Marker group for screening and diagnosing cervical cancer and application of marker group

Abstract

The invention discloses a marker group for screening and diagnosing cervical cancer and application of the marker group, and belongs to the technical field of molecular biology. The marker group comprises at least four of polypeptides as shown in sequences SEQ ID NO.1-42. The invention further discloses a method for detecting the biomarker group. The marker combination constructed by the invention has more excellent sensitivity and specificity when being used for auxiliary diagnosis and early screening of cervical cancer. The auxiliary diagnosis result is superior to the efficacy (the sensitivity is 81.1%, and the specificity is 78.9%) of serum CEA, SCC-Ag and CA125 combined detection in early stage cervical cancer diagnosis; the diagnostic sensitivity and specificity of the serum cf-DNA are 72.2% and 81.7% respectively. The early screening result is better than that of the current internationally recognized cervical cancer screening means, namely a combined screening mode of cytological detection (TCT) and human papilloma virus (HPV) virology detection.

Description

Technical Field

[0001] This invention belongs to the field of molecular biotechnology, specifically relating to a biomarker group for cervical cancer screening and diagnosis and its uses. Background Technology

[0002] Cervical cancer is one of the most common malignant tumors among women worldwide. It is caused by long-term, persistent infection of the cervix with high-risk human papillomavirus (HR-HPV).

[0003] Early diagnosis and postoperative follow-up are crucial for the prevention and treatment of cervical cancer. Currently, the main clinical methods for cervical cancer examination include exfoliative cytology and colposcopy. However, due to factors such as the subjectivity of the examinee and the uncertainty of cervical lesions, these two examinations still have limitations, often resulting in misdiagnosis or missed diagnosis. Today, HPV and cytology screening for cervical cancer are very mature. HPV testing has high sensitivity but slightly lower specificity, while cytology examination has lower sensitivity but slightly higher specificity; the two have complementary roles. However, highly specific and sensitive tumor markers are still lacking to guide postoperative patient follow-up, early detection of recurrence, and timely treatment.

[0004] Liquid biopsy, as an emerging diagnostic technology, can overcome the influence of tumor heterogeneity, providing more comprehensive molecular information about tumors and offering strong support for guiding clinical treatment and assessing prognosis. Currently, cervical cancer is considered a disease associated with HPV infection, with HPV types 16 and 18 being the most common. In addition to cervical secretions, the serum of cervical cancer patients also contains HPV DNA. Related studies suggest that the HPV DNA in serum originates from tumor cells because the types of HPV DNA in serum are the same as those in tumor tissue. Therefore, quantitative detection of serum HPV DNA can replace quantitative detection of ctDNA. HPV infection alone does not lead to cervical cancer; tumor development is necessarily accompanied by changes in genetic material. Many studies have shown that abnormal methylation also exists in the genome of cervical cancer cells, including hypermethylation of tumor suppressor gene promoters leading to tumor suppressor gene inactivation. Liquid biopsy can detect abnormally methylated genes in the blood, thereby achieving early diagnosis. However, which genes to screen for is currently undetermined.

[0005] Researchers used a high-precision gas chromatography-mass spectrometry (GC-MS) platform to identify and validate a series of metabolic biomarkers that could potentially diagnose cervical intraepithelial neoplasia (CIN) and cervical cancer. The diagnostic performance of single metabolites and combinations was evaluated using receiver operating characteristic (ROC) curves. The results showed that the combination of 2-methyl-1-propylamine (isobutylamine) and estrone performed best in all cross-validations, achieving excellent values ​​in area under the curve (AUC), sensitivity, and specificity. This combination demonstrated high diagnostic accuracy in distinguishing healthy women without HPV infection from patients with different degrees of CIN (including CIN I, II, and III), with an AUC of 0.908, a sensitivity of 93.0%, and a specificity of 87.2%. However, GC-MS requires derivatization of non-volatile metabolites, which is cumbersome and may introduce errors, affecting the reproducibility of results. Clinical application still requires a more stable and highly reproducible monitoring platform and standardized data. Summary of the Invention

[0006] In view of the above-mentioned shortcomings in the prior art, the present invention provides a set of biomarkers for cervical cancer screening and diagnosis and their uses. When used for auxiliary diagnosis and early screening of cervical cancer, it has excellent sensitivity and specificity and is expected to be applied to the diagnosis and treatment of cervical cancer.

[0007] To achieve the above objectives, the technical solution adopted by the present invention to solve its technical problem is as follows: A biomarker set for cervical cancer screening and diagnosis includes at least four of the polypeptides shown in SEQ ID NO. 1-42, with specific sequences shown in Table 1.

[0008] Table 1. Peptide Sequences

[0009] Among them, the first amino acid Q of peptide 13 is modified with Gln->pyro-Glu, and the sixth amino acid N is modified with Dehydrated; the fourth amino acid G of peptide 16 is modified with Dehydrated; the fourth amino acid G of peptide 28 is modified with Phospho; the amino acid K of peptide 33 is modified with Acetyl; and the third amino acid G of peptide 38 is modified with Phospho.

[0010] Furthermore, the biomarker set includes the peptides shown in sequences 1 and 2, as well as the following combinations of peptides: The polypeptide combination is one of sequence 3 and sequence 4; sequence 5 and sequence 6; sequence 15 and sequence 16; sequence 30 and sequence 42; sequence 3, sequence 4 and sequence 5; sequence 5, sequence 6 and sequence 7; sequence 15, sequence 16 and sequence 20; sequence 28, sequence 30 and sequence 40.

[0011] Furthermore, the biomarker set includes the peptides shown in sequences 7 and 8, as well as the following combinations of peptides: The polypeptide combination is one of the following: sequence 3 and sequence 4; sequence 9 and sequence 10; sequence 3, sequence 4 and sequence 9; or sequence 9, sequence 10 and sequence 11.

[0012] Furthermore, the biomarker group includes peptides shown in sequences 1, 10, and 30, as well as peptides shown in sequences 42, 20, 20, and 28; or sequences 28 and 42.

[0013] Furthermore, the biomarker set includes the peptides shown in sequences 5 and 18, as well as the following combinations of peptides: The polypeptide combination is sequence 2 and sequence 19; or sequence 2, sequence 19 and sequence 20.

[0014] Furthermore, the biomarker set includes the peptides shown in sequences 4 and 8, as well as the following combinations of peptides: The polypeptide combination is the polypeptide shown in sequences 19 and 20; sequences 2 and 6; or sequences 19, 20 and 22.

[0015] Furthermore, the biomarker set includes the polypeptides shown in sequences 7, 10, 17, 18 and / or 20.

[0016] Furthermore, the marker set includes sequence 5, sequence 15, sequence 25, and sequence 42, or sequence 28 and sequence 42.

[0017] Furthermore, the biomarker group includes polypeptides as shown in SEQ ID NO. 1~42.

[0018] The use of the above biomarkers in the preparation of formulations for cervical cancer screening and diagnosis.

[0019] The above biomarkers may be used in basic medical research for non-diagnostic / therapeutic purposes.

[0020] Further, basic medical research includes Western blotting, immunohistochemistry, or flow cytometry.

[0021] The beneficial effects of this invention are: The biomarker combination constructed in this invention exhibits superior sensitivity and specificity in the auxiliary diagnosis and early screening of cervical cancer. Its auxiliary diagnostic results are superior to the efficacy of combined detection of serum CEA, SCC-Ag, and CA125 in the early diagnosis of cervical cancer (sensitivity 81.1%, specificity 78.9%); the diagnostic sensitivity and specificity of serum cf-DNA are 72.2% and 81.7%, respectively. The early screening results are also superior to the currently internationally recognized cervical cancer screening method—the combined screening of cytological testing (TCT) and human papillomavirus (HPV) virological testing (TCT combined with HPV testing has a sensitivity of 84.21% and a specificity of 78.13%). Attached Figure Description

[0022] Figure 1 ROC curve for marker combination 1; Figure 2 ROC curve for marker combination 2; Figure 3 ROC curve for marker combination 3; Figure 4 ROC curve for marker combination 4; Figure 5 ROC curve for marker combination 5; Figure 6 ROC curve for marker combination 6; Figure 7 ROC curve for marker combination 7; Figure 8 ROC curve for marker combination 8; Figure 9 ROC curve for marker combination 9; Figure 10 ROC curve for marker combination 10; Figure 11 ROC curve for marker combination 11; Figure 12 ROC curve for marker combination 12; Figure 13 ROC curve for marker combination 13; Figure 14 ROC curve for marker combination 14; Figure 15 ROC curve for marker combination 15; Figure 16 ROC curve for marker combination 16; Figure 17 ROC curve for marker combination 17; Figure 18ROC curve for marker combination 18; Figure 19 ROC curve for marker combination 19; Figure 20 ROC curve for marker combination 20; Figure 21 ROC curve for marker combination 21; Figure 22 ROC curve for marker combination 22; Figure 23 ROC curve for marker combination 23; Figure 24 ROC curve for marker combination 24; Figure 25 ROC curve for marker combination 25; Figure 26 ROC curve for marker combination 26. Detailed Implementation

[0023] The specific embodiments of the present invention are described below to enable those skilled in the art to understand the present invention. However, it should be understood that the present invention is not limited to the scope of the specific embodiments. For those skilled in the art, various changes are obvious as long as they are within the spirit and scope of the present invention as defined and determined by the appended claims. All inventions utilizing the concept of the present invention are protected.

[0024] The patient samples used in this invention are all from Zhongshan Hospital affiliated with Fudan University and have passed ethical review.

[0025] The experimental methods used in this invention are as follows: I. Serum Sample Collection 1) Sample type: serum.

[0026] 2) Collection requirements: Fasting is required. Use a coagulation tube to draw 5 mL of venous blood, let it stand for 30 min, centrifuge at 3000 rpm for 15 min, and take out about 1 mL of serum and put it into a cryopreservation tube.

[0027] 3) Sample storage: Use on the same day; store at 2-8℃. If not used on the same day, store at -20℃ for up to 30 days; If stored for an extended period (more than one month), it should be kept at -80°C.

[0028] The freeze-thaw cycle should not exceed 3 times.

[0029] II. Extraction of analytes from serum 1) After calibrating the mass spectrometer, turn on the Solid Bio Fully Automated Sample Analysis System SPS1000 / SPS4000, and put in the consumables, matching reagent kits and the sample to be tested; Select the procedure method "Concentrated loading"; Run the program: a. Opening a hole; b. Take at least 10µL of serum sample and activation reagent, mix them in a 1:1 ratio, and place them in the G-row pre-reserved well for later use; c. Clean the custom pipette tip in cleaning reagent 1 and cleaning reagent 2 in sequence. Each time, aspirate at least 10µL of liquid and repeat the aspiration and dispensing process at least 3 times. d. Process the serum mixture in the G-row wells using the cleaned custom pipette tips. Aspirate at least 10 µL of solution each time, repeating the process at least three times. e. Clean the custom pipette tip after adsorbing the serum mixture using cleaning reagent 3. During cleaning, aspirate at least 10µL of liquid each time, repeating the aspiration and dispensing process at least 3 times. f. Transfer no less than 10µL of buffer reagent into the H-row pre-reserved hole, and place the customized pipette tip after using cleaning reagent 3 into the liquid to draw no less than 10µL of liquid. Repeat the suction and aspiration at least 3 times. g. Transfer at least 10µL of sample matrix solution into the H-row pre-reserved well to complete sample processing; h. Spot 2.0 µL of the solution from well H onto the hydrophobic-coated biochip (Wuxi Pimo Technology Co., Ltd.). i. Vacuum drying for 240 seconds.

[0030] The main components of each reagent are shown in Table 2.

[0031] Table 2 Reagent Composition

[0032] III. Mass Spectrometry Data Acquisition and Upload The hydrophobic coated biochip (Wuxi Pimo Technology Co., Ltd.) was vacuum dried and placed into a mass spectrometer; Data acquisition is performed using the pre-defined SP1 voltage (target high voltage), SP2 voltage (pulse high voltage), focusing voltage (lens high voltage), detector voltage (MCP voltage), pulse delay time, acquisition card range, target diameter, laser frequency, calibration method, and laser intensity.

[0033] IV. Quality Control 1) After data collection, the data will be uploaded to the "Mass Spectrometry Data Analysis Software"; 2) The software reads the sample information and signal spectrum, and judges whether the sample and sample pretreatment are qualified according to the quality control model; quality control failure may include a variety of possibilities, including the sample is not a cervical lesion sample, the signal spectrum intensity is not up to standard, etc.

[0034] 3) If the quality control fails, adjust the corresponding parameters according to the quality control results and repeat the serum analyte extraction process; 4) If the quality control is qualified, proceed to the next process.

[0035] V. Establishment of Positive Criterion Value and Result Analysis The study of positive cutoff values ​​used cervical cancer samples with clear diagnostic information and normal human samples. Benign lesion samples included samples of chronic cervicitis and low-grade CIN (CIN1), among other benign non-cervical cancer types. The core algorithm was based on supervised learning using known cervical cancer sample atlases. Through a series of processes such as smoothing, noise reduction, and baseline removal, characteristic peaks were selected, a classification model was constructed, and the similarity between the hormone signal atlas and known hormone signal atlases stored in the software was calculated (Cannataro M, Guzzi PH, Mazza T, et al. Preprocessing, Management, and Analysis of Mass Spectrometry Proteomics Data[J]. 2005.). Finally, the similarity score of the kit was determined using the Youden exponent maximum method to determine the positive cutoff value. When the similarity score < the positive cutoff value, the sample test result was negative; when the similarity score ≥ the positive cutoff value, the sample test result was positive. When using the maximum similarity score as the positive cutoff value to assist in the diagnosis of cervical cancer or to perform early screening for cervical cancer, the sensitivity is calculated as: Sensitivity = (Number of true positives / (Number of true positives + Number of false negatives)) × 100%, and Specificity is calculated as: (Number of true negatives / (Number of true negatives + Number of false positives)) × 100%.

[0036] Example 1: Screening and Identification of Biomarkers This study analyzed 400 healthy individuals (all female, aged 20-65 years, mean age 42.3 ± 11.6 years; specific age distribution: 68 cases aged 20-29, 107 cases aged 30-39, 118 cases aged 40-49, 83 cases aged 50-59, and 24 cases aged 60-65) and 400 cervical cancer samples (all female, aged 25-68 years, mean age 48.7 ± 10.2 years; specific age distribution: 43 cases aged 25-34, 43 cases aged 35-65, 43 cases aged 40-49, 43 cases aged 50-59, and 24 cases aged 60-65). Time-of-flight mass spectrometry (TOF-MS) was performed on 112 patients aged 44-44, 146 patients aged 45-54, 78 patients aged 55-64, and 21 patients aged 65-68. After primary mass spectrometry testing, the relative abundance differences of characteristic peaks in normal individuals and cervical cancer patients were considered, along with statistical differences (p<0.05, t-test). A machine learning algorithm (random forest) was used to rank the impact factors for feature selection, and the matching degree of data in the database was also considered. This identified 42 blood peptides with diagnostic capabilities for cervical cancer. The mass-to-charge ratio (m / z), relative abundance, and impact factors of these peptides are shown in Table 1. The relative abundance was normalized to ln(mean signal intensity of cancer patient samples / mean signal intensity of normal samples) based on the normal human sample. The feature peak selection in machine learning was based on feature importance assessment using ensemble learning. Multiple decision trees were constructed to quantify the contribution of each mass-to-charge ratio (m / z) peak in classification / prediction. The feature importance score, or influence factor, is obtained by calculating the mean reduction in impurity caused by the feature when splitting across all tree nodes (Biau, G., Scornet, E. A randomforest guided tour. TEST 25, 197–227 (2016). https: / / doi.org / 10.1007 / s11749-016-0481-7).

[0037] The specific parameters for first-order mass spectrometry are as follows: Ionization method: Matrix-assisted laser desorption / ionization (MALDI), with α-cyano-4-hydroxycinnamic acid (CHCA) as the matrix.

[0038] Quality range: 100-4000 Da.

[0039] Resolution: 20000 (full quality range).

[0040] Laser energy: 30-40%.

[0041] Acquisition mode: Positive ion mode.

[0042] Calibration: External quality calibration was performed using the Bruker Peptide Calibration Standard.

[0043] Subsequently, the sequences of these 42 substances in the clinical serum were confirmed using secondary mass spectrometry (MS / MS or TOF / TOF is a peptide identification method recommended by the guidelines of the China Food and Drug Administration and the U.S. Food and Drug Administration). The secondary mass spectrometry data analysis process is as follows: Data analysis methods: Mascot software (version 2.8) was used for database searching, with the UniProt Human Proteome Database (released in 2023) as the target database. Search parameters: Enzyme was set to "no digestion", parent ion mass error was allowed ±0.5 Da, fragment ion mass error was allowed ±0.3 Da, fixed modification was cysteine ​​urea methylation, and variable modification was methionine oxidation.

[0044] Sequence confirmation criteria: The confirmation of peptide sequences is based on the matching of fragment ion spectra (b- and y- ions) with theoretical spectra. A Mascot score higher than 30 (p<0.05) is considered significant.

[0045] False positive exclusion: Specificity was verified through reverse database search, and the false positive rate was controlled to below 1%.

[0046] Secondary mass spectrometry parameters: Collision-induced dissociation (CID).

[0047] Collision energy: 30 eV.

[0048] Fragment ion mass range: 100-3500 Da.

[0049] Data acquisition: Each sample is scanned at least 1000 times with lasers to improve the signal-to-noise ratio.

[0050] The sequences and specificity of these 42 biomarkers were confirmed by secondary mass spectrometry, ruling out false positives. The specific sequences are shown in Table 1.

[0051] Example 2: Validation of Marker Combinations Based on the 42 polypeptide biomarkers identified and confirmed in Example 1 (sequences and mass-to-charge ratios are shown in Table 1), different biomarker combinations were formed as shown in Table 3, and the sensitivity and specificity of the biomarker combinations in the auxiliary diagnosis and early screening of cervical cancer were verified. The analysis process is as follows: For all validation cohort samples, the same MALDI-TOF MS platform and parameters as in Example 1 were used for detection to obtain the mass spectrometry peak intensity data of all biomarkers in Table 1. The entire detection process was blinded, meaning that the experimental operators were unaware of the sample grouping information. The raw mass spectrometry data were processed by baseline correction, smoothing, and normalization (based on the internal standard peak intensity), and then the peak area or intensity value of each biomarker was extracted. Statistical analysis was performed using R software (version 4.0.2). The preprocessed biomarker intensity data were input into a logistic regression model. For auxiliary diagnostic validation, ten-fold cross-validation was performed using all samples from this cohort (Sun T, Liu J, Yuan H, Li X, Yan H. Construction of a risk prediction model for lung infection after chemotherapy in lung cancer patients based on the machine learning algorithm. Front Oncol. 2024 Aug 9;14:1403392. doi: 10.3389 / fonc.2024.1403392. PMID:39184040; PMCID: PMC11341396.). For early screening validation, given the imbalanced sample size, oversampling (SMOTE) was used to process the data before model training and testing (van den Goorbergh R, van Smeden M, Timmerman D, Van Calster B. The harm of class imbalance corrections for riskprediction models: illustration and simulation using logistic regression. JAm Med Inform Assoc. 2022 Aug 16;29(9):1525-1534. doi: 10.1093 / jamia / ocac093.PMID: 35686364; PMCID: PMC9382395.).The data analysis process referenced general guidelines for constructing clinical predictive models (Zweig MH, Campbell G. Receiver-operating characteristic (ROC) plots: a fundamental evaluation tool in clinical medicine. Clin Chem. 1993 Apr;39(4):561-77. Erratum in: Clin Chem 1993 Aug;39(8):1589. PMID: 8472349.). Sensitivity, specificity, and area under the receiver operating characteristic (AUC) curve were calculated for each biomarker combination. The specific calculation results for the performance indicators are shown in Table 3, and the corresponding ROC curves are shown in [Table data missing]. Figures 1-26 .

[0052] 1) Using 500 cervical cancer patients (all female, aged 27 to 67 years, mean age 49.2 ± 9.8 years; specifically distributed as follows: 58 cases aged 27-35, 113 cases aged 36-42, 147 cases aged 43-49, 118 cases aged 50-56, and 64 cases aged 57-67) and 500 healthy individuals (all female, aged 23 to 65 years, mean age 44.7 ± 10.3 years; specifically distributed as follows: 72 cases aged 23-31, 108 cases aged 32-38, 126 cases aged 39-45, 115 cases aged 46-52, and 79 cases aged 53-65), the auxiliary diagnosis of cervical cancer based on various biomarker combinations was validated. After score conversion, the positive judgment value of the model was finally determined to be 0.377 using the Youden index maximization method.

[0053] 2) Using a sample of 600 cervical cancer patients (all female, aged 25 to 65 years, mean age 47.8 ± 10.1 years; specifically distributed as follows: 63 cases aged 25-33, 137 cases aged 34-41, 168 cases aged 42-48, 157 cases aged 49-55, and 75 cases aged 56-65) and 3000 healthy individuals (all female, aged 20 to 64 years, mean age 43.2 ± 11.4 years; specifically distributed as follows: 423 cases aged 20-28, 587 cases aged 29-36, 712 cases aged 37-44, 698 cases aged 45-52, and 580 cases aged 53-64), the early screening of cervical cancer based on various biomarker combinations was validated. After score conversion, the positive judgment value of the model was finally determined to be 0.416 using the Youden index maximization method.

[0054] Table 3 Sensitivity and specificity of biomarker combinations for assisted diagnosis and early screening

[0055] (Continued from the table above)

[0056] According to Table 3 and Figures 1-26 The test results show that the combination of biomarkers selected in this invention has better auxiliary diagnostic results than the combined detection of serum CEA, SCC-Ag, and CA125 in the early diagnosis of cervical cancer (sensitivity 81.1%, specificity 78.9%); the diagnostic sensitivity and specificity of serum cf-DNA are 72.2% and 81.7%, respectively. The early screening results are also superior to the currently internationally recognized cervical cancer screening method—the combined screening of cytology test (TCT) and human papillomavirus (HPV) virology test (TCT combined with HPV test has a sensitivity of 84.21% and a specificity of 78.13%).

[0057] Finally, it should be noted that the above specific embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to examples, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications and substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A biomarker set for cervical cancer screening and diagnosis, characterized in that, The biomarker group includes at least four of the polypeptides represented by sequences SEQ ID NO.1 to 42.

2. The marker set according to claim 1, characterized in that, The biomarker set includes the peptides shown in Sequence 1 and Sequence 2, as well as the following combinations of peptides: The polypeptide combination is one of sequence 3 and sequence 4; sequence 5 and sequence 6; sequence 15 and sequence 16; sequence 30 and sequence 42; sequence 3, sequence 4 and sequence 5; sequence 5, sequence 6 and sequence 7; sequence 15, sequence 16 and sequence 20; sequence 28, sequence 30 and sequence 40.

3. The marker set according to claim 1, characterized in that, The biomarker set includes the peptides shown in sequences 7 and 8, as well as the following combinations of peptides: The polypeptide combination is one of the following: sequence 3 and sequence 4; sequence 9 and sequence 10; sequence 3, sequence 4 and sequence 9; sequence 9, sequence 10 and sequence 11.

4. The marker set according to claim 1, characterized in that, The biomarker set includes polypeptides represented by sequences 1, 10, and 30, as well as polypeptides represented by sequences 42, 20, 20, and 28, or 28 and 42.

5. The set of markers according to claim 1, characterized in that, The biomarker set includes the peptides shown in sequences 5 and 18, as well as the following combinations of peptides: The polypeptide combination is sequence 2 and sequence 19; or sequence 2, sequence 19 and sequence 20.

6. The set of markers according to claim 1, characterized in that, The biomarker set includes the peptides shown in sequences 4 and 8, as well as the following combinations of peptides: The polypeptide combination is the polypeptide shown in sequence 19 and sequence 20; sequence 2 and sequence 6; or sequence 19, sequence 20 and sequence 22.

7. The set of markers according to claim 1, characterized in that, The biomarker set includes the polypeptides shown in sequence 7, sequence 10, sequence 17, sequence 18 and / or sequence 20.

8. The set of markers according to claim 1, characterized in that, The group of markers includes sequence 5, sequence 15, sequence 25, and sequence 42 or sequence 28 and sequence 42.

9. The set of markers according to claim 1, characterized in that, The biomarker group includes polypeptides as shown in SEQ ID NO. 1~42.

10. Use of the biomarker group according to any one of claims 1 to 9 in the preparation of a formulation for cervical cancer screening and diagnosis.

Images