A biomarker of epithelial ovarian cancer and application thereof

Abstract

The application discloses a biomarker of epithelial ovarian cancer and application thereof. The biomarker comprises a basal cell adhesion molecule, a mesothelin and a folate receptor alpha, or coding nucleic acids of the basal cell adhesion molecule, the mesothelin and the folate receptor alpha, wherein the coding nucleic acids comprise mRNA or cDNA. An EOC CTC RNA diagnosis model is constructed by taking the biomarker as a feature, the sensitivity and the specificity of which can reach 88% and 83% respectively, and the model can dynamically monitor the progress of a patient's disease, and the patient's recurrence can be found earlier than by using imaging and existing serum indexes.

Description

Technical Field

[0001] This invention belongs to the field of molecular biology technology and relates to a biomarker for epithelial ovarian cancer and its application, particularly to a diagnostic biomarker for epithelial ovarian cancer based on a combination of circulating tumor cell mRNAs. Background Technology

[0002] Among the various pathological types of ovarian cancer, epithelial ovarian cancer (EOC) is the most common, accounting for approximately 80% of malignant ovarian tumors. Because ovarian cancer lacks typical clinical manifestations in its early stages, and there are currently no effective screening methods, although ovarian cancer patients initially respond well to comprehensive treatments such as surgery combined with chemotherapy, the vast majority of patients still develop drug resistance, metastasis, and recurrence. Therefore, timely diagnosis of ovarian cancer is of great significance for improving the prognosis and quality of life of ovarian cancer patients.

[0003] Current research data based on the general population shows that serum carbohydrate antigen (CA)125 testing, transvaginal ultrasound (TVUS), or a combination of both screening methods do not achieve satisfactory screening results. Although CA125 is currently the most widely used biomarker for ovarian cancer diagnosis and disease monitoring, indicating tumor progression or regression, studies have shown that CA125 levels are elevated (>35 U / mL) only in 50% of early-stage ovarian cancer patients, and elevated levels are also observed in the serum of 1% of healthy women, 3% of patients with benign ovarian tumors, and 6% of patients with extraovarian benign diseases. This indicates that the sensitivity and specificity of CA125 are insufficient, and expression levels vary across different ovarian cancer subtypes. Therefore, there is an urgent need to develop a highly sensitive, highly specific, and more stable cancer detection method to achieve timely diagnosis, treatment evaluation, and recurrence monitoring of ovarian cancer.

[0004] Circulating tumor cells (CTCs) refer to tumor cells originating from primary or metastatic tumors that detach and enter the peripheral blood circulation. Because they carry important information about primary or metastatic lesions and can spread to other tissues and organs throughout the body via the bloodstream, CTCs are considered a promising non-invasive cancer diagnostic biomarker. By purifying CTCs from peripheral blood samples and analyzing their characteristic molecules, such as proteins, mRNA, and lncRNA, it is hoped that non-invasive, continuous, and real-time dynamic monitoring can be achieved, providing a new direction for the timely diagnosis and disease monitoring of ovarian cancer. For example, CN108085392A discloses biomarkers for epithelial ovarian cancer and their uses, including hsa-miR-34b, hsa-miR-335, hsa-miR-136, hsa-let-7e, hsa-miR-1297, hsa-miR-374b, and hsa-miR-891b.

[0005] In conclusion, developing novel biomarkers and corresponding detection methods for epithelial ovarian cancer, and expanding the technical means for diagnosis, efficacy evaluation, and recurrence monitoring of epithelial ovarian cancer, is of great significance for the treatment of epithelial ovarian cancer. Summary of the Invention

[0006] In response to the shortcomings of existing technologies and practical needs, this invention provides a biomarker for epithelial ovarian cancer and its application, offering new methods and ideas for the diagnosis, efficacy evaluation, and disease monitoring of ovarian cancer.

[0007] To achieve the above objectives, the present invention adopts the following technical solution:

[0008] In a first aspect, the present invention provides a biomarker for epithelial ovarian cancer, the biomarker comprising basal cell adhesion molecule (BCAM), mesothelin (MSLN), and folate receptor α (FOLR1), or, the nucleic acid encoding basal cell adhesion molecule, the nucleic acid encoding mesothelin, and the nucleic acid encoding folate receptor α; the encoded nucleic acid comprising mRNA or cDNA.

[0009] This invention is the first to discover that basal cell adhesion molecules, mesothelin, and folate receptor α and their encoded nucleic acids in circulating tumor cells of epithelial ovarian cancer are associated with the development and prognosis of epithelial ovarian cancer. These molecules are overexpressed in patients with epithelial ovarian cancer, but are expressed at low levels or not at all in normal ovarian tissue and benign ovarian tumor tissue. This invention can be effectively applied to the diagnosis and progression monitoring of epithelial ovarian cancer.

[0010] Secondly, the present invention provides the application of the biomarkers for epithelial ovarian cancer described in the first aspect or their detection reagents in the preparation of products for detecting epithelial ovarian cancer.

[0011] Thirdly, the present invention provides a kit for detecting epithelial ovarian cancer, the kit comprising a detection reagent for the presence or level of biomarkers of epithelial ovarian cancer as described in the first aspect.

[0012] Fourthly, the present invention provides the application of the biomarkers for epithelial ovarian cancer described in the first aspect or their detection reagents in constructing diagnostic models or devices for epithelial ovarian cancer.

[0013] Fifthly, the present invention provides a method for constructing a diagnostic model for epithelial ovarian cancer, the method comprising: establishing a diagnostic model using multivariate logistic regression based on the expression levels of biomarkers for epithelial ovarian cancer as described in the first aspect.

[0014] Sixthly, the present invention provides a diagnostic model for epithelial ovarian cancer, wherein the input variables of the diagnostic model include the expression levels of biomarkers for epithelial ovarian cancer as described in the first aspect of the invention, and the output variable of the diagnostic model is the subject's EOC CTC Score, the formula for which the EOC CTC Score is calculated is:

[0015] EOC CTC Score=-3.904+0.482×A1+0.314×A2+1.7×A3.

[0016] Wherein, A1 represents the expression level of basal cell adhesion molecule or its encoded nucleic acid, preferably the expression level of basal cell adhesion molecule mRNA; A12 represents the expression level of mesothelin or its encoded nucleic acid, preferably the expression level of mesothelin mRNA; A3 represents the expression level of folate receptor α or its encoded nucleic acid, preferably the expression level of folate receptor α mRNA.

[0017] The diagnostic criteria for epithelial ovarian cancer are: EOC CTC Score > -1.64.

[0018] In a seventh aspect, the present invention provides a diagnostic device for epithelial ovarian cancer, the diagnostic device comprising an information acquisition module, a calculation module, and a diagnostic module.

[0019] The information acquisition module is used to perform the following:

[0020] The operation of obtaining subject detection information, the detection information including the expression level information of biomarkers for epithelial ovarian cancer as described in the first aspect.

[0021] The computing module is used to perform the following:

[0022] The expression level information of the biomarkers is substituted into the diagnostic model of epithelial ovarian cancer described in the sixth aspect to calculate the EOC CTC Score.

[0023] The diagnostic module is used to perform the following:

[0024] The procedure for determining the health status of the subject based on the EOC CTC Score value is as follows: the criterion for a positive result for epithelial ovarian cancer is: EOC CTC Score > -1.64.

[0025] Preferably, the method for obtaining the expression level information of the biomarker includes:

[0026] Ovarian cancer circulating tumor cells were isolated from the subjects' biological samples.

[0027] RNA was extracted from ovarian cancer circulating tumor cells after lysis and then reverse transcribed into cDNA.

[0028] The concentration of cDNA was quantitatively detected using droplet digital PCR, and the expression level of the corresponding mRNA was calculated.

[0029] Preferably, the method for separating circulating tumor cells of ovarian cancer includes: centrifuging to separate peripheral blood mononuclear cells from the peripheral blood of the subject, mixing the peripheral blood mononuclear cells with antibodies and immunomagnetic beads, and collecting the immunomagnetic beads.

[0030] Preferably, the antibodies include antibodies against epithelial adhesion molecules (EpCAM), N-cadherin, and mesothelin (MSLN).

[0031] Preferably, the immunomagnetic beads include a silica shell, on which an anti-adhesion layer and streptavidin are sequentially modified. The anti-adhesion layer is composed of polycarboxybetaine methacrylate (pCBMA), which can reduce non-specific adhesion of peripheral blood mononuclear cells and other cells, thereby improving the capture efficiency of CTCs.

[0032] Preferably, the biological sample includes peripheral blood.

[0033] In this invention, regarding the sample source, biological information of EOC cancer lesions can be effectively obtained for diagnosis by capturing CTCs in peripheral blood, and the tumor status can be monitored dynamically without damage.

[0034] In this invention, antibodies containing EpCAM, N-cadherin, and MSLN target ovarian cancer-specific circulating tumor cells (CTCs), and modified immunomagnetic beads are used to specifically capture CTCs from peripheral blood containing ovarian cancer (EOC). The immunomagnetic beads comprise a silica shell, an anti-adhesion layer, and streptavidin. The anti-adhesion layer is composed of polycarboxylated betaine methacrylate (pCBMA), which reduces non-specific adhesion of peripheral blood mononuclear cells, thereby improving the capture efficiency of CTCs. The antibodies can be mixed with the circulating tumor cell sample first, and then mixed with the immunomagnetic beads, or vice versa.

[0035] In this invention, the diagnostic model and device can be used to assist in the diagnosis of ovarian cancer and the detection of disease progression or recurrence.

[0036] Eighthly, the present invention provides the use of the biomarkers of epithelial ovarian cancer described in the first aspect in the diagnosis and evaluation of medicaments for the treatment of epithelial ovarian cancer.

[0037] Preferably, the biomarkers for epithelial ovarian cancer are used for the detection of ovarian cancer, as well as for diagnosing disease progression based on changes in biomarkers before, during, and after treatment, thereby determining the patient's treatment efficacy and timely intervention with subsequent drugs.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] This invention is the first to discover that basal cell adhesion molecules, mesothelin, and folate receptor α, and their encoded nucleic acids, are associated with the occurrence and development of epithelial ovarian cancer (EOC). By detecting their mRNA levels and using multivariate logistic regression, a CTC RNA diagnostic model for EOC was established. This model demonstrates superior diagnostic performance compared to the commonly used serum tumor marker CA125, with sensitivity and specificity reaching 88% and 83%, respectively, indicating excellent diagnostic capability for EOC. Furthermore, the EOC CTC RNA diagnostic model established in this invention shows excellent potential in the dynamic monitoring of EOC patients. In patients with no tumor progression, the trend of CTC RNA detection results is largely consistent with the clinical trends observed in imaging and serological examinations. For patients with disease progression, CTC RNA detection can more promptly reflect disease progression and detect early warning signals of tumor recurrence. Attached Figure Description

[0040] Figure 1 A flowchart illustrating the process of establishing a diagnostic model;

[0041] Figure 2 Flowchart for screening characteristic mRNA genes of EOC;

[0042] Figure 3A Heatmap of EOC characteristic mRNA gene expression levels in peripheral blood PBMCs of patients with benign ovarian tumors;

[0043] Figure 3B Heatmap of EOC-characteristic mRNA gene expression levels in peripheral blood CTCs before and after treatment in EOC patients;

[0044] Figure 4 ROC curve for a single target gene in distinguishing between EOC patients and patients with benign ovarian tumors;

[0045] Figure 5 EOC CTC score for patients with benign ovarian tumors, untreated EOC patients, and patients undergoing EOC treatment;

[0046] Figure 6 ROC curves for distinguishing EOC from benign ovarian tumors by EOC CTC Score and CA125 (Cut off = 35 U / mL);

[0047] Figure 7 A schematic diagram illustrating continuous sampling time and sample processing for dynamic monitoring of EOC patient efficacy;

[0048] Figure 8 The above are the EOC CTC Score and clinical hematological tumor markers of four EOC patients under dynamic monitoring before tumor cytoreductive surgery and chemotherapy in Example 2 of this invention.

[0049] Figure 9A Preoperative MRI images of EOC-26 patients;

[0050] Figure 9B PET-CT images of EOC-26 patients 366 days post-surgery. Detailed Implementation

[0051] To further illustrate the technical means and effects of this invention, the following description, in conjunction with embodiments and accompanying drawings, provides a further explanation of the invention. It is understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it.

[0052] Where specific techniques or conditions are not specified in the examples, they shall be performed in accordance with the techniques or conditions described in the literature in this field, or in accordance with the product instructions. Reagents or instruments whose manufacturers are not specified are all conventional products that can be purchased through legitimate channels.

[0053] In specific embodiments of the present invention, such as Figure 1 As shown, CTCs from peripheral blood of subjects were captured using magnetic beads modified with multiple antibodies (EpCAM / MSLN / N-cadherin) to capture EOC-derived CTCs. The expression levels of characteristic mRNAs within CTCs were detected using droplet digital PCR (ddPCR) technology to determine whether subjects had EOC. CTC mRNA follow-up was used to diagnose disease progression and recurrence. An EOCCTC RNA diagnostic model established using multivariate logistic regression was used to score the CTC mRNA detection results of subjects.

[0054] Example 1

[0055] This embodiment describes the construction and evaluation of an EOC diagnostic model based on CTCs characteristic mRNA.

[0056] A total of 47 participants were recruited, including patients with benign ovarian tumors, ovarian cancer, and fallopian tube cancer. All of these participants were enrolled in the study from January 2022 to December 2022. Among them were 15 patients with stage I-IV ovarian cancer, 2 patients with fallopian tube cancer, and 30 patients with benign ovarian tumors. Peripheral blood samples were collected from 77 participants.

[0057] The inclusion criteria for the study participants were as follows: 1) Those who had not yet received anti-tumor treatments such as surgery, radiotherapy, or chemotherapy at the time of initial diagnosis; 2) Those who understood and agreed to participate in this study and had signed an informed consent form; 3) Those without hematological diseases. The exclusion criteria for the study participants and blood samples were as follows: 1) Patients with a history of other malignant tumors; 2) Those with severe infections or heart, lung, or kidney dysfunction; 3) Those who experienced insufficient blood volume, coagulation, or hemolysis after blood sample collection. This study has been approved by the Ethics Review Committee of the Second Affiliated Hospital of Soochow University (Approval No. #JD-LK-2022-079-01), and all participants provided informed consent for this study.

[0058] Step 1, Screening for EOC-characteristic mRNA genes:

[0059] First, using the Cancer Genome Atlas (TCGA), the Genotype-Tissue Expression (GTEx) database, and the GSE4122 microarray data from the GeneExpression Omnibus (GEO) dataset, differentially expressed genes that were overexpressed in EOC tissues but underexpressed or absent in normal ovarian tissues and benign ovarian tumor tissues were screened using the limma package in R as candidate genes. The screening criteria were set as |logFC|≥2 and P<0.001. Subsequently, the candidate genes were screened again using the HPA immune cell dataset from the Human Protein Atlas (HPA) database to exclude genes that are highly expressed in immune cells. Finally, nine target gene mRNAs that are specifically highly expressed in EOC were identified: BCAM (basal cell adhesion molecule), KLK10 (kallikrein-associated peptidase 10), MSLN (mesothelin), LYPD1 (containing the LY6 / PLAUR domain 1), MUC16 (mucin 16), SCGB2A1 (secretory globin family 2A member 1), WFDC2 (human WAP tetradisulfide core domain protein 2), KLK7 (kallikrein-associated peptidase 7), and FOLR1 (folate receptor α). Figure 2 ).

[0060] Step 2: Isolation of CTCs and RNA extraction from clinical patient blood samples:

[0061] 5 mL of peripheral venous blood was collected from all subjects in EDTA vacuum anticoagulation blood collection tubes. After sampling, the tubes were inverted 5 times and transported at 4°C. The blood was processed and used within 24 hours. To avoid epithelial cell contamination, all blood samples were from peripheral blood outside the first tube. The blood collection tubes were centrifuged at 300g for 15 min in a horizontal centrifuge at room temperature to obtain plasma and the lower layer of blood cells. After aspirating the upper plasma, 1×PBS solution was added to the blood cell pellet to dilute it to 10 mL (twice the initial peripheral blood volume), and the mixture was thoroughly mixed. SepMate was then used to collect the final sample. TM Peripheral blood mononuclear cell (PBMC) layers were separated by centrifuge tubes. After washing, 500 μL (0.1 times the initial peripheral blood volume) of 1×PBS solution was added to resuspend the PBMCs. 2 mL of the PBMC suspension corresponding to whole blood was incubated with 0.1 mg of multi-antibody (EpCAM / MSLN / N-cadherin, antibody purchase information is detailed in Table 1) modified magnetic beads at 25°C for 30 min to capture ovarian cancer CTCs. After washing the CTC samples, 700 μL of QIAzol lysis buffer was added, and Direct-zol lysis was performed. TM RNA was extracted using the RNAMicroprep kit (ZYMO RESEARCH) and reverse transcribed into cDNA template using the reverse transcription kit (Takara). The cDNA template was then stored at -20°C for later use.

[0062] Table 1

[0063]

[0064] Step 3: Quantify the characteristic mRNA genes of CTCs using ddPCR:

[0065] Based on the target gene mRNA coding gene sequence screened in step one, target gene primers and probes (Thermo Fisher) were ordered. The target gene mRNA molecules in the sample were detected using a QX200 AutoDG droplet digital PCR (ddPCR) system to obtain the mRNA expression level of the EOC CTCs target gene captured in each 2 mL peripheral blood sample. The ddPCR detection system had a volume of 20 μL and consisted of CTCs target gene cDNA template, ddPCR probe premix (dUTP-free), sterile enzyme-free water, target gene primers, and probes. After PCR amplification of the target gene cDNA template, the cDNA concentration corresponding to the target gene mRNAs in the sample (transcripts / μL) was quantitatively detected using a droplet reader (QX200 Droplet Reader), and the mRNA expression level of the EOC CTCs target gene captured in each 2 mL peripheral blood sample was calculated. Figure 3A and Figure 3B ).

[0066] Step 4: Construction of the ovarian cancer diagnostic model and evaluation of its diagnostic efficacy:

[0067] For the preoperative blood samples of EOC patients and patients with benign ovarian tumors obtained in step three, ROC analysis was performed to calculate the area under the curve (AUC) to evaluate the diagnostic efficacy of individual target genes. Genes with the best diagnostic efficacy were selected as modeling features, namely BCAM, MSLN, and FOLR1. Figure 4 );

[0068] An EOC CTC RNA diagnostic model was established using multivariate logistic regression analysis. In this example, the EOC CTC Score = -3.904 + 0.482 × BCAM + 0.314 × MSLN + 1.7 × FOLR. Substituting the expression levels of modeling characteristic genes in the samples into the model yields the EOC CTC Score for each subject sample. Figure 5 ).

[0069] The diagnostic efficacy of the EOC CTC RNA diagnostic model was evaluated using ROC curves. The results showed that the model achieved a sensitivity of 88% and a specificity of 83% under optimal conditions, with a diagnostic accuracy of 85% and an AUC of 0.96. Figure 5 To compare the diagnostic capabilities of the EOC CTC RNA diagnostic model with the traditional ovarian cancer marker CA125, at a cutoff value of 35 U / mL, CA125 showed a sensitivity and specificity of 94% and 57%, respectively, a diagnostic accuracy of 70%, and an AUC of 0.75. Figure 6 Therefore, the EOC CTC RNA diagnostic model established in this invention has better diagnostic performance for EOC than the commonly used clinical serum tumor marker CA125.

[0070] Example 2

[0071] This embodiment evaluates the dynamic monitoring performance of the EOC CTC RNA diagnostic model.

[0072] This study tracked and evaluated the treatment outcomes of four patients with endocrine disorders (EOC) during tumor debulking surgery and each chemotherapy cycle. Blood samples were collected from patients before surgery and before the start of chemotherapy for continuous evaluation. The EOC CTC Score was calculated using an EOC CTC RNA diagnostic model. Figure 7 ).

[0073] Following tumor resection and postoperative chemotherapy, the EOC CTCS score of both patients (EOC-74 and EOC-88) showed a gradual decreasing trend during treatment. The dynamic trend of the EOC CTC score in these two patients was consistent with the trends of commonly used serum tumor markers CA125, HE4, and the ROMA index. Figure 8 In patients with EOC-36, the EOC CTC score steadily decreased after the initial surgery but significantly increased on postoperative day 179, suggesting a possible abnormality. In patients with EOC-26, the EOC CTC score steadily increased from postoperative day 114, while serum CA125 levels showed abnormalities on postoperative day 341. Figure 8 Ultimately, the patient was diagnosed with ovarian cancer with multiple recurrent lesions 366 days after surgery. Figure 9A and Figure 9B In the latter two patients, the EOC CTC Score detected disease progression earlier than traditional serum tumor biomarkers, indicating that the EOC CTC Score can reflect changes in the disease status of cancer patients more promptly, providing an opportunity to initiate relevant treatment measures as early as possible. These results suggest that the EOC CTC Score may become a potential auxiliary tool for monitoring treatment response in EOC patients.

[0074] In summary, the EOC CTC RNA diagnostic model based on this invention can accurately diagnose EOC from benign controls. This diagnostic model has a superior diagnostic effect on EOC compared to the clinical serum tumor marker CA125. Its receiver operating characteristic (ROC) area under the curve (AUC) is 0.96, which is significantly higher than that of CA125 (0.75), and the difference is statistically significant (P<0.0001). The diagnostic specificity and accuracy are both higher than those of CA125. In terms of dynamic monitoring, the EOC CTC RNA diagnostic model can reflect the disease progression earlier and detect the patient's recurrence more timely compared to imaging and existing serum detection indicators such as CA125.

[0075] The applicant declares that the detailed method of the present invention is illustrated by the above embodiments, but the present invention is not limited to the above detailed method, that is, it does not mean that the present invention must rely on the above detailed method to be implemented. Those skilled in the art should understand that any improvements to the present invention, equivalent substitutions of the raw materials of the product of the present invention, addition of auxiliary components, selection of specific methods, etc., all fall within the protection scope and disclosure scope of the present invention.

Claims

1. A diagnostic model for epithelial ovarian cancer, characterized in that, The input variables of the diagnostic model include the expression levels of biomarkers for epithelial ovarian cancer in the subject's circulating tumor cells; The output variable of the diagnostic model is the subject's EOC CTC Score, which is calculated using the following formula: EOC CTC Score= -3.904+0.482×A1 +0.314×A2 +1.7×A3; Wherein, A1 represents the expression level of basal cell adhesion molecule or its encoded nucleic acid in circulating tumor cells, A2 represents the expression level of interthelin or its encoded nucleic acid in circulating tumor cells, and A3 represents the expression level of folate receptor α or its encoded nucleic acid in circulating tumor cells. The diagnostic criteria for epithelial ovarian cancer are: EOC CTC Score > -1.

64.

2. A diagnostic device for epithelial ovarian cancer, characterized in that, The diagnostic device includes an information acquisition module, a calculation module, and a diagnostic module; The information acquisition module is used to perform the following: The procedure for obtaining subject testing information includes the expression level information of biomarkers for epithelial ovarian cancer in circulating tumor cells; the biomarkers include basal cell adhesion molecules, mesothelin, and folate receptor α, or, the nucleic acid encoding basal cell adhesion molecules, the nucleic acid encoding mesothelin, and the nucleic acid encoding folate receptor α; the encoded nucleic acid includes mRNA or cDNA; The computing module is used to perform the following: The expression level information of the biomarkers is substituted into the diagnostic model of epithelial ovarian cancer according to claim 1 to calculate the EOC CTC Score. The diagnostic module is used to perform the following: The procedure for determining the health status of the subject based on the EOC CTC Score value is as follows: the criterion for a positive epithelial ovarian cancer is: EOC CTC Score > -1.

64.

3. The diagnostic device for epithelial ovarian cancer according to claim 2, characterized in that, The method for obtaining the expression level information of the biomarker includes: Isolate ovarian cancer circulating tumor cells from biological samples of subjects; RNA was extracted from ovarian cancer circulating tumor cells after lysis and then reverse transcribed into cDNA. The concentration of cDNA was quantitatively detected using droplet digital PCR, and the expression level of the corresponding mRNA was calculated.

4. The diagnostic device according to claim 3, characterized in that, The method for separating circulating tumor cells of ovarian cancer includes: centrifuging to separate peripheral blood mononuclear cells from the peripheral blood of the subject, mixing the peripheral blood mononuclear cells with antibodies and immunomagnetic beads, and collecting the immunomagnetic beads.

5. The diagnostic device according to claim 4, characterized in that, The antibodies include antibodies against epithelial adhesion molecules, antibodies against calcium adhesion proteins, and antibodies against mesothelin.

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