintenance protein 1B (SMC1B), cadherin EGF-LAG seven channel G-type receptor 3 (CELSR3) and other mRNAs and FEZF1-AS1 (Fez family zinc finger protein 1 antisense RNA 1) and LINC01305 (long non-coding RNA01305) and other non-coding RNAs were significantly increased in cervical squamous cell carcinoma and adenocarcinoma compared with normal cervical tissues, indicating that these single molecules with significant expression differences in cervical squamous cell carcinoma and adenocarcinoma may have the potential to be used as molecular markers for the diagnosis of cervical squamous cell carcinoma and adenocarcinoma. On this basis, the present invention uses these single molecular markers screened out and uses statistical analysis to establish a joint model of mRNA and LncRNA for the establishment of molecular markers that can simultaneously cover the diagnosis of cervical squamous cell carcinoma and adenocarcinoma. At the same time, we further extended the combined model to peripheral blood samples for verification and found that the combined model can be used for cervical cancer screening based on peripheral blood samples, laying a good experimental foundation for its application as a non-invasive screening for cervical cancer. The establishment of this combined molecular model provides new ideas for new screening programs for cervical cancer and helps to improve the quality and efficiency of cervical cancer screening.

[0042] Unless otherwise specified, the experimental methods used in the following examples are conventional methods.

[0043] Unless otherwise specified, the materials and reagents used in the following examples can be obtained from commercial sources.

[0044] Example 1. Establishment and application of a model for combined detection of mRNA and lncRNA for diagnosis of cervical squamous cell carcinoma and adenocarcinoma.

[0045] 1. Study Population

[0046] A total of 396 subjects were included in the study of the present invention, of which 316 subjects were randomly divided into a training group and a validation group in a ratio of 8:2 for modeling and validation of the diagnostic model based on cervical tissue; and 80 subjects were used as the validation group for the constructed diagnostic model based on peripheral blood samples.

[0047] Cervical cancer tissue or peripheral blood samples were collected from patients with cervical cancer who met the following criteria: (1) pathologically confirmed cervical cancer (squamous cell carcinoma or adenocarcinoma); (2) no history of other specific malignant diseases; and (3) no chemotherapy, radiotherapy or other anti-tumor treatment before specimen collection.

[0048] The control group was selected from patients who had their uterus removed due to benign diseases during the same period; pathological confirmation showed that the cervical epithelial tissue was normal and no abnormal cells were found, and the control group had no history of malignant tumors.

[0049] This study followed the national institutional ethical policies and was approved by the Ethics Review Committee of the Obstetrics and Gynecology Hospital, Zhejiang University School of Medicine.

[0050] 2. Experimental Methods

[0051] 1. Determination of the relative expression levels of differentially expressed mRNA and LncRNA in normal cervical and tumor tissues and peripheral blood samples.

[0052] To ensure objectivity, laboratory staff were unaware of the information of the samples. Fresh cervical tissue (about 0.5 g per specimen) was collected from the operating room, and the specimen was immediately stored in liquid nitrogen after being rinsed with saline, and then transferred to a -80°C refrigerator for proper storage; fresh peripheral blood samples (about 3-5 mL per specimen) were collected from the outpatient clinic or operating room, placed on ice, centrifuged at 4°C, 3000 rpm for 10 minutes within half an hour, and the supernatant was separated and packaged (about 200 μL of supernatant per specimen was used for total RNA extraction) and transferred to a -80°C refrigerator in an RNase-free 1.5 mL EP tube for proper storage.

[0053] (1) Extraction and reverse transcription of total RNA from cervical tissue / peripheral blood samples

[0054] RNA was extracted from normal cervical tissue, cervical tumor tissue, and peripheral blood samples using Invitrogen's Trizol reagent, catalog number 15596026. For cervical tissue samples, normal cervical tissue or cervical tumor tissue was fully ground using a pre-cooled tissue grinder, and 1 mL of Trizol solution was added to each tube of sample, which was fully blown and allowed to stand at room temperature for 15 minutes, then transferred to an RNase-free EP tube and continued on ice; for peripheral blood samples, the frozen samples (about 200 μL each) were directly dissolved on ice, and 1 mL of Trizol solution was added to each tube of sample, which was fully blown and allowed to stand at room temperature for 15 minutes.

[0055] Then add 200μL chloroform to each tube sample, cover the tube tightly and shake vigorously for 15 seconds, then let it stand for 15 minutes; after centrifugation at 14000rpm for 15 minutes, the liquid is separated into 3 layers, carefully aspirate the supernatant and transfer it to a new EP tube; add 500μL isopropanol to the newly taken supernatant and shake carefully, let it stand for 15 minutes; after centrifugation at 12000rpm for 15 minutes, white RNA precipitate can be seen at the bottom of the tube, discard the supernatant; wash the RNA precipitate with 75% ethanol 1-2 times, open the lid for 5-10 minutes to dry the precipitate; dissolve the RNA precipitate with an appropriate amount of DEPC water and measure the RNA concentration. If the test is not performed immediately, the RNA solution needs to be stored in a -80℃ refrigerator.

[0056] RNA was reverse transcribed using PrimeScript from Takara TM RT kit, catalog number RR047A.

[0057] ① Prepare the reaction mixture on ice according to the system in Table 1 below to remove gDNA (the present invention takes 20 μL of qPCR system as an example)

[0058] Table 1 Reaction system for removing gDNA

[0059]

[0060]

[0061] Set up the following program in the PCR instrument to complete the reaction: 42℃2min→4℃forever

[0062] ②Reverse transcription reaction

[0063] The reaction system for each 10 μL Master Mix is ​​shown in Table 2 below:

[0064] Table 2 Master Mix reaction system

[0065]

[0066] After mixing 10 μL of Master Mix with 10 μL of the reaction solution in ①, perform the following reaction in a PCR instrument:

[0067] 37℃15min→85℃5s→4℃forever

[0068] Normal cervical tissue, cervical tumor tissue, and peripheral blood samples can be stored at -80℃ for a long time after RNA extraction and reverse transcription into cDNA.

[0069] (2) Quantitative Real-time PCR detection

[0070] ① Design and ordering of primer sequences

[0071] The designed primer sequences of SMC1B, CELSR3, FEZF1-AS1 and LINC01305 are shown in Table 3 . The above primers were synthesized by Shanghai Jima Biotechnology Co., Ltd.

[0072] Table 3. Primer sequences in qRT-PCR reactions

[0073]

[0074]

[0075] ②qRT-PCR reaction

[0076] The configuration of the qRT-PCR reaction system uses TaKaRa TB Green's qRT-PCR kit, catalog number: RR430A. In this study, a 384-well plate system qRT-PCR reaction system was used, and each well was 10 μL of the reaction system configuration as follows:

[0077] Table 4 qRT-PCR reaction system

[0078]

[0079] Fluorescence quantitative PCR reaction was carried out on ABI / ViiA7 fluorescence quantitative PCR instrument, and the reaction conditions were as follows:

[0080] 95℃10sec

[0081] [95℃×5sec+60℃×30sec]×40 cycles

[0082] 4℃forever

[0083] After the PCR reaction, use QuantStudio TM Real TimePCR software V1.3 was used for analysis to confirm that the primer melting curve and amplification efficiency were available and output the detection Ct value of each sample.

[0084] For normal cervical epithelial tissue, cervical cancer tissue, peripheral blood of normal cervical control group and peripheral blood of cervical cancer group, 18s rRNA was used as internal reference. -ΔΔCT Methods The relative expression of the target gene was calculated.

[0085] 3. Statistical Analysis of Experimental Data

[0086] Data statistical analysis ANOVA test or t test was used to perform statistical analysis on the biomarker levels of the normal control group and patients with cervical squamous cell carcinoma and adenocarcinoma. In order to establish a prediction model that can cover both cervical squamous cell carcinoma and adenocarcinoma, the forward logistic regression method was used to screen variables. The area under the receiver operating characteristic curve (ROC) with a 95% confidence interval (CI) (AUC) was used to compare the diagnostic performance of a single marker and the model. In addition, the Youden index of the training group was used to determine the cut-off value of the prediction model. For the validation based on peripheral blood samples, the data statistical analysis used a t test to perform statistical analysis on the biomarker levels of the normal control group and patients with cervical cancer. In the present invention, the diagnostic performance of the model in different subgroups was analyzed by comparing the AUC of the ROC curve. Other descriptive statistics, such as sensitivity, specificity, standard error (SEM), were statistically analyzed using software such as SPSS26.0, GraphPadPrism 9.0, and Microsoft Excel. The P value is two-tailed, and a P value less than 0.05 indicates that the difference is statistically significant.

[0087] IV. Experimental Results

[0088] In the present study, the 316 cervical tissue populations collected were randomly divided into a training group and a validation group at a ratio of 8:2. The training group had 253 candidates (45 normal cervical control groups, 126 cervical squamous cell carcinoma patients and 82 cervical adenocarcinoma patients), which were used to verify the previous sequencing results, and preliminarily screened out molecules with significant expression differences in cervical squamous cell carcinoma and adenocarcinoma, and established a joint diagnosis prediction model of mRNA-LncRNA through statistical analysis; the validation group had 63 candidates (11 normal cervical control groups; 32 cervical squamous cell carcinoma patients and 20 cervical adenocarcinoma patients), which were used to further verify the joint diagnosis prediction model of mRNA-LncRNA constructed in the training group. The ratio of candidates in the training group to the validation group was about 8:2. In the two groups, the ratio of healthy control group to cervical cancer patients was about 1:4.5; among cervical cancer patients, squamous cell carcinoma and adenocarcinoma patients accounted for 60% and 40%, respectively. The grouping of cervical tissue samples included in this research invention is shown in Table 5.

[0089] Table 5. Composition of cervical tissue samples included in each group in this study

[0090]

[0091] In order to further expand the application of this combined diagnostic prediction model, a validation group based on peripheral blood sample testing was set up, and another 80 subjects (30 normal cervical control group and 50 cervical cancer patients) were included to verify the diagnostic efficacy of this combined model in diagnosing cervical cancer in serum.

[0092] First, in the training group, the mRNA and LncRNA molecules with significant differential expression screened out in the previous whole transcriptome sequencing results were verified. Figure 1 The expression levels of SMC1B and CELSR3 mRNA and long non-coding RNA FEZF1-AS1 and LINC01305 in normal cervical tissue, cervical squamous cell carcinoma and cervical adenocarcinoma in the training group are shown. In the training group, the expression levels of SMC1B and CELSR3 mRNA, LncRNA FEZF1-AS1 and LINC01305 in cervical tissue of patients with cervical squamous cell carcinoma and cervical adenocarcinoma were significantly higher than those in the normal control group. This verifies the reliability of the previous whole transcriptome sequencing results ( Figure 1 ).

[0093] For the molecules with significant expression differences obtained in the preliminary validation, ROC analysis was further used to evaluate the expression levels of these single biomarkers in the training group in patients with cervical squamous cell carcinoma, adenocarcinoma and the normal control group, and the variables were screened by forward logistic regression method. According to the results of logistic regression analysis, a prediction model for cervical squamous cell carcinoma and cervical adenocarcinoma based on cervical tissue was established (Table 6). Finally, mRNA SMC1B and CELSR3 as well as LncRNA FEZF1-AS1 and LINC01305 were included to obtain the joint diagnosis prediction model as follows:

[0094] Predictive value of cervical squamous cell carcinoma

[0095] =-1.980+0.070×SMC1B+0.157×CELSR3+0.137×FEZF1-AS1+0.066×LINC01305

[0096] Predictive value of cervical adenocarcinoma

[0097] =-3.495+0.158×SMC1B+0.114×CELSR3+0.176×FEZF1-AS1+0.138×LINC01305

[0098] Table 6. Multivariate regression analysis of the diagnostic model for cervical squamous cell carcinoma in the training group

[0099] Study Variables Beta coefficient Standard error Wald value Degrees of Freedom P-value SMC1B 0.070 0.035 4.015 1 0.045 CELSR3 0.157 0.079 3.890 1 0.049 FEZF1-AS1 0.137 0.068 4.109 1 0.043 LINC01305 0.066 0.032 4.121 1 0.042 Constant term -1.980 0.443 19.967 1 0.000

[0100] Table 7. Multivariate regression analysis of the diagnostic model for cervical adenocarcinoma in the training group

[0101] Study Variables Beta coefficient Standard error Wald value Degrees of Freedom P-value SMC1B 0.158 0.061 6.793 1 0.009 CELSR3 0.114 0.053 4.672 1 0.031 FEZF1-AS1 0.176 0.087 4.044 1 0.044 LINC01305 0.138 0.062 5.023 1 0.025 Constant term -3.495 0.754 21.464 1 0.000

[0102] In the two formulas, SMC1B and CELSR3 represent the relative expression levels of SMC1B and CELSR3 gene mRNA in the cervical tissue of the subject to be tested (the substitute value is the 2 calculated by the real time PCR reaction of SMC1B and CELSR3 genes in the cervical tissue of the subject to be tested). -△△Ct FEZF1-AS1 and LINC01305 represent the relative expression levels of non-coding RNA FEZF1-AS1 and LINC01305 in the cervical tissue of the subjects (the input value is the 2 calculated by the real time PCR reaction of FEZF1-AS1 and LINC01305 genes in the cervical tissue of the subjects). -△△Ct value);

[0103] Plot the ROC curve with the predicted values ​​in the training set ( Figure 2 ), and the cut-off values ​​for diagnosing squamous cell carcinoma and adenocarcinoma were determined according to the Youden index value. The cut-off value corresponding to the Youden index of cervical squamous cell carcinoma of 0.821 is 0.743, that is, when the prediction value of the diagnostic model is ≤0.743, it is considered that the subject is not or is not a candidate for cervical squamous cell carcinoma (the possibility that the subject is a cervical squamous cell carcinoma patient is low); when the model prediction value is >0.743, it is considered that the subject is or is a candidate for cervical squamous cell carcinoma (the possibility that the subject is a cervical squamous cell carcinoma patient is high); the cut-off value corresponding to the Youden index of cervical adenocarcinoma of 0.824 is 0.675, that is, when the prediction value of the diagnostic model is ≤0.675, it is considered that the subject is not or is not a candidate for cervical adenocarcinoma (the possibility that the subject is a cervical adenocarcinoma patient is low); when the model prediction value is >0.675, it is considered that the subject is or is a candidate for cervical adenocarcinoma (the possibility that the subject is a cervical adenocarcinoma patient is high);

[0104] As shown in Table 8, among the above biomarkers, the area under the curve (AUC) of SMC1B for diagnosing cervical squamous cell carcinoma in the training group was 0.875 (95% CI, 0.824-0.926); the area under the curve (AUC) of CELSR3 for diagnosing cervical squamous cell carcinoma in the training group was 0.915 (95% CI, 0.874-0.956); the area under the curve (AUC) of FEZF1-AS1 for diagnosing cervical squamous cell carcinoma in the training group was 0.887 (95% CI, 0. 839-0.935); the area under the curve (AUC) of LINC01305 for diagnosing cervical squamous cell carcinoma in the training group was 0.818 (95% CI, 0.752-0.884); however, when the above markers were used as a combined diagnostic prediction model, the area under the curve (AUC) for diagnosing cervical squamous cell carcinoma in the training group was 0.952 (95% CI, 0.923-0.981). Compared with any single biomarker, the combined diagnostic prediction model has improved the diagnostic value of cervical squamous cell carcinoma.

[0105] As shown in Table 9, in the diagnosis of cervical adenocarcinoma, the area under the curve (AUC) of SMC1B in the training group was 0.842 (95% CI, 0.774-0.910); the area under the curve (AUC) of CELSR3 in the training group was 0.904 (95% CI, 0.853-0.954); the area under the curve (AUC) of FEZF1-AS1 in the training group was 0.930 (95% CI, 0.887-0.9 73); the area under the curve (AUC) of LINC01305 in the training group was 0.865 (95% CI, 0.801-0.929); when the above markers were used as a combined diagnostic model, the area under the curve (AUC) for diagnosing cervical adenocarcinoma in the training group was 0.975 (95% CI, 0.954-0.996). Compared with any single biomarker, the combined diagnostic prediction model has improved the diagnostic value of cervical adenocarcinoma.

[0106] Table 8. Comparison of the effectiveness of the combined diagnosis model and a single indicator in diagnosing cervical squamous cell carcinoma in the training group

[0107] Study Variables AUC P-value 95% Confidence Interval SMC1B 0.875 <0.0001 0.824-0.926 CELSR3 0.915 <0.0001 0.874-0.956 FEZF1-AS1 0.887 <0.0001 0.839-0.935 LINC01305 0.818 <0.0001 0.752-0.884 Joint Model 0.952 <0.0001 0.923-0.981

[0108] Table 9. Comparison of the effectiveness of the combined diagnosis model and a single indicator in diagnosing cervical adenocarcinoma in the training group

[0109] Study Variables AUC P-value 95% Confidence Interval SMC1B 0.842 <0.0001 0.774-0.910 CELSR3 0.904 <0.0001 0.853-0.954 FEZF1-AS1 0.930 <0.0001 0.887-0.973 LINC01305 0.865 <0.0001 0.801-0.929 Joint Model 0.975 <0.0001 0.954-0.996

[0110] The effectiveness of the combined diagnostic prediction model established in the training group was further evaluated in the validation group. Figure 3As shown in the results, the expression levels of SMC1B and CELSR3 mRNA, LncRNA FEZF1-AS1 and LINC01305 in cervical squamous cell carcinoma and cervical adenocarcinoma tissues in the validation group were significantly higher than those in normal cervical tissues (all P < 0.05).

[0111] ROC curve analysis was further used to evaluate the diagnostic efficacy of the combined model for cervical squamous cell carcinoma and adenocarcinoma in the validation group based on cervical tissue. As shown in Table 10, the area under the curve (AUC) of SMC1B in the validation group for diagnosing cervical squamous cell carcinoma was 0.918 (95% CI, 0.836-0.999); the area under the curve (AUC) of CELSR3 in the validation group for diagnosing cervical squamous cell carcinoma was 0.849 (95% CI, 0.724-0.975); the area under the curve (AUC) of FEZF1-AS1 in the validation group for diagnosing cervical squamous cell carcinoma was 0.892 (95% CI, 0.798 -0.986); the area under the curve (AUC) of LINC01305 for diagnosing cervical squamous cell carcinoma in the validation group was 0.892 (95% CI, 0.787-0.997); however, when the above markers were used as a combined diagnostic model, the area under the curve (AUC) for diagnosing cervical squamous cell carcinoma in the validation group was 0.966 (95% CI, 0.915-1.000). Compared with any single biomarker, the combined detection model has improved the diagnostic value of cervical squamous cell carcinoma ( Figure 4 A).

[0112] As shown in Table 11, in the diagnosis of cervical adenocarcinoma, the area under the curve (AUC) of SMC1B in the validation group was 0.891 (95% CI, 0.759-1.000); the area under the curve (AUC) of CELSR3 in the validation group was 0.864 (95% CI, 0.735-0.992); the area under the curve (AUC) of FEZF1-AS1 in the validation group was 0.905 (95% CI, 0.801-1. 000); the area under the curve (AUC) of LINC01305 in the validation group was 0.809 (95% CI, 0.652-0.967); when the above markers were used as a combined diagnostic model, the area under the curve (AUC) for diagnosing cervical adenocarcinoma in the validation group was 0.964 (95% CI, 0.898-1.000). Compared with any single biomarker, the combined diagnostic prediction model has improved the diagnostic value of cervical adenocarcinoma ( Figure 4 B).

[0113] Table 10. Comparison of the efficacy of the combined diagnostic model and a single indicator in diagnosing cervical squamous cell carcinoma in the cervical tissue validation group

[0114]

[0115]

[0116] Table 11. Comparison of the efficacy of the combined diagnostic model and a single indicator in diagnosing cervical adenocarcinoma in the cervical tissue validation group

[0117] Study Variables AUC P-value 95% Confidence Interval SMC1B 0.891 0.0004 0.759-1.000 CELSR3 0.864 0.0010 0.735-0.992 FEZF1-AS1 0.905 0.0002 0.801-1.000 LINC01305 0.809 0.0050 0.652-0.967 Joint Model 0.964 <0.0001 0.898-1.000

[0118] In the training group, the cut-off value of 0.743 for the diagnosis of cervical squamous cell carcinoma was determined by selecting the Youden index of 0.821. Based on this cut-off value, the sensitivity and specificity of the combined diagnosis model for the diagnosis of cervical squamous cell carcinoma in the training group were 86.5% and 95.6%, respectively. In the validation group based on cervical tissue, the sensitivity and specificity of the combined diagnosis model for the diagnosis of cervical squamous cell carcinoma can reach 93.75% and 100%. The cut-off value of 0.675 for the diagnosis of cervical adenocarcinoma was determined by selecting the Youden index of 0.824. Based on this cut-off value, the sensitivity and specificity of the combined diagnosis model for the diagnosis of cervical adenocarcinoma in the training group were 89.0% and 93.3%, respectively. In the validation group based on cervical tissue, the sensitivity and specificity of the combined diagnosis model for the diagnosis of cervical adenocarcinoma can reach 90.0% and 100%. Therefore, the combined diagnosis model has a high diagnostic efficacy in the diagnosis of cervical squamous cell carcinoma and adenocarcinoma.

[0119] To further expand the application of this combined diagnostic prediction model, the diagnostic efficacy of this combined model for cervical cancer was verified in another validation group based on peripheral blood sample testing.

[0120] like Figure 5 As shown in the figure, the expression levels of SMC1B and CELSR3 mRNA, LncRNA FEZF1-AS1 and LINC01305 in the peripheral blood samples of cervical cancer patients were significantly higher than those in the serum of normal controls (all P<0.05).

[0121] ROC curve analysis was further used to evaluate the diagnostic efficacy of the combined model for cervical cancer in the validation group based on peripheral blood samples. As shown in Table 8, the area under the curve (AUC) of SMC1B for diagnosing cervical cancer in the validation group based on peripheral blood samples was 0.899 (95% CI, 0.832-0.966); the area under the curve (AUC) of CELSR3 for diagnosing cervical cancer in the validation group based on peripheral blood samples was 0.840 (95% CI, 0.754-0.926); the area under the curve (AUC) of FEZF1-AS1 for diagnosing cervical cancer in the validation group based on peripheral blood samples was 0.798 (95% CI, 0.704 -0.892); the area under the curve (AUC) of LINC01305 for diagnosing cervical cancer in the validation group based on peripheral blood samples was 0.891 (95% CI, 0.820-0.962); however, when the above markers were used as a joint diagnostic prediction model, the area under the curve (AUC) for diagnosing cervical cancer in the validation group based on peripheral blood samples was 0.945 (95% CI, 0.899-0.992). Compared with any single biomarker, the diagnostic value of the combined detection model for cervical cancer in serum was also improved.

[0122] Table 8. Comparison of the efficacy of the combined diagnostic model and a single indicator in diagnosing cervical cancer in the peripheral blood sample validation group

[0123] Study Variables AUC P-value 95% Confidence Interval SMC1B 0.899 <0.0001 0.832-0.966 CELSR3 0.840 <0.0001 0.754-0.926 FEZF1-AS1 0.798 <0.0001 0.704-0.892 LINC01305 0.891 <0.0001 0.820-0.962 Joint Model 0.945 <0.0001 0.899-0.992

[0124] In the validation group based on peripheral blood samples, the cut-off value of 0.614 for cervical cancer diagnosis was determined by selecting the Youden index of 0.807. Based on this cut-off value, the sensitivity of the combined diagnosis model for cervical cancer diagnosis in the validation group based on peripheral blood samples was 84.0%, and its specificity was 96.7%. The above experiments show that the combined diagnosis model also has the potential to diagnose cervical cancer in peripheral blood samples and has a high diagnostic efficacy.

[0125] It should be noted that, in each figure, SMC1B represents the chromosome structure maintenance protein 1B gene; CELSR3 represents the cadherin EGF-LAG seven-channel G-type receptor 3 gene; FEZF1-AS1 represents the non-coding RNA FEZ family zinc finger protein 1 antisense RNA 1; LINC01305 represents long non-coding RNA01305; and the combined model is a combined diagnostic prediction model composed of the above markers.

[0126] V. Conclusion

[0127] The research of the present invention confirms that, compared with a single detection index, the constructed mRNA-LncRNA joint diagnosis prediction model can significantly improve the diagnostic efficacy of cervical squamous cell carcinoma and adenocarcinoma. This joint diagnosis model can be used not only for the diagnosis of cervical cancer based on tissue samples, but also for the diagnosis of cervical cancer based on peripheral blood samples, and has the potential value of non-invasive screening for cervical cancer. Therefore, the screening quality and efficiency of cervical cancer can be improved.

[0128] Those skilled in the art will readily appreciate other embodiments of the present application after considering the specification and practicing the contents disclosed herein. The present application is intended to cover any variations, uses or adaptations of the present application, which follow the general principles of the present application and include common knowledge or customary technical means in the art that are not disclosed in the present application.

Claims

1. A biomarker for the diagnosis of cervical squamous cell carcinoma and cervical adenocarcinoma, characterized in that: The biomarkers included mRNA of SMC1B and CELSR3 genes and long noncoding RNAs FEZF1-AS1 and LINC01305.

2. The biomarker according to claim 1, characterized in that: SMC1B forward primer: 5′-AGCTGGCTGATTTAGATGCTG-3′; SMC1B reverse primer: 5′-TCCAGTTGTTGAGTCATTGTAGC-3′; CELSR3 forward primer: 5′-CCCTCCCTTCAGACTTTTTGATT-3′; CELSR3 reverse primer: 5′-CTGTTGCCCATAATTCCCCAC-3′; RNA FEZF1-AS1 forward primer: 5′-CACACCACAAGCAACCTTGACTC-3; RNA FEZF1-AS1 reverse primer: 5′-CTTCTCCGCTCGGCATTCAGTAT-3′; LINC01305 forward primer: 5′-GGTGTCACCTCCACGAAGTC-3′; LINC01305 5'-TCGGTCCAGCTCAATTCCAC-3'.

3. A diagnostic system for cervical squamous cell carcinoma and cervical adenocarcinoma, characterized in that: The system includes: Reagents and / or instruments for detecting the mRNA expression levels of SMC1B and CELSR3 in a sample; Reagents and / or instruments for detecting the expression levels of LncRNA FEZF1-AS1 and LINC01305 in samples; A diagnosis unit, the diagnosis unit includes a data acquisition module, a calculation module and a diagnosis module, the data acquisition module is used to obtain the relative expression levels of SMC1B and CELSR3 mRNA and the relative expression levels of LncRNA FEZF1-AS1 and LINC01305 in the sample; the calculation module is used to calculate the cervical squamous cell carcinoma prediction value and the cervical adenocarcinoma prediction value according to the relative expression levels of SMC1B and CELSR3 mRNA and the relative expression levels of LncRNA FEZF1-AS1 and LINC01305; the diagnosis module is used to compare the cervical squamous cell carcinoma prediction value with the cervical squamous cell carcinoma threshold value to determine whether the subject to be tested is or is a candidate for a cervical squamous cell carcinoma patient, and compare the cervical adenocarcinoma prediction value with the cervical adenocarcinoma threshold value to determine whether the subject to be tested is or is a candidate for a cervical adenocarcinoma patient.

4. The system according to claim 3, characterized in that The sample is a cervical tissue sample or a peripheral blood sample of the subject to be tested.

5. The system according to claim 3, characterized in that Predictive value of cervical squamous cell carcinoma =-1.980+0.070×SMC1B+0.157×CELSR3+0.137×FEZF1-AS1+0.066×LINC01305; Predictive value of cervical adenocarcinoma =-3.495+0.158×SMC1B+0.114×CELSR3+0.176×FEZF1-AS1+0.138×LINC01305; Wherein, SMC1B and CELSR3 represent the relative expression levels of SMC1B and CELSR3 gene mRNA in the sample, respectively; FEZF1-AS1 and LINC01305 represent the relative expression levels of non-coding RNA FEZF1-AS1 and LINC01305 in the sample, respectively.

6. The system according to claim 4, characterized in that: The relative expression levels of SMC1B and CELSR3 mRNA and LncRNAFEZF1-AS1 and LINC01305 in the sample are reflected by the Ct value of SMC1B and CELSR3 mRNA and LncRNAFEZF1-AS and LINC01305 in the sample when performing Real-time PCR reaction.

7. The system according to claim 3, characterized in that The cervical squamous cell carcinoma threshold is 0.

743. When the cervical squamous cell carcinoma prediction value is ≤0.743, it is judged that the subject is not or is not a candidate for cervical squamous cell carcinoma; when the cervical squamous cell carcinoma prediction value is greater than 0.743, it is judged that the subject is or is a candidate for cervical squamous cell carcinoma. The cervical adenocarcinoma threshold is 0.

675. When the cervical adenocarcinoma prediction value is ≤0.675, it is judged that the subject is not or is not a candidate for a cervical adenocarcinoma patient; when the cervical adenocarcinoma prediction value is greater than 0.675, it is judged that the subject is or is a candidate for a cervical adenocarcinoma patient.

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