Application of cervical neuroendocrine carcinoma markers in the preparation of diagnostic reagents for cervical neuroendocrine carcinoma

Abstract

The present invention discloses an application of a cervical neuroendocrine carcinoma marker in the preparation of a cervical neuroendocrine carcinoma diagnostic reagent. The target marker is secretagogue (SCGN), adenylate cyclase-associated protein 2 (CAP2), and / or calcicyclin binding protein (CACYBP). Compared with the biomarkers currently used in the clinical diagnosis and treatment of cervical neuroendocrine carcinoma, such as synaptophysin (SYP), neural cell adhesion molecule (NCAM), chromogranin (CHGA), and neuron-specific enolase (ENO2), the SCGN, CAP2, and / or CACYBP disclosed in the present invention are used as target markers for cervical neuroendocrine carcinoma and have great application and promotion value in the diagnosis and treatment of cervical neuroendocrine carcinoma.

Description

Technical Field

[0001] The present invention belongs to the technical field of biomedical technology, and specifically relates to a target marker for diagnosis or treatment of cervical neuroendocrine carcinoma and its application. Background Art

[0002] Cervical cancer is a malignant tumor that seriously endangers women's health. According to the latest statistics from the International Agency for Research on Cancer (IARC) of the World Health Organization (WHO), there are as many as 9.2 million new cases of cancer in women worldwide, of which cervical cancer accounts for 6.5%; there are 4.4 million cases of cancer deaths in women, of which cervical cancer accounts for 7.7%; it ranks fourth in the incidence and mortality of malignant tumors in women. According to the latest statistics from the National Cancer Center, the incidence and mortality of cervical cancer in China ranked fourth and seventh respectively in 2016. Although the screening of cervical cancer is relatively complete, and surgical techniques, radiotherapy equipment, chemotherapy and other treatment methods have gradually improved, the clinical treatment effect of advanced and recurrent cervical cancer and some rare cervical cancer subtypes is still unsatisfactory.

[0003] Neuroendocrine cervix carcinoma (NECC) is an aggressive subtype of malignant cervical cancer originating from neuroendocrine cells, accounting for approximately 1.4% of all cervical malignancies. Unlike typical cervical squamous cell carcinoma and adenocarcinoma, NECC shows a more aggressive tendency to lymph node metastasis, high malignancy and mortality, and extremely poor prognosis. In addition, due to the rarity of NECC, there is currently no prospective clinical study on the standard diagnosis and treatment process of NECC. The treatment strategy for NECC is also mainly derived from the treatment of small cell lung cancer (the most common neuroendocrine cancer) and general cervical cancer. Two documents published by the Society of Gynecologic Oncology (SGO) and the Gynecologic Cancer InterGroup (GCIG) in 2011 and 2014, respectively, proposed a multifaceted treatment strategy for NECC, including surgery, chemotherapy, and radiotherapy. However, there has been little progress in treatment over the years.

[0004] Due to the rarity of cervical neuroendocrine carcinoma, there is currently a lack of effective commercial cell and animal models, and the main diagnostic and therapeutic markers are all derived from common neuroendocrine tumors and cervical squamous cell carcinoma, such as synaptophysin (SYP), chromogranin (CHGA), neural cell adhesion molecule (NCAM), and neuron-specific enolase (ENO2). However, not all patients have differential expression of the above proteins, and effective and specific biomarkers are urgently needed for the diagnosis and treatment of cervical neuroendocrine carcinoma. Summary of the invention

[0005] To solve the above technical problems, the present invention provides target markers for the diagnosis or treatment of cervical neuroendocrine carcinoma, wherein the target markers are SCGN (secretinin), CAP2 (adenylate cyclase-associated protein 2), and / or CACYBP (calcium cyclin binding protein), which have higher sensitivity and accuracy than the biomarkers currently used in the clinical diagnosis and treatment of cervical neuroendocrine carcinoma, such as synaptophysin (SYP), neural cell adhesion molecule (NCAM), chromogranin (CHGA), and neuron-specific enolase (ENO2), and show good specificity and recognition as diagnostic targets.

[0006] The present invention also provides the use of SCGN, CAP2, and / or CACYBP as cervical neuroendocrine carcinoma markers in the preparation of a diagnostic reagent for cervical neuroendocrine carcinoma.

[0007] The present invention also provides the use of SCGN, CAP2, and / or CACYBP proteins as cervical neuroendocrine carcinoma markers in the preparation of cervical neuroendocrine carcinoma therapeutic agents.

[0008] The present invention also provides druggable small molecules capable of targeting SCGN, CAP2, and / or CACYBP.

[0009] The technical solution adopted by the present invention is as follows:

[0010] The present invention provides a target marker for diagnosis or treatment of cervical neuroendocrine carcinoma, wherein the target marker is SCGN, CAP2, and / or CACYBP.

[0011] Furthermore, the amino acid sequence of the SCGN is shown in SEQ ID No.1.

[0012] The amino acid sequence of CAP2 is shown in SEQ ID No.2.

[0013] The amino acid sequence of CACYBP is shown in SEQ ID No.3.

[0014] The SCGN, CAP2, and / or CACYBP can be used as cervical neuroendocrine carcinoma markers in the preparation of cervical neuroendocrine carcinoma diagnostic reagents.

[0015] The SCGN, CAP2, and / or CACYBP proteins can be used as cervical neuroendocrine carcinoma markers in the preparation of cervical neuroendocrine carcinoma therapeutic agents.

[0016] The present invention provides a druggable small molecule capable of co-targeting SCGN, CAP2 and CACYBP, wherein the druggable small molecule is hydrogen peroxide.

[0017] The present invention provides a drug-forming small molecule capable of co-targeting CAP2 and CACYBP, wherein the drug-forming small molecule is resveratrol.

[0018] The present invention provides a druggable small molecule capable of targeting CACYBP, wherein the druggable small molecule is celastrol.

[0019] The present invention provides a drug-forming small molecule capable of targeting CACYBP, wherein the drug-forming small molecule is gedunin.

[0020] Compared with the prior art, the present invention has the following beneficial effects:

[0021] The present invention discloses for the first time that SCGN, CAP2, and / or CACYBP can be used as target markers for the diagnosis or treatment of cervical neuroendocrine carcinoma. Using them as target markers has good specificity and recognition, and compared with the current biomarkers used in the clinical diagnosis and treatment of cervical neuroendocrine carcinoma, such as synaptophysin (SYP), neural cell adhesion molecule (NCAM), chromogranin (CHGA), neuron-specific enolase (ENO2), it has higher sensitivity and accuracy. SCGN, CAP2, and / or CACYBP disclosed in the present invention as target markers for cervical neuroendocrine carcinoma have great application and promotion value in the diagnosis and treatment of cervical neuroendocrine carcinoma. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 It is the protein expression profile specific for cervical neuroendocrine carcinoma;

[0023] Figure 2Identification of key proteins in cervical neuroendocrine carcinoma by machine learning algorithms; A: Least absolute shrinkage and selection operator (LASSO) coefficient curve of specific differentially expressed proteins in cervical neuroendocrine carcinoma (left), 6 candidate proteins were identified after cross-validation selection and adjustment parameters (right), B: Support vector machine recursive feature elimination (SVM-RFE) algorithm identified 26 candidate proteins with an accuracy of 1 (left) and an error of 0 (right), C: Random Forest algorithm identified 23 candidate proteins, showing the error rate and the number of classification trees (left) and protein importance score (right), D: Venn diagram showing the specific differentially expressed proteins in cervical neuroendocrine carcinoma identified by the three machine learning methods;

[0024] Figure 3 Figure 1 is a receiver operating characteristic curve (ROC) model diagram. A: Single protein diagnostic ROC curves of SCGN, CAP2 and CACYBP. B: Single protein diagnostic ROC curves of SYP, NCAM, CHGA and ENO2. The figure shows that secretagogue (SCGN), adenylate cyclase-associated protein 2 (CAP2) and calcyclin binding protein (CACYBP) have the potential to be independent diagnostic targets.

[0025] Figure 4 Figure 1 is an artificial neural network (ANN) model diagram; A: ANN model for predicting NECC based on SCGN, CAP2 and CACYBP, B: ROC curve for evaluating the ANN model based on SCGN, CAP2 and CACYBP, C: ANN model for predicting NECC based on SYP, NCAM1, CHGA and ENO2, D: ROC curve for evaluating the ANN model based on SYP, NCAM1, CHGA and ENO2; the figure shows that the protein expression signatures of secretagogue (SCGN), adenylate cyclase-associated protein 2 (CAP2) and calcyclin binding protein (CACYBP) have good predictive accuracy;

[0026] Figure 5Figure 1 Tumor specimens were tested using the existing clinical targets synaptophysin (SYP) and neural cell adhesion molecule (NCAM) for cervical neuroendocrine carcinoma; A: Representative photomicrographs of HE and IHC staining of SYP and NCAM in patient 1, B: Representative photomicrographs of HE and IHC staining of SYP and NCAM in patient 2, C: Representative photomicrographs of HE and IHC staining of SYP and NCAM in patient 3, D: Representative photomicrographs of HE and IHC staining of SYP and NCAM in patient 4, E: Representative photomicrographs of HE and IHC staining of SYP and NCAM in patient 5, F: Representative photomicrographs of HE and IHC staining of SYP and NCAM in patient 6, G: Representative photomicrographs of HE and IHC staining of SYP and NCAM in patient 7;

[0027] Figure 6 Figure 1. The dysregulated expression of SCGN, adenylate cyclase-associated protein 2 (CAP2), and calcyclin-binding protein (CACYBP) in cervical neuroendocrine carcinoma tumors and adjacent paracancerous specimens; A: Representative micrographs of IHC staining of SCGN in adjacent paracancerous tissues (upper panel) and NECC specimens (lower panel), B: Representative micrographs of IHC staining of CAP2 in adjacent paracancerous tissues (upper panel) and NECC specimens (lower panel), C: Representative micrographs of IHC staining of CACYBP in adjacent paracancerous tissues (upper panel) and NECC specimens (lower panel), D: Protein expression level of SCGN, E: Protein expression level of CAP2, F: Protein expression level of CACYBP;

[0028] Figure 7 Schematic diagram of the molecular docking results of hydrogen peroxide and various target markers; A: molecular docking results of hydrogen peroxide and CACYBP, B: molecular docking results of hydrogen peroxide and CAP2, C: molecular docking results of hydrogen peroxide and SCGN; the figure shows that hydrogen peroxide can jointly target secretagogue (SCGN), adenylate cyclase-associated protein 2 (CAP2) and calcyclin binding protein (CACYBP);

[0029] Figure 8 Schematic diagram of molecular docking results of resveratrol and various target markers; A: molecular docking results of resveratrol and CACYBP, B: molecular docking results of resveratrol and CAP2; the results show that resveratrol can jointly target adenylate cyclase-associated protein 2 (CAP2) and calcyclin binding protein (CACYBP);

[0030] Fig. 9 Schematic diagram of the molecular docking results of celastrol and gedunin with CACYBP; the figure shows that celastrol and gedunin can target calcyclin binding protein (CACYBP). DETAILED DESCRIPTION

[0031] The present invention is described in detail below with reference to the embodiments.

[0032] The following documents are referenced:

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[0039] Neuroendocrine carcinoma of the cervix:a systematic review of theliterature.

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[0045] The data sources and processing procedures used in the present invention are as follows: The present invention retrieved gynecological cases in Wuhu City, Anhui Province and its subordinate county-level hospitals, and finally retrieved 7 specimens of cervical neuroendocrine carcinoma (NECC) including paired paracancerous tissues and tumor samples from the Wuhu Affiliated Hospital of East China Normal University (Wuhu Second People's Hospital). In general, the present invention obtained 14 pairs of paracancerous tissues and tumor samples from 7 patients. Approved by the Human Experiment Ethics Committee of the Wuhu Affiliated Hospital of East China Normal University (Wuhu Second People's Hospital) (approval number 2024-KY-009), all patients participating in this study gave written informed consent for the use of their data. The proteomic analysis of the above-mentioned NECC specimens was performed by Wuhan Maiwei Biotechnology Co., Ltd.

[0046] Quantitative proteomic analysis was performed using 4D-DIA. Total protein was determined by SDS-PAGE and trypsin digestion. After desalting, samples were identified by liquid chromatography-tandem mass spectrometry (LC-MS / MS).

[0047] First, the traditional DDA method was used to establish a protein spectrum database, and then the DIA technology was used to collect mass spectrometry data of each sample. Finally, spectral matching, quantitative information extraction and subsequent statistical analysis were performed based on the DDA database. This study sorted and analyzed the quantitative proteomics data. In addition, the present invention also downloaded the transcriptome data of 279 cervical cancer patients (CESC) from the TCGA database. Patients need to have complete clinical information (gender, age, race, survival status and survival time, etc.) to be included in the study. Subsequently, the present invention used the R language "limma" package to screen the dysregulated expression proteins in NECC (|logFC|>0.5 and pvalue<0.05), and screened out the dysregulated expression genes of CESC (|logFC|>1 and pvalue<0.001). The specific method is referred to the literature [1]. That is, the present invention compares the proteomic and transcriptomic data of common cervical tumor types (cervical squamous cell carcinoma and cervical adenocarcinoma), collects samples of cervical neuroendocrine carcinoma patients and performs 4D-DIA proteomic sequencing, and identifies 83 cervical neuroendocrine carcinoma-specific differential proteins. Subsequently, the present invention uses the R software package "heatmap" to visualize the expression profiles of these 83 cervical neuroendocrine carcinoma-specific differential proteins, such as Figure 1shown.

[0048] In order to find the key differential proteins from 83 cervical neuroendocrine carcinoma-specific differential proteins, the present invention applied three machine learning algorithms, including least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE) and random forest. The specific operation process is as follows: the data is imported using the read.table() function in the R language, and the data is preprocessed (missing values ​​are removed / standardized data) before machine learning.

[0049] LASSO: The present invention uses the Lasso package (such as the glmnet package) in R to construct a Lasso model. The algorithm used by the Lasso model is to select relevant variables by minimizing the loss function and reduce the coefficients corresponding to irrelevant variables to zero. Then, the optimal parameters of the Lasso model are selected using methods such as cross-validation and the cv.glmnet() function is used to perform cross-validation.

[0050] ●SVM-RFE: The present invention uses the e1071 package in R or to construct the SVM model, which is a supervised learning method for binary or multi-classification and uses various evaluation indicators such as accuracy, recall, F1 score, etc. to evaluate the model. Then, the feature set selected according to the feature selection step is used.

[0051] ● Random forest: The present invention uses the randomForest package in R to build a random forest model. The rf() function can be used to build the model and specify relevant parameters such as the number of trees and node splitting rules. Then, the optimal parameters of the random forest model can be selected by cross-validation and the tuneRF() function can be used to perform parameter tuning. Then, the importance of each variable is obtained by the variable importance evaluation function of the random forest model and the varImp() function is used to calculate the variable importance.

[0052] The present invention first uses the LASSO algorithm to identify the changes in the regression coefficients of 83 cervical neuroendocrine carcinoma-specific differential proteins. The specific method is referred to in reference [2]. This screening process identified 6 candidate proteins. Then the present invention also uses the SVM-RFE model to identify the protein with the smallest cross-validation error, thereby screening out 26 candidate proteins. The specific method is referred to in reference [3]. In addition, the present invention also incorporates 83 cervical neuroendocrine carcinoma-specific differential proteins into the random forest model. The specific method is referred to in reference [4]. The results show that the cross-validation error of 39 trees is the smallest. Therefore, 23 candidate proteins with important scores greater than 0.15 were identified. Six candidate proteins were identified by the LASSO algorithm, 26 candidate proteins were identified by the SVM-RFE algorithm, and 23 candidate proteins were identified by the random forest algorithm. After crossing all candidate proteins, it was found that secretagogue (SCGN), adenylate cyclase-associated protein 2 (CAP2) and calcicycline binding protein (CACYBP) were consistently identified by the three machine learning algorithms and were therefore defined as key proteins for cervical neuroendocrine carcinoma. Figure 2 shown.

[0053] The present invention uses the R software package pROC to perform receiver operating characteristic curve (ROC curve) analysis to obtain the area under the ROC curve (AUC). The specific method is referred to in the literature [5].

[0054] The protein expression data of secretagogue (SCGN), adenylate cyclase-associated protein 2 (CAP2), calcicycline binding protein (CACYBP), synaptophysin (SYP), neural cell adhesion molecule (NCAM), chromogranin (CHGA), and neuron-specific enolase (ENO2) from patients with cervical neuroendocrine carcinoma in Wuhu Affiliated Hospital of East China Normal University (Wuhu Second People's Hospital) were obtained. The ROC analysis of paracancerous and tumor was performed using the roc function of pROC, and the AUC and confidence interval were evaluated using the ci function of pROC to obtain the final AUC result. The results are as follows: Figure 3As shown. That is, through the construction of the diagnostic model, a diagnostic model based on the expression characteristics of secretagogue (SCGN), adenylate cyclase-associated protein 2 (CAP2) and calcicycline binding protein (CACYBP) proteins was provided. The AUC values ​​of the single gene diagnosis ROC curves of secretagogue (SCGN), adenylate cyclase-associated protein 2 (CAP2) and calcicycline binding protein (CACYBP) were 0.816, 1.000 and 1.000, respectively. Compared with the current biomarkers used in the clinical diagnosis and treatment of cervical neuroendocrine carcinoma (synaptophysin (SYP), neural cell adhesion molecule (NCAM), chromogranin (CHGA), neuron-specific enolase (ENO2)), it showed higher sensitivity [6]

[0055] The present invention uses the R software package "neuralnet" to construct an artificial neural network diagnostic model based on the expression levels of secretagogue (SCGN), adenylate cyclase-associated protein 2 (CAP2) and calcicycline binding protein (CACYBP) proteins [7]. After the protein expression data were normalized using the min-max normalization method, the seed was set to 123. The constructed artificial neural network model mainly consists of three layers: 1) input layer, which mainly consists of normalized secretagogue (SCGN), adenylate cyclase-associated protein 2 (CAP2) and calcicycline binding protein (CACYBP) protein expressions. 2) hidden layer, which mainly includes normalized secretagogue (SCGN), adenylate cyclase-associated protein 2 (CAP2) and calcicycline binding protein (CACYBP) protein expressions and weights, as well as the weights between the two hidden layers. 3) output layer, which indicates whether the sample is normal or tumor. Finally, in order to speed up the convergence speed of the neural network and improve its accuracy, the first hidden layer is set to 8 neurons, the second hidden layer is set to 3 neurons, and ROC is used to evaluate the prediction performance of the neural network. That is, by constructing an artificial neural network model, the present invention also provides a diagnostic model based on the protein expression characteristics of secretagogue (SCGN), adenylate cyclase-associated protein 2 (CAP2) and calcicycline binding protein (CACYBP), and the AUC value under the ROC curve of the diagnostic model is 0.929. In addition, the artificial neural network diagnostic model constructed based on the protein expression data of the three proteins of secretagogue (SCGN), adenylate cyclase-associated protein 2 (CAP2) and calcicycline binding protein (CACYBP) of the present invention shows higher accuracy in comparison with the biomarkers currently used in the clinical diagnosis and treatment of cervical neuroendocrine carcinoma (synaptophysin (SYP), neural cell adhesion molecule (NCAM), chromogranin (CHGA), neuron-specific enolase (ENO2)), such as Figure 4 And as shown in Table 1.

[0056] Table 1. Diagnostic effect of ANN constructed based on secretagogue (SCGN), adenylate cyclase-associated protein 2 (CAP2) and calcyclin binding protein (CACYBP) for NECC

[0057]

[0058] Note: “+” means the predicted result is the same as the actual result; “-” means the predicted result is opposite to the actual result.

[0059] The cervical neuroendocrine carcinoma tissue used in the present invention is from the Wuhu Affiliated Hospital of East China Normal University (Wuhu Second People's Hospital). The present invention first diagnoses patients with cervical neuroendocrine carcinoma through existing clinical targets. The results show that synaptophysin (SYP) and neural cell adhesion molecule (NCAM) are detected in most cervical neuroendocrine carcinoma tissues, but not all patients show synaptophysin (SYP) and neural cell adhesion molecule (NCAM) positive. Figure 5 As shown. Secondly, the present invention explored and verified the dysregulated expression of three proteins, secretagogue (SCGN), adenylate cyclase-associated protein 2 (CAP2) and calcyclin binding protein (CACYBP). The results showed that in all 7 pairs of cervical neuroendocrine carcinoma tissues and adjacent tissues, secretagogue (SCGN), adenylate cyclase-associated protein 2 (CAP2) and calcyclin binding protein (CACYBP) were significantly differentially expressed, as shown in Figure 2. Figure 6 As shown, it shows good specificity and recognition as a diagnostic target. The specific steps of immunohistochemistry are as follows:

[0060] ●Tissue paraffin embedding and sectioning

[0061] 1. Immersion and preservation: Soak the fixed tissue in PBS solution for 12 hours, and replace the fixative solution several times;

[0062] 2. Dehydration and wax immersion: Take out the washed tissue, rinse it with running water for about 4 hours, then put it into the dehydration basket, and dehydrate it in the dehydrator with gradient alcohol: 75% alcohol for 2 hours-85% alcohol for 2 hours-90% alcohol for 1.5 hours-95% alcohol for 2 hours-absolute ethanol I for 2 hours-absolute ethanol II for 2 hours, then put the tissue block into alcohol benzene for 40 minutes-xylene I for 40 minutes-xylene II for 40 minutes at 65 degrees for transparency, and finally perform wax permeation: melt paraffin I for 0.5 hours-65 degrees, melt paraffin II for 1 hour-65 degrees, melt paraffin III for 2 hours;

[0063] 3. Embedding: Use a wax encapsulation machine to embed the tissue processed in step 2. First, place the embedding mold in an oven for preheating, add paraffin wax first and then put the tissue block in, fix it in the embedding frame and affix the corresponding label. Cool it in a -20℃ freezer, wait for the wax to solidify, take out the wax block from the embedding frame, trim the wax block appropriately, and be careful not to let the wax block solidify during the embedding process, and keep the wax block uniform;

[0064] 4. Slice: Use a paraffin slicer to slice the embedded wax block, set the slice thickness to 4μm. Then float the sliced ​​slices on the spreader, flatten the wax slices in 40℃ warm water, and after fully unfolding, use an adhesive slide to fish out the tissue and place it in a 60℃ oven for baking. After the end, store it at room temperature for later use.

[0065] HE staining

[0066] 1. Dewaxing of paraffin sections to water: Take out the sections to be stained, put them into xylene I and xylene II for 20 minutes each, then put them into anhydrous ethanol I and anhydrous ethanol II for 5 minutes each, and finally put them into 75% alcohol for 5 minutes, take out the sections and wash them with running water;

[0067] 2. Hematoxylin staining: Put the cleaned slices into hematoxylin staining solution for about 5 minutes, wash with running water, and then perform the following steps: differentiation with 1% hydrochloric acid alcohol differentiation solution - washing with running water - blueing with blueing solution - washing with running water, and set aside;

[0068] 3. Eosin staining: Dehydrate the hematoxylin-stained sections in 85% and 95% gradient alcohol for 5 minutes each, and then stain them in eosin staining solution for about 5 minutes;

[0069] 4. Dehydration and sealing: Put the slices into anhydrous ethanol I, anhydrous ethanol II, anhydrous ethanol III, xylene I, and xylene II in turn for 5 minutes each, and finally seal with neutral gum;

[0070] 5. Observe under a microscope and take photos.

[0071] Immunohistochemistry

[0072] 1. After dewaxing, the sections were rinsed in distilled water for 2 minutes;

[0073] 2. Antigen repair: Treat with 3% hydrogen peroxide for 10 minutes to remove endogenous catalase, and wash with water 3 times;

[0074] 3. Circle drawing, 10% serum blocking;

[0075] 4. The slides were removed, excess serum was wiped off, and anti-SYP (1:150, proteintech), anti-NCAM (1:200, proteintech), Rabbit anti-CACYBP (1:200, proteintech), anti-CAP2 (1:50, Proteintech) and anti-SCGN (1:250, Abcam) were added and incubated overnight at 4°C;

[0076] 5. After removing the slides, wash them with PBS three times, 5 min each time, wipe off the excess PBS, and drop biotinylated secondary antibody (1:200, Roche Diagnostics) incubate at room temperature for 30 min;

[0077] 6. Color development, hematoxylin turns blue;

[0078] 7. Dehydrate, make transparent, and seal the slides. Observe and take photos under a microscope.

[0079] That is, the present invention further detected the differentially expressed secretagogue (SCGN), adenylate cyclase-associated protein 2 (CAP2) and calcyclin binding protein (CACYBP) proteins in patients with cervical neuroendocrine carcinoma through immunohistochemistry and other experiments, that is, the dysregulated expression of their protein levels was significant:

[0080] The present invention uses the Enrichr platform (https: / / maayanlab.cloud / Enrichr / ) for online identification and analysis. First, the present invention inputs the gene symbols of secretagogue (SCGN), adenylate cyclase-associated protein 2 (CAP2) and calcyclin binding protein (CACYBP) on the homepage of the Enrichr platform, and then screens the DSigDB database in the "Disease / Drug" module to find drugs targeting secretagogue (SCGN), adenylate cyclase-associated protein 2 (CAP2) and calcyclin binding protein (CACYBP), and sets P<0.05 as statistically significant. Subsequently, the present invention uses molecular docking technology to study the binding affinity of the screened drugs with secretagogue (SCGN), adenylate cyclase-associated protein 2 (CAP2) and calcyclin binding protein (CACYBP) to determine the most promising drugs. Molecular docking technology is a process in which two or more molecules recognize each other through geometric matching and energy matching to find the best matching pattern. Specifically, the present invention obtained the molecular structure of the targeted drug from the PubChem database (https: / / pubchem.ncbi.nlm.nih.gov / ). At the same time, the calcicycline binding protein (CACYBP) (PDB ID, 2A25; resolution, ), adenylate cyclase-associated protein 2 (CAP2) (PDB ID, 1K8F; resolution, ) and secretagogue (SCGN) (PDB ID, 6JLH; resolution, ) three-dimensional coordinates. Protein and molecule files were converted to PDBQT format, water molecules were excluded and polar hydrogen atoms were added. In order to allow unrestricted molecular motion, each protein domain was surrounded by a centered grid box. The ligand and protein files were prepared using the AutoDock tool. Protein-ligand docking was performed using the AutoDock tool [8], and the interaction between the receptor and the ligand was visualized using PyMOL (version 2.5) [9]. That is, through molecular docking technology for targeted drug identification, the present invention has the following findings:

[0081] The present invention has discovered a key drug-forming small molecule that jointly targets secretagogue (SCGN), adenylate cyclase-associated protein 2 (CAP2) and calcyclin binding protein (CACYBP): hydrogen peroxide, such as Figure 7 shown.

[0082] The present invention has discovered a key drug-forming small molecule that targets adenylate cyclase-associated protein 2 (CAP2) and calcyclin binding protein (CACYBP): resveratrol. Figure 8 shown.

[0083] The present invention has discovered key drug-forming small molecules targeting calcyclin binding protein (CACYBP): celastrol and Gedunin, such as Fig. 9 shown.

[0084] In summary, the present invention discloses secretagogue (SCGN), adenylate cyclase associated protein 2 (CAP2) and calcyclin binding protein (CACYBP) and their applications in the diagnosis and treatment of cervical neuroendocrine carcinoma. The present invention not only provides the application of secretagogue (SCGN), adenylate cyclase associated protein 2 (CAP2) and calcyclin binding protein (CACYBP) as independent factors in diagnosis, but also explains their common application in diagnosis by jointly constructing clinical indicators based on the expression characteristics of the three proteins.

[0085] The present invention also discloses the ability of hydrogen peroxide, resveratrol, celastrol and gedunin to target the above molecules. Hydrogen peroxide, resveratrol, celastrol and gedunin have potential application value in the treatment of cervical neuroendocrine carcinoma; product functions: regulating cancer cell proliferation; regulating cancer cell migration; regulating tumor growth; regulating tumor metastasis; treating cancer. SCGN, CAP2, and / or CACYBP disclosed in the present invention as target markers for cervical neuroendocrine carcinoma have great application and promotion value in the diagnosis and treatment of cervical neuroendocrine carcinoma.

[0086] The above reference embodiments provide a detailed description of a target marker for the diagnosis or treatment of cervical neuroendocrine carcinoma and its application, which is illustrative rather than restrictive, and several embodiments can be listed according to the limited scope. Therefore, changes and modifications without departing from the overall concept of the present invention should fall within the scope of protection of the present invention.

Claims

1. Application of CAP2 and / or CACYBP as markers of cervical neuroendocrine carcinoma in the preparation of diagnostic reagents for cervical neuroendocrine carcinoma.

Images