A marker group for esophageal cancer screening and diagnosis and use thereof

Abstract

The application discloses a marker group for esophageal cancer screening and diagnosis and application thereof, and belongs to the field of molecular biological technology. The marker group comprises at least four polypeptides shown in sequences SEQ ID NO. 1-40. The marker group constructed by the application has more excellent sensitivity and specificity when used for auxiliary diagnosis and early screening of esophageal cancer. The auxiliary diagnosis result of the marker group selected by the application is superior to that of an imaging X-ray barium meal contrast (sensitivity and specificity are 87.06% and 61.54% respectively) and a detection method based on 5hmC combination low-throughput whole exon sequencing (sensitivity and specificity are 82.4% and 88.2% respectively). The early screening result is superior to that of an esophageal new cell collector cytological screening method.

Description

Technical Field

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

[0002] Esophageal cancer (EC) is a malignant tumor originating from the esophageal mucosal epithelium. It is one of the most common malignant tumors in clinical practice. It is usually diagnosed at a locally advanced or late stage, missing the opportunity for optimal early surgical treatment. Even with surgical intervention, it is difficult to achieve a good prognosis.

[0003] In 2008, a prospective evaluation of screening in an asymptomatic population (n=740) showed that traditional mechanical balloon cytology had a sensitivity of 39% and a specificity of 85% in diagnosing esophageal squamous cell dysplasia or tumors, while inflatable balloon cytology had a sensitivity of 46% and a specificity of 84%. This demonstrates that traditional cytology has low sensitivity and lacks strong supporting evidence. Currently, multiple expert consensuses in China no longer recommend traditional mechanical balloon and inflatable balloon cytology as methods for early esophageal cancer screening. Recently, a Chinese team improved the shape of the esophageal cell collector, achieving an average cell collection quantity of over 6 million cells per session with good safety and patient tolerability; and developed an AI-assisted cell diagnostic system, which achieved a sensitivity of 90% and a specificity of 93.7% in community screening populations. In 2019, the consensus of domestic experts recommended that a novel esophageal cell collector combined with biomarker detection be used for the initial screening of Barrett's esophagus-associated dysplasia and early esophageal adenocarcinoma. At the same time, the combination of biomarker detection should be further applied in the screening of esophageal squamous epithelial dysplasia and early squamous cell carcinoma.

[0004] Due to the limitations of traditional imaging techniques and tumor marker detection in the diagnosis of early esophageal cancer, novel biomarkers have received increasing attention in recent years. While previous barium swallow radiography can visualize the morphology and structure of the esophageal wall, it is difficult to assess the extent of esophageal cancer invasion, lymph node metastasis, and vascular invasion. Furthermore, barium swallow radiography has relatively low sensitivity and specificity, easily leading to missed diagnoses and misdiagnoses. A prospective study conducted by a team at Nanfang Hospital, Southern Medical University, used either the biomarker 5-hydroxymethylcytosine (5hmC) or a combination of 5hmC and low-throughput whole-exon sequencing (WES) to detect early esophageal squamous cell carcinoma (ESCC). This combination showed a sensitivity of 82.4%, a specificity of 88.2%, an accuracy of 84.3%, and an AUC of 0.934 in ESCC patients. Circulating tumor DNA (ctDNA) testing is convenient to obtain, highly sensitive, and can detect tumor burden in real time, showing great potential in the diagnosis of early esophageal cancer. However, randomized controlled trials are still lacking to demonstrate the value of ctDNA in the diagnosis of early esophageal cancer. Although several biomarkers have been proposed for the auxiliary diagnosis of esophageal cancer, they cannot confirm the diagnosis of precancerous lesions or predict the risk of esophageal cancer progression in high-risk populations. Therefore, current expert consensus does not recommend the use of a single biomarker or a combination of biomarkers for the screening or diagnosis of esophageal cancer. Summary of the Invention

[0005] In view of the above-mentioned shortcomings in the prior art, the present invention provides a biomarker set for esophageal cancer screening and diagnosis and its uses. It has excellent sensitivity and specificity when used for auxiliary diagnosis and early screening of esophageal cancer, and is expected to be applied to the diagnosis and treatment of esophageal cancer.

[0006] To achieve the above objectives, the technical solution adopted by the present invention to solve its technical problem is as follows:

[0007] A biomarker group for esophageal cancer screening and diagnosis includes at least four of the polypeptides shown in SEQ ID NO. 1 to 40, with specific sequences shown in Table 1.

[0008] Table 1. Peptide Sequences

[0009]

[0010] Among them, the second amino acid K of peptide 1 is modified with Acetyl, the third amino acid G of peptide 3 is modified with Phospho, the fourth amino acid G of peptide 11 is modified with Phospho, the first amino acid Q of peptide 21 is modified with Gln->pyro-Glu, the sixth amino acid N is modified with Dehydrated, and the fourth amino acid G of peptide 33 is modified with Dehydrated.

[0011] Furthermore, the biomarker set includes the peptides shown in sequences 1 and 2, as well as the following combinations of peptides:

[0012] The polypeptide combination is one of sequence 3 and sequence 4; sequence 5 and sequence 6; sequence 11 and sequence 12; sequence 30 and sequence 40; sequence 3, sequence 4 and sequence 5; sequence 5, sequence 6 and sequence 7; sequence 11, sequence 12 and sequence 20; sequence 9, sequence 30 and sequence 40.

[0013] Furthermore, the biomarker set includes the peptides shown in sequences 7 and 8, as well as the following combinations of peptides:

[0014] 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.

[0015] Furthermore, the biomarker group includes peptides shown in sequences 1, 10, and 30, as well as peptides shown in sequences 40, 20, 20, and 22; or sequences 9 and 40.

[0016] Furthermore, the biomarker set includes the peptides shown in sequences 5 and 15, as well as the following combinations of peptides:

[0017] The polypeptide combinations are sequences 2 and 16; sequences 25 and 40; sequences 2, 16 and 20; or sequences 9, 25 and 40.

[0018] Furthermore, the biomarker set includes the peptides shown in sequences 4 and 8, as well as the following combinations of peptides:

[0019] The polypeptide combination is sequence 19 and sequence 20; sequence 2 and sequence 6; or sequence 19, sequence 20 and sequence 22.

[0020] Furthermore, the biomarker set includes the peptides shown in sequences 7, 10, 17, and 18.

[0021] Furthermore, the biomarker set includes the polypeptides shown in sequences 7, 10, 17, 18, and 22.

[0022] Furthermore, the biomarker group includes polypeptides as shown in SEQ ID NO. 1~40.

[0023] The use of the above biomarkers in the preparation of formulations for esophageal cancer screening and diagnosis.

[0024] The above biomarkers may be used in basic medical research for non-diagnostic / therapeutic purposes.

[0025] Further, basic medical research includes Western blotting, immunohistochemistry, or flow cytometry.

[0026] The beneficial effects of this invention are:

[0027] The biomarker combination constructed in this invention exhibits superior sensitivity and specificity in the auxiliary diagnosis and early screening of esophageal cancer. The auxiliary diagnostic results of the biomarker combination selected in this invention are superior to those of imaging barium swallow radiography (sensitivity and specificity of 87.06% and 61.54%, respectively) and detection methods based on 5hmC combined low-throughput whole-exome sequencing (sensitivity and specificity of 82.4% and 88.2%, respectively). The early screening results are superior to the esophageal novel cell collector cytology screening method (sensitivity and specificity of 84.2% and 96.2%, respectively), which is currently promoted in various health check-up institutions and recommended as the "only highly efficient non-invasive primary screening method" in the "Guidelines for Esophageal Cancer Screening and Early Diagnosis and Treatment in China (2022)". Attached Figure Description

[0028] Figure 1 ROC curve for marker combination 1;

[0029] Figure 2 ROC curve for marker combination 2;

[0030] Figure 3 ROC curve for marker combination 3;

[0031] Figure 4 ROC curve for marker combination 4;

[0032] Figure 5 ROC curve for marker combination 5;

[0033] Figure 6 ROC curve for marker combination 6;

[0034] Figure 7 ROC curve for marker combination 7;

[0035] Figure 8 ROC curve for marker combination 8;

[0036] Figure 9ROC curve for marker combination 9;

[0037] Figure 10 ROC curve for marker combination 10;

[0038] Figure 11 ROC curve for marker combination 11;

[0039] Figure 12 ROC curve for marker combination 12;

[0040] Figure 13 ROC curve for marker combination 13;

[0041] Figure 14 ROC curve for marker combination 14;

[0042] Figure 15 ROC curve for marker combination 15;

[0043] Figure 16 ROC curve for marker combination 16;

[0044] Figure 17 ROC curve for marker combination 17;

[0045] Figure 18 ROC curve for marker combination 18;

[0046] Figure 19 ROC curve for marker combination 19;

[0047] Figure 20 ROC curve for marker combination 20;

[0048] Figure 21 ROC curve for marker combination 21;

[0049] Figure 22 ROC curve for marker combination 22;

[0050] Figure 23 ROC curve for marker combination 23;

[0051] Figure 24 ROC curve for marker combination 24;

[0052] Figure 25 ROC curve for marker combination 25;

[0053] Figure 26 ROC curve for marker combination 26. Detailed Implementation

[0054] 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.

[0055] The patient samples used in this invention are all from Zhongshan Hospital affiliated with Fudan University and have passed ethical review.

[0056] The experimental methods used in this invention are as follows:

[0057] I. Serum Sample Collection

[0058] 1) Sample type: serum.

[0059] 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.

[0060] 3) Sample storage:

[0061] Use on the same day; store at 2-8℃.

[0062] If not used on the same day, store at -20℃ for up to 30 days;

[0063] If stored for an extended period (more than one month), it should be kept at -80°C.

[0064] The freeze-thaw cycle should not exceed 3 times.

[0065] II. Extraction of analytes from serum

[0066] 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;

[0067] Select the procedure method "Concentrated loading";

[0068] Run the program:

[0069] a. Opening a hole;

[0070] 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;

[0071] 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.

[0072] 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.

[0073] 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.

[0074] 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.

[0075] g. Transfer at least 10µL of sample matrix solution into the H-row pre-reserved well to complete sample processing;

[0076] h. Spot 2.0 µL of the solution from well H onto the hydrophobic-coated biochip (Wuxi Pimo Technology Co., Ltd.).

[0077] i. Vacuum drying for 240 seconds.

[0078] The main components of each reagent are shown in Table 2.

[0079] Table 2 Reagent Composition

[0080]

[0081] III. Mass Spectrometry Data Acquisition and Upload

[0082] The hydrophobic coated biochip (Wuxi Pimo Technology Co., Ltd.) was vacuum dried and placed into a mass spectrometer;

[0083] 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.

[0084] IV. Quality Control

[0085] 1) After data collection, the data will be uploaded to the "Mass Spectrometry Data Analysis Software";

[0086] 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 an esophageal lesion sample, the signal spectrum intensity is not up to standard, etc.

[0087] 3) If the quality control fails, adjust the corresponding parameters according to the quality control results and repeat the serum analyte extraction process;

[0088] 4) If the quality control is qualified, proceed to the next process.

[0089] V. Establishment of Positive Criterion Value and Result Analysis

[0090] The study of positive cutoff values ​​used esophageal cancer samples with clear diagnostic information and normal human samples, covering patients with benign esophageal diseases such as reflux esophagitis, esophageal diverticulum, and achalasia. The core algorithm is based on supervised learning of known esophageal cancer sample atlases. Through a series of processes such as smoothing, noise reduction, and baseline removal, characteristic peaks are screened, a classification model is constructed, and the similarity between hormone signal atlases and known hormone signal atlases stored in the software is calculated (Cannataro M, Guzzi PH, Mazza T, et al. Preprocessing, Management, and Analysis of Mass Spectrometry Proteomics Data[J]. 2005.). Finally, the similarity score positive cutoff value of the kit is determined by the Youden index maximization method. When the similarity score < positive cutoff value, the sample test result is negative; when the similarity score ≥ positive cutoff value, the sample test result is positive. When using the maximum similarity score as the positive cutoff value to assist in the diagnosis of esophageal cancer or to perform early screening for esophageal 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%.

[0091] Example 1: Screening and Identification of Biomarkers

[0092] This invention analyzed 460 normal human samples (238 males (51.7%), 222 females (48.3%), aged 25 to 70 years, with a mean age of 47.2 ± 11.6 years; specifically, the age distribution was: 63 cases aged 25-34, 107 cases aged 35-44, 118 cases aged 45-54, 126 cases aged 55-64, and 46 cases aged 65-70) and 420 esophageal cancer samples (318 males (75.7%), 102 females (24.3%), aged 42 to 78 years, with a mean age of 61.3 ± 11.6 years). The age distribution was as follows: 42-49 years (48 cases), 50-57 years (126 cases), 58-65 years (163 cases), 66-73 years (68 cases), and 74-78 years (15 cases). Time-of-flight mass spectrometry (TOF-MS) was performed. Through first-level mass spectrometry testing, the relative abundance differences of characteristic peak data in normal individuals and esophageal cancer patients were considered, along with statistical differences (p<0.05, t-test). A machine learning algorithm (random forest) was used to rank the influence factors for feature selection and to assess the matching degree of data in the database. This identified 40 blood peptides with diagnostic capabilities for esophageal cancer. The mass-to-charge ratio (m / z), relative abundance, and influence 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 random forest guidedtour. TEST 25, 197–227 (2016). https: / / doi.org / 10.1007 / s11749-016-0481-7).

[0093] The specific parameters for first-order mass spectrometry are as follows:

[0094] Ionization method: Matrix-assisted laser desorption / ionization (MALDI), matrix: α-cyano-4-hydroxycinnamic acid (CHCA); mass range: 100-4000 Da; resolution: 20000 (full mass range); laser energy: 30-40%; acquisition mode: positive ion mode; calibration: external mass calibration using Bruker Peptide Calibration Standard.

[0095] Subsequently, the sequences of these 40 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:

[0096] 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.

[0097] Sequence confirmation criteria: Peptide sequence confirmation 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. False positive exclusion: Specificity is verified through reverse database searches, and the false positive rate is controlled below 1%.

[0098] Secondary mass spectrometry parameters:

[0099] Collision-induced dissociation (CID); Collision energy: 30 eV; Fragment ion mass range: 100-3500 Da; Data acquisition: At least 1000 laser scans per sample to improve signal-to-noise ratio.

[0100] The sequences and specificity of these 40 biomarkers were confirmed by secondary mass spectrometry, ruling out false positives. The specific sequences are shown in Table 1.

[0101] Example 2: Validation of Marker Combinations

[0102] Based on the 40 peptide 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 esophageal 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 prediction 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.). Specific calculation results for performance indicators are shown in Table 3, and the corresponding ROC curves are shown below. Figures 1-26 .

[0103] 1) The use of various biomarker combinations for the auxiliary diagnosis of esophageal cancer was validated in 400 patients (303 males (75.8%), 97 females (24.2%), aged 43 to 76 years, with a mean age of 60.8 ± 9.5 years. Specifically, the distribution was as follows: 46 patients aged 43-49, 118 patients aged 50-57, 143 patients aged 58-65, 78 patients aged 66-73, and 15 patients aged 74-76) and 400 healthy individuals (204 males (51.0%), 196 females (49.0%), aged 28 to 72 years, with a mean age of 46.3 ± 11.2 years. Specifically, the distribution was as follows: 53 patients aged 28-36, 108 patients aged 37-44, 119 patients aged 45-52, 87 patients aged 53-60, and 33 patients aged 61-72).

[0104] 2) A sample of 500 esophageal cancer patients (378 males (75.6%) and 122 females (24.4%), aged 45 to 77 years, with a mean age of 59.7 ± 9.8 years. The specific distribution was: 58 patients aged 45-52, 132 patients aged 53-60, 163 patients aged 61-68, and 147 patients aged 69-77) and 3000 healthy individuals (1523 males, ...) were compared with a sample of 3000 healthy individuals. 50.8% were female and 1477 were male (49.2%), with an age range of 30 to 75 years and a mean age of 47.6 ± 11.9 years. The specific distribution was as follows: 412 cases aged 30-38 years, 587 cases aged 39-46 years, 623 cases aged 47-54 years, 698 cases aged 55-62 years, 518 cases aged 63-70 years, and 162 cases aged 71-75 years. The combination of various biomarkers was validated for early screening of esophageal cancer.

[0105] Table 3 Sensitivity and specificity of biomarker combinations for assisted diagnosis and early screening

[0106]

[0107] (Continued from the table above)

[0108]

[0109] (Continued from the table above)

[0110]

[0111] According to Table 3 and Figures 1-26 The test results show that the auxiliary diagnostic results of the biomarker combination selected in this invention are superior to those of X-ray barium meal imaging (sensitivity and specificity of 87.06% and 61.54%, respectively) and the detection method based on 5hmC combined low-throughput whole-exome sequencing (sensitivity and specificity of 82.4% and 88.2%, respectively). The early screening results are superior to the esophageal novel cell collector cytology screening method (sensitivity and specificity of 84.2% and 96.2%, respectively), which is currently being promoted in various health and medical examination institutions and recommended as the "only highly efficient non-invasive primary screening method" in the "Guidelines for Esophageal Cancer Screening and Early Diagnosis and Treatment in China (2022)".

[0112] 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 esophageal cancer screening and diagnosis, characterized in that, The biomarker group is selected from one of the following combinations of peptides: A) Sequence 1, Sequence 2, Sequence 3 and Sequence 4; B) Sequences 1, 2, 5, and 6; C) Sequence 1, Sequence 2, Sequence 11 and Sequence 12; D) Sequences 1, 2, 30, and 40; E) Sequence 1, Sequence 2, Sequence 3, Sequence 4 and Sequence 5; F) Sequence 1, Sequence 2, Sequence 5, Sequence 6 and Sequence 7; G) Sequence 1, Sequence 2, Sequence 11, Sequence 12 and Sequence 20; H) Sequence 1, Sequence 2, Sequence 9, Sequence 30 and Sequence 40; I) Sequence 2, Sequence 16, Sequence 20, Sequence 5 and Sequence 15; J) Sequences 2, 16, 5, and 15; K) Sequence 2, Sequence 6, Sequence 4 and Sequence 8; L) Sequences 1 to 40; The amino acid sequences of sequences 1 to 40 are shown in SEQ ID NO. 1 to 40; In sequence 1, the second amino acid K has an Acetyl modification; in sequence 3, the third amino acid G has a Phospho modification; in sequence 11, the fourth amino acid G has a Phospho modification; in sequence 21, the first amino acid Q has a Gln->pyro-Glu modification, and the sixth amino acid N has a Dehydrated modification; in sequence 33, the fourth amino acid G has a Dehydrated modification.

2. Use of the reagent for detecting the biomarker group of claim 1 in the preparation of preparations for esophageal cancer screening and diagnosis.

Images