Product for diagnosing colorectal cancer based on proteomics and application

Abstract

The invention discloses a product for diagnosing colorectal cancer based on proteomics and application. The invention also discloses application of a product for detecting at least one of CLU, CFI, PON1, IGLC2, C8B, SERPINC1, ORM2 and APOA4 proteins in preparation of a product for diagnosing colorectal cancer. The protein markers CLU, CFI, PON1, IGLC2, C8B, SERPINC1, ORM2 and APOA4 have significantly higher diagnostic sensitivity and specificity, the invasiveness of enteroscopy is avoided, the compliance of patients is improved, high-throughput detection and quantitative analysis are performed on proteins by using a liquid chromatography-mass spectrometry (LC-MS / MS) technology, the detection resolution and accuracy are effectively improved, and the detection cost is reduced. And false positive and false negative results are obviously reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of clinical testing and diagnosis, and in particular to a product and application for diagnosing colorectal cancer based on proteomics. Background Art

[0002] Colorectal cancer (CRC) is one of the cancers with the highest morbidity and mortality rates worldwide. Early-onset colorectal cancer (EO-CRC) refers to colorectal cancer that occurs before the age of 50. The incidence has been on the rise in recent years, while the traditionally more common late-onset colorectal cancer (LO-CRC) mainly occurs in the elderly population over 50 years old. Compared with LO-CRC patients, EO-CRC patients are often in a more advanced stage when diagnosed, their disease progresses rapidly, and their five-year survival rate is relatively low. Although a variety of treatments such as surgery, radiotherapy, chemotherapy and targeted therapy can improve the survival prognosis of some patients, the overall prognosis of colorectal cancer still needs to be improved. Especially for young patients, early screening and diagnosis are particularly important.

[0003] Currently, colorectal cancer screening mainly relies on colonoscopy, fecal occult blood test (FOBT) and stool DNA testing, and traditional blood biomarkers. Current colorectal cancer screening technology still has many limitations, especially the lack of sensitivity and specificity in early screening. Although colorectal cancer is one of the most common cancers worldwide, early diagnosis still poses great challenges. Existing screening methods mainly include the following categories:

[0004] Colonoscopy: Although it is the most commonly used and effective screening method, colonoscopy is an invasive procedure that may cause discomfort or complications and has low compliance, which makes it difficult for many patients, especially those at high risk, to undergo effective screening at an early stage.

[0005] FOBT and stool DNA testing: These tests are relatively noninvasive and easy to accept, but their sensitivity and specificity are limited, especially in the detection of early cancers, it is difficult to accurately identify tiny lesions.

[0006] Traditional blood biomarkers: Biomarkers are abnormal molecules in the process of tumor occurrence and development, widely present in body fluids such as blood, feces, and urine, and can be used for cancer screening, diagnosis, and prognosis evaluation. Although traditional biomarkers such as CEA (carcinoembryonic antigen) and CA19-9 (carbohydrate antigen 19-9) are widely used in clinical practice, their performance in the diagnosis of early colorectal cancer is not ideal, with low sensitivity and specificity, and it is difficult to meet the needs of accurate diagnosis. Summary of the invention

[0007] The purpose of the present invention is to overcome the above-mentioned deficiencies of the prior art and provide a product and application for diagnosing colorectal cancer based on proteomics, which utilizes liquid chromatography-mass spectrometry (LC-MS / MS) to perform high-throughput detection and quantitative analysis of proteins, effectively improve the resolution and accuracy of detection, and significantly reduce false positive and false negative results.

[0008] The purpose of the present invention is to provide an application of a product for detecting at least one of CLU, CFI, PON1, IGLC2, C8B, SERPINC1, ORM2 and APOA4 proteins in the preparation of a product for diagnosing colorectal cancer.

[0009] In order to achieve the above object, the present invention is implemented by the following scheme:

[0010] Based on proteomics analysis of the composition and dynamic changes of proteins in organisms, potential plasma protein markers related to early-onset and late-onset colorectal cancer can be discovered. This technology plays a key role in the development of cancer markers and can identify new, highly specific and sensitive protein markers, which also provide strong support for the formulation of individualized treatment plans. The present invention screens out colorectal cancer protein markers with high specificity, sensitivity and accuracy by applying proteomics technology. Therefore, diagnostic products and methods based on plasma protein markers can provide a non-invasive or minimally invasive screening method for early-onset and late-onset colorectal cancer, effectively making up for the shortcomings of traditional diagnostic technology in accuracy, sensitivity and specificity, thereby improving the efficiency and accuracy of early diagnosis of colorectal cancer.

[0011] The present invention provides the use of a product for detecting at least one of CLU, CFI, PON1, IGLC2, C8B, SERPINC1, ORM2 and APOA4 proteins in the preparation of a product for diagnosing colorectal cancer. The present invention identified a total of 542 proteins through quality control filtering, data normalization, and protein qualitative and quantitative analysis. The proteins detected in at least 70% of the samples in each group were retained and analyzed by t-test, and the top eight differentially expressed proteins were screened out according to the p value.

[0012] Preferably, the product detects at least one of PON1, IGLC2, SERPINC1 and ORM2 proteins. The present invention finally screens out the four protein markers as universal early-onset and late-onset colorectal cancer diagnostic protein markers through data modeling and verification.

[0013] More preferably, the product detects at least three of PON1, IGLC2, SERPINC1 and ORM2 proteins. The combined marker results have an improved effect over single protein markers.

[0014] Most preferably, the product detects at least one of the following three groups of proteins: the first group is PON1, IGLC2 and SERPINC1 proteins, the second group is PON1, IGLC2 and ORM2 proteins, and the third group is IGLC2, SERPINC1 and ORM2 proteins. The three groups of proteins can achieve an AUC area of ​​1.

[0015] In the present invention, the PON1 protein is the protein marker with the largest AUC area. Its expression in the healthy control group and the colorectal cancer group is significantly different, and it performs well in distinguishing colorectal cancer from the healthy control group. The protein shows high sensitivity and specificity in clinical diagnosis, providing strong support for accurate diagnosis.

[0016] The present invention also verified through experiments that the tissue RNA expression level of PON1 protein is consistent with the plasma PON1 protein expression level. In the UKBB population, it was also found that the plasma PNO1 protein expression level has a protective effect on colorectal cancer. Among the four protein markers, PON1 protein plays the most important role in the accurate diagnosis of colorectal cancer.

[0017] Furthermore, the product is a reagent and / or instrument for detecting the protein using at least one of liquid chromatography tandem mass spectrometry, chemiluminescence, radioisotope method, fluorescence luminescence, enzyme labeling, colloidal gold method, biochip detection method and protein blotting.

[0018] In a specific embodiment of the present invention, the product is a reagent and / or instrument for detecting the protein by liquid chromatography tandem mass spectrometry, and the product contains a mobile phase for liquid chromatography, and the mobile phase includes reagent A and reagent B, and the reagent A and / or reagent B contain at least one of formic acid, acetonitrile and water; the reagent A is a formic acid aqueous solution, and the concentration of formic acid is 0.05% to 0.2 v / v; the reagent B is a formic acid-acetonitrile aqueous solution, and the formic acid-acetonitrile aqueous solution is obtained by mixing formic acid and acetonitrile aqueous solution, the concentration of acetonitrile in the acetonitrile aqueous solution is 5% to 95% v / v, and the concentration of formic acid in the formic acid-acetonitrile aqueous solution is 0.05% to 0.2% v / v.

[0019] Preferably, in the mobile phase, the concentration of formic acid in the reagent A is 0.1% v / v, in the reagent B, the concentration of acetonitrile in the acetonitrile aqueous solution is 80% v / v, and the concentration of formic acid in the formic acid-acetonitrile aqueous solution is 0.1% v / v.

[0020] Furthermore, the product also contains a chromatographic column, the chromatographic column filler is ReproSil-PurC18 particles with a particle size of 1.9 μm, and the manufacturer is: Dr. Maisch GmbH, Germany.

[0021] Furthermore, the method for using the product comprises the following steps:

[0022] S1: using urea to dissolve the protein in the sample to be tested, adding dithiothreitol to reduce the protein in the sample to be tested, and adding iodoacetamide to alkylate the protein in the sample to be tested;

[0023] S2: using trypsin to enzymatically digest the sample treated in step S1 to obtain a protease hydrolysate;

[0024] S3: desalting the protease hydrolysate from step S2 using a solid phase extraction column to obtain peptide fragments to be tested;

[0025] S4: Detect the peptide fragments to be detected in step S3 by liquid chromatography-tandem mass spectrometry:

[0026] The elution condition in step S4 is gradient elution:

[0027] 0-45 min, 10%-27% v / v reagent B, 73%-90% v / v reagent A; 45-53 min, 27%-37% v / v reagent B, 63%-73% v / v reagent A; 53-57 min, 37%-100% v / v reagent B, 0-63% v / v reagent A; 57-60 min, 100% v / v reagent B; the flow rate is 0.3-0.6 mL / min.

[0028] In a specific embodiment of the present invention, the flow rate is 0.45 mL / min.

[0029] Furthermore, the column temperature is set to 30-60°C, preferably 50°C.

[0030] Furthermore, in step S1, 8-10 mol / L urea solution is used to dissolve the protein in the sample to be tested, preferably 8 mol / L. Too high a urea concentration will affect the activity of the enzyme.

[0031] Furthermore, in step S1, the final concentration of dithiothreitol is 0.01-0.02 mol / L, preferably 0.01 mol / L.

[0032] Furthermore, in step S1, the final concentration of iodoacetamide is 0.04-0.08 mol / L, preferably 0.04 mol / L. Excessive iodoacetamide may cause the problem of over-alkylation.

[0033] Furthermore, in step S1, after adding iodoacetamide, the mixture is placed away from light for 15 to 30 minutes, preferably 30 minutes.

[0034] Furthermore, in step S1, after being placed away from light, the urea concentration is reduced to below 1 mol / L.

[0035] In a specific embodiment of the present invention, in step S1, urea, dithiothreitol and iodoacetamide solutions can be prepared with sodium bicarbonate solution or water, preferably sodium bicarbonate solution; the concentration of the sodium bicarbonate solution is 0.05-0.005 mol / L, wherein the concentration of the sodium bicarbonate solution for preparing the urea solution is 0.005 mol / L, and the concentration of the sodium bicarbonate solution for preparing dithiothreitol and / or iodoacetamide is 0.05 mol / L.

[0036] Furthermore, in step S2, the mass ratio of trypsin to the sample processed in step S1 is 1:40-50, preferably 1:50.

[0037] Furthermore, in step S3, formic acid is added to mix with the protease hydrolysate of step S2, and then desalting is performed using a solid phase extraction column.

[0038] Furthermore, in step S3, the concentration of formic acid is 0.05% to 0.2% v / v, preferably 0.1% v / v.

[0039] In a specific embodiment of the present invention, the mass spectrometer detector conditions in step S4 are: electrospray ion source, negative ion scanning, ion source voltage of 2600V, ion source temperature of 320°C, primary mass spectrometer scanning range of 350-1500m / z, scanning resolution of 60000; secondary mass spectrometer scanning range is fixed at 200m / z, and secondary scanning resolution is 30000.

[0040] Further, the sample includes a clinical sample obtained or obtained from a subject. In some instances, the clinical sample is synovial fluid, whole blood, serum, plasma, urine, sputum, tissue, saliva, tears, spinal fluid, tissue sections obtained by biopsy, cells placed in tissue culture or adapted to tissue culture, sweat, mucus, fecal matter, gastric juice, peritoneal fluid, amniotic fluid, cyst fluid, peritoneal fluid, pancreatic juice, breast milk, lung lavage fluid, bone marrow, gastric acid, bile, semen, pus, aqueous humor, exudate, or a combination of the above substances.

[0041] In some embodiments, the clinical sample is selected from the group consisting of: blood, plasma, saliva, mucus, urine, feces, tissue, sweat, tears, hair, or a combination thereof. In some embodiments, the clinical sample is a blood sample.

[0042] In the present embodiment, the clinical sample is a plasma sample.

[0043] The present invention relates to plasma proteomic biomarkers: which are found to be differentially present in plasma biological samples of subjects with colorectal cancer compared to "normal" subjects. If the difference between the expression levels of a plasma biomarker in the samples is determined to be statistically significant, then the plasma protein biomarker is differentially present between the samples. Common tests for statistical significance include, but are not limited to: t-tests, ANOVA, Kniskal-Wallis, Wilcoxon, Mann-Whitney, and odds ratios. Plasma protein biomarkers alone or in combination can be used to provide a measure of the relative risk of colorectal cancer in a subject.

[0044] The plasma biomarkers described herein can be used in screening tests to assess the colorectal cancer status of a subject. Disease status refers to the presence or absence of colorectal cancer. Based on the colorectal cancer status of the subject, other operations can be indicated, including, for example, other diagnostic tests or therapeutic operations.

[0045] The ability of a diagnostic test to correctly predict a disease state is usually measured in the form of the accuracy of the assay, the sensitivity of the assay, the specificity of the assay, or the "area under the curve" (AUC) (e.g., the area under the receiver operating characteristic ROC curve). The accuracy used herein is a measure of the ratio of samples that are misclassified. The accuracy can be calculated as the total number of correctly classified samples divided by the total number of samples (e.g., in a test population). Sensitivity is a measure of the "true positive" predicted as positive by the test, and can be calculated as the number of correctly identified colorectal cancer samples divided by the total number of colorectal cancer samples. Specificity is a measure of the "true negative" predicted as negative by the test, and can be calculated as the number of correctly identified normal samples divided by the total number of normal samples. AUC is a measure of the area under the receiver operating characteristic curve, and the curve is a graph of sensitivity relative to the false positive rate (1-specificity). The larger the AUC, the more effective the predictive value of the test. Other useful measures of the practicality of the test include "positive predictive value" (it is the percentage of the actual positive that the test is positive) and "negative predictive value" (it is the percentage of the actual negative that the test is negative).

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] Based on proteomics, the present invention proposes a non-invasive colorectal cancer screening product based on plasma protein biomarkers and a method for using the product. This product and method are superior to the existing technology in many aspects, specifically:

[0048] High sensitivity and specificity: Compared with existing blood biomarkers, the protein markers CLU, CFI, PON1, IGLC2, C8B, SERPINC1, ORM2 and APOA4 of the present invention have significantly higher diagnostic sensitivity and specificity.

[0049] Non-invasive: The present invention is based on the detection of body fluid proteins, which avoids the invasiveness of colonoscopy, greatly improves patient compliance, shortens cancer diagnosis time, and is suitable for extensive screening of high-risk populations.

[0050] High-throughput and precise detection: Liquid chromatography-mass spectrometry (LC-MS / MS) is used to perform high-throughput detection and quantitative analysis of proteins, effectively improving the resolution and accuracy of detection and significantly reducing false positive and false negative results. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 Log2Intensity distribution of mass spectrometry test data of 40 early-onset control samples.

[0052] Figure 2 This is the ROC curve of specific protein markers for distinguishing the healthy control group and the colorectal cancer group in the entire validation group of plasma samples.

[0053] Figure 3 This is the multivariate ROC curve diagram of specific protein markers for distinguishing the healthy control group and colorectal cancer group in the entire validation group of plasma samples.

[0054] Figure 4 The expression of PON1 in all plasma samples in the healthy control group and colorectal cancer group.

[0055] Figure 5 The expression of PON1 protein RNA in colorectal cancer tissues and healthy control tissues.

[0056] Figure 6 Nonlinear association between plasma PNO1 expression level and colorectal cancer risk in UKBB population. DETAILED DESCRIPTION

[0057] The present invention is further described in detail below in conjunction with the accompanying drawings and specific examples of the specification. The examples are only used to explain the present invention and are not used to limit the scope of the present invention. The test methods used in the following examples are conventional methods unless otherwise specified; the materials and reagents used are reagents and materials that can be obtained from commercial channels unless otherwise specified.

[0058] The sample selection criteria are as follows:

[0059] (1) Patients with colorectal cancer of various stages confirmed by histopathology: the early-onset group was aged 18 to 50 years, and the late-onset group was aged over 50 years.

[0060] (2) The control group consisted of healthy volunteers with no history of colorectal cancer or other malignant tumors, matched with the patient group in terms of gender and age.

[0061] (3) All subjects signed informed consent.

[0062] The sample exclusion criteria are as follows:

[0063] (1) Failure to obtain informed consent from patients or patients refusing to participate in the study.

[0064] (2) Patients with incomplete clinical data.

[0065] (3) Patients who have received systemic chemotherapy or other targeted therapies in the past.

[0066] (4) Patients with severe medical complications.

[0067] Plasma samples were collected from colorectal cancer patients and healthy controls. All samples were self-collected (Table 1):

[0068] Discovery cohort: included 40 patients with early-onset colorectal cancer (EO-CRC), 40 patients with late-onset colorectal cancer (LO-CRC), and 80 matched healthy controls, totaling 160 samples.

[0069] Validation cohort: included 10 patients with early-onset colorectal cancer and 10 patients with late-onset colorectal cancer and their healthy controls, a total of 40 samples.

[0070] Table 1 Plasma sample information of the discovery cohort and validation cohort

[0071]

[0072] Example 1 Screening for proteins differentially expressed in colorectal cancer

[0073] 1. Experimental Methods

[0074] 1. Collect samples

[0075] Whole blood samples from the discovery cohort were collected using purple-capped anticoagulant tubes (BD, America) containing ethylene diamine tetraacetic acid (EDTA) anticoagulant. The samples were centrifuged at 1000 × g for 10 min at 4 °C within 6 h, and the upper plasma layer was taken and transferred to a 1.5 mL sterile centrifuge tube, which was numbered and labeled. The upper supernatant was taken and stored in a -80 °C refrigerator after further centrifugation at 12000 × g for 2 min at 4 °C.

[0076] 2. Sample pretreatment

[0077] (1) Protein enzymatic hydrolysis: 8 mol / L urea solution (prepared in 0.005 mol / L sodium bicarbonate solution) was added to each plasma sample to dissolve the protein; 0.2 mol / L dithiothreitol (prepared in 0.05 mol / L sodium bicarbonate solution) was added to control the final concentration of dithiothreitol to 0.01 mol / L, and incubated at 95°C for 10 min to reduce the disulfide bonds in the protein; 0.8 mol / L iodoacetamide (prepared in 0.05 mol / L sodium bicarbonate solution) was then added to control its final concentration to 0.04 mol / L for alkylation treatment, and the sample was placed at room temperature in the dark for 30 min; 0.005 mol / L sodium bicarbonate solution was added to dilute the urea concentration to less than 1 mol / L.

[0078] (2) Trypsin hydrolysis: Trypsin and the sample treated in step (1) are mixed at a mass ratio of 1:50, shaken and mixed, and incubated at 37° C. overnight for enzymatic hydrolysis to obtain a trypsin hydrolyzed sample.

[0079] (3) Desalting: The trypsin enzymatic hydrolyzed sample in step (2) was centrifuged at 12,000 × g for 10 min, the supernatant was taken, formic acid was added to a final formic acid concentration of 0.1% (v / v), and desalting and impurity removal were performed using a Strata X C18 solid phase extraction column (America, Phenomenexcx) to obtain peptide fragments, which were vacuum dried and stored in a -80°C ultra-low temperature refrigerator for future use.

[0080] 3. Protein spectrum analysis

[0081] Liquid chromatography-mass spectrometry analysis: The peptides obtained in step 2 were dissolved in 0.1 (v / v) formic acid aqueous solution (concentration of 200-500 ng / μL) by high performance liquid chromatography-mass spectrometry (LC-MS / MS), and 500 ng was taken for analysis. The sample was loaded into a capillary analytical column filled with C18 1.9 mm ReproSil particles (Dr. Maisch GmbH), equipped with an EASY-nLC 1200 system (Thermo Fisher Scientific) and MS (Orbitrap 480, Thermo Fisher Scientific). The column temperature was maintained at 50°C in the column oven. High-throughput mass spectrometry detection of the proteome was performed using data independent acquisition (DIA) data acquisition mode.

[0082] Chromatographic conditions: mobile phase A is 0.1% (v / v) formic acid aqueous solution; mobile phase B is formic acid-acetonitrile aqueous solution, the formic acid-acetonitrile aqueous solution is obtained by mixing formic acid and acetonitrile aqueous solution, the concentration of acetonitrile in the acetonitrile aqueous solution is 80% (v / v), and the concentration of formic acid in the formic acid-acetonitrile aqueous solution is 0.1% (v / v).

[0083] Gradient elution, four liquid phase gradient settings are as follows:

[0084] 0-45 min, 10%-27% (v / v) mobile phase B, 73%-90% (v / v) mobile phase A; 45-53 min, 27%-37% (v / v) mobile phase B, 63%-73% (v / v) mobile phase A; 53-57 min, 37%-100% (v / v) mobile phase B, 0-63% (v / v) mobile phase A; 57-60 min, 100% (v / v) mobile phase B. The flow rate was 0.45 mL / min.

[0085] Mass spectrometry conditions: electrospray ion source, negative ion scanning, ion source voltage of 2600 V, ion source temperature of 320 ° C. MRM scanning analysis settings: primary mass spectrometry scanning range of 350-1500 m / z, scanning resolution of 60000; secondary mass spectrometry scanning range is fixed at 200 m / z, secondary scanning resolution of 30000.

[0086] 4. Data analysis and marker screening

[0087] Data acquisition: Use the Data Independent Acquisition (DIA) mode to collect protein mass spectrometry data and obtain protein information.

[0088] Bioinformatics analysis: DIANN software was used to perform qualitative and quantitative analysis of peptides and proteins on mass spectrometry data. Proteins detected in at least 70% of the samples in each group were retained for t-test analysis (FC>1.5; P<0.05), and the top eight differentially expressed proteins were screened as candidate markers according to the p value.

[0089] Data retrieval and analysis: DIANN software was used to perform qualitative and quantitative analysis of peptides and proteins on the secondary mass spectrometry data.

[0090] 2. Experimental Results

[0091] The present invention identified 542 proteins in total through quality control filtering, data normalization, and protein qualitative and quantitative analysis. Here, a certain set of data is used as an example to evaluate the data quality. Figure 1It can be seen that the mass spectrometry test data of 40 samples in a group have good consistency, and the CV value fluctuation does not exceed 20%, which is a favorable guarantee for subsequent differential analysis and marker screening. Subsequently, we retained the proteins detected in at least 70% of the samples in each group for t-test analysis, and screened out the top eight differentially expressed proteins according to the p value, namely CLU (NCBI accession number: P10909, clusterin), CFI (NCBI accession number: P05156, complement factor I), PON1 (NCBI accession number: P27169, paraoxonase 1), IGLC2 (NCBI accession number: P0DOY2, human immunoglobulin light chain lambda 2 chain C region), C8B (NCBI accession number: P07358, complement component 8B), SERPINC1 (NCBI accession number: P01008, serine protease inhibitor C1), ORM2 (NCBI accession number: P19652, mucin-like protein 2) and APOA4 (NCBI accession number: P06727, human apolipoprotein A4), which were subsequently used as candidate protein markers.

[0092] Example 2 Data Modeling and Validation

[0093] 1. Experimental Methods

[0094] 1. Machine Learning Model Construction

[0095] Diagnostic models supported by machine learning: Diagnostic models built using machine learning algorithms can further improve the predictive performance of protein markers, facilitate early detection and support personalized treatment plans.

[0096] The data of 8 differentially expressed candidate proteins in the plasma samples of the cohort (n=160) in Example 1 were modeled using a machine learning algorithm (such as a random forest algorithm), and ROC analysis was performed to output all protein markers and their combinations with AUC values ​​greater than 0.85.

[0097] Three-quarters of the modeling group sample data were randomly divided into a training set and one-quarter into a test set. The training set was used to build and train the machine learning classification model, and the test set was used to verify the model's discriminative ability. The evaluation model was constructed by iterating 1,000 random cycles of the random forest, performing 10-fold cross validation, and calculating the average accuracy of the final model.

[0098] The ROC curve is a method to study the relationship between model sensitivity and specificity. It uses sensitivity as the ordinate and 1-specificity as the abscissa to evaluate model performance by comparing the AUC under the curve. The closer the AUC is to 1, the better the model performance and the better the diagnostic effect. If it is less than 0.5, it means that the model accuracy is poor. The AUC value of the diagnostic model reaches above 0.85, showing good predictive performance.

[0099] 2. The constructed diagnostic model was validated using samples from an independent validation cohort group (n=40).

[0100] In order to verify the effectiveness of the constructed diagnostic model, the plasma samples of the validation cohort group were used as unknown samples for verification, using the same detection and analysis method as the discovery cohort group in Example 1.

[0101] 2. Experimental Results

[0102] 1. Finally, four protein markers, PON1, IGLC2, SERPINC1 and ORM2, were screened out as universal diagnostic protein markers for early-onset and late-onset colorectal cancer.

[0103] 2. The validation results of individual protein markers showed that PON1 protein had an AUC of 0.96, a sensitivity of 95%, and a specificity of 95% in distinguishing colorectal cancer patients; IGLC2 protein had an AUC of 0.92, a sensitivity of 85%, and a specificity of 100% in distinguishing colorectal cancer patients; SERPINC1 protein had an AUC of 0.88, a sensitivity of 95%, and a specificity of 70% in distinguishing colorectal cancer patients; ORM2 protein had an AUC of 0.91, a sensitivity of 70%, and a specificity of 100% in distinguishing colorectal cancer patients. Figure 2 ). The AUC of other proteins was less than 0.85.

[0104] The results of the combined marker model construction showed that the combined marker results were better than those of single protein markers. When three proteins were combined (IGLC2+SERPINC1+PON1, IGLC2+ORM2+PON1 and IGLC2+ORM2+SERPINC1), the AUC area could reach 1 ( Figure 3 ). The results showed that the constructed colorectal cancer diagnosis model showed good performance on unknown samples and could support its application in clinical practice.

[0105] Among them, the protein marker with the largest AUC area, PON1, had a similar expression among the groups. Figure 4As shown, the protein performed well in distinguishing colorectal cancer from healthy controls. It showed high sensitivity and specificity in clinical diagnosis, providing strong support for accurate diagnosis.

[0106] Example 3 Expression of PON1 protein RNA in intestinal cancer tissues and healthy control tissues

[0107] 1. Experimental Methods

[0108] PON1 is a universal protein for colorectal cancer, and the RNA level was verified at the tissue level corresponding to PON1. The colorectal cancer tissues and paired healthy control tissue samples matched in the cohort of Example 1 were collected, and the total RNA was extracted and quality tested. Subsequently, the raw sequencing data was generated using a high-throughput RNA sequencing platform (Illumina). After the sequencing data was quality controlled (Trimmomatic cleaned up low-quality sequences and adapter sequences), it was aligned with the reference genome (GRCh38) and the software STAR. Next, FeatureCounts was used to quantify the gene expression level, and the FPKM (Fragments Per Kilobase of transcript per Million mapped reads) method was used for standardization to obtain the RNA expression value of the PON1 gene in colorectal cancer tissues and healthy control tissues. Finally, statistical analysis was used to compare the expression levels of PON1 in cancer tissues and healthy tissues, and a scatter plot was drawn using a visualization tool (such as R's ggplot2 package) to show the expression distribution and significance of the difference (P value) between the two groups of data.

[0109] 2. Experimental Results

[0110] The tissue RNA expression level of PON1 protein also showed consistent results with the plasma PON1 protein expression level ( Figure 5 ).

[0111] Example 4 Nonlinear association between plasma PNO1 protein expression level and colorectal cancer risk in UKBB population

[0112] 1. Experimental Methods

[0113] Restricted cubic splines (RCS) were constructed in the Cox proportional hazards regression model to determine the nonlinear association between plasma PNO1 levels and colorectal cancer risk. First, the PNO1 expression level was standardized and the spline nodes (usually the 50% quantile of the exposure variable) were selected. Subsequently, after adjusting for potential confounders (such as age and sex I), the relationship between PNO1 expression level and colorectal cancer risk was modeled based on restricted cubic splines, and the hazard ratio (HR) and its 95% confidence interval were extracted. Finally, a curve graph was generated by visualization tools (such as the rms package and ggplot2 package in R language). The blue curve represents the HR estimate and the orange shaded interval represents the 95% confidence interval. At the same time, a reference line (HR = 1) and significance test results were added to assist interpretation, clearly presenting the protective effect of PNO1 levels and its nonlinear trend.

[0114] 2. Experimental Results

[0115] In the UKBB population, it was also found that the expression level of plasma PNO1 protein was associated with a protective effect against colorectal cancer ( Figure 6 ).

[0116] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit the protection scope of the present invention. For ordinary technicians in this field, other different forms of changes or modifications can be made based on the above descriptions and ideas. It is not necessary and impossible to list all the implementation methods here. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the claims of the present invention.

Claims

1. Use of a product for detecting at least one of CLU, CFI, PON1, IGLC2, C8B, SERPINC1, ORM2 and APOA4 proteins in the preparation of a product for diagnosing colorectal cancer.

2. The use according to claim 1, characterized in that: The product detects at least one of PON1, IGLC2, SERPINC1 and ORM2 proteins.

3. The use according to claim 2, characterized in that: The product detects at least three of the PON1, IGLC2, SERPINC1 and ORM2 proteins.

4. The use according to claim 3, characterized in that: The product detects at least one of the following three groups of proteins: the first group is PON1, IGLC2 and SERPINC1 proteins, the second group is PON1, IGLC2 and ORM2 proteins, and the third group is IGLC2, SERPINC1 and ORM2 proteins.

5. The use according to any one of claims 1 to 4, characterized in that: The product is a reagent and / or instrument for detecting the protein using at least one of liquid chromatography tandem mass spectrometry, chemiluminescence, radioisotope method, fluorescence luminescence, enzyme labeling, colloidal gold method, biochip detection method and protein blotting.

6. The use according to claim 5, characterized in that: The product is a reagent and / or instrument for detecting the protein by liquid chromatography tandem mass spectrometry analysis. The product contains a mobile phase for liquid chromatography, wherein the mobile phase comprises a reagent A and a reagent B, and the reagent A and / or the reagent B contain at least one of formic acid, acetonitrile and water; the reagent A is a formic acid aqueous solution, and the concentration of the formic acid is 0.05% to 0.2 v / v; the reagent B is a formic acid-acetonitrile aqueous solution, which is obtained by mixing formic acid and acetonitrile aqueous solution, wherein the concentration of acetonitrile in the acetonitrile aqueous solution is 5% to 95% v / v, and the concentration of formic acid in the formic acid-acetonitrile aqueous solution is 0.05% to 0.2% v / v.

7. The use according to claim 6, characterized in that: The product also contains a chromatographic column, wherein the chromatographic column filler is ReproSil-Pur C18 particles with a particle size of 1.9 μm.

8. The use according to claim 7, characterized in that: The method of using the product comprises the following steps: S1: using urea to dissolve the protein in the sample to be tested, adding dithiothreitol to reduce the protein in the sample to be tested, and adding iodoacetamide to alkylate the protein in the sample to be tested; S2: using trypsin to enzymatically digest the sample treated in step S1 to obtain a protease hydrolysate; S3: desalting the protease hydrolysate from step S2 using a solid phase extraction column to obtain peptide fragments to be tested; S4: Detect the peptide fragments to be detected in step S3 by liquid chromatography-tandem mass spectrometry: The elution condition in step S4 is gradient elution: 0-45 min, 10%-27% v / v reagent B, 73%-90% v / v reagent A; 45-53 min, 27%-37% v / v reagent B, 63%-73% v / v reagent A; 53-57 min, 37%-100% v / v reagent B, 0-63% v / v reagent A; 57-60 min, 100% v / v reagent B; the flow rate is 0.3-0.6 mL / min.

9. The use according to claim 8, characterized in that: The flow rate was 0.45 mL / min.

10. The use according to claim 8, characterized in that: The mass spectrometer detector conditions in step S4 are: electrospray ion source, negative ion scanning, ion source voltage of 2600 V, ion source temperature of 320° C., primary mass spectrometer scanning range of 350 to 1500 m / z, scanning resolution of 60,000; secondary mass spectrometer scanning range is fixed at 200 m / z, and secondary scanning resolution is 30,000.

Images