A biomarker for detecting advanced colorectal adenoma and its application

Abstract

The present invention provides a biomarker for the detection of advanced colorectal adenoma and its application. Using an immunoprecipitation sequencing platform (mDIP-Seq) based on high-resolution mRNA display technology, plasma antibody diagnostic markers for advanced colorectal adenoma are screened, and a multiple antibody panel suitable for the diagnosis of advanced colorectal adenoma is developed in combination with a random forest algorithm, a disease prediction model is constructed, and the accuracy of the diagnosis of advanced colorectal adenoma is improved. Compared with invasive colonoscopy, this method has the characteristics of low cost, scalability, and high throughput, and is suitable for large-scale advanced colorectal adenoma screening in the population.

Description

Technical Field

[0001] The present invention relates to the field of diagnosis of advanced colorectal adenoma, and in particular to a biomarker for detecting advanced colorectal adenoma and an application thereof. Background Art

[0002] Colorectal cancer (CRC) has an insidious onset and early symptoms are not easy to detect, but it actually has a long intervention window. Colorectal adenoma (CRA) is a common benign intestinal tumor that is closely related to the occurrence of CRC and is widely considered to be a precancerous lesion. Patients who are discovered and diagnosed in the precancerous adenoma stage have a higher 5-year survival rate after radical surgery, so early screening and intervention can significantly improve the survival rate of patients.

[0003] However, most CRA patients have no obvious symptoms, and only a few show clinical manifestations such as abdominal discomfort, changes in bowel habits or blood in the stool, and the early symptoms of CRC patients lack specificity. Therefore, the diagnosis of colorectal tumors relies on the highly invasive colonoscopy biopsy as the most reliable method. Colonoscopy biopsy is highly invasive, the examination process is painful, and the patient compliance is low, often missing the best time for treatment. Other existing clinical tests also have many unfavorable factors in terms of accuracy, sensitivity, and testing costs. For example, serum carcinoembryonic antigen (CEA) has large individual differences, and there are also positive results in other tumors such as gastric cancer, lung cancer, and pancreatic cancer. The sensitivity is low and the specificity is insufficient; the false positive rate of fecal occult blood test is high, and the sensitivity for early colorectal cancer is low; the cost of fecal DNA methylation detection is high; imaging examination methods such as magnetic resonance imaging and computed tomography have high requirements for equipment and technology, and are expensive, making it difficult to be widely used for large-scale screening. Therefore, it is urgent to develop a sensitive, reliable, and suitable early detection marker for large-scale screening of advanced colorectal adenomas and early colorectal cancer.

[0004] Autoantibodies targeting tumor-associated antigens (TAAs) have gradually become a hot topic in the study of new markers for tumor plasma diagnosis because they can simultaneously reflect the state of cancer cells and the body's immune system, are more stable and easier to detect than TAAs, and have the advantage of being detected in the early stages of tumor development and before clinical symptoms appear. For example, anti-p53 antibodies are the most studied autoantibodies related to the diagnosis of colorectal cancer, and can distinguish colorectal cancer from healthy controls or benign diseases. However, due to their low sensitivity, they must be used in combination with other indicators, and it is difficult to diagnose advanced colorectal adenomas at an early stage.

[0005] In addition, a large number of studies have shown that the intestinal microbiome is an important etiological factor in the occurrence and progression of colorectal cancer, and some fecal microbial markers associated with colorectal tumors have also been identified. However, fecal microbial markers have defects such as low biomass and easy interference by reagents or environmental pollutants. In addition, the human intestinal microbiome contains a variety of species and expresses a large number of potential antigens. Potential transient intestinal microbial translocation may trigger a persistent systemic immune response. Studies have shown that the temporal stability of the antibody epitope library is higher than the microbiome abundance inferred from metagenomic sequencing data of fecal samples. However, there is currently no research on intestinal microbial antibodies associated with colorectal tumors.

[0006] Therefore, exploring the plasma anti-tumor and intestinal microbial antibody repertoire is of great significance for discovering new plasma biomarkers that can be used for the early diagnosis of colorectal tumors. Summary of the invention

[0007] In view of the problems existing in the prior art, the present invention provides a biomarker for the detection of advanced colorectal adenoma and early colorectal cancer and its application. The plasma antibody diagnostic markers of advanced colorectal adenoma and early colorectal cancer are screened by using an immunoprecipitation sequencing platform (mDIP-Seq) based on high-resolution mRNA display technology, and a multiple antibody panel suitable for the diagnosis of advanced colorectal adenoma and early colorectal cancer is developed in combination with a random forest algorithm, a disease prediction model is constructed, and the accuracy of the diagnosis of advanced colorectal adenoma and early colorectal cancer is improved. Compared with invasive colonoscopy, this method has the characteristics of low cost, scalability, and high throughput, and is suitable for large-scale screening of advanced colorectal adenoma and early colorectal cancer in the population.

[0008] The immunoprecipitation sequencing platform (mDIP-Seq) of mRNA display technology is a new type of biomarker screening platform technology constructed for the first time in the present invention. By constructing an antigen peptide library, magnetic beads carrying human plasma antibodies are reacted with the antigen peptide library respectively, and antigen peptides that can bind to the magnetic beads are selected. Then, by analyzing the enrichment of antigen peptides in patients with advanced colorectal adenoma, early colorectal cancer and healthy controls, the differences in antibody spectra among patients with advanced colorectal adenoma, early colorectal cancer and healthy controls are explored, and antigen peptides that can significantly distinguish between patients with advanced colorectal adenoma and healthy people and antigen peptides that distinguish between patients with early colorectal cancer and healthy people are selected. The antibody corresponding to the antigen peptide is a biomarker that can be used to diagnose and predict whether an individual has advanced colorectal adenoma or early colorectal cancer.

[0009] On the one hand, the present invention provides a use of a biomarker in the preparation of a reagent for predicting whether an individual has advanced colorectal adenoma, the biomarker comprising plasma autoantibodies and anti-intestinal microbial antibodies, the plasma autoantibodies comprising autoantibodies to antigenic peptides as shown in any one or more of Seq ID NO.6 to Seq ID NO.7, and Seq ID NO.9 in the sequence table; the anti-intestinal microbial antibodies comprising antibodies to antigenic peptides as shown in any one or more of Seq ID NO.1 to Seq ID NO.5, Seq ID NO.8, and Seq ID NO.10 in the sequence table.

[0010] Advanced CRA is a precancerous lesion that is ≥1 cm in size or has ≥25% of any size of villous components or high-grade dysplasia. Over time, it is easy to develop into CRC. If tumor biomarkers with certain early warning effects can be found in the early stages of colorectal cancer, early diagnosis of CRA will be of great significance for improving the treatment effect and prognosis of patients.

[0011] According to databases such as UniProt and NCBI, as well as other existing literature, the present invention designed and synthesized a human protein oligonucleotide library and an oligonucleotide library covering colorectal tumor-related intestinal bacterial antigens in high throughput, collected plasma samples from patients with advanced colorectal adenoma and healthy controls, and mapped the plasma anti-tumor and intestinal microbial IgG antibody fingerprints during the progression of advanced colorectal adenoma using a high-resolution immunoprecipitation sequencing platform (mDIP-Seq) based on mRNA display technology. Ultimately, 10 antibodies to antigenic peptides that are significantly associated with advanced colorectal adenoma were screened, including 3 plasma autoantibodies and 7 anti-intestinal microbial antibodies.

[0012] Furthermore, the biomarker includes antibodies to any one or more of the antigenic peptides shown in Seq ID NO.1 to Seq ID NO.8.

[0013] In order to improve the diagnostic efficacy of advanced colorectal adenoma, three plasma autoantibodies and seven anti-intestinal microbial antibodies were combined for screening, and finally 8 to 10 antibody markers of antigenic peptides were screened. Combined with the random forest algorithm, a multiple antibody panel suitable for the diagnosis of advanced colorectal adenoma was developed, and a prediction model for advanced colorectal adenoma was constructed, which has good risk prediction ability.

[0014] In some embodiments, the reagent for predicting whether a patient has advanced colorectal adenoma is a detection reagent prepared with the biomarker as the detection target, such as sample pretreatment reagents, antigens or antibodies, and other biological reagents and kits suitable for the detection of the biomarker; it can also be developed into a standardized reagent or kit suitable for liquid chromatography-ultraviolet (LC-UV) or liquid chromatography-mass spectrometry (LC-MS) detection of the biomarker.

[0015] Therefore, the present invention screened antibodies to 10 antigenic peptides from a large number of antigenic peptides as biomarkers in the sample to be tested, and then screened antibodies to the first 8 antigenic peptides as preferred biomarkers, which are antibodies to antigenic peptides with sequences as described in the sequence table Seq ID NO.1 to Seq ID NO.8. Because the effect of constructing a model with 10 antibody markers can be achieved due to the smaller number of markers when 8 types are used, it is preferred to use antibody markers to construct a model for constructing a diagnostic model, which has good clinical diagnostic value for advanced colorectal adenoma, can significantly improve the diagnostic identification ability and diagnostic efficacy of advanced colorectal adenoma, and realize risk prediction of advanced colorectal adenoma.

[0016] Furthermore, the reagent is used to detect the content of biomarkers in a body fluid sample; the body fluid sample includes any one or more of saliva, blood, urine, plasma, serum, and cerebrospinal fluid.

[0017] Furthermore, the reagent is used to detect the presence of biomarkers in a body fluid sample.

[0018] The present invention screens biomarkers of advanced colorectal adenoma from blood. These biomarkers show significant differences in the blood of patients with advanced colorectal adenoma and healthy people. By collecting blood samples, the possibility of an individual suffering from advanced colorectal adenoma can be predicted or assisted in diagnosis by detecting the positive rate of these biomarkers in the individual's blood.

[0019] The present invention determines whether the individual suffers from advanced colorectal adenoma by determining whether the antibody exists in the sample, that is, determining its positive rate. The present invention also constructs a random forest model through a machine learning method to determine whether the individual suffers from advanced colorectal adenoma.

[0020] Furthermore, the detection method includes a radiation method, an immunological method, a fluorescence method, a flow cytometry, a latex turbidimetry, a biochemical method, an enzyme method, a hybridization method, a gas chromatography-mass spectrometry method, a liquid chromatography-mass spectrometry method, a chromatography method, a chemiluminescence method, a magnetoelectric method or a photoelectric conversion method.

[0021] The presence or absence of markers or the level of their content is a relative concept. For example, when comparing the diseased group with the non-diseased group, the content of these specific markers is compared with the diseased group or the non-diseased group as a benchmark. It may be that for some markers, the content in the diseased group is higher than that in the non-diseased group, and this high has a statistical difference, such as a significant or extremely significant increase. Therefore, when these markers are judged, if it is a single marker, if the probability of the marker occurring at a certain risk increases, the content of the marker changes. The change here may be a relative increase or a relative decrease. This relative increase or relative decrease has a significant difference, and of course it can also be an extremely significant difference. Therefore, no matter what means are used for detection, a predetermined value can be used as a standard (cut-off value). If it is higher than this value, it is considered that the content has changed. Such results can be used as predictive or diagnostic values.

[0022] Therefore, in some aspects, the biomarkers described in the present invention can be obtained by detecting the content of the markers in the sample by any known method, such as liquid chromatography, gas chromatography, mass spectrometry, LC-MS, gas chromatography-mass spectrometry (GC-MS), chromatography-mass spectrometry (CC-MS), liquid chromatography-tandem mass spectrometry (LC-MS-MS), nuclear magnetic resonance spectroscopy (NMR), immunochromatography test strips, immunoreaction chips, capillary electrophoresis, infrared spectroscopy, etc. As long as they can be used to detect the content of protein markers in the sample, they can be used for the diagnosis of advanced colorectal adenoma. As long as the content of protein markers in the sample can be detected, it can be used to predict or diagnose the probability of a certain disease. It can be understood that the detection here is to detect the sample of the individual, and then compare it with the pre-set standard, and the comparison result is used to judge or predict the occurrence state of the disease, for example, it can be used to predict the probability of the occurrence of advanced colorectal adenoma. This prediction or diagnosis is whether it occurs within a certain period of time. Of course, such detection can be continuous detection, and the progress of the disease is inferred as the content of certain substances changes.

[0023] In another aspect, the present invention provides a kit for predicting whether an individual suffers from advanced colorectal adenoma, wherein the kit comprises a detection reagent for the biomarker for the use described above.

[0024] In another aspect, the present invention provides a biomarker combination for predicting whether an individual has advanced colorectal adenoma, wherein the biomarker combination comprises antibodies to any one or more antigenic peptides shown in sequence listings Seq ID NO.1 to Seq ID NO.10.

[0025] On the other hand, the present invention provides a system for predicting whether an individual has advanced colorectal adenoma, the system comprising a data analysis module, the data analysis module being used to analyze the detection values ​​of biomarkers, the biomarkers comprising plasma autoantibodies and anti-intestinal microbial antibodies, the plasma autoantibodies comprising autoantibodies to antigenic peptides as shown in any one or more of the sequence tables Seq ID NO.6 to Seq ID NO.7, and Seq ID NO.9; the anti-intestinal microbial antibodies comprising antibodies to antigenic peptides as shown in any one or more of the sequence tables Seq ID NO.1 to Seq ID NO.5, Seq ID NO.8, and Seq ID NO.10.

[0026] Furthermore, the biomarker includes antibodies to antigenic peptides as shown in any one or more of the sequence listings Seq ID NO.1 to Seq ID NO.8.

[0027] In some embodiments, a random forest model is constructed using a combination of antibody biomarkers of 2 to 10 antigenic peptides. It is found that models constructed using antibodies of the top 8 to 10 antigenic peptides have good diagnostic efficacy. Preferably, a diagnostic model is constructed using antibodies of the 8 antigenic peptides in the sequence table Seq ID NO.1 to Seq ID NO.8. The changes in antibody concentrations can be used to distinguish patients with advanced colorectal adenoma from healthy people, and have extremely high diagnostic value.

[0028] In summary, the combined diagnostic model of antibodies containing 8 to 10 antigen peptides constructed in this example has good clinical diagnostic value for advanced colorectal adenoma, and can significantly improve the diagnostic identification ability and diagnostic efficacy of advanced colorectal adenoma.

[0029] Furthermore, the data analysis module uses the detection values ​​of markers of known samples as a training set, and divides them into an advanced colorectal adenoma group and a healthy group according to whether they have advanced colorectal adenoma, analyzes the relationship between the detection values ​​of the advanced colorectal adenoma group and the healthy group, and constructs a model.

[0030] Furthermore, the system also includes a data storage module, a data input interface and a data output interface; the data storage module is used to store the detection values ​​of biomarkers; the data input interface is used to input the detection values ​​of biomarkers, and the data output interface is used to output the prediction results.

[0031] Furthermore, the detection value is the presence or relative abundance or concentration value of antibodies against the first 8 antigenic peptides in the sequence list Seq ID NO.1 to Seq ID NO.10.

[0032] On the other hand, the present invention provides a use of a biomarker in the preparation of a reagent for predicting whether an individual has early colorectal cancer, the biomarker comprising plasma autoantibodies and anti-intestinal microbial antibodies, the plasma autoantibodies comprising autoantibodies to antigenic peptides as shown in any one or more of the sequence listings Seq ID NO.12, Seq ID NO.14 to Seq ID NO.18, and Seq ID NO.10; the anti-intestinal microbial antibodies comprising antibodies to antigenic peptides as shown in any one or more of the sequence listings Seq ID NO.11, SeqID NO.13, and Seq ID NO.19.

[0033] According to databases such as UniProt and NCBI, as well as other existing literature, the present invention has established a human protein oligonucleotide library and an oligonucleotide library covering intestinal bacterial antigens. At the same time, plasma samples from patients with early colorectal cancer and healthy controls were collected. A high-resolution immunoprecipitation sequencing platform (mDIP-Seq) based on mRNA display technology was used to map the plasma anti-tumor and intestinal microbial IgG antibody fingerprints during the progression of early colorectal cancer. Finally, 10 antibodies to antigenic peptides that are significantly associated with early colorectal cancer were screened, including 7 plasma autoantibodies and 3 anti-intestinal microbial antibodies.

[0034] Furthermore, the biomarker includes any one or more polypeptide sequences shown in Seq ID NO.11 to Seq ID NO.19.

[0035] In order to improve the diagnostic efficacy of early colorectal cancer, 7 plasma autoantibodies and 3 anti-intestinal microbial antibodies were combined for screening, and finally 9 to 10 antibody markers of antigenic peptides were screened. Combined with the random forest algorithm, a multiple antibody panel suitable for the diagnosis of early colorectal cancer was developed, and an early colorectal cancer prediction model was constructed with good risk prediction ability.

[0036] In some embodiments, the reagent for predicting whether a patient has early colorectal cancer is a detection reagent prepared with the biomarker as the detection target, such as sample pretreatment reagents, antigens or antibodies, and other biological reagents and kits suitable for the detection of the biomarker; it can also be developed into a standardized reagent or kit suitable for liquid chromatography-ultraviolet (LC-UV) or liquid chromatography-mass spectrometry (LC-MS) detection of the biomarker.

[0037] Therefore, the present invention screened out antibodies to 10 antigenic peptides from a large number of antigenic peptides as biomarkers in the sample to be tested, and then screened out antibodies to the first 9 antigenic peptides as preferred biomarkers, which are antibodies to antigenic peptides with sequences as described in the sequence table Seq ID NO.11 to Seq ID NO.19. Because the effect of constructing a model with 10 antibody markers can be achieved due to the smaller number of markers when 9 types are used, it is preferred to use antibody markers to construct a model for constructing a diagnostic model, which has good clinical diagnostic value for early colorectal cancer, can significantly improve the diagnostic identification ability and diagnostic efficacy of early colorectal cancer, and realize the risk prediction of early colorectal cancer.

[0038] Furthermore, the reagent is used to detect the content of biomarkers in a body fluid sample; the body fluid sample includes any one or more of saliva, blood, urine, plasma, serum, and cerebrospinal fluid.

[0039] Furthermore, the reagent is used to detect the presence of biomarkers in a body fluid sample.

[0040] By determining whether antibodies are present in the sample, the probability of developing colorectal cancer can be predicted.

[0041] In yet another aspect, the present invention provides a kit for predicting whether an individual suffers from early-stage colorectal cancer, the kit comprising a detection reagent for the biomarker for the use described above.

[0042] In another aspect, the present invention provides a biomarker combination for predicting whether an individual has early colorectal cancer, wherein the biomarker combination comprises antibodies to any one or more antigenic peptides shown in sequence listings Seq ID NO.11 to Seq ID NO.20.

[0043] On the other hand, the present invention provides a system for predicting whether an individual has early colorectal cancer, the system comprising a data analysis module, the data analysis module being used to analyze the detection values ​​of biomarkers, the biomarkers comprising plasma autoantibodies and anti-intestinal microbial antibodies, the plasma autoantibodies comprising autoantibodies to antigenic peptides as shown in any one or more of the sequence tables Seq ID NO.12, SeqID NO.14~Seq ID NO.18, and Seq ID NO.10; the anti-intestinal microbial antibodies comprising antibodies to antigenic peptides as shown in any one or more of the sequence tables Seq ID NO.11, Seq ID NO.13, and Seq ID NO.19.

[0044] Furthermore, the biomarker includes antibodies to antigenic peptides as shown in any one or more of the sequence listings Seq ID NO.11 to Seq ID NO.19.

[0045] Furthermore, the data analysis module uses the detection values ​​of markers of known samples as a training set, divides the samples into an early colorectal cancer group and a healthy group according to whether they have early colorectal cancer, analyzes the relationship between the detection values ​​of the early colorectal cancer group and the healthy group, and constructs a model.

[0046] Furthermore, the system also includes a data storage module, a data input interface and a data output interface; the data storage module is used to store the detection values ​​of biomarkers; the data input interface is used to input the detection values ​​of biomarkers, and the data output interface is used to output the prediction results.

[0047] Furthermore, the detection value is the presence or relative abundance or concentration value of antibodies against the first 9 antigenic peptides in the sequence listing Seq ID NO.11 to Seq ID NO.19.

[0048] The beneficial effects of the present invention are:

[0049] 1. The present invention screened out 10 new antibody biomarkers that can distinguish patients with advanced colorectal adenoma from healthy people. It can effectively evaluate and diagnose advanced colorectal adenoma and healthy people, and can more accurately identify patients with advanced colorectal adenoma. Compared with traditional detection methods, it reduces the risk of misdiagnosis and missed diagnosis, and provides strong support for early detection and intervention of the disease.

[0050] 2. A combined differential diagnosis model for advanced colorectal adenoma containing 2 to 10 antibody biomarkers was constructed. It is convenient and fast, and the test results are highly consistent with the clinical gold standard test results. At the same time, it significantly reduces the cost of diagnosing advanced colorectal adenoma and has good application prospects.

[0051] 3. We have screened 10 new antibody biomarkers that can distinguish early colorectal cancer patients from healthy people. They can effectively evaluate and diagnose early colorectal cancer patients and healthy people, and can more accurately identify early colorectal cancer patients. Compared with traditional detection methods, they reduce the risk of misdiagnosis and missed diagnosis, and provide strong support for early detection and intervention of the disease.

[0052] 4. A combined differential diagnosis model for early colorectal cancer containing 2 to 10 antibody biomarkers was constructed. It is convenient and fast, and the test results are highly consistent with the clinical gold standard test results. At the same time, it significantly reduces the cost of diagnosing early colorectal cancer and has good application prospects.

[0053] Detailed description

[0054] (1) Diagnosis or testing

[0055] The diagnosis or detection here refers to the detection or analysis of biomarkers in the sample, or the content of the target biomarker, such as the absolute content or relative content, and then the presence or amount of the target marker is used to explain whether the individual providing the sample may have or suffer from a certain disease, or the possibility of having a certain disease. The meanings of diagnosis and detection here are interchangeable. The result of this test or diagnosis cannot be directly used as a direct result of illness, but is an intermediate result. If a direct result is obtained, other auxiliary means such as pathology or anatomy are required to confirm that a certain disease is present. For example, the present invention provides a variety of new biomarkers that are associated with advanced colorectal adenomas and early colorectal cancer. Changes in the content of these markers are directly correlated with whether or not the patient suffers from early colorectal cancer or advanced colorectal adenomas.

[0056] (2) The relationship between markers or biomarkers and patients with early colorectal cancer and advanced colorectal adenoma

[0057] Markers and biomarkers have the same meaning in the present invention. The connection here means that the appearance or change in the content of a certain biomarker in a sample has a direct correlation with a specific disease, for example, a relative increase or decrease in the content indicates that the possibility of suffering from the disease is higher than that of healthy people.

[0058] If multiple different markers appear in a sample at the same time or their contents change relatively, it means that the possibility of suffering from the disease is higher than that of healthy people. That is to say, among the types of markers, some markers are strongly associated with the disease, some markers are weakly associated with the disease, or some are even not associated with a specific disease. One or more of the markers with strong correlation can be used as markers for diagnosing the disease, and those markers with weak correlation can be combined with strong markers to diagnose a disease, increasing the accuracy of the test results.

[0059] For the numerous biomarkers in plasma discovered by the present invention, these markers can be used to distinguish early colorectal cancer, advanced colorectal adenoma from healthy people or benign disease populations. The markers here can be used alone as a single marker for direct detection or diagnosis. The selection of such a marker indicates that the relative change in the content of the marker has a strong correlation with early colorectal cancer and advanced colorectal adenoma. Of course, it is understandable that one or more markers with strong correlation with early colorectal cancer and advanced colorectal adenoma can be selected for simultaneous detection. It is normal to understand that in some ways, selecting a biomarker with strong correlation for detection or diagnosis can achieve a certain standard of accuracy, such as 60%, 65%, 70%, 80%, 85%, 90% or 95% accuracy, which can explain that these markers can obtain the intermediate value of diagnosing a certain disease, but it does not mean that a certain disease can be directly confirmed.

[0060] (3) Definition of disease terms

[0061] Colorectal cancer: also known as colorectal cancer, refers to cancer originating from the large intestine epithelium, including colon cancer and rectal cancer. Adenocarcinoma is the most common pathological type, and very few are squamous cell carcinomas. The treatment of colorectal cancer should adopt the principle of individualized treatment. According to the patient's age, constitution, pathological type of the tumor, and invasion range (staging), appropriate treatment methods should be selected, including radical surgery, chemotherapy, targeted therapy, radiotherapy, etc. The formation of colorectal cancer generally goes through the development process of normal mucosal hyperplasia, advanced adenoma (malignant), and adenocarcinoma (malignant), which generally takes 5-10 years. Therefore, early screening, diagnosis and treatment are the most effective means to reduce the mortality rate of colorectal cancer. In particular, if intervention can be carried out at the polyp adenoma (malignant) stage, the occurrence of colorectal cancer can be effectively prevented. Advanced adenoma (AA) is a precancerous lesion, which refers to a villous component of any size or high degree of dysplasia of ≥1cm or ≥25%. Over time, it is easy to develop into colorectal cancer. If we can find tumor biomarkers with certain early warning effects in the early stages of colorectal cancer and diagnose colorectal cancer and advanced adenomas (malignant tumors), it will be of great significance for improving the treatment effect and prognosis of patients.

[0062] Early colorectal cancer: Early colorectal cancer refers to colorectal epithelial tumors of any size whose invasion depth is limited to the mucosa and submucosa, regardless of the presence or absence of lymph node metastasis.

[0063] Advanced adenoma: refers to tubulovillous adenoma, villous adenoma and / or adenoma with high-grade dysplasia with a diameter greater than 1 cm. It is considered a precancerous lesion because of its close relationship with the occurrence of colorectal cancer. Advanced adenoma should be removed in time to prevent it from evolving into colorectal cancer.

[0064] Therefore, the prediction model provided by the present invention can quickly distinguish patients with advanced colorectal adenoma and healthy people based on changes in the concentration of biomarkers in body fluid samples.

[0065] (4) Gold standard for diagnosis of early colorectal cancer: Patients with early colorectal cancer often have no clinical symptoms or signs. Diagnosis relies on standardized colonoscopy performed by qualified physicians, with biopsy tissue pathology as the basis for diagnosis. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 This is a schematic diagram of the mDIP-Seq screening process in Example 1;

[0067] Figure 2 Schematic diagram of the composition of the plasma antigen peptide and intestinal bacterial antigen peptide libraries constructed in Example 1;

[0068] Figure 3 Schematic diagram of the results of verifying the reliability and repeatability of the mDIP-Seq platform in Example 1; Figure 3 A in the figure is the reliability verification result of the mDIP-Seq platform. Figure 3 B in the figure is the technical repeatability verification result;

[0069] Figure 4 The reported autoantibody markers were detected in the plasma of colorectal tumor patients by the mDIP-Seq platform in Example 1;

[0070] Figure 5 is the number of antigenic peptides related to HC, CRA, and CRC screened from the human protein antigen library by the mDIP-Seq platform in Example 1;

[0071] Figure 6 The relationship between the plasma antibodies binding to antigenic peptides of HC, CRA and CRC groups screened from the human protein antigen library in Example 1;

[0072] Figure 7 is the number of antigenic peptides related to HC, CRA, and CRC screened from the intestinal bacterial antibody epitope library by the mDIP-Seq platform in Example 1;

[0073] Figure 8 The relationship between the plasma antibodies and antigenic peptides of the HC, CRA, and CRC groups screened from the intestinal bacterial antibody epitope library in Example 1;

[0074] Fig. 9 This is a graph showing the results of orthogonal partial least squares discriminant analysis of the differences in plasma antibody binding to antigenic peptides in HC, CRA, and CRC groups in Example 1;

[0075] Fig.10 The advanced colorectal adenoma diagnostic model constructed in Example 2;

[0076] Fig.11 This is the early colorectal cancer diagnostic model constructed in Example 2. DETAILED DESCRIPTION

[0077] The present invention is further described in detail below in conjunction with the accompanying drawings and examples. It should be noted that the following examples are intended to facilitate understanding of the present invention and do not have any limiting effect on it. The reagents used in this example are all known products and were obtained by purchasing commercially available products.

[0078] Example 1: Screening of antigen peptide antibody biomarkers for CRA and CRC detection

[0079] According to databases such as UniProt and NCBI, as well as other existing literature, the present invention has established a human protein oligonucleotide library and an oligonucleotide library covering intestinal bacterial antigens, collected plasma samples from patients with early colorectal cancer, patients with advanced colorectal adenoma, and healthy controls, and screened antigen peptides that can bind to antibodies in the plasma of patients with colorectal cancer and advanced colorectal adenoma using a high-resolution immunoprecipitation sequencing platform (mDIP-Seq) based on mRNA display technology. The screening process flow chart using the mDIP-Seq platform (immunoprecipitation sequencing platform based on high-resolution mRNA display technology) is shown in Figure 1 . Ten antigenic peptides that can be used for efficient detection of patients with advanced colorectal adenoma were screened out, including three antigenic peptides for detecting plasma autoantibodies and seven antigenic peptides for detecting anti-intestinal microbial antibodies; at the same time, 10 antigenic peptides that can be used for efficient detection of patients with colorectal cancer were screened out, including seven antigenic peptides for detecting plasma autoantibodies and three antigenic peptides for detecting anti-intestinal microbial antibodies.

[0080] 1. Research subjects and samples

[0081] In this example, plasma samples from patients in the Department of Colorectal Surgery of the Second Affiliated Hospital of Zhejiang University School of Medicine were collected, including 34 patients with advanced adenoma (CRA), 47 patients with early colorectal cancer (CRC), and 42 healthy controls (HC) (intestinal diseases were excluded by colonoscopy). Inclusion criteria are: 1) No antibiotics, probiotics, Chinese medicine, proton pump inhibitors, or hormone drugs were taken within three months before sampling; 2) The sampling time of patients with adenoma and colorectal cancer was before intervention treatment (including drugs and surgery). Exclusion criteria are: vegetarians; patients with diabetes; history of autoimmune diseases or ongoing immunomodulatory treatment; history of malignant diseases or ongoing tumor radiotherapy and chemotherapy; history of other types of gastrointestinal cancer or colitis-related diseases. Blood samples were collected using EDTK-2K anticoagulant tubes, stored at 4°C, centrifuged at 2,500rpm for 10 minutes at room temperature, and the supernatant (plasma) was collected and dispensed into cryopreservation tubes and stored at -80°C. All participants voluntarily participated in the study and signed informed consent.

[0082] Table 1. Basic information of research subjects

[0083]

[0084] 2. Composition and design of self- and bacterial antigen libraries

[0085] In this example, two antigen libraries were designed and synthesized in high-throughput manner, one is a human protein antigen library (covering 211,940 antigenic peptides), and the other is an intestinal bacterial antigen library (covering 277,321 antigenic peptides).

[0086] 1) Composition and design of human antigen library (Agilent SurePrint platform)

[0087] The entire human proteome was downloaded from the UniProt database and folded at 100% sequence identity (download time was December 18, 2018). Subsequently, a peptide library with a length of 66 amino acids and 12 overlapping amino acid sequences was created, and these peptide sequences were reverse translated into DNA codons according to human codons. The linker sequence "GGCTTCCGGAACCTCA" was added to the 5' end of the library, and the "TCTGCCGGTGTAGGCA" sequence was added to the 3' end of the library to generate a collection of oligonucleotide sequences with a length of 230nt. Finally, the Agilent SurePrint platform was used to synthesize an oligonucleotide library containing 211,940 different sequences covering nearly 20,600 human proteins. The composition of the human antigen library can be found in Figure 2 A in, contains 211,940 antigens covering human proteins.

[0088] 2) Composition and design of human intestinal bacterial antigen library (Genewiz platform)

[0089] This example includes colorectal adenoma and colorectal cancer-related fecal bacterial markers based on the work of Wu Y. et al., Thomas AM et al., and Wirbel J et al. 8 bacteria that differ in the feces of healthy people and patients with colorectal adenoma: Aminipila butyrica, Christensenellaceae R-7 group sp., Eubacteriumcoprostanoligenes, Eubacterium ruminantium, Rothia dentocariosa, Ruminiclostridium 9 sp., Ruminococcaceae UCG-005 sp., and eillonella parvula. 24 bacteria that were differentially expressed in patients with colorectal adenoma and colorectal cancer included [Clostridium] scindens, [Eubacterium] coprostanoligenes group sp., [Eubacterium] ventriosum groupsp., [Ruminococcus] gnavus group sp., Bacteroides dorei, Bacteroides nordii, Blautia faecis, Blautia sp., Erysipelatoclostridium ramosum, Eubacteriumruminantium, Hungatella hathewayi WAL-18680, Lachnospira pectinoschiza, Lachnospiraceae UCG-010 sp., Merdibacter massiliensis, Parvimonas micra, Porphyromonas sp. 2007b, Porphyromonas sp. HMSC077F02, Roseburia hominis A2-183, Roseburia intestinalis, Ruminococcaceae UCG-002 sp., Ruminococcusbromii, Streptococcus infantarius, Streptococcus thermophilus TH1435 and Tyzzerella 3 sp.The 13 bacteria that differed in the feces of healthy people and colorectal cancer patients included Clostridium symbiosum, Faecalibacterium prausnitzii (domaint), Fusobacterium nucleatum, Gemella morbillorum, Hungatella hathewayi, Parvimonas micra, Peptostreptococcus stomatis, Porphyromonas asaccharolytica, Porphyromonassomerae, Porphyromonas uenonis, Prevotella intermedia, Ruminococcus torques and Solobacterium moorei.

[0090] At the same time, this example also includes Prevotella copri, which can induce antibody response in more than 95% of individuals, as a positive control; according to the report on the epidemiological characteristics of acute diarrhea in China from 2009 to 2018 issued by the Chinese Center for Disease Control and Prevention, three of the most common intestinal bacterial pathogens in my country are included, including diarrheagenicescherichia coli, nontyphoidal Salmonella (S.enterica subsp.enterica) and Vibrio parahaemolyticus. In addition, this example also includes Enterococcus faecalis V583, Escherichia coli (Strain: NF73-1), Klebsiellapneumoniae 30660 / NJST258_1, which are reported to be able to transfer to extraintestinal organs and tissues. In addition, 9 IgA antibody-coated bacteria were added, including Akkermansia muciniphila ATCC BAA835, Ruminococcus torques ATCC 27756, Doreaformicigenerans ATCC 27755, Ruminococcus bromii, Ruminococcus gnavus AGR2154, Blautia producta ATCC 27340 = DSM 2950, ​​Streptococcus lutetiensis 033, Bacteroides fragilis YCH46 and Haemophilus parainfluenzae T3T1.

[0091] Subsequently, this example downloaded the proteome of the above bacteria from Uniprot and NCBI (download time was November 19, 2021). And the GO annotation terms of these proteins were obtained from Uniprot and Interproscan. Proteins with specified membrane localization or flagella GO terms were selected for further processing. For membrane proteins, in order to extract the extracellular domain and avoid hydrophobic transmembrane domains, this example applied the TMHMM (transmembrane prediction using hidden Markov model) program to predict transmembrane helices based on hidden Markov models. For the selection of secretory proteins, this example uses the SignalP 4.0 program to predict and remove secretory peptide sequences to obtain mature sequences. In addition, this example also includes secretory proteins belonging to type I, III, IV and VI secretion systems in Gram-negative bacteria that lack signal peptides based on GO annotations. Subsequently, a set of peptide libraries with a length of 56 amino acids and 12 overlapping amino acid sequences were created. The peptide sequences were clustered at 75% sequence identity using the CDHIT program, and cluster representatives were selected for the next step. These peptide sequences were then reverse translated into DNA codons according to the E. coli codons. The linker sequence "GGGAAACAGCAACGGA" was added to the 5' end of the library, while the "ACATCGTCAGCGTCGA" sequence was added to the 3' end of the library to generate a collection of oligonucleotide sequences with a length of 200 nt. Finally, the Genewiz platform was used to synthesize an oligonucleotide library containing 277,321 different sequences covering intestinal bacterial antigens. The composition of the human intestinal bacterial antigen library is shown in Figure 2 B in the dataset contains 277,321 antigens covering the intestinal flora associated with colorectal tumors.

[0092] After obtaining the two DNA libraries, the libraries were PCR amplified using the following two rounds of amplification primers. The primers were designed to contain sequences encoding T7 promoter and Kozak sequence at the 5' end and Flag tag at the 3' end, which were used for in vitro transcription, in vitro translation reaction, and affinity purification of peptides.

[0093] The first round of PCR primers include the upstream primer (Seq ID NO.41) and downstream primer (Seq ID NO.42) of the human proteome library; the upstream primer (Seq ID NO.43) and downstream primer (Seq ID NO.44) of the intestinal bacterial antigen library.

[0094] Second round PCR primers: including upstream primer (Recovery-F, Seq ID NO.45) and downstream primer (Recovery-R, Seq ID NO.46).

[0095] 3. Screening of disease-related antigenic peptides based on the mDIP-Seq platform

[0096] This example is based on the mDIP-Seq platform technology and is used to screen antigen peptides associated with advanced colorectal adenomas and early colorectal cancer. The specific process is as follows:

[0097] (1) Prepare diluted input library

[0098] The exon library was transcribed by T7 in vitro transcription kit, and the pF30P Linker with puromycin was connected to RNA using T4 DNA ligase and Split20 splint DNA fragment. Lambda exonuclease was then used to remove the splint DNA on the RNA, and the RNA successfully connected to the Linker was purified using the Dynabeads® mRNA purification kit. 0.4 μM of purified RNA was taken and a 25 μL in vitro translation system was constructed using the NEB PURExpress in vitro translation kit. Each 25 μL translation product was used for the screening experiment of plasma antibodies from 3 patients. 500 mM KCl and 60 mM MgCl2 were then added and incubated at room temperature for 30 minutes to promote the formation of mRNA-peptide fusion products. Then, 200 μL of TBK buffer (25 mM Tris, 150 mM KCl, 0.02% Tween-20) was added to each 25 μL translation system for dilution, and 10 μL of M2 Anti-Flag magnetic beads were added, incubated at 4°C for 2 h, and then washed 5 times with 1 mL of pre-cooled TBK buffer (25 mM Tris, 150 mM KCl, 0.02% Tween-20), affinity purified to remove unfused template RNA and sequences containing nonsense mutations. The mRNA-peptide complex was eluted by incubating with 50 μL of 1X RT reaction solution containing 200 μg / mL Flag polypeptide at room temperature for half an hour, and then HiscriptIII reverse transcriptase (Vazyme) and RT primer (Lib-rev, Seq ID NO.47) were added for reverse transcription to generate mRNA / cDNA-peptide complex (input library). After the reverse transcription, a portion of the purified sample was retained for sequencing to determine the distribution frequency of polypeptides in the input library.

[0099] (2) Human plasma IgG enrichment

[0100] The operation was performed in a 96-well plate (Biorad, HSP9601). 10 μL of protein G-coupled magnetic beads (Dynabeads™ Protein G) / sample were taken, and the magnetic beads were washed 3 times with TBK buffer using a 96-well plate magnetic stand. Then 1 mL of PrG blocking solution (50% ULtra Block buffer, 20% BSA, 10 μL / mL yeast tRNA, 1 mg / mL sperm DNA) was added and rotated for 1-2 hours at room temperature, and then washed 3 times with TBK. The plasma sample placed at -80°C was taken out and placed at 4°C until it was completely thawed. The patient plasma was diluted 1:50, and 50 ul of the diluted serum was added and mixed with the PrG-coated magnetic beads for incubation to enrich the patient plasma IgG, and the mixture was incubated at 4°C overnight.

[0101] (3) Screening of disease-related antigenic peptides

[0102] Finally, the beads were washed 3 times with about 200 μL of TBK buffer, and the input library diluted with Buffer X (20% BSA, 10 μL / mL yeast tRNA, 1 mg / mL sperm DNA, 25 mM Tris-HCl, 150 mM KCl, 0.02% Tween-20) was added for incubation at room temperature for 3 hours. After incubation, the beads were fully washed 3 times with 200 μL TBK buffer, and the eluate was collected after adding Nuclease-free water and heated at 95°C for 4 minutes, which was the output library. The input library and the output library were PCR amplified, and finally the input and output libraries were sequenced. Unique Dual Index was added to each sample for distinction, and finally high-throughput sequencing was performed on the Illumina Hiseq PE150 platform.

[0103] PCR amplification primers: including upstream primers (Human-Seq-F, Seq ID NO.48) and downstream primers (Human-Seq-R, Seq ID NO.49) of the human proteome library; upstream primers (Bac-Seq-F, SeqID NO.50) and downstream primers (Bac-Seq-R, Seq ID NO.51) of the intestinal bacterial antigen library.

[0104] (4) Verification of screening sequence results

[0105] The screened peptides were further validated using the Split Luciferase Binding Assay (SLBA), which confirmed that antibodies corresponding to the antigenic peptides did exist in plasma.

[0106] The above process is the workflow of the mDIP-Seq platform developed in this embodiment, which combines the in vitro synthesized linear epitope library covering the human proteome library and the colorectal tumor-related intestinal flora antigen, and reacts with the antigen peptides in the library through PrG magnetic beads enriched with human plasma IgG antibodies, and selects antigen peptides that can bind to antibodies in the plasma of healthy people, patients with advanced colorectal adenoma and colorectal cancer patients, respectively. The relevant antigen peptides can be used as biomarkers to predict whether an individual is a patient with colorectal cancer or advanced colorectal adenoma.

[0107] In short, the antigen library was amplified and recovered by PCR, and the mDIP-Seq input library (polypeptide-mRNA complex) was generated through a series of steps including in vitro transcription, in vitro translation, Flag-tag purification, and reverse transcription. The input library was incubated with PrG magnetic beads that captured human plasma IgG antibodies and blank magnetic beads at low temperature overnight, and after sufficient washing, the DNA was eluted by heating at 95°C, and the eluate was recovered to obtain the output library.

[0108] The gamma-poisson model was used to fit the abundance distribution of peptides in each sample, and the significant p-value of each antigenic peptide enrichment was calculated to analyze the enriched antigenic peptides in each sample.

[0109] Quality control and validation of the mDIP-Seq platform: The mDIP-Seq platform allows 96 samples to be run completely in parallel, which can relatively reduce any batch or plate-to-plate variability. Figure 3 The composition, feasibility and repeatability of two antigen libraries are shown, including membrane proteins, secretory proteins, bacterial flagella and virulence proteins. Before the formal screening, this example used a human antigen library and a commercial anti-TLR4 antibody with known antigen peptides to verify the feasibility of the mDIP-Seq platform. A single round of immunoprecipitation sequencing found that mDIP-Seq successfully enriched the corresponding red-labeled TLR4 antigen peptide ( Figure 3 The peptide enrichment in the output library of three independent experiments showed high reproducibility (Pearson correlation coefficient > 0.95) ( Figure 3 These results indicate that the mDIP-Seq platform can effectively identify antigenic peptides corresponding to antibodies and that the platform can also be applied to antibodies against membrane proteins.

[0110] 4. mDIP-Seq Data Processing

[0111] Sequencing data were analyzed using python and bash scripts. Each sample was identified by the 10bp sequence that matched the corresponding Unique DualIndex. To improve data quality, the sequencing company filtered the original fastq files of the double-end sequencing, filtered out reads with N content exceeding 10% of the base number or low-quality (Q<=5) base number exceeding 50%, and obtained clean fastq files.

[0112] Subsequently, the present embodiment utilizes Cutadapt (version 4.8.0) to excise the constant region of each reads to generate a new clean fastq file. Next, the index file of the human reference genome peptide library and the bacterial reference genome peptide library was constructed using bowtie2-build command. Bowtie2 (version 2.5.0) was used to compare the new cleanfastq file with these index files, and the --very-sensitive parameter was used to ensure the accurate matching of the double-end sequencing data, and the comparison result was output with SAM (Sequence Alignment Map) format file. Samtools (version 1.16.1) was subsequently used to convert it to BAM (Binary Alignment Map) format file, and the Pysam (version 0.22.1) module in Python (version 3.11.5) was used to count the number of peptides in each sample, and the reading was standardized to RPM (read counts per million) file.

[0113] Finally, this example uses the Phip-stat (version 0.5.1) module in the Bash command line tool and Python (version 3.11.5) to fit the Gamma distribution parameters using the gamma-poisson model and calculate the significant p value of peptide enrichment in each sample. The code is as follows "phip gamma-poisson-model -t 99.9 -i RPM_file.tsv -o gamma-poisson-outputfile." The larger the -Log10 p value of the peptide, the greater the probability of enrichment.

[0114] The study cohort included 123 participants, including 34 patients with advanced CRA, 47 patients with early CRC, and 42 HC controls (intestinal diseases were excluded by colonoscopy). All patients had been diagnosed with the disease and before interventional treatment (including medication and surgery) when plasma samples were collected. First, this example analyzed the enrichment of colorectal tumor-related autoantibodies that have been reported in the literature. mDIP-Seq detected these autoantibody responses in the CRC population, which indirectly verified the feasibility of the mDIP-Seq platform ( Figure 4 In order to reduce the influence of false positive data, a blank PrG control group (Mock IP) without adding plasma was set up in parallel.

[0115] For the human antigen library, in order to reduce the false positive rate, in this example, the peptides that were significantly enriched in the patient plasma sample (-Log10 p value > 2, i.e., p < 0.01) and whose enrichment multiple was 5 times that of the input library, and were not significantly enriched in the blank control group (-Log10 p value < 1.3, i.e., p > 0.05) were defined as individual-specifically enriched autoantigen peptides. The number of peptides specifically enriched in each individual was calculated in each group ( Figure 5 ). At the same time, we defined the peptides that appeared in at least 10% but less than 95% of the subjects in the HC, CRA, and CRC cohorts as population-specific enriched antigenic peptides. Therefore, 2,261 autoantibody-binding peptides were selected for case-control analysis, 1,237 autoantibody-binding peptides were used for CRA cohort-specific analysis, and 7,296 autoantibody-binding peptides were used for early CRC cohort-specific analysis ( Figure 6 ).

[0116] Regarding the screening of enteric bacterial antibody epitope libraries, in this example, peptides that were significantly enriched in plasma samples (-Log10 p value > 10) and whose enrichment multiple was 10 times that of the input library, and that were not significantly enriched in the blank control group (-Log10 p value < 1.3, i.e., p > 0.05) were defined as individual-specifically enriched bacterial antigen peptides ( Figure 7 ). Similarly, this example selected 2,514 bacterial antibody binding peptides for case-control analysis, 1,625 bacterial antibody binding peptides for CRA cohort-specific analysis, and 3,318 bacterial antibody binding peptides for early CRC cohort-specific analysis ( Figure 8 ). Subsequently, orthogonal partial least squares discriminant analysis (OPLS-DA) showed that the enrichment spectra of plasma IgG antibodies binding to self- and bacterial antigen peptides in CRA patients, early CRC patients and HC populations were significantly different (p < 0.05) ( Fig. 9 ).

[0117] 5. Filter results

[0118] (1) Biomarkers for distinguishing CRA patients from HC population

[0119] Finally, through comprehensive screening of positive rate, importance coefficient and importance ranking, for biomarkers used to distinguish CRA patients from HC populations, a total of 10 antigenic peptide antibodies as shown in Table 2 were screened. The corresponding antibody content in plasma was detected by these 10 antigenic peptides, which can be used to efficiently distinguish patients with advanced colorectal adenoma from healthy people. Therefore, the antibodies corresponding to these 10 antigenic peptides are biomarkers that can be used to efficiently predict whether an individual is a patient with advanced colorectal adenoma. Among them, among the antibodies corresponding to the 10 antigenic peptides, the antibodies to 3 autoantigen peptides are human autoantibodies, and the antibodies to 7 microbial antigenic peptides are anti-intestinal microbial antibodies. The positive rate, importance coefficient and importance ranking results of the 10 antigenic peptides are shown in Table 3.

[0120] Table 2. Antibodies to antigenic peptides that can be used to predict patients with advanced colorectal adenoma

[0121]

[0122] Table 3. Positive rate, importance coefficient and importance ranking results of antigenic peptides that can be used to predict patients with advanced colorectal adenoma

[0123]

[0124] The positive rate in Table 3 indicates the probability that the antibody of the antigen peptide appears positive in the plasma of healthy people and patients with advanced adenoma. The greater the difference between the two probabilities, the more obvious the effect of the antibody of the antigen peptide in distinguishing patients with advanced colorectal adenoma from healthy people. The importance coefficient refers to the importance ratio of using the antibody of the antigen peptide to distinguish patients with advanced colorectal adenoma from healthy people. The greater the importance coefficient, the better the effect of the antibody of the antigen peptide in distinguishing patients with advanced colorectal adenoma from healthy people, and the greater the importance ratio. The higher the importance ranking, the better the effect of the antibody of the antigen peptide in distinguishing patients with advanced colorectal adenoma from healthy people. The AUC values, ROC sensitivity and specificity of the 10 antigen peptide antibodies that are screened and can effectively distinguish patients with advanced colorectal adenoma from healthy people are shown in Table 4.

[0125] Table 4. AUC values, ROC sensitivity and specificity of 10 antibodies for distinguishing CRA from HC

[0126]

[0127] For the antibodies of the 10 antigenic peptides screened out, this example also verifies their AUC values, ROC sensitivity and specificity through a validation set, which is a patient plasma sample collected from the Department of Colorectal Surgery of the Second Affiliated Hospital of Zhejiang University School of Medicine, including 17 patients with advanced adenoma (CRA) and 21 healthy controls (HC) (intestinal diseases are excluded by colonoscopy). After analysis, the AUC values, ROC sensitivity and specificity of the antibodies of the 10 antigenic peptides for the validation set are shown in Table 5.

[0128] Table 5. AUC values, ROC sensitivity and specificity of the 10 antibodies for distinguishing CRA from HC in the validation set

[0129]

[0130] By detecting the content of antibodies corresponding to the 10 antigenic peptides in plasma, it can be used to predict whether an individual has advanced colorectal adenoma. The degree of correlation between the antibodies of the 10 antigenic peptides and advanced colorectal adenoma can be distinguished by the AUC values, sensitivity, specificity, etc. in Tables 3 and 4, among which the AUC value is the most intuitive and obvious. The higher the AUC value, the more accurately the biomarker can distinguish patients with advanced colorectal adenoma from healthy people.

[0131] It can be seen from Tables 3 and 4 that the content of antibodies to the 10 antigenic peptides is significantly correlated with whether or not a patient has advanced colorectal adenoma. Any of the antibodies to the 10 antigenic peptides alone can be used to distinguish patients with advanced colorectal adenoma from healthy people.

[0132] (2) Biomarkers for distinguishing early-stage CRC patients from HC population

[0133] Through comprehensive screening of positive rate, importance coefficient and importance ranking, for biomarkers used to distinguish early CRC patients from HC populations, a total of 10 antigenic peptide antibodies as shown in Table 6 were screened. The corresponding antibody content in plasma was detected by these 10 antigenic peptides, which can be used to efficiently distinguish early colorectal cancer patients from healthy people. Therefore, the antibodies corresponding to these 10 antigenic peptides are biomarkers that can be used to efficiently predict whether an individual is an early colorectal cancer patient. Among them, among the antibodies corresponding to the 10 antigenic peptides, the antibodies to 7 antigenic peptides are human protein antigen autoantibodies, and the antibodies to 3 antigenic peptides are anti-intestinal microbial antibodies. The positive rate, importance coefficient and importance ranking results of the 10 antigenic peptides are shown in Table 7.

[0134] Table 6. Antibodies to antigenic peptides that can be used to predict early colorectal cancer patients

[0135]

[0136] Table 7. Positive rate, importance coefficient and importance ranking results of antigenic peptides that can be used to predict patients with early colorectal cancer

[0137]

[0138] The positive rate in Table 7 indicates the probability that the antibody of the antigen peptide appears positive in the plasma of healthy people and early colorectal cancer patients, respectively. The greater the difference between the two probabilities, the more obvious the effect of the antibody of the antigen peptide in distinguishing early colorectal cancer patients from healthy people. The importance coefficient refers to the importance ratio of using the antibody of the antigen peptide to distinguish early colorectal cancer patients from healthy people. The larger the importance coefficient, the better the effect of the antibody of the antigen peptide in distinguishing early colorectal cancer patients from healthy people, and the greater the importance ratio. The higher the importance ranking, the better the effect of the antibody of the antigen peptide in distinguishing early colorectal cancer patients from healthy people. The AUC values, ROC sensitivity and specificity of the 10 antigen peptide antibodies that are screened and can effectively distinguish early colorectal cancer patients from healthy people are shown in Table 8.

[0139] Table 8 AUC values, ROC sensitivity and specificity of 10 antibodies for distinguishing early CRC from HC

[0140]

[0141] For the antibodies of the 10 antigenic peptides screened out, this example also verifies their AUC values, ROC sensitivity and specificity through a validation set, which is a patient plasma sample collected from the Department of Colorectal Surgery of the Second Affiliated Hospital of Zhejiang University School of Medicine, including 23 patients with early colorectal cancer (CRC) and 21 healthy controls (HC) (intestinal diseases are excluded by colonoscopy). After analysis, the AUC values, ROC sensitivity and specificity of the antibodies of the 10 antigenic peptides for the validation set are shown in Table 9.

[0142] Table 9 AUC values, ROC sensitivity and specificity of the 10 antibodies for distinguishing early CRC from HC in the validation set

[0143]

[0144] By detecting the content of antibodies corresponding to the 10 antigenic peptides in plasma, it can be used to predict whether an individual has early colorectal cancer. The degree of correlation between the antibodies of the 10 antigenic peptides and early colorectal cancer can be distinguished by the AUC values, sensitivity, specificity, etc. in Tables 6 and 7, among which the AUC value is the most intuitive and obvious. The higher the AUC value, the more accurately the biomarker can distinguish between early colorectal cancer patients and healthy people.

[0145] It can be seen from Tables 8 and 9 that the content of antibodies to the 10 antigenic peptides is significantly correlated with whether or not a patient has early colorectal cancer. Any of the antibodies to the 10 antigenic peptides can be used to distinguish early colorectal cancer patients from healthy people.

[0146] Example 2: Construction of diagnostic model

[0147] This example is based on the 10 antibody markers for predicting advanced colorectal adenoma and 10 antibody markers for predicting early colorectal cancer screened in Example 1, and respectively constructs a diagnostic model for predicting whether an individual is a patient with advanced colorectal adenoma and a diagnostic model for predicting whether an individual is a patient with early colorectal cancer. Plasma samples from patients in the Department of Colorectal Surgery of the Second Affiliated Hospital of Zhejiang University School of Medicine were collected, including 56 patients with advanced adenoma (CRA), 48 patients with early colorectal cancer (CRC) and 64 healthy controls (HC) (intestinal diseases were excluded by colonoscopy).

[0148] Then, according to Table 3 and Table 7, this example examines the ability of the antibody epitope library to distinguish classification tasks (CRA vs. HC, early CRC vs. HC), and identifies the antibody with the largest contribution of antibody-binding peptides as a diagnostic marker. Machine learning analysis was performed using Python code and the scikit-learn library, and the random forest algorithm was used to evaluate the importance of peptide features and classification performance for the screened antibodies that specifically enriched antigen peptides.

[0149] The data were randomly divided into a training set (including 28 patients with advanced adenoma, 24 patients with early colorectal cancer, and 32 healthy controls) and a validation set (including 28 patients with advanced adenoma, 24 patients with early colorectal cancer, and 32 healthy controls). The mDIP-Seq platform technology provided in Example 1 was used to detect the presence of antibodies to the antigenic peptides in each sample for the antibodies and their stepwise combinations of the top 2, 3, 4, 5, 6, 7, 8, 9, and 10 antigenic peptides in Tables 2 and 5, respectively. A random forest model was constructed by machine learning, and the AUC of the model on the training set and validation set was calculated. The classification accuracy and specificity of the validation set were evaluated in combination with the confusion matrix of the prediction results. The parameters of the random forest model are shown in Table 10.

[0150] Table 10. Random forest model parameters

[0151]

[0152] The diagnostic model constructed by the random forest model for predicting patients with advanced colorectal adenoma and the diagnostic effects of different numbers of antibody combinations are shown in Table 11.

[0153] Table 11. Diagnostic effect of random forest models constructed with different antibody combinations to predict CRA (training set)

[0154]

[0155] According to Table 11, and compared with Tables 4 and 5, it can be seen that the antibody of the combined antigen peptide has a higher ability to distinguish patients with advanced colorectal adenoma and healthy people than the antibody of a single antigen peptide. The random forest model constructed with antibodies of the first 8, 9, and 10 antigen peptides has an AUC value of 0.99, an accuracy of 86%, and a specificity of 90%, showing excellent discrimination ability for patients with advanced colorectal adenoma and healthy people. Subsequently, this embodiment evaluates the model performance on the validation set, and the evaluation results are shown in Table 12.

[0156] Table 12. Diagnostic effect of random forest models constructed with different antibody combinations to predict CRA (validation set)

[0157]

[0158] The random forest classification model constructed by antibodies of the first 8 antigenic peptides is shown in Fig.10 When the default prediction probability threshold was 0.5, the AUC value for predicting patients with advanced colorectal adenoma reached 0.93, with an accuracy of 83% and a specificity of 90%.

[0159] It can be seen that the random forest models constructed using antibodies to the first 8, 9, and 10 antigen peptides can be used to accurately distinguish patients with advanced colorectal adenoma from healthy people. The most preferred is the random forest model constructed using antibodies to the first 8 antigen peptides, which requires the least number of markers and achieves the best effect.

[0160] The diagnostic model constructed by the random forest model for predicting early colorectal cancer patients and the diagnostic effects of different numbers of antibody combinations are shown in Table 13.

[0161] Table 13. Diagnostic effect of random forest models constructed with different antibody combinations for predicting early CRC (training set)

[0162]

[0163] According to Table 13, and compared with Tables 8 and 9, it can be seen that the antibody of the combined antigen peptide has a higher ability to distinguish early colorectal cancer patients and healthy people than the antibody of a single antigen peptide. The random forest model constructed using antibodies of the first 9 and 10 antigen peptides has an AUC value of 0.98, an accuracy of 86%, and a specificity of 90%, showing excellent discrimination ability for early colorectal cancer patients and healthy people. Subsequently, this embodiment evaluates the model performance on the validation set, and the evaluation results are shown in Table 14.

[0164] Table 14. Diagnostic effect of random forest models constructed with different antibody combinations for predicting early CRC (validation set)

[0165]

[0166] The random forest classification model constructed by antibodies of the first 9 antigenic peptides is shown in Fig.11 ,When the default prediction probability threshold was 0.5, the AUC value for predicting early colorectal cancer patients reached 0.88, with an accuracy of 89% and a specificity of 100%.

[0167] It can be seen that the random forest models constructed using antibodies to the first 9 or 10 antigen peptides can be used to accurately distinguish between early colorectal cancer patients and healthy people. The most preferred is the random forest model constructed using antibodies to the first 9 antigen peptides, which requires the least number of markers and achieves the best effect.

[0168] The antibodies with a total of 20 antigen peptides provided by the present invention have shown good accuracy and specificity in diagnosing CRA patients and early CRC patients, and have great clinical significance for improving the survival rate of colorectal tumor patients.

[0169] It can be understood that the embodiments described in the present invention are some preferred embodiments and features. Any person skilled in the art can make some changes and variations based on the essence of the description of the present invention. These changes and variations are also considered to be within the scope of the present invention and the scope limited by the independent claims and the appended claims.

Claims

1. Use of a biomarker in the preparation of a reagent for predicting whether an individual has advanced colorectal adenoma, characterized in that: The biomarkers include plasma autoantibodies and anti-intestinal microbial antibodies, the plasma autoantibodies include autoantibodies to antigenic peptides as shown in any one or more of the sequence lists Seq ID NO.6~Seq ID NO.7, and Seq ID NO.9; the anti-intestinal microbial antibodies include antibodies to antigenic peptides as shown in any one or more of the sequence lists Seq ID NO.1~Seq ID NO.5, Seq ID NO.8, and SeqID NO.

10.

2. The use according to claim 1, characterized in that The biomarker includes any one or more polypeptide sequences shown in Seq ID NO.1 to Seq ID NO.

8.

3. The use according to claim 2, characterized in that The reagent is used to detect the content of biomarkers in a body fluid sample; the body fluid sample includes any one or more of saliva, blood, urine, plasma, serum, and cerebrospinal fluid.

4. The use according to claim 3, characterized in that The reagent is used to detect the presence or absence of biomarkers in a body fluid sample.

5. A kit for predicting whether an individual has advanced colorectal adenoma, characterized in that: A detection reagent for a biomarker for use as described in any one of claims 1 to 4.

6. A system for predicting whether an individual has advanced colorectal adenoma, characterized in that: The system includes a data analysis module, which is used to analyze the detection values ​​of biomarkers, wherein the biomarkers include plasma autoantibodies and anti-intestinal microorganism antibodies, wherein the plasma autoantibodies include autoantibodies to antigenic peptides as shown in any one or more of the sequence tables Seq ID NO.6~Seq ID NO.7, and Seq ID NO.9; the anti-intestinal microorganism antibodies include antibodies to antigenic peptides as shown in any one or more of the sequence tables Seq ID NO.1~Seq ID NO.5, Seq ID NO.8, and Seq ID NO.

10.

7. The system according to claim 6, characterized in that The biomarkers include antibodies to antigenic peptides as shown in any one or more of the sequence listings Seq ID NO.1 to Seq ID NO.

8.

8. The system according to claim 7, characterized in that The data analysis module uses the detection values ​​of markers of known samples as a training set, divides the samples into an advanced colorectal adenoma group and a healthy group according to whether the samples have advanced colorectal adenoma, analyzes the relationship between the detection values ​​of the advanced colorectal adenoma group and the healthy group, and constructs a model.

9. The system according to claim 8, characterized in that The system also includes a data storage module, a data input interface and a data output interface; the data storage module is used to store the detection values ​​of the biomarkers; the data input interface is used to input the detection values ​​of the biomarkers, and the data output interface is used to output the prediction results.

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