CN117051113B - Application of biomarker combination in preparation of kit for predicting colorectal cancer - Google Patents

Application of biomarker combination in preparation of kit for predicting colorectal cancer Download PDF

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CN117051113B
CN117051113B CN202311316320.3A CN202311316320A CN117051113B CN 117051113 B CN117051113 B CN 117051113B CN 202311316320 A CN202311316320 A CN 202311316320A CN 117051113 B CN117051113 B CN 117051113B
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李明珠
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Abstract

The invention discloses application of a biomarker combination in preparation of a kit for predicting colorectal cancer. In particular to application of a biomarker combination in preparing a kit for predicting and/or diagnosing colorectal cancer; the biomarker combination consists of 104 biomarkers, and the kit comprises reagents for detecting the expression level of the biomarkers in the biomarker combination. The biomarker combination has the advantages of high sensitivity and high specificity in predicting early colorectal cancer risk, provides favorable technical support for predicting colorectal cancer occurrence and development, has wide scientific research value and provides great convenience for early clinical diagnosis, intervention treatment and the like.

Description

Application of biomarker combination in preparation of kit for predicting colorectal cancer
Technical Field
The invention belongs to the field of biomedical technology and diagnosis, and particularly relates to application of a biomarker combination in preparation of a kit for predicting colorectal cancer.
Background
Colorectal cancer (CRC) is the highest incidence of malignancy of the digestive tract, and according to 2020 global tumor burden report, colorectal cancer is one of the three most frequently developed cancers, second only to breast cancer and lung cancer, second only to lung cancer.
Therefore, accurate and sensitive early screening means are necessary. Traditional cancer tumor screening means include imaging examinations, pathology examinations, radiological examinations, immunological examinations, and the like. Various blood examination indexes in physical examination, X-ray and the like are all commonly used methods for screening colorectal cancer. However, these methods have the disadvantages of excessively high false positive rate and relatively late discovery time.
Therefore, a high-sensitivity and high-accuracy diagnostic method is urgently needed to achieve early cancer screening.
Proteomics plays a major role in revealing complex molecular events of tumorigenesis, such as tumorigenesis, invasion, metastasis, and tolerance to therapy. Proteomics tumor diagnosis has the advantages of high sensitivity, strong specificity and clear background mechanism, and is increasingly applied to tumor detection in recent years. Moreover, the study of these tumor markers is often based on a certain amount of experimental data, with relatively limited numbers of cancer types and sample sizes involved. In recent years, as proteomics continues to develop, the body fluid proteomics has grown in size. Therefore, by collecting body fluid proteome data and utilizing a big data analysis method, a tumor risk model with wide application range and high accuracy is found, thereby being beneficial to realizing early diagnosis and having important clinical significance for early diagnosis and early treatment of patients.
Disclosure of Invention
The invention aims to solve the technical problem that the prior art lacks a biomarker capable of accurately predicting colorectal cancer risk in early stage, and provides application of a biomarker combination in preparation of a kit for predicting colorectal cancer. The biomarker combination has the advantages of high sensitivity and high specificity in predicting early colorectal cancer risk, provides favorable technical support for predicting colorectal cancer occurrence and development, has wide scientific research value and provides great convenience for early clinical diagnosis, intervention treatment and the like.
The invention solves the technical problems through the following technical proposal.
The first aspect of the invention provides the use of a biomarker combination for the manufacture of a kit for the prediction and/or diagnosis of colorectal cancer;
wherein, the biomarker combination is selected from the group consisting of ACTA1, ACTA2, ACTB, ACTBL2, ACTC1, ACTG2, ACTR3C, ADAM, ALAD, ALDH9A1, ARHGDIB, ARHGEF, ARMC8, ATP5F1D, BIN2, CALM1, CALM2, CALM3, CALML3, CAVIN2, CD14, CDC37, CFL1, CHGB, CHST3, CLIC1, CPPED1, CRP, CSRP1, DSC3, DUSP3, EIF5A2, EIF5AL1, EIF5B, EWSR1, F8, FBLN5, FHL1, FKBP1A, GAPDH, GOLGA, GP1BB, GP6, GSTO 1' H3-4, H3C1, H3C15, HNRNPA0, HTRA1, IL18BP, KLHL13, KLHL9, LEMD2, LGALSL, LRG1, MAP4, MCM7, MIF, MRPL37, MTPN, MYL6B, MYOF, NAXD, NCALD, NEDD1, NIT2, NUP155, PCBP3, PDCD10, PFN1, PGPEP1, PHGDH, PKLR, PKM, PKP4, PLBD2, PMVK, POSTN, POTEE, POTEF, POTEI, POTEKP, PSD4, PTGIS, RCN1, RDH10, RECK, RUVBL1, S100A9, SAA1, SAA2, SARM1, SMC1A, SMC, SPG21, TAGLN2, THBS1, TMSB4X, TNIK, TXN, VTI1B, VWF and YKT.
In a second aspect the invention provides a reagent for detecting a biomarker combination, the biomarker combination is selected from the group consisting of ACTA1, ACTA2, ACTB, ACTBL2, ACTC1, ACTG2, ACTR3C, ADAM, ALAD, ALDH9A1, ARHGDIB, ARHGEF, ARMC8, ATP5F1D, BIN2, CALM1, CALM2, CALM3, CALML3, CAVIN2, CD14, CDC37, CFL1, CHGB, CHST3, CLIC1, CPPED1, CRP, CSRP1, DSC3, DUSP3, EIF5A2, EIF5AL1, EIF5B, EWSR1, F8, FBLN5, FHL1, FKBP1A, GAPDH, GOLGA, GP1BB, GP6, GSTO 1' H3-4, H3C1, H3C15, HNRNPA0, HTRA1, IL18BP, KLHL13, KLHL9, LEMD2, LGALSL, LRG1, MAP4, MCM7, MIF, MRPL37, MTPN, MYL6B, MYOF, NAXD, NCALD, NEDD1, NIT2, NUP155, PCBP3, PDCD10, PFN1, PGPEP1, PHGDH, PKLR, PKM, PKP4, PLBD2, PMVK, POSTN, POTEE, POTEF, POTEI, POTEKP, PSD4, PTGIS, RCN1, RDH10, RECK, RUVBL1, S100A9, SAA1, SAA2, SARM1, SMC1A, SMC, SPG21, TAGLN2, THBS1, TMSB4X, TNIK, TXN, VTI1B, VWF and YKT.
In some embodiments of the invention, the agent is used to detect the expression level of the biomarker combination; the expression level is protein expression level and/or mRNA transcription level.
In some preferred embodiments of the invention, the agent is a biomolecular agent that specifically binds to the biomarker, or specifically hybridizes to a nucleic acid encoding the biomarker.
In some embodiments of the invention, the biomolecular reagent is selected from the group consisting of a primer, a probe, and an antibody.
In some embodiments of the invention, the agent is an agent for genomic, transcriptomic, and/or proteomic sequencing.
In a third aspect the invention provides the use of a reagent for detecting a biomarker combination in the manufacture of a kit for predicting and/or diagnosing colorectal cancer;
wherein, the biomarker combination is selected from the group consisting of ACTA1, ACTA2, ACTB, ACTBL2, ACTC1, ACTG2, ACTR3C, ADAM, ALAD, ALDH9A1, ARHGDIB, ARHGEF, ARMC8, ATP5F1D, BIN2, CALM1, CALM2, CALM3, CALML3, CAVIN2, CD14, CDC37, CFL1, CHGB, CHST3, CLIC1, CPPED1, CRP, CSRP1, DSC3, DUSP3, EIF5A2, EIF5AL1, EIF5B, EWSR1, F8, FBLN5, FHL1, FKBP1A, GAPDH, GOLGA, GP1BB, GP6, GSTO 1' H3-4, H3C1, H3C15, HNRNPA0, HTRA1, IL18BP, KLHL13, KLHL9, LEMD2, LGALSL, LRG1, MAP4, MCM7, MIF, MRPL37, MTPN, MYL6B, MYOF, NAXD, NCALD, NEDD1, NIT2, NUP155, PCBP3, PDCD10, PFN1, PGPEP1, PHGDH, PKLR, PKM, PKP4, PLBD2, PMVK, POSTN, POTEE, POTEF, POTEI, POTEKP, PSD4, PTGIS, RCN1, RDH10, RECK, RUVBL1, S100A9, SAA1, SAA2, SARM1, SMC1A, SMC, SPG21, TAGLN2, THBS1, TMSB4X, TNIK, TXN, VTI1B, VWF and YKT.
In some embodiments of the invention, the agent is as described in the second aspect.
In a fourth aspect the invention provides a kit comprising a reagent and biomarker combination according to the second aspect, wherein, the biomarker combination is selected from the group consisting of ACTA1, ACTA2, ACTB, ACTBL2, ACTC1, ACTG2, ACTR3C, ADAM, ALAD, ALDH9A1, ARHGDIB, ARHGEF, ARMC8, ATP5F1D, BIN2, CALM1, CALM2, CALM3, CALML3, CAVIN2, CD14, CDC37, CFL1, CHGB, CHST3, CLIC1, CPPED1, CRP, CSRP1, DSC3, DUSP3, EIF5A2, EIF5AL1, EIF5B, EWSR1, F8, FBLN5, FHL1, FKBP1A, GAPDH, GOLGA, GP1BB, GP6, GSTO 1' H3-4, H3C1, H3C15, HNRNPA0, HTRA1, IL18BP, KLHL13, KLHL9, LEMD2, LGALSL, LRG1, MAP4, MCM7, MIF, MRPL37, MTPN, MYL6B, MYOF, NAXD, NCALD, NEDD1, NIT2, NUP155, PCBP3, PDCD10, PFN1, PGPEP1, PHGDH, PKLR, PKM, PKP4, PLBD2, PMVK, POSTN, POTEE, POTEF, POTEI, POTEKP, PSD4, PTGIS, RCN1, RDH10, RECK, RUVBL1, S100A9, SAA1, SAA2, SARM1, SMC1A, SMC, SPG21, TAGLN2, THBS1, TMSB4X, TNIK, TXN, VTI1B, VWF and YKT.
A fifth aspect of the invention provides a method for detecting colorectal cancer for non-diagnostic purposes, the method comprising detecting the expression level of a biomarker combination in a test sample;
wherein, the biomarker combination is selected from the group consisting of ACTA1, ACTA2, ACTB, ACTBL2, ACTC1, ACTG2, ACTR3C, ADAM, ALAD, ALDH9A1, ARHGDIB, ARHGEF, ARMC8, ATP5F1D, BIN2, CALM1, CALM2, CALM3, CALML3, CAVIN2, CD14, CDC37, CFL1, CHGB, CHST3, CLIC1, CPPED1, CRP, CSRP1, DSC3, DUSP3, EIF5A2, EIF5AL1, EIF5B, EWSR1, F8, FBLN5, FHL1, FKBP1A, GAPDH, GOLGA, GP1BB, GP6, GSTO 1' H3-4, H3C1, H3C15, HNRNPA0, HTRA1, IL18BP, KLHL13, KLHL9, LEMD2, LGALSL, LRG1, MAP4, MCM7, MIF, MRPL37, MTPN, MYL6B, MYOF, NAXD, NCALD, NEDD1, NIT2, NUP155, PCBP3, PDCD10, PFN1, PGPEP1, PHGDH, PKLR, PKM, PKP4, PLBD2, PMVK, POSTN, POTEE, POTEF, POTEI, POTEKP, PSD4, PTGIS, RCN1, RDH10, RECK, RUVBL1, S100A9, SAA1, SAA2, SARM1, SMC1A, SMC, SPG21, TAGLN2, THBS1, TMSB4X, TNIK, TXN, VTI1B, VWF and YKT;
the expression level is protein expression level and/or mRNA transcription level.
In the invention, the non-diagnostic purpose is the purpose of scientific research and pathological data statistics, and the applicable scene comprises verification of whether an animal model is successfully constructed, in-vitro efficacy experiments, epidemiological statistics of tumors and the like.
A sixth aspect of the present invention provides a prediction system for colorectal cancer risk, the prediction system comprising a detection module and an analysis and judgment module; the detection module detects the expression level of the biomarker combination in the sample to be detected and transmits the expression level data to the analysis and judgment module; the analysis judging module processes the expression level data through firmware software, presets a machine learning algorithm based on a generalized linear regression model, constructs a prediction model, predicts the probability of the sample suffering from colorectal cancer and the probability of the sample not suffering from colorectal cancer respectively, judges whether the expression level data accords with preset judging conditions so as to predict the risk of the sample suffering from colorectal cancer, and outputs a prediction result; the judging condition is that the probability of suffering from colorectal cancer is larger than or equal to the probability of not suffering from colorectal cancer;
outputting a prediction result of "having colorectal cancer risk" when the expression level data satisfies the judgment condition; when the expression level data does not meet the judgment condition, namely the probability of suffering from colorectal cancer is smaller than the probability of not suffering from colorectal cancer, outputting a prediction result as 'without colorectal cancer risk';
wherein, the biomarker combination is selected from the group consisting of ACTA1, ACTA2, ACTB, ACTBL2, ACTC1, ACTG2, ACTR3C, ADAM, ALAD, ALDH9A1, ARHGDIB, ARHGEF, ARMC8, ATP5F1D, BIN2, CALM1, CALM2, CALM3, CALML3, CAVIN2, CD14, CDC37, CFL1, CHGB, CHST3, CLIC1, CPPED1, CRP, CSRP1, DSC3, DUSP3, EIF5A2, EIF5AL1, EIF5B, EWSR1, F8, FBLN5, FHL1, FKBP1A, GAPDH, GOLGA, GP1BB, GP6, GSTO 1' H3-4, H3C1, H3C15, HNRNPA0, HTRA1, IL18BP, KLHL13, KLHL9, LEMD2, LGALSL, LRG1, MAP4, MCM7, MIF, MRPL37, MTPN, MYL6B, MYOF, NAXD, NCALD, NEDD1, NIT2, NUP155, PCBP3, PDCD10, PFN1, PGPEP1, PHGDH, PKLR, PKM, PKP4, PLBD2, PMVK, POSTN, POTEE, POTEF, POTEI, POTEKP, PSD4, PTGIS, RCN1, RDH10, RECK, RUVBL1, S100A9, SAA1, SAA2, SARM1, SMC1A, SMC, SPG21, TAGLN2, THBS1, TMSB4X, TNIK, TXN, VTI1B, VWF and YKT;
the expression level is protein expression level and/or mRNA transcription level.
In some embodiments of the present invention, the prediction system is configured to process the expression level data by Firmiana software after the receiving or inputting is completed, and preset as a machine learning algorithm based on a generalized linear regression model, so as to construct the prediction system.
In a preferred embodiment of the present invention, the parameters of the generalized linear regression model are: and screening the markers by adopting a backward regression method, and carrying out model training and prediction function prediction by utilizing the train function of the R packet Caret. Preferably, the R-package of the generalized linear regression model includes: model=train (formula, data=train_data, method= "glm", family= 'binominal') (formula: model formula, input molecular combination; train_data: training set); prediction code: prediction (prediction. Model: training set derived predictive model, test_data: internal or external validation set).
In some embodiments of the invention, the sample to be tested is a plasma sample.
In some embodiments of the invention, the prediction system further comprises a data collection module for collecting expression level data of the biomarker combinations in the test sample.
In some embodiments of the invention, the prediction system is a system for predicting early colorectal cancer.
In some embodiments of the present invention, in the analysis and judgment module, the training set parameter is set to 80%, and the verification set parameter is set to 20%.
A seventh aspect of the invention provides a computer readable storage medium storing a computer program which, when executed by a processor, performs the functions of the prediction system according to the sixth aspect of the invention or performs the steps of the method according to the fifth aspect of the invention.
An eighth aspect of the invention provides an electronic device comprising a memory storing a computer program for executing the computer program to perform the functions of the prediction system according to the sixth aspect of the invention or to perform the steps of the method according to the fifth aspect of the invention.
According to the invention, through the body fluid protein molecules for colorectal cancer screening, a tumor risk model is established, so that early diagnosis of colorectal cancer is facilitated.
The invention obtains a group of biomarkers capable of predicting colorectal cancer risk by screening a body fluid proteome, and the screening method comprises the following steps:
(1) Collecting body fluid samples from healthy and colorectal cancer patients;
(2) Preparing body fluid sample proteins of healthy people and colorectal cancer patients;
(3) Detecting the expression level of protein molecules in body fluid samples of healthy people and colorectal cancer patients;
(4) Finding out the protein group molecules with high body fluid specificity of tumor patients, and constructing a classifier to distinguish.
On the basis of conforming to the common knowledge in the field, the above preferred conditions can be arbitrarily combined to obtain the preferred examples of the invention.
The reagents and materials used in the present invention are commercially available.
The invention has the positive progress effects that:
the biological marker combination is used for risk prediction and detection of colorectal cancer, has the advantages of high sensitivity and high specificity, provides favorable technical support for predicting occurrence and development of colorectal cancer, has wide scientific research value, and provides great convenience for early clinical diagnosis, intervention treatment and the like.
Drawings
Fig. 1 is a schematic view of the area under the ROC curve.
FIG. 2 shows the predicted results of marker combinations in the validation set, including the prediction accuracy, sensitivity and specificity results.
Fig. 3 is a schematic diagram of the structure of a system for predicting colorectal cancer risk.
Fig. 4 is a schematic structural diagram of an electronic device.
Detailed Description
The invention is further illustrated by means of the following examples, which are not intended to limit the scope of the invention. The experimental methods, in which specific conditions are not noted in the following examples, were selected according to conventional methods and conditions, or according to the commercial specifications.
"biomarker" refers to a biochemical marker that can mark changes or possible changes in system, organ, tissue, cell and subcellular structure or function, and can be used for disease diagnosis, judgment of disease stage, or evaluation of the safety and efficacy of new drug therapies in a target population.
The specific techniques or conditions are not identified in the examples and are described in the literature in this field or are carried out in accordance with the product specifications. The reagents or apparatus used were conventional products commercially available through regular channels, with no manufacturer noted.
Examples include plasma samples from 200 normal populations and 65 colorectal cancer patients. The design and practice of this study has been approved and supervised ethically and written informed consent has been obtained for all patients.
Example 1 screening and validation of combinations of biomarkers for predicting colorectal cancer risk
1.1 Separation of plasma
Collecting whole blood sample, mixing in EDTA anticoagulant tube, centrifuging at 4deg.C for 10 min with 1,600Xg, centrifuging, collecting supernatant (blood plasma) in new EP tube, centrifuging at 16,000Xg for 10 min to remove cell debris, packaging blood plasma in centrifuge tube, and freezing at-80deg.C for use.
1.2 Plasma sample pretreatment
To 2. Mu.L of plasma sample was added 100. Mu.L of 50mM ammonium bicarbonate, vortexed and mixed for 1min, the sample was incubated at 95℃for 4 min to thermally denature the protein, cooled to room temperature, 2. Mu.g of pancreatic protease (Trypsin) was added to the system, 18℃was shaken for h at 37℃and then 10. Mu.L of aqueous ammonia was added to the system to stop the enzymatic hydrolysis. Desalting the peptide sample after enzymolysis, pumping, and freezing at-80 ℃ until mass spectrum detection.
1.3 Mass spectrometric detection of ASD plasma samples
The peptide sample was tested using a Orbitrap Fusion Lumos three-in-one high resolution mass spectrometry system (Thermo Fisher Scientific, rockford, USA) in tandem with a high performance liquid chromatography system (EASY-nLC 1200,Thermo Fisher) and mass spectrometry data was obtained for the whole protein corresponding to the peptide sample. The specific operation is as follows:
the nano-flow liquid chromatography is adopted, and the chromatographic column is a self-made C18 chromatographic column (150 μm ID multiplied by 8 cm,1.9 μm/120A filler). The temperature of the column temperature box is 60 ℃. The dry powder peptide was reconstituted with loading buffer (0.1% formic acid in water), separated by column chromatography, eluted with 600 nL/min linear 6-30% mobile phase B (ACN and 0.1% formic acid), and combined with mass spectrometry detection means for data independent acquisition (Data Independent Acquisition, DIA) using a 10 min liquid phase gradient. The DIA mass spectrometry detection parameters were set as follows: the ion mode is positive ions; the resolution of the primary mass spectrum is 30K, the maximum injection time is 20 ms, the AGC Target is 3e6, and the scanning range is 300-1400 m/z; the secondary scanning resolution is 15K, 30 variable isolation windows are acquired, and the collision energy is 27%. The liquid chromatography tandem mass spectrometry system uses Xcalibur software control for data acquisition.
1.4 Data analysis
All data were processed using Firmiana (V1.0). The Firmiana is a workflow based on Galaxy system, and consists of a plurality of functional modules such as a user login interface, raw data, identification and quantification, data analysis, knowledge mining and the like. DIA data was searched against the UniProt human protein database (updated at 2019.12.17, 20406 entries) using DIANN (v 12.1). The mass difference of the parent ion was 20 ppm and the mass difference of the daughter ion was 50 mmu. At most two leaky sites are allowed. The search engine sets cysteine carbamoyl methylation as the fixed modification and N-acetylation and oxidation of methionine as the variable modification. The parent ion charge range is set to +2, +3, and +4. The error discovery rate (False Discovery Rate, FDR) was set to 1%. The results of the DIA data were incorporated into the reference library using SpectraST software. A total of 327 libraries were used as reference libraries.
The identified peptide fragment quantification results are recorded as the average of the peak areas of chromatographic fragment ions in all reference spectra libraries. Protein quantification was performed using the unlabeled absolute intensity-based quantification (Intensity Based Absolute Auantification, iBAQ) method. We calculated the peak area values as part of the corresponding proteins. Total Fraction (FOT) is used to represent normalized abundance of a particular protein in a sample. FOT is defined as the iBAQ of the protein divided by the total iBAQ of all identified proteins in the sample. A protein having at least one proprietary peptide (unique peptide) and 1% FDR is selected.
The Firmiana selected in this embodiment is preset as a machine learning algorithm based on a generalized linear regression model, and a prediction model is constructed to predict the probability of the sample suffering from colorectal cancer and the probability of the sample not suffering from colorectal cancer, respectively. The parameters of the generalized linear regression model are as follows: and screening the markers by adopting a backward regression method, and carrying out model training and prediction function prediction by utilizing the train function of the R packet Caret. The R-package of the generalized linear regression model includes: model=train (formula, data=train_data, method= "glm", family= 'binominal') (formula: model formula, input molecular combination; train_data: training set); prediction code: prediction (prediction. Model: training set derived predictive model, test_data: internal or external validation set).
Experiments find that the expression level of part of proteins in body fluid samples of tumor patients and healthy people has significant change, 104 protein molecular markers (ACTA 1, ACTA2, ACTB, ACTBL2, ACTC1, ACTG2, ACTR3C, ADAM, ALAD, ALDH9A1, ARHGDIB, ARHGEF12, ARMC8, ATP5F1D, BIN, CALM1, CALM2, CALM3, CALML3, CALM2, CD14, CDC37, CFL1, CHGB, CHST3, CLIC1, cppled 1, CRP, CSRP1, DSC3, DUSP3, EIF5A2, EIF5AL1, EIF5B, EWSR1, F8, FBLN5, FHL1, FKBP1A, GAPDH, GOLGA3, GP1BB, GP6, GSTO 1) in a plasma sample of a patient with colorectal cancer relative expression levels of H3-4, H3C1, H3C15, HNRNPA0, HTRA1, IL18BP, KLHL13, KLHL9, LEMD2, LGALSL, LRG1, MAP4, MCM7, MIF, MRPL37, MTPN, MYL6B, MYOF, NAXD, NCALD, NEDD1, NIT2, NUP155, PCBP3, PDCD10, PFN1, PGPEP1, PHGDH, PKLR, PKM, PKP4, PLBD2, PMVK, POSTN, POTEE, POTEF, POTEI, POTEKP, PSD4, PTGIS, RCN1, RDH10, RECK, RUVBL1, S100A9, SAA1, SAA2, SARM1, SMC1A, SMC, SPG21, TAGLN2, THBS1, TMSB4X, TNIK, TXN, VTI1B, VWF and YKT 6) were plotted into ROC curves (Receiver Operating Curve) to calculate AUC (Area Under the ROC Curve), wherein the training set comprises 55 positive cases, 166 negative cases, auc=0.99, diagnostic sensitivity 100.00%, specificity 100.00% (see fig. 1), and the internal validation set comprises the remaining 10 positive cases, 34 negative cases, diagnostic sensitivity 100%, and specificity 100% (see fig. 2). Analytical methods are described in Karimollah Hajian-Tilaki, receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation, caspian J Intern Med2013; 4 (2): 627-635. Substituting the expression level of the biomarker into a model for an unknown sample to obtain colorectal cancer risk prediction of the sample and output a result, wherein when the probability of suffering from colorectal cancer is greater than or equal to the probability of not suffering from colorectal cancer, the output prediction result is' colorectal cancer risk; when the probability of suffering from colorectal cancer is smaller than the probability of not suffering from colorectal cancer, outputting a prediction result as 'no colorectal cancer risk'. The matrix information for the expression levels of each biomarker is shown in table 1.
As can be seen from the above-described results, 104 protein molecular markers (ACTA 1, ACTA2, ACTB, ACTBL2, ACTC1, ACTG2, ACTR3C, ADAM10, ALAD, ALDH9A1, ARHGDIB, ARHGEF12, ARMC8, ATP5F1D, BIN2, CALM1, CALM2, CALM3, CALML3, CAVIN2, CD14, CDC37, CFL1, CHGB, CHST3, CLIC1, CPPED1, CRP, CSRP1, DSC3, DUSP3, EIF5A2, EIF5AL1, EIF5B, EWSR1, F8, FBLN5, FHL1, FKBP1A, GAPDH, GOLGA3, GP1 BB) in the plasma of a tumor patient GP6, GSTO1, H3-4, H3C1, H3C15, HNRNPA0, HTRA1, IL18BP, KLHL13, KLHL9, LEMD2, LGALSL, LRG1, MAP4, MCM7, MIF, MRPL37, MTPN, MYL6B, MYOF, NAXD, NCALD, NEDD1, NIT2, NUP155, PCBP3, PDCD10, PFN1, PGPEP1, PHGDH, PKLR, PKM, PKP4, PLBD2, PMVK, POSTN, POTEE, POTEF, POTEI, POTEKP, PSD4, PTGIS, RCN1, RDH10, RECK, RUL 1, S100A9, SAA1, SAA2, SARM1, SMC1A, SMC, SPG21, TAGLN2, THBS1, TMSB4X, TNIK, TXN, VTI1B, VWF and YKT 6), can be used for predicting tumor risk.
Table 1 matrix information of expression levels of the respective biomarkers
Example 2 System for predicting colorectal cancer Risk
System 61 for predicting colorectal cancer risk: the data processing module 52 and the judging and outputting module 53 further include a data collecting module 51 (fig. 3).
The data collection module 51 is used to collect the expression level data of the biomarker combinations in the colorectal cancer tissue samples of the patient and transmit them to the data processing module.
The data processing module 52 is configured to analyze the expression level data of the received or input biomarker combinations according to the data analysis method described in example 1 to obtain a calculation result. Wherein the expression level data of the biomarker combinations can be collected by the data collection module 51, and the expression level data of the biomarker combinations can also be obtained from other sources.
The judging and outputting module 53 is configured to judge whether the calculated result meets a preset judging condition, that is, the risk probability of suffering from colorectal cancer is greater than or equal to the risk prediction probability of not suffering from colorectal cancer, so as to predict colorectal cancer risk, and output a prediction result; in the judging and outputting module, when the expression level data meets the judging condition, the colorectal cancer risk probability is greater than or equal to the colorectal cancer risk prediction probability without the judging condition, and a prediction result is output as' colorectal cancer risk; and outputting a prediction result as 'no colorectal cancer risk' when the expression level data does not meet the judgment condition and the colorectal cancer risk probability is smaller than the colorectal cancer risk prediction probability.
Example 3 electronic device
The present embodiment provides an electronic device, which may be expressed in the form of a computing device (e.g. may be a server device), including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor may implement the method for predicting colorectal cancer risk in embodiment 1 of the present invention when executing the computer program.
Fig. 4 shows a schematic diagram of a hardware structure of the present embodiment, where the electronic device specifically includes:
at least one processor 91, at least one memory 92, and a bus 93 for connecting the different system components (including the processor 91 and the memory 92), wherein:
the bus 93 includes a data bus, an address bus, and a control bus.
The memory 92 includes volatile memory such as Random Access Memory (RAM) 921 and/or cache memory 922, and may further include Read Only Memory (ROM) 923.
Memory 92 further includes a program having a set (at least one) of program modules 924 and/or program means 925, such program modules 924 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each or some combination of which may include an implementation of a network environment.
The processor 91 executes various functional applications and data processing, such as the data analysis method of embodiment 1 of the present invention, by running a computer program stored in the memory 92.
The electronic device 9 may further communicate with one or more external devices 94 (e.g., keyboard, pointing device, etc.). Such communication may occur through an input/output (I/O) interface 95. Also, the electronic device 9 may communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN) and/or a public network, such as the Internet, through a network adapter 96. The network adapter 96 communicates with other modules of the electronic device 9 via the bus 93. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in connection with the electronic device 9, including but not limited to: microcode, device drivers, redundant processors, external disk drive arrays, RAID (disk array) systems, tape drives, data backup storage systems, and the like.
It should be noted that although several units/modules or sub-units/modules of an electronic device are mentioned in the above detailed description, such a division is merely exemplary and not mandatory. Indeed, the features and functionality of two or more units/modules described above may be embodied in one unit/module according to embodiments of the present application. Conversely, the features and functions of one unit/module described above may be further divided into ones that are embodied by a plurality of units/modules.
Embodiment 4 computer-readable storage Medium
An embodiment of the present invention provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method of predicting colorectal cancer risk in embodiment 1 of the present invention.
More specifically, among others, readable storage media may be employed including, but not limited to: portable disk, hard disk, random access memory, read only memory, erasable programmable read only memory, optical storage device, magnetic storage device, or any suitable combination of the foregoing.
In a possible embodiment, the invention may also be implemented in the form of a program product comprising program code for causing a terminal device to carry out the steps of implementing the method of predicting colorectal cancer risk in embodiment 1 of the invention, when said program product is run on the terminal device.
Wherein the program code for carrying out the invention may be written in any combination of one or more programming languages, which program code may execute entirely on the user device, partly on the user device, as a stand-alone software package, partly on the user device and partly on the remote device or entirely on the remote device.
Finally, the above embodiments are only for illustrating the technical solution of the present invention, and are not limiting.
Biomarker name: (reference may be made to NCBI or genegards database)
ACTA1：actin alpha 1，Gene ID: 58
ACTA2：actin alpha 2，Gene ID: 59
ACTB：actin beta，Gene ID: 60
ACTBL2：actin beta like 2，Gene ID: 345651
ACTC1：Actin Alpha Cardiac Muscle 1，Gene ID: 70
ACTG1：Actin Gamma 1，Gene ID: 71
ACTG2：Actin Gamma 2，Gene ID: 72
ACTR3C：actin related protein 3C，Gene ID: 653857
ADAM10：ADAM metallopeptidase domain 10，Gene ID: 102
ALAD：aminolevulinate dehydratase，Gene ID: 210
ALDH9A1：aldehyde dehydrogenase 9 family member A1，Gene ID: 223
ARHGDIB：Rho GDP dissociation inhibitor beta，Gene ID: 397
ARHGEF12：Rho guanine nucleotide exchange factor 12，Gene ID: 23365
ARMC8：Armadillo Repeat Containing 8，Gene ID: 25852
ATP5F1D：ATP synthase F1 subunit delta，Gene ID: 513
BIN2：bridging integrator 2，Gene ID: 51411
CALM1：calmodulin 1，Gene ID: 801
CALM2：calmodulin 2，Gene ID: 805
CALM3：calmodulin 3，Gene ID: 808
CALML3：calmodulin like 3，Gene ID: 810
CAVIN2：caveolae associated protein 2，Gene ID: 8436
CD14：CD14 molecule，Gene ID: 929
CDC37：cell division cycle 37，Gene ID: 11140
CFL1：cofilin 1，Gene ID: 1072
CHGB：chromogranin B，Gene ID: 1114
CHST3：carbohydrate sulfotransferase 3，Gene ID: 9469
CLIC1：chloride intracellular channel 1，Gene ID: 1192
CPPED1：calcineurin like phosphoesterase domain containing 1，Gene ID: 55313
CRP：C-reactive protein，Gene ID: 1401
CSRP1：cysteine and glycine rich protein 1，Gene ID: 1465
DSC3：desmocollin 3，Gene ID: 1825
DUSP3：dual specificity phosphatase 3，Gene ID: 1845
EIF5A2：eukaryotic translation initiation factor 5A2，Gene ID: 56648
EIF5AL1：eukaryotic translation initiation factor 5A like 1，Gene ID: 143244
EIF5B：eukaryotic translation initiation factor 5B，Gene ID: 9669
EWSR1：EWS RNA binding protein 1，Gene ID: 2130
F8：coagulation factor VIII，Gene ID: 2157
FBLN5：fibulin 5，Gene ID: 10516
FHL1：four and a half LIM domains 1，Gene ID: 2273
FKBP1A：FKBP prolyl isomerase 1A，Gene ID: 2280
GAPDH：glyceraldehyde-3-phosphate dehydrogenase，Gene ID: 2597
GOLGA3：golgin A3，Gene ID: 2802
GP1BB：glycoprotein Ib platelet subunit beta，Gene ID: 2812
GP6：glycoprotein VI platelet，Gene ID: 51206
GSTO1：glutathione S-transferase omega 1，Gene ID: 9446
H3-4：H3.4 histone, cluster member，Gene ID: 8290
H3C1：H3 clustered histone 1，Gene ID: 8350
H3C15：H3 clustered histone 15，Gene ID: 333932
HNRNPA0：heterogeneous nuclear ribonucleoprotein A0，Gene ID: 10949
HTRA1：HtrA serine peptidase 1，Gene ID: 5654
IL18BP：interleukin 18 binding protein，Gene ID: 10068
KLHL13：kelch like family member 13，Gene ID: 90293
KLHL9：kelch like family member 9，Gene ID: 55958
LEMD2：LEM domain nuclear envelope protein 2，Gene ID: 221496
LGALSL：galectin like，Gene ID: 29094
LRG1：leucine rich alpha-2-glycoprotein 1，Gene ID: 116844
MAP4：microtubule associated protein 4，Gene ID: 4134
MCM7：minichromosome maintenance complex component 7，Gene ID: 4176
MIF：macrophage migration inhibitory factor，Gene ID: 4282
MRPL37：mitochondrial ribosomal protein L37，Gene ID: 51253
MTPN：myotrophin，Gene ID: 136319
MYL6B：myosin light chain 6B，Gene ID: 140465
MYOF：myoferlin，Gene ID: 26509
NAXD：NAD(P)HX dehydratase，Gene ID: 55739
NCALD：neurocalcin delta，Gene ID: 83988
NEDD1：NEDD1 gamma-tubulin ring complex targeting factor，Gene ID: 121441
NIT2：nitrilase family member 2，Gene ID: 56954
NUP155：nucleoporin 155，Gene ID: 9631
PCBP3：poly(rC) binding protein 3，Gene ID: 54039
PDCD10：programmed cell death 10，Gene ID: 11235
PFN1：profilin 1，Gene ID: 5216
PGPEP1：pyroglutamyl-peptidase I，Gene ID: 54858
PHGDH：phosphoglycerate dehydrogenase，Gene ID: 26227
PKLR：pyruvate kinase L/R，Gene ID: 5313
PKM：pyruvate kinase M1/2，Gene ID: 5315
PKP4：plakophilin 4，Gene ID: 8502
PLBD2：phospholipase B domain containing 2，Gene ID: 196463
PMVK：phosphomevalonate kinase，Gene ID: 10654
POSTN：periostin，Gene ID: 10631
POTEE：POTE ankyrin domain family member E，Gene ID: 445582
POTEF：POTE ankyrin domain family member F，Gene ID: 728378
POTEI：POTE ankyrin domain family member I，Gene ID: 653269
POTEKP：POTE ankyrin domain family member K, pseudogene，Gene ID: 440915
PSD4：pleckstrin and Sec7 domain containing 4，Gene ID: 23550
PTGIS：prostaglandin I2 synthase，Gene ID: 5740
RCN1：reticulocalbin 1，Gene ID: 5954
RDH10：retinol dehydrogenase 10，Gene ID: 157506
RECK：reversion inducing cysteine rich protein with kazal motifs，Gene ID: 8434
RUVBL1：RuvB like AAA ATPase 1，Gene ID: 8607
S100A9：S100 calcium binding protein A9，Gene ID: 6280
SAA1：serum amyloid A1，Gene ID: 6288
SAA2：serum amyloid A2，Gene ID: 6289
SARM1：sterile alpha and TIR motif containing 1，Gene ID: 23098
SMC1A：structural maintenance of chromosomes 1A，Gene ID: 8243
SMC2：structural maintenance of chromosomes 2，Gene ID: 10592
SPG21：SPG21 abhydrolase domain containing, maspardin，Gene ID: 51324
TAGLN2：transgelin 2，Gene ID: 8407
THBS1：thrombospondin 1，Gene ID: 7057
TMSB4X：thymosin beta 4 X-linked，Gene ID: 7114
TNIK：TRAF2 and NCK interacting kinase，Gene ID: 23043
TXN：thioredoxin，Gene ID: 7295
VTI1B：vesicle transport through interaction with t-SNAREs 1B，Gene ID: 10490
VWF：von Willebrand factor，Gene ID: 7450
YKT6：YKT6 v-SNARE homolog，Gene ID: 10652

Claims (14)

1. Use of a biomarker combination in the preparation of a kit for predicting and/or diagnosing colorectal cancer;
wherein, the biomarker combination is selected from the group consisting of ACTA1, ACTA2, ACTB, ACTBL2, ACTC1, ACTG2, ACTR3C, ADAM, ALAD, ALDH9A1, ARHGDIB, ARHGEF, ARMC8, ATP5F1D, BIN2, CALM1, CALM2, CALM3, CALML3, CAVIN2, CD14, CDC37, CFL1, CHGB, CHST3, CLIC1, CPPED1, CRP, CSRP1, DSC3, DUSP3, EIF5A2, EIF5AL1, EIF5B, EWSR1, F8, FBLN5, FHL1, FKBP1A, GAPDH, GOLGA, GP1BB, GP6, GSTO 1' H3-4, H3C1, H3C15, HNRNPA0, HTRA1, IL18BP, KLHL13, KLHL9, LEMD2, LGALSL, LRG1, MAP4, MCM7, MIF, MRPL37, MTPN, MYL6B, MYOF, NAXD, NCALD, NEDD1, NIT2, NUP155, PCBP3, PDCD10, PFN1, PGPEP1, PHGDH, PKLR, PKM, PKP4, PLBD2, PMVK, POSTN, POTEE, POTEF, POTEI, POTEKP, PSD4, PTGIS, RCN1, RDH10, RECK, RUVBL1, S100A9, SAA1, SAA2, SARM1, SMC1A, SMC, SPG21, TAGLN2, THBS1, TMSB4X, TNIK, TXN, VTI1B, VWF and YKT.
2. A reagent for detecting a biomarker combination, characterized in that, the biomarker combination is selected from the group consisting of ACTA1, ACTA2, ACTB, ACTBL2, ACTC1, ACTG2, ACTR3C, ADAM, ALAD, ALDH9A1, ARHGDIB, ARHGEF, ARMC8, ATP5F1D, BIN2, CALM1, CALM2, CALM3, CALML3, CAVIN2, CD14, CDC37, CFL1, CHGB, CHST3, CLIC1, CPPED1, CRP, CSRP1, DSC3, DUSP3, EIF5A2, EIF5AL1, EIF5B, EWSR1, F8, FBLN5, FHL1, FKBP1A, GAPDH, GOLGA, GP1BB, GP6, GSTO 1' H3-4, H3C1, H3C15, HNRNPA0, HTRA1, IL18BP, KLHL13, KLHL9, LEMD2, LGALSL, LRG1, MAP4, MCM7, MIF, MRPL37, MTPN, MYL6B, MYOF, NAXD, NCALD, NEDD1, NIT2, NUP155, PCBP3, PDCD10, PFN1, PGPEP1, PHGDH, PKLR, PKM, PKP4, PLBD2, PMVK, POSTN, POTEE, POTEF, POTEI, POTEKP, PSD4, PTGIS, RCN1, RDH10, RECK, RUVBL1, S100A9, SAA1, SAA2, SARM1, SMC1A, SMC, SPG21, TAGLN2, THBS1, TMSB4X, TNIK, TXN, VTI1B, VWF and YKT.
3. The reagent of claim 2, wherein the reagent is used to detect the expression level of the biomarker combination;
the expression level is protein expression level and/or mRNA transcription level.
4. The agent of claim 3, wherein the agent is a biomolecular agent that specifically binds to the biomarker, or specifically hybridizes to a nucleic acid encoding the biomarker.
5. The reagent of claim 4, wherein the biomolecular reagent is selected from the group consisting of a primer, a probe and an antibody.
6. The reagent of claim 3, wherein the reagent is a reagent for genomic, transcriptome, and/or proteomic sequencing.
7. Use of a reagent for detecting a biomarker combination in the preparation of a kit for predicting and/or diagnosing colorectal cancer;
wherein, the biomarker combination is selected from the group consisting of ACTA1, ACTA2, ACTB, ACTBL2, ACTC1, ACTG2, ACTR3C, ADAM, ALAD, ALDH9A1, ARHGDIB, ARHGEF, ARMC8, ATP5F1D, BIN2, CALM1, CALM2, CALM3, CALML3, CAVIN2, CD14, CDC37, CFL1, CHGB, CHST3, CLIC1, CPPED1, CRP, CSRP1, DSC3, DUSP3, EIF5A2, EIF5AL1, EIF5B, EWSR1, F8, FBLN5, FHL1, FKBP1A, GAPDH, GOLGA, GP1BB, GP6, GSTO 1' H3-4, H3C1, H3C15, HNRNPA0, HTRA1, IL18BP, KLHL13, KLHL9, LEMD2, LGALSL, LRG1, MAP4, MCM7, MIF, MRPL37, MTPN, MYL6B, MYOF, NAXD, NCALD, NEDD1, NIT2, NUP155, PCBP3, PDCD10, PFN1, PGPEP1, PHGDH, PKLR, PKM, PKP4, PLBD2, PMVK, POSTN, POTEE, POTEF, POTEI, POTEKP, PSD4, PTGIS, RCN1, RDH10, RECK, RUVBL1, S100A9, SAA1, SAA2, SARM1, SMC1A, SMC, SPG21, TAGLN2, THBS1, TMSB4X, TNIK, TXN, VTI1B, VWF and YKT.
8. The use according to claim 7, wherein the agent is as claimed in any one of claims 3 to 6.
9. A kit comprising a combination of a reagent according to any one of claims 2 to 6 and a biomarker, wherein, the biomarker combination is selected from the group consisting of ACTA1, ACTA2, ACTB, ACTBL2, ACTC1, ACTG2, ACTR3C, ADAM, ALAD, ALDH9A1, ARHGDIB, ARHGEF, ARMC8, ATP5F1D, BIN2, CALM1, CALM2, CALM3, CALML3, CAVIN2, CD14, CDC37, CFL1, CHGB, CHST3, CLIC1, CPPED1, CRP, CSRP1, DSC3, DUSP3, EIF5A2, EIF5AL1, EIF5B, EWSR1, F8, FBLN5, FHL1, FKBP1A, GAPDH, GOLGA, GP1BB, GP6, GSTO 1' H3-4, H3C1, H3C15, HNRNPA0, HTRA1, IL18BP, KLHL13, KLHL9, LEMD2, LGALSL, LRG1, MAP4, MCM7, MIF, MRPL37, MTPN, MYL6B, MYOF, NAXD, NCALD, NEDD1, NIT2, NUP155, PCBP3, PDCD10, PFN1, PGPEP1, PHGDH, PKLR, PKM, PKP4, PLBD2, PMVK, POSTN, POTEE, POTEF, POTEI, POTEKP, PSD4, PTGIS, RCN1, RDH10, RECK, RUVBL1, S100A9, SAA1, SAA2, SARM1, SMC1A, SMC, SPG21, TAGLN2, THBS1, TMSB4X, TNIK, TXN, VTI1B, VWF and YKT.
10. A colorectal cancer risk prediction system, which is characterized by comprising a detection module and an analysis and judgment module; the detection module detects the expression level of the biomarker combination in the sample to be detected and transmits the expression level data to the analysis and judgment module; the analysis judging module processes the expression level data through firmware software, presets a machine learning algorithm based on a generalized linear regression model, constructs a prediction model, predicts the probability of the sample suffering from colorectal cancer and the probability of the sample not suffering from colorectal cancer respectively, judges whether the expression level data accords with preset judging conditions so as to predict the risk of the sample suffering from colorectal cancer, and outputs a prediction result; the judging condition is that the probability of suffering from colorectal cancer is larger than or equal to the probability of not suffering from colorectal cancer;
outputting a prediction result as having colorectal cancer risk when the expression level data satisfies the judgment condition; when the expression level data does not meet the judging condition, namely the probability of suffering from colorectal cancer is smaller than the probability of not suffering from colorectal cancer, outputting a prediction result to be without colorectal cancer risk;
wherein, the biomarker combination is selected from the group consisting of ACTA1, ACTA2, ACTB, ACTBL2, ACTC1, ACTG2, ACTR3C, ADAM, ALAD, ALDH9A1, ARHGDIB, ARHGEF, ARMC8, ATP5F1D, BIN2, CALM1, CALM2, CALM3, CALML3, CAVIN2, CD14, CDC37, CFL1, CHGB, CHST3, CLIC1, CPPED1, CRP, CSRP1, DSC3, DUSP3, EIF5A2, EIF5AL1, EIF5B, EWSR1, F8, FBLN5, FHL1, FKBP1A, GAPDH, GOLGA, GP1BB, GP6, GSTO 1' H3-4, H3C1, H3C15, HNRNPA0, HTRA1, IL18BP, KLHL13, KLHL9, LEMD2, LGALSL, LRG1, MAP4, MCM7, MIF, MRPL37, MTPN, MYL6B, MYOF, NAXD, NCALD, NEDD1, NIT2, NUP155, PCBP3, PDCD10, PFN1, PGPEP1, PHGDH, PKLR, PKM, PKP4, PLBD2, PMVK, POSTN, POTEE, POTEF, POTEI, POTEKP, PSD4, PTGIS, RCN1, RDH10, RECK, RUVBL1, S100A9, SAA1, SAA2, SARM1, SMC1A, SMC, SPG21, TAGLN2, THBS1, TMSB4X, TNIK, TXN, VTI1B, VWF and YKT;
the expression level is protein expression level and/or mRNA transcription level.
11. The predictive system of claim 10 wherein the sample to be tested is a human plasma sample.
12. The prediction system of claim 10 or 11 further comprising a data collection module for collecting expression level data of the biomarker combinations in the test sample.
13. A computer readable storage medium storing a computer program, which, when executed by a processor, performs the functions of the prediction system of any of claims 10-12.
14. An electronic device comprising a memory storing a computer program and a processor for executing the computer program to implement the functionality of the prediction system of any of claims 10-12.
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2015132273A1 (en) * 2014-03-03 2015-09-11 Institut D´Investigació Biomèdica De Girona Dr. Josep Trueta Method for diagnosing colorectal cancer from a human feces sample by quantitive pcr, primers and kit
AU2016201170A1 (en) * 2005-12-23 2016-03-17 Pacific Edge Limited Prognosis prediction for colorectal cancer
WO2021231274A1 (en) * 2020-05-11 2021-11-18 Ampel Biosolutions, Llc Methods and systems for analyzing targetable pathologic processes in covid-19 via gene expression analysis
CN115678994A (en) * 2022-01-27 2023-02-03 上海爱谱蒂康生物科技有限公司 Biomarker combination, reagent containing biomarker combination and application of biomarker combination
CN115954045A (en) * 2022-12-02 2023-04-11 上海爱谱蒂康生物科技有限公司 Personalized treatment decision method and system for intestinal cancer and storage medium containing personalized treatment decision system

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
AU2016201170A1 (en) * 2005-12-23 2016-03-17 Pacific Edge Limited Prognosis prediction for colorectal cancer
WO2015132273A1 (en) * 2014-03-03 2015-09-11 Institut D´Investigació Biomèdica De Girona Dr. Josep Trueta Method for diagnosing colorectal cancer from a human feces sample by quantitive pcr, primers and kit
WO2021231274A1 (en) * 2020-05-11 2021-11-18 Ampel Biosolutions, Llc Methods and systems for analyzing targetable pathologic processes in covid-19 via gene expression analysis
CN115678994A (en) * 2022-01-27 2023-02-03 上海爱谱蒂康生物科技有限公司 Biomarker combination, reagent containing biomarker combination and application of biomarker combination
CN115954045A (en) * 2022-12-02 2023-04-11 上海爱谱蒂康生物科技有限公司 Personalized treatment decision method and system for intestinal cancer and storage medium containing personalized treatment decision system

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