CN111489829A - Method for constructing mathematical model for detecting pancreatic cancer in vitro and application thereof - Google Patents
Method for constructing mathematical model for detecting pancreatic cancer in vitro and application thereof Download PDFInfo
- Publication number
- CN111489829A CN111489829A CN202010482539.0A CN202010482539A CN111489829A CN 111489829 A CN111489829 A CN 111489829A CN 202010482539 A CN202010482539 A CN 202010482539A CN 111489829 A CN111489829 A CN 111489829A
- Authority
- CN
- China
- Prior art keywords
- pancreatic cancer
- mir
- markers
- marker
- mathematical model
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16H—HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
- G16H50/00—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
- G16H50/50—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for simulation or modelling of medical disorders
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
- G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
Landscapes
- Engineering & Computer Science (AREA)
- Medical Informatics (AREA)
- Health & Medical Sciences (AREA)
- Public Health (AREA)
- Life Sciences & Earth Sciences (AREA)
- Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Pathology (AREA)
- Proteomics, Peptides & Aminoacids (AREA)
- Databases & Information Systems (AREA)
- Primary Health Care (AREA)
- Data Mining & Analysis (AREA)
- Biomedical Technology (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- Biophysics (AREA)
- Epidemiology (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Biotechnology (AREA)
- Evolutionary Biology (AREA)
- Spectroscopy & Molecular Physics (AREA)
- Theoretical Computer Science (AREA)
- Investigating Or Analysing Biological Materials (AREA)
- Measuring Or Testing Involving Enzymes Or Micro-Organisms (AREA)
Abstract
The application provides a method for constructing a mathematical model for detecting pancreatic cancer in vitro, which comprises the steps of obtaining the concentrations of at least two pancreatic cancer markers from a sample, carrying out logistic regression on the concentration value of each marker, substituting the concentration obtained by detection into the logistic regression model to obtain an analysis result, and carrying out comprehensive pancreatic cancer analysis by using the concentration of each marker and the logistic regression analysis result. The application also provides an application of the method.
Description
Technical Field
The application relates to the technical field of medical diagnosis, in particular to a method for constructing a mathematical model for detecting pancreatic cancer in vitro and application thereof.
Background
While pancreatic cancer may not be well known to many, it is a common, highly malignant tumor of the digestive system. The cancer is extremely dangerous in onset, hidden in symptoms, strong in invasiveness, short in course of disease, fast in progress and high in death rate, the median survival time is only about 6 months, about 3/4 patients die within 1 year after diagnosis, and the 5-year survival rate is less than 5%. Therefore, it is also called "king in cancer". Worldwide, the incidence and mortality of pancreatic cancer is increasing. Assessing the burden of pancreatic cancer and its global, regional, and national patterns is critical to the formulation and better allocation of medical resources to control pancreatic cancer risk factors, to conduct early detection, and to provide faster and more effective treatment.
Researchers at The national institute of health metering and assessment, Washington university of America, utilized data in The 2017 research report on Global Burden of Disease (GBD) to investigate The incidence, mortality and disability-adjusted life-span of 195 national and regional pancreatic cancers from 1990 to 2017 (DA L Ys) and estimate The mortality rate due to pancreatic cancer risk factors (smoking, high fasting glucose and high body mass index). related research efforts are published in The L anchor Gastroenterology & Hepatology, and based on The reported data, 44.8 million new world pancreatic cancers are reported in 2017 and 44.1 million people die.
The number of deaths, morbidity and DA L Ys caused by pancreatic cancer has increased more than doubled worldwide from 1990 to 2017. with the trend toward an aging population, the incidence of pancreatic cancer may continue to rise.
CA19-9 is a serum tumor marker which is more commonly applied in pancreatic cancer diagnosis at present, but the specificity of a simple CA19-9 index for pancreatic cancer diagnosis is often low, and biliary tract inflammation, obstructive jaundice and systemic tumors such as digestive tract and gynecology can cause CA19-9 to be increased, on the other hand, a part of pancreatic cancer patients do not show the increase of CA19-9 due to L ewis antibody negative and the like, so that the sensitivity of the pancreatic cancer patients is reduced, and the early diagnosis capability of CA19-9 for pancreatic cancer is limited, so that the early diagnosis or auxiliary diagnosis needs to be combined with other markers.
The identification of pancreatic cancer is a general problem, and the treatment methods of pancreatic cancer mainly comprise surgery, radiotherapy, chemotherapy and cell biotherapy, and different pathological changes with different properties and different clinical stages and clinical treatment methods thereof, so that the early diagnosis of pancreatic cancer is particularly important, and a pancreatic cancer marker (TM) is one of the detection indexes which are currently considered. However, one pancreatic cancer marker can appear in multiple pancreatic cancers, and multiple pancreatic cancer markers can also appear in one pancreatic cancer, so that the trend of jointly detecting the relevant pancreatic cancer markers to improve the diagnostic sensitivity and specificity is towards the trend.
In more cases, one index is far from sufficient for pancreatic cancer detection diagnosis. In the case of multiple indicators, we also need to consider the problem of parameter integration. The invention provides a multi-dimensional combined method for diagnosing pancreatic cancer in vitro, which jointly detects protein markers, metabolites, cell-free DNA, cell-free non-coding RNA, autoantibodies, inflammatory factors, growth factors, circulating pancreatic cancer cells, exosomes and the like related to pancreatic cancer, and improves the sensitivity and specificity of pancreatic cancer detection.
Disclosure of Invention
The main objective of the present application is to provide a method for constructing a mathematical model for detecting pancreatic cancer in vitro, so as to improve the sensitivity and specificity of detecting pancreatic cancer clinically, none of the markers for detecting pancreatic cancer at present can diagnose pancreatic cancer with very high sensitivity and specificity results, most pancreatic cancers adopt a joint detection mode, but all adopt molecular diagnosis or immunodiagnosis to detect several markers of one type, and do not combine the detections of various dimensions, and in order to enhance the accuracy of prediction, it is better to combine the detection of both the longitudinal and the external dimensions: it is an object of the present invention to combine metabolites, exosomes, molecular diagnostics, immunodiagnosis.
The application provides a method for constructing a mathematical model for detecting pancreatic cancer in vitro, which comprises the steps of obtaining the concentrations of at least two pancreatic cancer markers from a sample, carrying out logistic regression on the concentration value of each marker, substituting the concentration value obtained by measurement into the logistic regression model to obtain an analysis result, and carrying out comprehensive pancreatic cancer analysis by using the concentration value of each marker and the logistic regression analysis result.
Preferably, the pancreatic cancer markers include at least one of the following categories:
pancreatic cancer protein markers, pancreatic cancer metabolite markers, pancreatic cancer molecular diagnostic markers, pancreatic cancer autoantibodies, pancreatic cancer-associated inflammatory and/or growth factors, and pancreatic cancer-associated exosomes.
Preferably, the pancreatic cancer protein marker is selected from any one or more of the group consisting of carbohydrate antigen 19-9, carcinoembryonic antigen-associated cell adhesion molecule 1(CEACAM1), Osteopontin (OPN), Osteoprotegerin (OPG), leucine-rich α 2-glycoprotein 1 (L RG1), human matrix metalloproteinase inhibitor 1(TIMP1), intercellular adhesion molecule-1 (ICAM-1), serum macrophage inhibitory cytokine-1 (MIC-1), malignancy-specific growth factor (TSGF), carbohydrate antigen CA242, U L16 binding protein 2(U L BP2), matrix metalloproteinase 9(MMP-9), high mobility group protein a1(HMGA1), parkinson's disease protein 7(PARK7), macrophage factor-1 (MIC-1), receptor-bound tumor antigen expressed by SiSo cells (RCAS1), leukemia inhibitory factor (L eukeukinhinithiory, 56 IF, L;
the pancreatic cancer metabolite markers are selected from any one or more of acetyl spermine, diacetyl spermine (DAS), indole derivatives, lysophosphatidylcholine, glucose, acetone and selenocysteine;
the pancreatic cancer molecular diagnostic marker is selected from any one or more of CDKN2A, TP53, M L H1, BRCA1/2, ATM, KRAS, miR-642b-3p, miR-885-5p, miR-196a, miR-505, miR-145, miR-150, miR-223, miR-636, miR-26b, miR-34a, miR-122, miR-106b, miR-126 and miR-106;
the pancreatic cancer autoantibody is selected from any one or more of S L P-2, VDAC-1, VDAC-2, VDAC3, CHCHHD 3, COMT and TOM 40;
the pancreatic cancer related inflammatory factors and growth factors are selected from any one or more of I L-6, I L-10, S100, I L-13, CRP and SAA;
the pancreatic cancer related exosomes are selected from any one or more of IGF-1, IGF-2, TGF- β, PDGF, VEGF, I L-6, IP-10, I L-10, S100, I L-13, CRP, SAA the pancreatic cancer related inflammatory and growth factors are selected from any one or more of I L-6, I L-10, S100, I L-13, CRP, SAA;
the pancreatic cancer related exosomes are selected from any one or more of L RG1, KIT, CD91, miR-30B, miR-30C, miR-122, miR-195, miR-203, miR-221 and miR-222.
Preferably, the formula of the logistic regression is:
wherein L ogit (P) is the logistic regression model result of the same or different pancreatic cancer markers, C is the natural constant obtained by regression, the coefficient of each marker obtained by regression analysis is a natural number, the marker concentration i is the marker concentration in the same or different classes, and n is an integer greater than or equal to 2.
Preferably, the sample tested comprises: human or animal tissue, a blood sample, urine, saliva, body fluid, feces.
Preferably, the detection technique comprises one or more of a radiological method, an immunological method, a fluorescence method, a flow fluorescence, a latex turbidimetry, a biochemical method, an enzymatic method, a PCR method, a sequencing method, a hybridization method, a gas chromatography, a liquid chromatography, a chemiluminescence method, a magnetoelectric conversion method, and a photoelectric conversion method.
Preferably, the pancreatic cancer marker is a pancreatic cancer protein marker, a pancreatic cancer molecular diagnostic marker and a pancreatic cancer metabolic marker are combined, the pancreatic cancer protein marker is CEACAM1, OPN, OPG, L RG1, TIMP1, ICAM-1, MIC-1, the pancreatic cancer molecular diagnostic marker is CDKN2A, TP53, M L H1, BRCA1/2, ATM, KRAS, miR-642b-3p, miR-885-5p, miR-196a, the pancreatic cancer metabolic marker is acetylspermine, Diacetylspermine (DAS), indole, lysophosphatidylcholine, the concentration values of these markers in the sample are obtained, natural logarithm conversion is performed, and after logistic regression analysis, the marker without contribution is eliminated, the regression model obtained is L g (L) + (L + L) + (opca L) + (L + L) + L + 360.3672 + L + 360 g + L + 360 g + L + 360.3672 + L + 360 g L + 3+ L + 3+ L + 3+ L + 3+ L + 3 g + 3+ L
The application has the following advantages: the pancreatic cancer detection method has the advantages that the pancreatic cancer detection method has different dimensionalities, different types of combination are combined horizontally and longitudinally, internal and external detection is realized, the defects that the detection sensitivity and specificity of one marker or one dimensionality are not high in the market are overcome, the accuracy and the precision of pancreatic cancer diagnosis are greatly improved, traditional CT or biopsy puncture and other invasive diagnoses can be replaced, the subtype of pancreatic cancer can be judged, early diagnosis, early screening, auxiliary diagnosis or prognosis observation can be provided at the same time, and good news is brought to patients.
Detailed Description
In order to make the technical solutions in the embodiments of the present application better understood, the technical solutions in the embodiments of the present application are clearly and completely described, and it is obvious that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The detection methodology used in the examples may be a commercially available reagent test kit or a self-made kit.
Example 1
The blood samples were tested for 9 pancreatic cancer protein marker concentrations (CA19-9, CEACAM1, OPN, OPG, L RG1, TIMP1, ICAM-1, U L BP2, L IF) using a commercially available chemiluminescence assay kit, for 7 pancreatic cancer molecular marker concentrations (miR-642b-3p, miR-885-5p, miR-196a, miR-505, miR-145, miR-150, miR-223) using fluorescence in situ hybridization or sequencing, for 5 pancreatic cancer-associated inflammatory factor concentrations (IGF-2, TGF- β, PDGF, VEGF, I L-6) in blood samples using flow fluorescence, and for 3 pancreatic cancer-associated metabolite concentrations (indole derivatives, lysophosphatidylcholine, Diacetylspermine (DAS)) in urine or blood using standard LC assays.
The tested concentration of the related marker is subjected to logistic regression analysis to obtain L ogit (P) ═ constant + lambda 1 x P1+ lambda 2 x P2+ η 3 x P3+ η 4 x P4 … …
And testing the concentrations of the markers of the unknown blood samples, substituting the concentrations into a regression model, and comprehensively diagnosing whether the blood samples suffer from pancreatic cancer and the risk of pancreatic cancer according to the judgment standards of L ogit (P) and L ogit (P) values of the regression model.
Example 2
The blood samples were tested for 6 pancreatic cancer protein marker concentrations (CA19-9, CEACAM1, OPN, OPG, L RG1, TIMP1) using a purchased or self-made chemiluminescence assay kit, 5 pancreatic cancer molecular markers (CDKN2A, TP53, M L H1, BRCA1/2, KRAS) in the blood samples were tested using fluorescence in situ hybridization, 7 pancreatic cancer autoantibodies (S L P-2, VDAC-1, VDAC-2, VDAC3, CHCHCHHD 3, COMT, TOM40) in the blood samples were tested using a purchased immunofluorescence assay, and 8 pancreatic cancer-related exosomes (CD44v6, Tspan8, EpCAM, MET, CD104, C L DN4, EPCAM, CD151) in the urine were tested using a LC-MS.
The tested concentration of the related marker is subjected to logistic regression analysis to obtain L ogit (P) ═ constant + lambda 1 x P1+ lambda 2 x P2+ η 3 x P3+ η 4 x P4 … …
And testing the concentrations of the markers of the unknown blood samples, substituting the concentrations into a regression model, and comprehensively diagnosing whether the blood samples suffer from pancreatic cancer and the risk of pancreatic cancer according to the judgment standards of L ogit (P) and L ogit (P) values of the regression model.
Example 3
The pancreatic cancer protein markers are CEACAM1, OPN, OPG, L RG1, TIMP1, ICAM-1 and MIC-1, the pancreatic cancer molecular diagnostic markers are CDKN2A, TP53, M L H1, BRCA1/2, ATM, KRAS, miR-642b-3p, miR-885-5p and miR-196a, and the pancreatic cancer metabolic markers are acetylspermine, Diacetylspermine (DAS), indole and lysophosphatidylcholine, the concentration values of the markers in the sample are obtained, natural logarithmic transformation is performed, and after the nondividing markers are eliminated, the regression model obtained is L log (P) + L + 1.141L n (CEACAM L) + 1.041L n) + L n (L + 36392) (CD L + 3672.3672 + L + 36.
And testing the concentrations of the markers of the unknown blood samples, substituting the concentrations into a regression model, and comprehensively diagnosing whether the blood samples suffer from pancreatic cancer and the risk of the pancreatic cancer according to the judgment standards of L ogit (P) and a regression model L ogit (P) value obtained by calculation.
Experimental research shows that the multi-dimensional combination and combination mode pancreatic cancer detection has higher sensitivity and specificity compared with single detection or single detection of one or more types, the sensitivity can reach 99 percent, and the specificity is 100 percent and is far superior to pancreatic cancer diagnosis markers on the market. The above description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (8)
1. A method of constructing a mathematical model for detecting pancreatic cancer in vitro, comprising obtaining the concentrations of at least two pancreatic cancer markers from a sample, performing logistic regression on the concentration values of each marker determined, substituting the concentrations detected into a logistic regression model to obtain an analysis result, and performing a comprehensive pancreatic cancer analysis using the concentration of each marker and the logistic regression analysis result.
2. The method of constructing a mathematical model for the in vitro detection of pancreatic cancer according to claim 1, wherein said pancreatic cancer markers comprise at least one of the following categories:
pancreatic cancer protein markers, pancreatic cancer metabolite markers, pancreatic cancer molecular diagnostic markers, pancreatic cancer autoantibodies, pancreatic cancer-associated inflammatory and/or growth factors, and pancreatic cancer-associated exosomes.
3. The method for constructing a mathematical model for in vitro detection of pancreatic cancer according to claim 1, wherein the pancreatic cancer protein marker is selected from any one or more of sugar chain antigen 19-9, carcinoembryonic antigen-associated cell adhesion molecule 1(CEACAM1), Osteopontin (OPN), Osteoprotegerin (OPG), leucine-rich α 2-glycoprotein 1 (L RG1), human matrix metalloproteinase inhibitor 1(TIMP1), intercellular adhesion molecule-1 (ICAM-1), malignancy-specific growth factor (TSGF), sugar chain antigen CA242, U L16 binding protein 2(U L BP2), matrix metalloproteinase 9(MMP-9), high mobility group protein a1(HMGA1), parkinson's disease protein 7(PARK7), macrophage factor-1 (MIC-1), receptor-bound tumor antigen expressed by SiSo cells (rca 1), leukemia inhibitory factor (leukakeyine L), leukemia inhibitory factor (bithiernit), L, 36if L;
the pancreatic cancer metabolite markers are selected from any one or more of acetyl spermine, diacetyl spermine (DAS), indole derivatives, lysophosphatidylcholine, glucose, acetone and selenocysteine;
the pancreatic cancer molecular diagnostic marker is selected from any one or more of CDKN2A, TP53, M L H1, BRCA1/2, ATM, KRAS, miR-642b-3p, miR-885-5p, miR-196a, miR-505, miR-145, miR-150, miR-223, miR-636, miR-26b, miR-34a, miR-122, miR-106b, miR-126 and miR-106;
the pancreatic cancer autoantibody is selected from any one or more of S L P-2, VDAC-1, VDAC-2, VDAC3, CHCHHD 3, COMT and TOM 40;
the pancreatic cancer related inflammatory factors and growth factors are selected from any one or more of I L-6, I L-10, S100, I L-13, CRP and SAA;
the pancreatic cancer related exosomes are selected from any one or more of IGF-1, IGF-2, TGF- β, PDGF, VEGF, I L-6, IP-10, I L-10, S100, I L-13, CRP and SAA.
4. The method for constructing a mathematical model for the in vitro detection of pancreatic cancer according to claim 3, wherein the logistic regression is formulated as:
wherein L ogit (P) is the logistic regression model result of the same or different pancreatic cancer markers, C is the natural constant obtained by regression, α is the coefficient of each marker obtained by regression analysis, which is a natural number, the marker concentration i is the marker concentration in the same or different classes, and n is an integer greater than or equal to 2.
5. The method for constructing a mathematical model for in vitro detection of pancreatic cancer according to claim 1, wherein the sample to be detected comprises: human or animal tissue, a blood sample, urine, saliva, body fluid, feces.
6. The method for constructing a mathematical model for in vitro pancreatic cancer detection according to claim 1, wherein the detection technique comprises one or more of a radiation method, an immunological method, a fluorescence method, a flow fluorescence, a latex turbidimetry, a biochemical method, an enzymatic method, a PCR method, a sequencing method, a hybridization method, a gas chromatography, a liquid chromatography, a chemiluminescence method, a magnetoelectric conversion method and a photoelectric conversion method.
7. The method for constructing a mathematical model for in vitro detection of pancreatic cancer according to claim 1, wherein the pancreatic cancer markers are pancreatic cancer protein markers, pancreatic cancer molecular diagnostic markers and pancreatic cancer metabolic markers, wherein the pancreatic cancer protein markers are CEACAM1, OPN, OPG, L RG1, TIMP1, ICAM-1, MIC-1, and the pancreatic cancer molecular diagnostic markers are CDKN2A, TP53, M L H1, BRCA1/2, ATM, KRAS, miR-642b-3p, miR-885-5p, miR-196a, and the pancreatic cancer metabolic markers are acetyl spermine, diacetyl spermine (DAS), indole, lysophospholipid, and the concentration values of these markers in the sample are obtained, naturally logarithmetically transformed, analyzed by logistic regression, and the regression model obtained after rejecting no contribution marker is L (L) + L + 3672.3672 + L (L + 3672.3672 + L + 3+ L g L + 3+ L g + 3+ L + 3+ L g L + 3+ L.
8. Use of a mathematical model obtained by the method of constructing a mathematical model for the in vitro detection of pancreatic cancer according to any of claims 1 to 7 for predicting the risk of cancer in a subject of a sample, wherein the subject of the sample is considered to be at risk of cancer when the value of the calculated analysis result obtained from the mathematical model is ≧ 0.234.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010482539.0A CN111489829A (en) | 2020-05-29 | 2020-05-29 | Method for constructing mathematical model for detecting pancreatic cancer in vitro and application thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010482539.0A CN111489829A (en) | 2020-05-29 | 2020-05-29 | Method for constructing mathematical model for detecting pancreatic cancer in vitro and application thereof |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN111489829A true CN111489829A (en) | 2020-08-04 |
Family
ID=71796045
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010482539.0A Pending CN111489829A (en) | 2020-05-29 | 2020-05-29 | Method for constructing mathematical model for detecting pancreatic cancer in vitro and application thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN111489829A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112420200A (en) * | 2020-10-27 | 2021-02-26 | 上海交通大学医学院附属瑞金医院 | A predictive system for early malignant transformation of intraductal papillary mucinous tumors of the pancreas |
| WO2021238086A1 (en) * | 2020-05-29 | 2021-12-02 | 杭州广科安德生物科技有限公司 | Method for constructing mathematical model for detecting lung cancer in vitro and application |
| CN115101127A (en) * | 2022-06-23 | 2022-09-23 | 康德（深圳）生物技术有限公司 | System and method for screening early cancer risk of human by automatically analyzing PCR detection result |
| CN116087530A (en) * | 2023-03-29 | 2023-05-09 | 臻和(北京)生物科技有限公司 | Protein composition, device, equipment and storage medium for detecting pancreatic cancer |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103782174A (en) * | 2011-06-07 | 2014-05-07 | 卡里斯生命科学卢森堡控股有限责任公司 | Circulating biomarkers for cancer |
| WO2014193999A2 (en) * | 2013-05-28 | 2014-12-04 | Caris Science, Inc. | Biomarker methods and compositions |
| CN105229470A (en) * | 2013-03-15 | 2016-01-06 | 艾克斯肯诊断股份有限公司 | Be used for the treatment of the method with diagnostic system lupus erythematosus |
| CN106461663A (en) * | 2013-11-21 | 2017-02-22 | 环太平洋有限公司 | Triaging of patients having asymptomatic hematuria using genotypic and phenotypic biomarkers |
| CN107058521A (en) * | 2017-03-17 | 2017-08-18 | 中国科学院北京基因组研究所 | A kind of detecting system for detecting human immunity state |
| CN107385050A (en) * | 2017-08-04 | 2017-11-24 | 上海易毕恩生物技术有限公司 | For detecting the gene marker, kit and cancer of pancreas detection method of cancer of pancreas |
| CN108474779A (en) * | 2016-03-08 | 2018-08-31 | 马格雷股份有限公司 | The protein and auto-antibody biomarker of diagnosing and treating for lung cancer |
| WO2019121942A1 (en) * | 2017-12-20 | 2019-06-27 | Metanomics Health Gmbh | Methods for diagnosing pancreatic cancer |
| CN110646554A (en) * | 2019-09-12 | 2020-01-03 | 北京博远精准医疗科技有限公司 | Metabolomics-based diagnostic markers for pancreatic cancer and their screening methods and applications |
-
2020
- 2020-05-29 CN CN202010482539.0A patent/CN111489829A/en active Pending
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103782174A (en) * | 2011-06-07 | 2014-05-07 | 卡里斯生命科学卢森堡控股有限责任公司 | Circulating biomarkers for cancer |
| CN105229470A (en) * | 2013-03-15 | 2016-01-06 | 艾克斯肯诊断股份有限公司 | Be used for the treatment of the method with diagnostic system lupus erythematosus |
| WO2014193999A2 (en) * | 2013-05-28 | 2014-12-04 | Caris Science, Inc. | Biomarker methods and compositions |
| CN106461663A (en) * | 2013-11-21 | 2017-02-22 | 环太平洋有限公司 | Triaging of patients having asymptomatic hematuria using genotypic and phenotypic biomarkers |
| CN108474779A (en) * | 2016-03-08 | 2018-08-31 | 马格雷股份有限公司 | The protein and auto-antibody biomarker of diagnosing and treating for lung cancer |
| CN107058521A (en) * | 2017-03-17 | 2017-08-18 | 中国科学院北京基因组研究所 | A kind of detecting system for detecting human immunity state |
| CN107385050A (en) * | 2017-08-04 | 2017-11-24 | 上海易毕恩生物技术有限公司 | For detecting the gene marker, kit and cancer of pancreas detection method of cancer of pancreas |
| WO2019121942A1 (en) * | 2017-12-20 | 2019-06-27 | Metanomics Health Gmbh | Methods for diagnosing pancreatic cancer |
| CN110646554A (en) * | 2019-09-12 | 2020-01-03 | 北京博远精准医疗科技有限公司 | Metabolomics-based diagnostic markers for pancreatic cancer and their screening methods and applications |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2021238086A1 (en) * | 2020-05-29 | 2021-12-02 | 杭州广科安德生物科技有限公司 | Method for constructing mathematical model for detecting lung cancer in vitro and application |
| CN112420200A (en) * | 2020-10-27 | 2021-02-26 | 上海交通大学医学院附属瑞金医院 | A predictive system for early malignant transformation of intraductal papillary mucinous tumors of the pancreas |
| CN115101127A (en) * | 2022-06-23 | 2022-09-23 | 康德（深圳）生物技术有限公司 | System and method for screening early cancer risk of human by automatically analyzing PCR detection result |
| CN116087530A (en) * | 2023-03-29 | 2023-05-09 | 臻和(北京)生物科技有限公司 | Protein composition, device, equipment and storage medium for detecting pancreatic cancer |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN111489829A (en) | Method for constructing mathematical model for detecting pancreatic cancer in vitro and application thereof | |
| CN111584008A (en) | Method for constructing mathematical model for detecting colorectal cancer in vitro and application thereof | |
| WO2021238086A1 (en) | Method for constructing mathematical model for detecting lung cancer in vitro and application | |
| CN105603101B (en) | Detect application of the system of 8 miRNA expression quantity in diagnosis or auxiliary diagnosis of hepatoma product is prepared | |
| CN105256014B (en) | Breast cancer combined diagnosis marker and detection kit | |
| CN111653315A (en) | Method for constructing mathematical model for detecting breast cancer in vitro and application thereof | |
| CN111540469A (en) | Method for constructing mathematical model for in-vitro detection of gastric cancer and application thereof | |
| JP2010243406A (en) | Method for detecting disease state progression degree of liver cancer and chronic liver disease using discriminant function characterized by measurement values of AFP and PIVKA-II | |
| CN111583993A (en) | Method for constructing mathematical model for in vitro cancer detection and application thereof | |
| TWI571514B (en) | Method for accessing the risk of having colorectal cancer | |
| JP7187081B2 (en) | Methods for early diagnosis and post-treatment monitoring of breast cancer using liquid biopsy multiplex oncogene biomarkers | |
| CN118501460A (en) | Application of ALDOA protein as a biomarker in diagnosing, predicting or monitoring the progression of gastric cancer | |
| Chen et al. | Fecal miRNAs as potential biomarkers for early detection of colorectal cancer: An updated review | |
| CN112501294A (en) | Colorectal cancer biomarker and application thereof | |
| Yang et al. | A nomogram for predicting colorectal cancer liver metastasis using circulating tumor cells from the first drainage vein | |
| CN114990220B (en) | Molecular marker group for early diagnosis of renal clear cell carcinoma | |
| JP6612509B2 (en) | Method, recording medium and determination device for assisting prognosis of colorectal cancer | |
| CN102732522A (en) | Liver cancer serum nucleic acid aptamers | |
| CN114457160A (en) | Application of miRNA (micro ribonucleic acid) molecule as early lung cancer detection marker | |
| Laude et al. | Clinical value of Circulating tumor DNA for patients with epithelial ovarian cancer | |
| RU2823255C1 (en) | Method for prediction of recurrent thyroid cancer after radical surgery | |
| CN115948545B (en) | A diagnostic kit for ESCC based on plasma exosome miRNA | |
| Habib et al. | Serum microRNA 106 and microRNA 223 as novel biomarkers in inflammatory bowel disease | |
| US20240309460A1 (en) | Use of reagent for detecting pir-hsa-120522 in plasma in preparation of lung cancer screening or diagnosis kit | |
| CN109182520B (en) | Cervical cancer and precancerous lesion detection kit and application thereof |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20200804 |