Fecal biomarker composition for early diagnosis of colorectal cancer and application of fecal biomarker composition

Abstract

The invention belongs to the field of colorectal cancer detection, and particularly relates to a fecal biomarker composition for early diagnosis of colorectal cancer and application of the fecal biomarker composition. According to the invention, firstly, a fecal biomarker combination for early diagnosis of colorectal cancer is obtained through screening, and the fecal biomarker combination is used for early diagnosis of colorectal cancer by measuring the content of markers; secondly, based on the specificity of high-throughput sequencing and metabolome detection results, a model for early diagnosis or prediction of the colorectal cancer is constructed through random forest, linear regression and other models, early diagnosis of the colorectal cancer is achieved, and the method has the advantages of being low in cost, convenient to detect, high in accuracy, good in sensitivity and high in specificity and has good application prospects. The kit can avoid pain, discomfort and complications caused by enteroscope invasive examination, obviously reduces the false positive rate of early diagnosis of colorectal cancer, and is widely used for early diagnosis of colorectal cancer.

Description

Technical Field

[0001] The present invention belongs to the field of colorectal cancer detection, and specifically relates to a fecal biomarker combination and application for early diagnosis of colorectal cancer. Background Art

[0002] Colorectal cancer is a common malignant tumor worldwide, accounting for about one in ten cancer cases, with an incidence and mortality rate of 6.1% and 9.2% respectively. If colorectal cancer lesions can be detected in the early stage and surgically removed, the survival rate exceeds 90%, while it drops to 13% in the late stage. Therefore, early screening is the most effective means to reduce the mortality rate of colorectal cancer. With the development of sequencing technology and information technology, artificial intelligence has become an important tool to assist clinicians, such as imaging diagnosis. The use of artificial intelligence algorithms and bioinformatics can deeply mine data information, establish diagnostic models, and improve diagnostic accuracy. Data show that artificial intelligence models based on algorithms such as neural networks and deep learning can achieve an accuracy of more than 80%, benefiting patients. Therefore, combining artificial intelligence learning with early diagnosis will further reduce the mortality rate and medical burden of colorectal cancer.

[0003] At present, invasive colonoscopy is still the gold standard for CRC diagnosis and the most commonly used clinical diagnosis method. The results of case-control and cohort studies show that colonoscopy has the potential to prevent CRC and cancer death. Although colonoscopy is considered the gold standard for CRC screening, it is an invasive procedure that requires a high level of expertise, is expensive, may cause postoperative complications, and has a poor patient experience, which limits its use as an option for early screening. For non-invasive detection methods, the American Cancer Society recommends high-sensitivity FOBT and FIT every three years, but their sensitivity is low (12%-56%) and is greatly affected by diet and medication, and needs to be further improved. The multi-target fecal DNA test Cologuard was approved by the FDA in 2014 and is recommended for screening every 3 years, but it has a low sensitivity for early adenomas (42.4%) and a high detection cost (US$599). Currently, its performance has only been verified in a few countries such as the United States and Canada. Therefore, new non-invasive diagnostic methods need to be developed.

[0004] The term “microbiome” refers to the entire habitat, including microorganisms (bacteria, archaea, lower and higher eukaryotes, and viruses), their genomes, and the surrounding environmental conditions. These factors change along the adenoma-carcinoma sequence, reflected in changes in abundance. Some microorganisms produce genotoxic compounds and cause inflammation, while others proliferate in tumor-associated microenvironments (referred to as “driver” and “passenger” bacteria, respectively). Systematic analysis of microbial communities and identification of microorganisms with different abundances as biomarkers could provide effective diagnostic strategies. The development of next-generation sequencing has generated a large amount of data on the CRC microbiome, and bioinformatics as well as machine learning methods have also provided powerful tools to improve our understanding. Metagenomics and 16S rRNA sequencing studies have shown that the abundance of certain microorganisms varies between patients and healthy people, and effective combinations of microbial biomarkers can be used for CRC diagnosis. The occurrence of cancer is accompanied by global changes in metabolic status, affecting tumor tissues as well as the surrounding micro- and macroenvironment. Metabolic changes in tumor cell status can be observed more directly than genomic and proteomic alterations, and are therefore a promising source of biomarkers for detecting tumorigenesis. Various studies have shown that metabolites produced by intestinal bacteria can enter the circulation and play regulatory functions in distal organs, and key metabolic pathways are disrupted in the pathogenesis of CRC. In recent years, fecal metabolites closely related to CRC have been intensively studied to develop diagnostic biomarkers. Gut microbiome reprogramming in patients with colorectal cancer is associated with changes in the fecal metabolome, and changes in gut microbiome-related serum metabolites (GMSM) can effectively distinguish CRC and adenoma patients from normal individuals. The GMSM-based model can distinguish CRC and adenoma patients from healthy normal subjects more effectively than the clinical marker carcinoembryonic antigen. The GMSM model is superior to clinical biomarkers CEA and FOBT in the detection of colorectal abnormalities. Using CEA to detect colorectal abnormalities at the clinically used critical value of 5U / mL, the AUC was 0.72, the sensitivity was 35.8%, and the specificity was 86.4%. In contrast, the AUC of the GMSM model reached 0.92 (sensitivity = 83.5%, specificity = 84.9%), which is much higher than CEA. The sensitivity of FOBT / FIT was 65.2%, which is comparable to previous reports. These results suggest that the GMSM model is superior to the FOBT / FIT test in detecting CRC.

[0005] The present invention screens specific fecal microorganisms as a biomarker composition for early diagnosis of colorectal cancer, wherein the biomarker composition includes 5,8,12-Trihome (9) (5,8,12-trihydroxyoctadecenoic acid), Carbamoyl (2r)-2,5-diaminopentanoate (carbamoyl (2r)-2,5-diaminopentanoate), 8-Tridecynoic Acid (8-triynoic acid), 5z,8z,11z,14z-Eicosatetraenedioic Acid (5z,8z,11z,14z-eicosatetraenoic acid) and Pyramidobacter piscolen s (Bacillus subtilis), through random forest, linear regression and other models, for the diagnosis of colorectal cancer results, the present invention utilizes the specificity based on high-throughput sequencing results, and combines it with the early diagnosis of colorectal cancer, with high sensitivity and strong specificity, can avoid the pain, discomfort and postoperative complications caused by invasive colonoscopy, significantly reduce the false positive rate of early diagnosis of colorectal cancer, and can be widely used in the early diagnosis of colorectal cancer. Summary of the invention

[0006] In view of the above technical problems, the purpose of the present invention is to provide a biomarker combination and application for early diagnosis of colorectal cancer. Specifically, it includes the following contents:

[0007] In the first aspect, the present invention provides a biomarker combination for the early diagnosis of colorectal cancer, wherein the biomarker combination includes 5,8,12-Trihome(9)(5,8,12-trihydroxyoctadecenoic acid), Carbamoyl(2r)-2,5-Diaminopentanoate(Carbamoyl(2r)-2,5-diaminopentanoate), 8-Tridecynoic Acid(8-triynoic acid), 5z,8z,11z,14z-Eicosatetraenedioic Acid(5z,8z,11z,14z-Eicosatetraenoic Acid) and Pyramidobacter piscolens(Pea cone bacterium).

[0008] In a second aspect, the present invention provides the use of a reagent for detecting the biomarker combination described in the first aspect in the preparation of a reagent, a kit or a chip for early diagnosis or prediction of colorectal cancer.

[0009] In a third aspect, the present invention provides the use of a reagent for detecting the biomarker combination described in the first aspect in preparing a screening and evaluation model for colorectal cancer patients.

[0010] In a fourth aspect, the present invention provides the use of the biomarker combination described in the first aspect above in the preparation of a reagent, a kit or a chip for early diagnosis or prediction of colorectal cancer.

[0011] In a fifth aspect, the present invention provides the use of the biomarker combination described in the first aspect in preparing a screening and evaluation model for colorectal cancer patients.

[0012] In a sixth aspect, the present invention provides a kit for early diagnosis or prediction of colorectal cancer, the kit comprising reagents or instruments for detecting the biomarker combination described in the first aspect above.

[0013] In a seventh aspect, the present invention provides a model for early diagnosis or prediction of colorectal cancer, the model comprising:

[0014] (1) Sample processing: extracting the biomarker combination of claim 1 from the stool sample to be tested, calculating the abundance of the Pyramid obacter piscolens gene, and detecting the abundance of 5,8,12-Trihome(9), Carbamoyl(2r)-2,5-Diaminopentanoate, 8-Tridecynoic Acid, and 5z,8z,11z,14z-Eicosatetraenedioic Acid by full scan LC-MS / MS;

[0015] (2) Model construction: The colorectal cancer samples (determined by colonoscopy and pathological examination) were marked as 1, and the healthy samples were marked as 0. The abundance values ​​of the biomarker combinations of different samples were input into the random forest to construct a model and predict the diagnosis results.

[0016] (3) Result determination: When the predicted value is 1, the diagnosis result of colorectal cancer is positive; when the predicted value is 0, the diagnosis result of colorectal cancer is negative.

[0017] In an eighth aspect, the present invention provides a method for determining a combination of fecal biomarkers for early diagnosis of colorectal cancer as described in the first aspect above:

[0018] (1) The markers of colorectal cancer group and healthy group were statistically analyzed (Wilcoxon test), and species with P < 0.05 were used as potential biomarkers;

[0019] (2) The R software package AUCRF (random forest method) was used to determine the optimal combination of microbial biomarkers;

[0020] (3) The best combination was determined by randomly removing markers and using AUC as the evaluation metric (pROC package).

[0021] (4) A logistic regression model was constructed using the above markers to compare the two.

[0022] In a ninth aspect, the present invention provides a model system for early diagnosis or prediction of colorectal cancer, comprising:

[0023] (1) a pre-input module, at least used to input the data to be evaluated;

[0024] (2) an evaluation module, at least used to evaluate the data to be evaluated, which is executed by the model described in the seventh aspect;

[0025] (3) A display module, at least used to display the evaluation results.

[0026] In a tenth aspect, the present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the system according to the eighth aspect is used to implement the following steps, including:

[0027] (1) Collect and / or input evaluation data for early diagnosis or prediction of colorectal cancer;

[0028] (2) Substitute the evaluation data into the model for calculation to obtain a conclusion on whether the sample is colorectal cancer;

[0029] (3) Output the judgment conclusion of whether the sample is colorectal cancer.

[0030] The beneficial effects of the present invention are as follows: ① After a large number of screenings, the present invention obtained for the first time the metabolites 5,8,12-Trihome(9) (5,8,12-trihydroxyoctadecenoic acid), carbamoyl(2r)-2,5-diaminopentanoate (carbamoyl(2r)-2,5-diaminopentanoate), 8-Tridecynoic Acid (8-triynoic acid), 5z,8z,11z,14z-Eicosatetraenoic Acid (5z,8z,11z,14z-eicosatetraenoic acid) and Pyramidobacter piscolens (Bacillus piscolens) as a marker for the early diagnosis of colorectal cancer; ② Secondly, the present invention constructs an early diagnosis or prediction model for colorectal cancer with the above markers, which provides a new direction for the clinical diagnosis of colorectal cancer, avoids the pain, discomfort and postoperative complications caused by the existing invasive colonoscopy in the early diagnosis of colorectal cancer, significantly reduces the false positive rate of early diagnosis of colorectal cancer, has the characteristics of good specificity and high sensitivity, has good clinical application value for the auxiliary diagnosis of colorectal cancer, and can be widely used in the early diagnosis of colorectal cancer. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 The AUC and marker curves were constructed from the random forest model using pROC from 250 markers; the curve showed that the model composed of 24 markers was the best.

[0032] Figure 2 AUC curve of the diagnostic model constructed using five fecal metabolites. RF represents the random forest model and Glm represents the logistic regression model. DETAILED DESCRIPTION

[0033] The following examples are provided to facilitate a better understanding of the present invention, but are not intended to limit the present invention. The experimental methods in the following examples are conventional methods unless otherwise specified. The experimental materials used in the following examples are commercially available unless otherwise specified.

[0034] In a specific embodiment, sensitivity, specificity, accuracy and other combinations are used to describe the goodness and reliability of the detection method described in the present invention. Several terms used together with the description of sensitivity, specificity and accuracy include: true positive (TP), true negative (TN), false positive (FP), false negative (FN); wherein, if the patient is proved to have a disease and a given screening test also shows the presence of the disease, the test result is considered to be a true positive; if the patient is proved not to have a disease and a given screening test also shows the absence of the disease, the test result is considered to be a true negative; if the result of the screening test shows that a patient who does not actually have the disease has the disease, the test result is a false positive; if the result of the screening test shows that a patient who actually has the disease does not have the disease, the test result is a false negative.

[0035] Sensitivity = TP / (TP+FN) = number of true positive assessments / number of all positive assessments;

[0036] Specificity = TN / (TN+FP) = number of true negative assessments / number of all negative assessments;

[0037] Accuracy = (TN + TP) / (TN + TP + FN + FP) = number of correct evaluations / number of all evaluations.

[0038] In the following examples, 58 patients with colorectal cancer and adenoma and 44 healthy subjects were selected, and the marker combination provided by the present invention was used to screen the above cases. Based on the test results, the sensitivity, specificity and accuracy of the marker combination provided by the present invention for colorectal cancer detection were determined.

[0039] Example 1 Screening of blood metabolites for early diagnosis of colorectal cancer

[0040] 1. Fecal sample collection

[0041] Prepare a sterile stool collection cup, collect about 0.5 g of the patient's stool, and immediately store it at -80°C.

[0042] 2. Fecal biomarker testing

[0043] 2.1 Fecal genome extraction

[0044] (1) The genomic DNA of the sample was extracted using a DNA extraction kit, and then the DNA concentration was detected using agarose gel electrophoresis and NanoDrop2000.

[0045] (2) Add 130 μl (260 ng) of DNA sample into the Covaris tube and shear the DNA using Covaris S220.

[0046] (3) Transfer the fragmented DNA to a new 1.5 ml centrifuge tube.

[0047] (4) Take 100 ng of the sheared DNA, add 15 μl of End Prep Mix 4 and add water to 65 μl. Pipette up and down 10 times to mix.

[0048] (5) On PCR instrument, 20°C, 15 min; 65°C, 15 min; 4°C hold.

[0049] (6) Add 25 μl Rapid Ligation buffer 2, 5 μl Rapid DNA ligase and 5 μl DNA Adapter to the PCR product from the previous step and mix well by pipetting.

[0050] (7) On PCR instrument, 20°C, 15 min; hold at 4°C.

[0051] (8) Add 12.5 μl ATL or ATL2 (A-Tailing Mix), pipette up and down 10 times, mix well. Add 60 μl of well-mixed AMPure XP beads, pipette up and down 10 times, mix well, and leave at room temperature for 5 min.

[0052] (9) Place the tube on the magnetic rack for 5-10 min until the supernatant is transparent and discard the supernatant.

[0053] (10) Keep the U-shaped plate on the magnetic rack and add 200 μl of freshly prepared 80% ethanol at room temperature for 30 seconds. Discard the supernatant.

[0054] (11) Repeat step (4) once.

[0055] (12) Incubate the plate at room temperature for 5–10 min, air dry, and remove the plate from the magnetic rack.

[0056] (13) Add 102.5 μl of Nuclease-free H2O and pipette up and down 10 times to mix thoroughly. Incubate at room temperature for 2 min and place on a magnetic rack for 5 min until the supernatant becomes clear.

[0057] (14) Transfer 100 μl of supernatant to a new PCR plate, add 56 μl of thoroughly mixed AMPure XP beads, pipette up and down 10 times, mix well, and leave at room temperature for 5 min.

[0058] (15) Place on the magnetic stand for 10 min until the supernatant is transparent. Pipette the supernatant into a new PCR plate and add 12 μl of well-mixed AMPure XP beads. Pipette 10 times to mix well and place at room temperature for 10 min.

[0059] (16) Place the plate on the magnetic rack for 10 min until the supernatant becomes transparent. Discard the supernatant and keep the plate on the magnetic rack. Add 200 μl of freshly prepared 80% ethanol and incubate at room temperature for 30 s. Discard the supernatant.

[0060] (17) Repeat once.

[0061] (18) Place the plate at room temperature for 5-10 min, air dry, remove the plate from the magnetic rack, add 22.5 μl of Nuclease-free H2O, pipette up and down 10 times, and mix thoroughly.

[0062] (19) Incubate at room temperature for 2 min. Place on a magnetic rack for 5 min until the supernatant becomes transparent. Transfer 20 μl of the supernatant to a new PCR plate.

[0063] 2.2 Library construction

[0064] (1) Add 5 μl of PCR Primer Mix 3 for Illumina and 25 μl of VAHTS HiFi Amplification Mix to the purified eluted product and mix well by pipetting.

[0065] (2) On the PCR machine, 95℃, 3min; 7-8cycle (98℃20s, 60℃15s, 72℃30s); 72℃, 5min; 4℃hold.

[0066] (3) Add 45 μl of thoroughly mixed AMPure XP beads, pipette 10 times to mix, and leave at room temperature for 5 min.

[0067] (4) Place the tube on the magnetic rack for 5-10 min until the supernatant becomes transparent. Aspirate the supernatant and discard.

[0068] (5) Place the plate on the magnetic stand and add 200 μl of freshly prepared 80% ethanol. Incubate at room temperature for 30 seconds, discard the supernatant, and repeat once.

[0069] (6) Place at room temperature for 5-10 min, air dry, remove the plate from the magnetic stand, add 22.5 μl Nuclease-free H2O, pipette up and down 10 times, and mix thoroughly.

[0070] (7) Incubate at room temperature for 2 min, place on a magnetic rack for 5 min until the supernatant becomes transparent, and transfer 20 μl of the supernatant to a new PCR plate.

[0071] (8) The library concentration was quantified using Qubit 3.0 and the library length was quality checked using Agilent 2100.

[0072] 2.3 Data processing and species annotation

[0073] First, fastp was used to remove the adapters, filter out low-quality bases, and remove reads containing N bases (ambiguous bases). MEGAHIT was used to assemble the metagenome. MMSeqs2 was used to construct a non-redundant gene set for the genes predicted in all samples. Salmon was used to align the clean reads of each sample with the non-redundant gene set (95% identity), and the abundance information of the genes in the corresponding samples was counted.

[0074] The DIAMOND software was used to compare and annotate the representative sequences of the gene set with the NR library data, and the expected value e-value of the BLAST comparison parameter was set to 1e-5. The species annotation was obtained through the taxonomic information database corresponding to the NR library, and then the abundance of the species was comprehensively calculated using the gene abundance corresponding to the species. The abundance of species in each sample was counted at the taxonomic levels of Domain, Kingdom, Phylum, Class, Order, Family, Genus, and Species, so as to construct the abundance spectrum at the corresponding taxonomic level.

[0075] 2.4 Metabolite extraction

[0076] (1) Weigh 60 mg of sample into a 1.5 mL centrifuge tube, add two small steel beads and 600 μL of methanol-water (V:V = 4:1, containing mixed internal standard, 4 μg / mL);

[0077] (2) After precooling in a -40°C refrigerator for 2 min, the mixture was placed in a grinder and ground (60 Hz, 2 min);

[0078] (3) Ultrasonic extraction in an ice-water bath for 10 min and then standing at -40°C overnight;

[0079] (4) Centrifuge for 10 min (12000 rpm, 4°C), draw 150 μL of the supernatant with a syringe, filter using a 0.22 μm organic phase pinhole filter, transfer to an LC injection vial, and store at -80°C until LC-MS analysis.

[0080] (5) Quality control samples (QC) were prepared by mixing equal volumes of extracts from all samples.

[0081] (6) All extraction reagents were precooled at -20°C before use.

[0082] 2.5 Liquid chromatography-mass spectrometry analysis

[0083] (1) The analytical instrument used in the experiment was a liquid chromatography-mass spectrometry system consisting of a Waters ACQUITY UPLC I-Class plus / Thermo QE ultra-high performance liquid chromatography-tandem high-resolution mass spectrometer.

[0084] (2) Chromatographic column: Chromatographic column: ACQUITY UPLC HSS T3 (100 mm×2.1 mm, 1.8 um); column temperature: 45°C; mobile phase: A-water (containing 0.1% formic acid), B-acetonitrile; flow rate: 0.35 mL / min; injection volume: 2 μL.

[0085] (3) Mass spectrometry conditions: Spray Voltage (V) (3800 -3000), Capillary Temperature (℃) (320), Aux gas heater temperature (℃) (350), Sheath Gas Flow Rate (Arb) (35), Auxgas flow rate (Arb) (8), S-lens RF level (50), Mass range (m / z) (70-1050), Full msresolution(70000), MS / MS resolution(17500), NCE / stepped NCE(10, 20, 40).

[0086] 2.6 Data processing

[0087] (1) Data preprocessing Before pattern recognition, the raw data were processed by the metabolomics processing software ProgenesisQI v3.0 for baseline filtering, peak identification, integration, retention time correction, peak alignment and normalization. The main parameters were: pre cursor tolerance: 5 ppm (HMDB+Lipidmaps) / 10 ppm (LuMet-Animal+METLIN); product tolerance: 10 ppm (HMDB+Lipidmaps) / 20 ppm (LuMet-Animal+METLIN).

[0088] (2) Compound identification was based on multiple dimensions including RT (retention time), accurate mass, secondary fragmentation, and isotope distribution using the Human Metabolome Database (HMDB), Lipidmaps (v2.3), METLIN database, and LuMet-Animal3.0 local database.

[0089] (3) The extracted data were processed by missing value processing, 0 value replacement, score screening, data merging, etc. Missing value processing and 0 value replacement: Delete the ion peaks with missing values ​​(0 value) > 50% in the group, and replace the remaining 0 values ​​with half of the minimum ion intensity of all samples. Score screening: The qualitative compounds were screened according to the qualitative result score (Score) of the compound. The screening standard was 36 points (out of 80 points). The qualitative results below 36 points were considered inaccurate and deleted.

[0090] (4) Multivariate statistical analysis will first use unsupervised principal component analysis (PCA) to observe the overall distribution between samples and the stability of the entire analysis process, and then use supervised partial least squares analysis (PLS-DA) and orthogonal partial least squares analysis (OPLS-DA) to distinguish the overall differences in metabolic profiles between groups and find the differential metabolites between groups.

[0091] 2.7 Marker combination screening

[0092] (1) The markers of colorectal cancer group and healthy group were statistically analyzed for differences between groups (Wilcoxon test), and species with P < 0.05 were used as potential biomarkers. Through the Wilcoxon intergroup difference test, markers with Bonferroni-corrected P values ​​less than 0.05 were selected, a total of 250 metabolites.

[0093] (2) The colorectal cancer samples (determined by colonoscopy and pathological examination) were marked as 1, and the healthy samples were marked as 0. The species abundance matrix of different samples was modeled, analyzed and predicted. The R software package AUCRF (random forest method) was used to determine the optimal combination of microbial biomarkers. The results are shown in Figure 1 As shown, the diagnostic effect is best when the combination contains 24 markers.

[0094] (3) The best combination was determined by randomly removing markers and using AUC as the evaluation metric (pROC package). Seven metabolic markers were finally determined to retain the diagnostic performance of the model: 5,8,12-Trihome(9) (5,8,12-trihydroxyoctadecenoic acid), Carbamoyl(2r)-2,5-Diaminopentanoate (Carbamoyl(2r)-2,5-Diaminopentanoate), 8-Tridecynoi c Acid (8-Tridecynoiic acid), 5z,8z,11z,14z-Eicosatetraenedioic Acid (5z,8z,11z,14z-Eicosatetraenoic Acid) and Pyramidobacter piscolens (Pea cone bacterium).

[0095] (4) A logistic regression model was constructed using the above markers to compare the two.

[0096] Through the above analysis, five biomarkers, including metabolites 5,8,12-Trihome(9), Carbamoyl(2r)-2,5-Diaminopentanoate, 8-Tridecynoic Acid, 5z,8z,11z,14z-Eicosatetraenedioic Acid and Pyramidobacter piscolens, were selected as a marker combination for early diagnosis of colorectal cancer, which was used to distinguish between healthy and colorectal cancer groups.

[0097] Example 2 Establishment of an early diagnosis or prediction model for colorectal cancer

[0098] 1. Model construction

[0099] Five biomarkers, including metabolites 5,8,12-Trihome(9), Carbamoyl(2r)-2,5-Diaminopentanoate, 8-Tridecynoic Acid, 5z,8z,11z,14z-Eicosatetraenedioic Acid and Pyramidobacterpiscolens, were selected as a marker combination for early diagnosis of colorectal cancer to construct an early diagnosis or prediction model for colorectal cancer, specifically:

[0100] (1) Extract metabolites 5,8,12-Trihome(9), Carbamoyl(2r)-2,5-Diaminopentanoate, 8-Tr idecynoic acid, 5z,8z,11z,14z-Eicosatetraenedioic acid, and Pyramidobacter piscolens from fecal samples and obtain the abundance values ​​of the above markers;

[0101] (2) The colorectal cancer samples (determined by colonoscopy and pathological examination) were marked as 1, and the healthy samples were marked as 0. The abundance values ​​of the biomarker combinations of different samples (n*n table) were input into the random forest to build a model and predict the diagnosis results.

[0102] (3) When the abundance matrix of the marker is used and the prediction value of the random forest algorithm is 1, the diagnosis result of the sample is judged to be positive, that is, colorectal cancer; when the prediction value is 0, the diagnosis result of the sample is judged to be negative, that is, healthy.

[0103] 2. Accuracy Verification

[0104] The above-mentioned predicted diagnosis results were compared with the actual diagnosis results (colonoscopy and pathological examination confirmed colorectal cancer) to determine the accuracy.

[0105] 3. Results Analysis

[0106] Five markers, 5,8,12-Trihome(9), Carbamoyl(2r)-2,5-Diaminopentanoate, 8-Tridecynoic Acid, 5z,8z,11z,14z-Eicosatetraenedioic Acid and Pyramidobacterpiscolens, were used to distinguish between healthy and colorectal cancer groups. The results are as follows Figure 2 As shown, when the sensitivity was 90.5%, the specificity was 90.0% and the accuracy was 89.3%.

[0107] The above results show that the biomarker combination described in the invention has high accuracy, specificity and sensitivity for diagnosing colorectal cancer.

[0108] Example 3 Colorectal cancer early diagnosis or prediction system

[0109] This embodiment provides a system for early diagnosis or prediction of colorectal cancer, including:

[0110] (1) a pre-input module, at least for inputting the biomarker combination abundance data in the stool sample and transmitting it to the evaluation module;

[0111] (2) an evaluation module, at least for analyzing the abundance data of the biomarker combination in the stool sample according to the model described in Example 2 to obtain a result; wherein the abundance data of the biomarker combination can be collected through a pre-input module, or the abundance data of the biomarker combination can be obtained from other sources.

[0112] (3) A display module, at least used to display the evaluation result. When the prediction value is 0, the diagnosis result of colorectal cancer is negative; when the prediction value is 1, the diagnosis result of colorectal cancer is positive.

[0113] Embodiment 4 Computer equipment

[0114] The present embodiment provides an electronic device, which can be expressed in the form of a computing device (for example, a server device), including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor can implement the colorectal cancer early diagnosis or prediction model in Embodiment 2 of the present invention when executing the computer program.

[0115] When the processor executes the program, the system implements the following steps, including:

[0116] (1) Collecting and / or inputting biomarker combination abundance data for early diagnosis or prediction of colorectal cancer;

[0117] (2) Substituting the evaluation data into the model for calculation, and obtaining a conclusion on whether the sample is positive or negative for colorectal cancer;

[0118] (3) Output the judgment conclusion whether the sample is positive or negative for colorectal cancer.

[0119] It should be noted that although several units / modules or sub-units / modules of the electronic device are mentioned in the above detailed description, this division is merely exemplary and not mandatory. In fact, according to the embodiments of the present application, the features and functions of two or more units / modules described above can be embodied in one unit / module. Conversely, the features and functions of one unit / module described above can be further divided into multiple units / modules to be embodied.

[0120] Embodiment 5 Computer readable storage medium

[0121] This embodiment provides a computer-readable storage medium on which the computer device is stored. When the program is executed by a processor, the steps of the method for evaluating the geography of the Great Zodiac in the first embodiment of the present invention are implemented.

[0122] The readable storage medium may include but is not limited to: a portable disk, a hard disk, a random access memory, a read-only memory, an erasable programmable read-only memory, an optical storage device, a magnetic storage device or any suitable combination of the above.

[0123] In a possible implementation, the present invention can also be implemented in the form of a program product, which includes a program code. When the program product is run on a terminal device, the program code is used to enable the terminal device to execute the model for early diagnosis or prediction of colorectal cancer in Example 2 of the present invention.

[0124] The program code for executing the present invention may be written in any combination of one or more programming languages, and may be executed entirely on a user device, partially on a user device, as an independent software package, partially on a user device and partially on a remote device, or entirely on a remote device.

[0125] The above description is a detailed description of the preferred feasible embodiments of the present invention, but the embodiments are not intended to limit the scope of the patent application of the present invention. All equivalent changes or modified changes completed under the technical spirit suggested by the present invention should fall within the patent scope covered by the present invention.

Claims

1. A fecal biomarker combination for early diagnosis of colorectal cancer, characterized in that: The biomarker combination includes 5,8,12-Trihome (9), Carbamoyl (2r)-2,5-Diaminopentanoate, 8-Tridecynoic Acid, 5z,8z,11z,14z-Eicosatetraenedioic Acid and Pyramidobacter piscolens.

2. Use of a reagent for detecting the biomarker combination described in claim 1 in the preparation of a reagent, a kit or a chip for early diagnosis or prediction of colorectal cancer.

3. Use of a reagent for detecting the biomarker combination described in claim 1 in preparing a screening and evaluation model for colorectal cancer patients.

4. Use of the biomarker combination as claimed in claim 1 in the preparation of a reagent, a kit or a chip for early diagnosis or prediction of colorectal cancer.

5. Use of the biomarker combination as described in claim 1 in preparing a screening and evaluation model for colorectal cancer patients.

6. A kit for early diagnosis or prediction of colorectal cancer, comprising reagents or instruments for detecting the biomarker combination of claim 1.

7. A model for early diagnosis or prediction of colorectal cancer, characterized in that: The model includes: (1) Sample processing: extracting the biomarker combination of claim 1 from the stool sample to be tested, calculating the abundance of Pyramidobacter piscolens genes, and detecting the abundance of 5,8,12-Trihome(9), Carbamoyl(2r)-2,5-Diaminopentanoate, 8-Tridecynoic Acid, and 5z,8z,11z,14z-Eicosatetraenedioic Acid by LC-MS / MS full scan; (2) Model construction: The colorectal cancer samples (determined by colonoscopy and pathological examination) were marked as 1, and the healthy samples were marked as 0. The abundance values ​​of the biomarker combinations of different samples were input into the random forest to construct a model and predict the diagnosis results. (3) Result determination: When the predicted value is 1, the diagnosis result of colorectal cancer is positive; when the predicted value is 0, the diagnosis result of colorectal cancer is negative.

8. The method for determining a combination of fecal biomarkers for early diagnosis of colorectal cancer according to claim 1: (1) The markers of colorectal cancer group and healthy group were statistically analyzed (Wilcoxon test), and species with P < 0.05 were used as potential biomarkers; (2) The R software package AUCRF (random forest method) was used to determine the optimal combination of microbial biomarkers; (3) The best combination was determined by randomly removing markers and using AUC as the evaluation metric (pROC package). (4) A logistic regression model was constructed using the above markers to compare the two.

9. A model system for early diagnosis or prediction of colorectal cancer, characterized in that: include: (1) a pre-input module, at least used to input the data to be evaluated; (2) an evaluation module, at least used to evaluate the data to be evaluated, executed by the model described in claim 7 or 8; (3) A display module, at least used to display the evaluation results.

10. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the system according to claim 9 is used to implement the following steps, including: (1) Collect and / or input evaluation data for early diagnosis or prediction of colorectal cancer; (2) Substitute the evaluation data into the model for calculation to obtain a conclusion on whether the sample is colorectal cancer; (3) Output the judgment conclusion of whether the sample is colorectal cancer.

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