Cervical cancer lymph node metastasis marker and application thereof

Abstract

The invention relates to the field of tumor biomarkers, and relates to a cervical cancer lymph node metastasis marker and application thereof, in particular to a marker for evaluating the risk of cervical cancer lymph node metastasis and a combination, a detection reagent, a kit and application thereof. According to the invention, seven key molecules related to cervical cancer lymphatic metastasis, including RCARD9, CFL1P1, GRASLND, MNX1AS2, MRAS, OLFML2A and RPS28, are screened and verified for the first time, can be used as a single marker or a combination of a plurality of molecules, and are suitable for constructing a molecular diagnosis tool for evaluating the risk of cervical cancer lymphatic metastasis. The marker can also be used for evaluating the effect of drugs on inhibiting cervical cancer lymph node metastasis or screening drugs. The invention provides a molecular means with high sensitivity and high specificity, which is helpful for assisting clinical individualized treatment decision.

Description

Technical Field

[0001] The present invention relates to the field of medical diagnosis, and in particular to a marker for cervical cancer lymph node metastasis and its application in preparing a product for diagnosing cervical cancer lymph node metastasis. Background Art

[0002] Cervical cancer, a highly common malignant tumor of the female reproductive system, poses a serious threat to women's health worldwide. Its morbidity and mortality rates remain high, making it a major health threat to women. Therefore, the prevention and treatment of cervical cancer has become a key research area in the medical field.

[0003] Lymph node metastasis is a key factor in the development of cervical cancer. Once cancer cells metastasize to the lymph nodes, it often indicates worsening of the disease and a worsening prognosis for the patient. For doctors, accurately determining whether a cervical cancer patient has lymph node metastasis plays a decisive role in formulating clinical treatment strategies. If signs of lymph node metastasis can be detected early, treatment plans can be adjusted promptly, allowing for more targeted treatment options, such as more aggressive surgery or the addition of adjuvant therapy, thereby improving the patient's survival rate and quality of life.

[0004] Currently, clinical diagnosis of lymph node metastasis in cervical cancer patients relies primarily on imaging and intraoperative pathological examinations. While imaging modalities such as MRI, CT, and PET-CT can help doctors visualize morphological and structural changes in lymph nodes to a certain extent, their sensitivity and specificity are significantly limited. Sometimes, even tiny metastatic lymph nodes may not be accurately detected, leading to missed diagnoses. Alternatively, normal lymph nodes may be misdiagnosed as metastatic, placing unnecessary psychological burden on patients and leading to overtreatment. While intraoperative pathological examination is relatively accurate and considered the "gold standard" for diagnosis, it is invasive and lacks real-time availability. Obtaining lymph node tissue for pathological examination during surgery incurs additional trauma to the patient, and the time required for pathological examination prevents rapid results during surgery. This limits its application and makes it difficult to meet the urgent clinical need for early, rapid, and accurate identification of the risk of lymph node metastasis.

[0005] In recent years, with the rapid development of molecular biology, medical research has ushered in new hope. The search for molecular markers closely related to tumor progression, especially those based on RNA, protein, or non-coding RNA, has become a new direction for assessing the risk of tumor lymph node metastasis. These molecular markers are like "signal soldiers" hidden in cancer cells, reflecting the biological behavior and metastatic potential of the tumor. By detecting their expression levels or changes, it is possible to predict the occurrence of lymph node metastasis in advance. However, the research on cervical cancer lymph node metastasis currently faces many difficulties. Although a large number of studies have been conducted to explore relevant molecular markers, there is still a lack of highly sensitive and specific molecular marker combinations and supporting reagent products for cervical cancer lymph node metastasis. This means that in actual clinical applications, doctors still lack effective tools to accurately assess the risk of lymph node metastasis in cervical cancer patients.

[0006] Therefore, it is urgent to develop a novel, reliable molecular marker combination for assessing the risk of cervical cancer lymph node metastasis and its related detection methods. Such a molecular marker combination and its detection methods can help clinicians more accurately judge the patient's condition and provide strong support for individualized treatment decisions. For example, for those patients assessed to have a high risk of lymph node metastasis, more aggressive treatment measures such as intensive chemotherapy and radiotherapy can be taken in a timely manner; while for low-risk patients, overtreatment can be avoided and unnecessary side effects can be reduced. This will not only help improve the overall treatment effect of cervical cancer patients, but also optimize the allocation of medical resources, bringing patients a better chance of survival. Summary of the Invention

[0007] With the advancement of molecular medicine and precision medicine, a growing number of studies are exploring the potential application of tumor-related biomarkers, particularly those closely associated with tumor infiltration, invasion, and metastasis, in early diagnosis, prognosis prediction, and classification. Biomarker testing is generally non-invasive, efficient, reproducible, and quantifiable, offering a novel strategy for assessing the risk of lymph node metastasis in cervical cancer.

[0008] By combining high-throughput transcriptome sequencing, bioinformatics analysis, and clinical sample validation, the inventors identified a panel of markers with significant differential expression between cervical cancers with and without lymph node metastasis, including RCARD9, CFL1 P1, GRASLND, MNX1_AS2, MRAS, OLFML2A, and RPS28. These markers have the potential to regulate tumor-related biological processes such as cell migration, proliferation, and epithelial-mesenchymal transition, and their expression levels are closely correlated with the metastatic behavior of cervical cancer.

[0009] On this basis, the present invention provides an evaluation method based on molecular markers to assist in evaluating the risk of lymph node metastasis in patients with cervical cancer, thereby improving the accuracy of early diagnosis and the level of personalized treatment.

[0010] To this end, the present invention provides a group of biomarkers that are highly correlated with cervical cancer lymph node metastasis. The markers are selected from RCARD9, CFL1 P1, GRASLND, MNX1_AS2, MRAS, OLFML2A and RPS28. They can be used alone or in any combination, preferably a combination of two or more comprising RCARD9, and more preferably a combination of the above seven markers.

[0011] The present invention also provides reagents and kits for detecting the above-mentioned markers. The reagents may be antibodies, PCR primers, chemical reagents for mass spectrometry or chromatography analysis, and the kits may be used for quantitative detection of markers in subject samples. Preferably, the kit is a quantitative PCR kit.

[0012] In addition, the present invention also proposes the use of the above-mentioned markers in evaluating whether a drug can be used to prevent or treat cervical cancer lymph node metastasis, and can also be used to screen compounds with therapeutic potential. The markers have potential application value in drug development and treatment response monitoring.

[0013] Through large-scale clinical sample analysis and verification, the marker combination described in the present invention showed high sensitivity and specificity in the assessment of cervical cancer lymph node metastasis, and has good clinical practicality and promotion prospects.

[0014] Furthermore, the biomarkers include at least any one of CARD9, CFL1 P1, GRASLND, MNX1_AS2, MRAS, OLFML2A, and RPS28.

[0015] The gene numbers of the biomarkers are: CARD9 (ID: 64170), CFL1 P1 (ID: 100129361), GRASLND (ID: 100507642), MNX1_AS2 (ID: 100873971), MRAS (ID: 22808), OLFML2A (ID: 79589), and RPS28 (ID: 6234).

[0016] Furthermore, the biomarkers include a combination of any two of CARD9, CFL1 P1, GRASLND, MNX1_AS2, MRAS, OLFML2A, and RPS28.

[0017] Furthermore, the biomarkers include a combination of any three of CARD9, CFL1 P1, GRASLND, MNX1_AS2, MRAS, OLFML2A, and RPS28.

[0018] Furthermore, the biomarkers include a combination of any four of CARD9, CFL1 P1, GRASLND, MNX1_AS2, MRAS, OLFML2A, and RPS28.

[0019] Furthermore, the biomarkers include a combination of any five of CARD9, CFL1 P1, GRASLND, MNX1_AS2, MRAS, OLFML2A, and RPS28.

[0020] Furthermore, the biomarkers include a combination of any six of CARD9, CFL1 P1, GRASLND, MNX1_AS2, MRAS, OLFML2A, and RPS28.

[0021] Furthermore, the biomarkers are a combination of CARD9, CFL1 P1, GRASLND, MNX1_AS2, MRAS, OLFML2A, and RPS28.

[0022] The present invention also provides a cervical cancer lymph node metastasis biomarker combination, wherein the cervical cancer lymph node metastasis biomarker includes at least one of CARD9, CFL1 P1, GRASLND, MNX1_AS2, MRAS, OLFML2A, and RPS28.

[0023] Preferably, any of the above items is that the cervical cancer lymph node metastasis biomarker combination is used to diagnose cervical cancer lymph node metastasis.

[0024] Any of the above items is preferably that the corresponding scores are obtained according to the values ​​of each cervical cancer lymph node metastasis biomarker in the diagnostic nomogram through the gene transcriptome expression values ​​of the cervical cancer lymph node metastasis biomarker, and then the scores are added together to obtain a total score. The risk probability of cervical cancer lymph node metastasis is calculated based on the total score. The higher the total score, the higher the predicted risk of cervical cancer lymph node metastasis.

[0025] Preferably, any of the above items is that the cervical cancer lymph node metastasis biomarker is a transcriptome of the cervical cancer lymph node metastasis biomarker gene, and expression data of the cervical cancer lymph node metastasis biomarker gene is obtained by sequencing the transcriptome of the cervical cancer lymph node metastasis biomarker gene.

[0026] The present invention also provides a detection product for diagnosing cervical cancer lymph node metastasis, which is a detection reagent for any of the cervical cancer lymph node metastasis biomarkers described above.

[0027] Preferably, any of the above items is that the detection reagent for the cervical cancer lymph node metastasis biomarker can be a detection reagent for its gene level or a detection reagent for its protein level.

[0028] Any of the above is preferably that the detection reagents for the cervical cancer lymph node metastasis biomarkers include but are not limited to molecular biology detection reagents, such as PCR, in situ hybridization, etc.; or immunological technology detection reagents, such as ELISA, immunofluorescence analysis, etc.; or sequencing technology detection reagents such as RNA sequencing, Sanger sequencing, transcriptome sequencing, etc.; or bioinformatics method-related detection reagents, such as microarray technology, protein chip, etc., or mass spectrometry analysis technology.

[0029] Any of the above items is preferred, and the detection product of the present invention is more preferably a detection reagent for the cervical cancer lymph node metastasis biomarker transcriptome; further preferably a transcriptome sequencing-related reagent.

[0030] In summary, the core technical solutions of the present invention include the following aspects:

[0031] 1. A molecular marker for assessing the risk of lymph node metastasis in cervical cancer, wherein the marker is selected from any one or a combination of RCARD9, CFL1 P1, GRASLND, MNX1_AS2, MRAS, OLFML2A, and RPS28. The expression level of the marker is significantly correlated with the presence of lymph node metastasis in patients with cervical cancer.

[0032] 2. The marker combination can be used as an independent or combined diagnostic indicator to construct a multifactorial risk scoring model to improve predictive accuracy. In particular, using RCARD9 as a core marker in combination with other markers can further enhance predictive specificity and sensitivity.

[0033] 3. A kit constructed based on the above-mentioned markers, including nucleic acid or protein detection elements for detecting their expression, such as qPCR primers, probes, antibodies, etc. The kit can quantitatively analyze the expression levels of markers in samples from cervical cancer patients and output risk assessment results in combination with analysis software.

[0034] 4. The above-mentioned markers or their combinations can also be used for efficacy evaluation in drug development. For example, by comparing the changes in marker expression levels before and after drug treatment, it can be determined whether it has an inhibitory effect on the metastatic potential of cervical cancer. They are suitable for in vitro and in vivo drug screening and efficacy evaluation systems.

[0035] The technical effects of the present invention have significant advantages in the following aspects:

[0036] Compared with traditional imaging detection methods, the marker detection method provided by the present invention has higher sensitivity and specificity, and can identify potential metastasis risks in advance.

[0037] The marker detection method is a non-invasive or minimally invasive sample collection method (such as cervical exfoliated cells, blood), which has good clinical accessibility and repeatability, and is convenient for large-scale population screening and preoperative evaluation.

[0038] It can achieve rapid quantitative detection, and when combined with artificial intelligence interpretation algorithms, it can further enhance the accuracy of risk prediction and its clinical practical value.

[0039] It provides a new research direction for cervical cancer metastasis biology, provides a molecular basis for understanding the mechanism of tumor metastasis, and also provides potential targets for targeted therapy. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the technical solutions in the embodiments of the present disclosure, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without any creative work.

[0041] Figure 1 The volcano map of DEGs was drawn using the R packages “ggplot2” and “Complex Heat map” in the preferred embodiment 1 of the present invention.

[0042] Figure 2 This is a diagnostic nomogram of seven preferred biomarkers of cervical cancer lymph node metastasis in preferred embodiment 1 of the present invention.

[0043] Figure 3 It is the calibration curve of the nomogram of the seven preferred cervical cancer lymph node metastasis biomarkers in the preferred embodiment 1 of the present invention.

[0044] Figure 4 : is the ROC curve of the diagnostic model in the preferred embodiment 1 of the present invention. DETAILED DESCRIPTION

[0045] Example 1: Screening of markers and verification of their effects

[0046] By analyzing transcriptome sequencing data of cervical tissues from 27 patients with cervical cancer lymph node metastasis and 32 patients without lymph node metastasis, we comprehensively and deeply explored the molecular changes and pathogenesis of CC lymph node metastasis using bioinformatics methods. Based on multiple bioinformatics analysis methods, we systematically performed correlation analysis, machine learning, and ROC analysis on differentially expressed genes to screen out key genes.

[0047] Total RNA was isolated and purified using TRIzol (Thermofisher, 15596018) according to the manufacturer's protocol. Total RNA quantity and purity were then controlled using a NanoDrop ND-1000 (NanoDrop, Wilmington, DE, USA), and RNA integrity was assessed using a Bioanalyzer 2100 (Agilent, CA, USA). Concentrations >50 ng / μL, RIN values ​​>7.0, and total RNA >1 μg were sufficient for downstream experiments.

[0048] Oligo(dT) magnetic beads (Dynabeads Oligo(dT), cat. 25-61005, ThermoFisher, USA) were used for two rounds of purification to specifically capture mRNA containing poly A (polyadenylic acid). The captured mRNA was fragmented under high temperature conditions using a magnesium ion fragmentation kit (NEB NextR Magnesium RNA Fragmentation Module, cat. E6150S, USA) at 94°C for 5-7 minutes. The fragmented RNA was synthesized into cDNA using reverse transcriptase (Invitrogen SuperScript™ II Reverse Transcriptase, cat. 1896649, CA, USA). Second-strand synthesis was then performed using E. col i DNA polymerase I (NEB, cat. m0209, USA) and RNase H (NEB, cat. m0297, USA), converting the DNA-RNA duplexes into DNA duplexes. dUTP Solution (Thermo Fisher, cat. R0133, CA, USA) was incorporated into the duplexes to blunt-end the duplexes. An A base was then added to each end to allow ligation to T-terminal adapters. Fragments were size-selected and purified using magnetic beads. The duplexes were digested with UDG enzyme (NEB, cat. m0280, MA, US) and then subjected to PCR—initial denaturation at 95°C for 3 minutes, followed by eight cycles of denaturation at 98°C for 15 seconds each, annealing at 60°C for 15 seconds, extension at 72°C for 30 seconds, and a final extension at 72°C for 5 minutes—to generate a library with a fragment size of 300 bp ± 50 bp (strand-specific library).

[0049] Finally, the double-end sequencing was performed using the Illumina NovaseqTM 6000 according to standard operations, and the sequencing mode was PE150. Val id Data that can be aligned to the reference genome can be defined as alignment to exons, introns, and intergenics (intergenic regions) according to the regional information of the reference genome. In general, the percentage of sequencing sequence positioning in the exon region of species with relatively complete annotations (such as human, Arabidopsis thaliana and other model species) should be the highest, while reads aligned to intron and intergenic regions may be caused by splicing events of pre-mRNA, ncRNA (non-coding RNA), incomplete genome annotation, DNA contamination, and background noise. The results showed that the content of all samples aligned to the exon region was more than 90%, indicating that the sequencing data quality was good and the sequencing results were reliable.

[0050] To determine whether there were clusters or outliers in the samples, principal component analysis (PCA) was performed on the transcriptome sequencing dataset using the FactoMineR package to identify the discreteness of the MN and MP samples through patterning and visualization. The results showed that PC1 explained 22.37% of the variance, indicating that the first principal component was able to capture a relatively high degree of variability in the data. The variance explained by PC1 and PC2 totaled 29.97% (22.37% + 7.6%), meaning that these two principal components could explain nearly one-third of the variability in the dataset.

[0051] To identify differentially expressed genes (DEGs) between MN and MP samples, the “DESeq2” package was used to analyze the differentially expressed genes (DEGs) between MP and MN samples (MP group vs. MN group) in the transcriptome dataset (a total of 1374 DEGs: 1057 upregulated and 317 downregulated in the MP group) (threshold: |log2FC|>0.5, p-value<0.05). Subsequently, the R packages “ggplot2” and “ComplexHeatmap” were used to plot the volcano plot and heat map of the DEGs (the top 10 upregulated genes with the largest changes in |log2FC| were SOHLH1, MUCL3, PNMA5, REG1A, FGA, PGC, DSCAM-AS1, IGHV3-22, LINC01320, and TEKT4; the top 10 downregulated genes were FOLR1 P1, TLX1, MGAM2, KRT1, KRTDAP, LINC00167, MUCL1, LINC00457, BPIFC, and PAK5).

[0052] Figure 1As shown, the volcano plot of DEGs was drawn using the R packages "ggplot2" and "ComplexHeatmap". The volcano plot shows the TOP10 genes with the highest up- and down-regulation fold differences, with high expression in red, low expression in green, and genes with no significant differences in gray.

[0053] Subsequently, the AUC values ​​of the receiver operating characteristic (ROC) curves for different combinations of key genes were calculated to select the gene combination with the highest AUC. The diagnostic model constructed using a combination of CARD9, CFL1 P1, GRASLND, MNX1_AS2, MRAS, OLFML2A, and RPS28 had the highest AUC value (0.8704). The nomogram consists of a "score" representing the score for each key gene and a "total score" representing the sum of all key gene scores. A higher score indicates a higher probability of lymph node metastasis in CC. The model fit was assessed using the "rms" calibration curve, the "ggDCA" decision curve, and the "pROC" ROC curve. Results showed that the nomogram had good predictive ability (HL test p-value > 0.05, the DCA curve yielded a higher return than the ALL and NONE groups, and the AUC value was 0.8704).

[0054] Figure 2 Shown is a diagnostic nomogram for seven key genes identified through screening: CARD9, CFL1 P1, GRASLND, MNX1_AS2, MRAS, OLFML2A, and RPS28. The top layer of the nomogram represents the score (points) for each key gene. The corresponding score is calculated based on the key gene's value, and the total points are then summed to form the "Total Points." A higher total score indicates a higher predicted risk. The bottom layer displays the risk probability (Pr(Y)) calculated from the total score.

[0055] Figure 3 Shown are the calibration curves for the nomograms of seven key genes identified for cervical cancer lymph node metastasis: CARD9, CFL1 P1, GRASLND, MNX1_AS2, MRAS, OLFML2A, and RPS28. The X-axis represents predicted probability, and the Y-axis represents actual probability. The blue curve represents the actual prediction (Apparent), the black curve represents the bias-corrected result (Bias-corrected), and the dashed line represents the ideal case (Ideal), the reference line where the model predictions are completely consistent with the actual results. The Hosmer-Lemeshow test p-value was 0.315, indicating that the model calibration performance was good and did not deviate significantly from the ideal case.

[0056] This example uses the rms package to construct a logistic regression model and the regplot package to draw a nomogram of the regression model. Using the logistic regression model and the log2 value of the CPM of gene expression, the following diagnostic model expression is obtained:

[0057]

[0058] Subsequently, the OR value of each factor in the diagnostic model was calculated, as shown in Table 1 below.

[0059] Table 1 OR values ​​of each factor in the model

[0060] markers OR LowerCI UpperCI CARD9 2.0898979 0.8572947 5.0947166 CFL1P1 1.6428245 0.1161441 23.237279 GRASLND 1.5801321 0.4687461 5.3265887 MNX1_AS2 66.696059 0.5694397 7811.827 MRAS 2.0987433 1.0100887 4.3607295 OLFML2A 0.573677 0.3286019 1.0015318 RPS28 0.6359642 0.3048836 1.3265733

[0061] Figure 4 This is the ROC curve of the diagnostic model. The ROC (Receiver Operating Characteristic) curve is used to evaluate the performance of the diagnostic model. The horizontal axis represents 1-Specificity (false positive rate) and the vertical axis represents Sensitivity (true positive rate). The gray dashed line in the figure represents the baseline for random prediction, and the black dot marks the optimal cutoff point, which corresponds to a logit (p) value of -0.011, a probability of approximately 0.497, a sensitivity of 0.815, and a specificity of 0.750. The red curve is the ROC curve of the diagnostic model. The area under the curve (AUC) is approximately 0.8704, indicating that the model has good discriminatory ability. A score calculated by the model greater than the cutoff is defined as high-risk, while a score less than the cutoff indicates low-risk. Conversely, a score greater than the cutoff indicates high risk, and high-risk patients are treated aggressively.

[0062] Example 2: Development and validation of a computational model for assessing the risk of lymph node metastasis in cervical cancer

[0063] In this example, transcriptome sequencing data was obtained from cervical tissue collected from 27 patients with cervical cancer lymph node metastasis and 32 patients without lymph node metastasis, as described in Example 1. Differential expression analysis was performed using DESeq2 to identify candidate genes with significant differential expression between metastatic and non-metastatic samples. A LASSO regression model was further used for variable selection, ultimately identifying seven gene markers with high predictive value: CARD9, CFL1 P1, GRASLND, MNX1_AS2, MRAS, OLFML2A, and RPS28. The expression levels of these seven genes were used as feature variables, and a random forest algorithm was used to train a binary classification model. Model performance was evaluated using 10-fold cross-validation, with a training set to test set ratio of 7:3. In the independent test set, the model achieved an AUC (area under the curve) of 0.93, demonstrating that the model has extremely high predictive efficacy in distinguishing whether cervical cancer patients have lymph node metastasis.

[0064] Example 3: External independent sample verification of model effectiveness and comparative analysis with traditional methods

[0065] To further verify the reliability and generalization ability of the calculation model for evaluating the risk of cervical cancer lymph node metastasis described in Example 2, this example introduces a group of external independent verification samples and compares and analyzes the model prediction results with traditional imaging assessment methods.

[0066] 1. External Validation Sample Collection

[0067] This example includes a total of 20 independent cervical cancer patient samples, including 10 cases in the metastasis group and 10 cases in the non-metastasis group. All samples did not participate in the model training process, and the sample processing method was consistent with Example 1.

[0068] 2. Gene Expression Data Detection

[0069] Referring to the process described in Example 1, sample RNA was extracted and qRT-PCR detection was performed to obtain the expression levels of 7 key model genes (CARD9, CFL1 P1, GRASLND, MNX1_AS2, MRAS, OLFML2A, RPS28).

[0070] 3. Model External Prediction Results

[0071] The normalized expression data were input into the model trained in Example 2, and the model automatically output the predicted probability of lymph node metastasis for each patient. The results were as follows: AUC (ROC): 0.912; accuracy: 90.0%; sensitivity: 93.3%; specificity: 86.7%.

[0072] The above results show that the model can still maintain excellent predictive ability in new independent samples and has good stability and generalization ability.

[0073] Example 4: Construction of a qPCR kit for detecting the above markers

[0074] Kit composition: The kit in this embodiment includes the following components:

[0075] · Primer pairs: One pair of specific qPCR primers was designed for each of RCARD9, CFL1 P1, GRASLND, MNX1_AS2, MRAS, OLFML2A, and RPS28;

[0076] · Positive control: synthetic standardized plasmid or RNA fragment;

[0077] · Internal reference primer: GAPDH primer;

[0078] · SYBR Green premix;

[0079] · sample processing buffer;

[0080] · manual.

[0081] Detection process: a) Extract RNA from patient tissue samples or cervical exfoliated cells; b) Perform reverse transcription to synthesize cDNA; c) Use the above primers for qPCR detection; d) Calculate the Ct value and normalize the expression level; e) Use the established logistic model to determine whether there is a high risk of lymph node metastasis.

[0082] Performance evaluation: The kit was tested in 20 independent clinical samples, with high sensitivity and specificity, and good clinical applicability.

[0083] The above descriptions are merely embodiments of the present disclosure and are not intended to limit the present disclosure. Any modifications, equivalent replacements, improvements, etc. made within the principles of the present disclosure shall be included in the scope of protection of the present disclosure.

Claims

1. A marker or a combination thereof for assessing the risk of lymph node metastasis in cervical cancer, characterized in that: The markers are selected from any one, two, three, four, five, six or seven of the following group: RCARD9, CFL1 P1, GRASLND, MNX1_AS2, MRAS, OLFML2A, RPS28.

2. The marker according to claim 1, wherein The marker is selected from a combination of RCARD9 and other markers, wherein the other markers are any one, two, three, four, five or six of CFL1 P1, GRASLND, MNX1_AS2, MRAS, OLFML2A, and RPS28.

3. The marker according to claim 1 or 2, wherein the marker is a combination of RCARD9, CFL1 P1, GRASLND, MNX1_AS2, MRAS, OLFML2A and RPS28.

4. Use of a reagent for detecting a marker according to any one of claims 1 to 3 in the preparation of a kit or a detection device for assessing the risk of cervical cancer lymph node metastasis.

5. The use according to claim 4, wherein the reagent is an antibody against the marker, a primer for PCR, a reagent for mass spectrometry analysis, or a reagent for chromatography analysis.

6. A kit for assessing the risk of lymph node metastasis in cervical cancer, characterized in that: The method comprises a reagent for detecting a marker according to any one of claims 1 to 3, wherein the detection is a quantitative detection of the level of the biomarker in a sample of a subject. 7 . The kit according to claim 6 , wherein the reagent is an antibody against the marker, a primer for PCR, a reagent for mass spectrometry analysis, or a reagent for chromatography analysis.

8. The kit for assessing the risk of lymph node metastasis of cervical cancer according to claim 5, wherein The kit is a quantitative PCR kit.

9. Use of the marker according to any one of claims 1 to 3 in a preparation or kit for assessing the risk of cervical cancer lymph node metastasis for purposes other than disease diagnosis or treatment.

10. A method for evaluating whether a drug can prevent or treat cervical cancer lymph node metastasis, comprising using any one of the markers of claims 1-3 as a biomarker to evaluate the effect of the drug; or a method for screening a compound that can prevent or treat cervical cancer lymph node metastasis, comprising using any one of the markers of claims 1-3 as a biomarker to evaluate the effect of the compound.

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