CN111667918A - Method for constructing mathematical model for in vitro detection of lung cancer and application - Google Patents
Method for constructing mathematical model for in vitro detection of lung cancer and application Download PDFInfo
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Abstract
The application provides a method for constructing a mathematical model for detecting lung cancer in vitro, which comprises the steps of obtaining the concentrations of at least two lung cancer markers from a sample, carrying out logistic regression on the concentration value of each marker, substituting the concentration of the detected marker into the logistic regression model to obtain an analysis result, and carrying out comprehensive lung cancer analysis by using the concentration of each marker and the logistic regression analysis result. The application also discloses an application of the method.
Description
Technical Field
The application relates to the technical field of biological detection, in particular to a method for constructing a mathematical model for in vitro lung cancer detection and application.
Background
The World Health Organization (WHO), the International agency for research on cancer (IARC), the latest report is published in 2018, the number of people suffering from cancer worldwide is estimated to be rapidly increased, 1810 ten thousand cases are newly added in 2018 for one year, the number of deaths is up to 960 ten thousand, and the cancer becomes the first number of killers in the world and is the largest 'road blocking tiger' which hinders the expectation of human beings and prolongs the service life.
Wherein Asian cancer accounts for 48.4% of the world, corresponding to 1/2% of the world, and Asian accounts for nearly 60% of 960 ten thousand cancer deaths. In 1810 ten new cancer cases, more than half of men and 950 ten patients have 50% of the total incidence and 60% of the mortality, while in 860 ten new cancer cases, women have 47.5% of the total incidence and slightly more than half of the mortality.
In China, the incidence rate of cancer is on the rise, and in China, more than 400 people are diagnosed with cancer every year on average, more than 1 ten thousand people are diagnosed with cancer every day on average, 7 people die of cancer every minute on average, 6000 people die of cancer every day, and nearly 5 people die of cancer every minute.
For the whole population, lung cancer is the "first male killer". The data show that the first place in the overall incidence and mortality of cancer, whether global or chinese, is lung cancer, with incidence and mortality rates that account for 11.6% and 18.4% (global), 20% and 27.3% (chinese) of the population of patients with total cancer.
The identification of benign lung cancer and lung cancer is a general problem, the treatment methods of lung cancer mainly comprise operations, radiotherapy, chemotherapy and cell biotherapy, and the clinical treatment methods of different pathological changes and different clinical stages are different, so that the early diagnosis of lung cancer is particularly important, and a lung cancer marker (TM) is one of the detection indexes which are considered at present. However, one lung cancer marker can appear in a plurality of lung cancers, and one lung cancer can also appear in a plurality of lung cancer markers, so that the trend of jointly detecting the related lung cancer markers to improve the diagnosis sensitivity and specificity is formed.
In more cases, one index is far from sufficient for lung cancer detection and diagnosis. In the case of multiple indicators, we also need to consider the problem of parameter integration. The invention provides a multi-dimensional combined in-vitro lung cancer diagnosis method, which jointly detects lung cancer related protein markers, metabolites, cell-free DNA, cell-free non-coding RNA, autoantibodies, inflammatory factors, growth factors, circulating lung cancer cells, exosomes and the like, and improves the sensitivity and specificity of lung cancer detection.
Disclosure of Invention
The main objective of the present application is to provide a method for constructing a mathematical model, which can be applied to multi-dimensional in vitro lung cancer diagnosis to improve the sensitivity and specificity of clinical lung cancer detection, no marker for lung cancer detection can be used to diagnose lung cancer with very high sensitivity and specificity results, most of lung cancer adopts a joint inspection form, but several markers of one type are detected by molecular diagnosis or immunodiagnosis, and the detection of various dimensions is not combined, so as to enhance the prediction accuracy, preferably, the combination of transverse integration, longitudinal integration, internal integration and external integration is adopted: 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 lung cancer in vitro, which comprises the steps of obtaining the concentrations of at least two lung cancer markers from a sample, carrying out logistic regression on the concentration value of each marker, substituting the concentration of the detected marker into the logistic regression model to obtain an analysis result, and carrying out comprehensive lung cancer analysis by using the concentration of each marker and the logistic regression analysis result. Preferably, the lung cancer markers include at least one of the following categories:
lung cancer protein markers, lung cancer metabolite markers, lung cancer molecular diagnostic markers, lung cancer autoantibodies, lung cancer-associated DNA methylation markers, lung cancer-associated inflammatory factors and/or growth factors, and lung cancer-associated exosomes.
Preferably, the lung cancer protein marker is selected from any one or more of Pro-SFTBP, CEA, CA125, SCC-Ag, CYFRA21-1, Pro-GRP, NSE, CA15-3, CA19-9, CA242, HSPG, NCAM, preferably any one or more combination of Pro-SFTBP, CEA, CA125, SCC-Ag, CYFRA21-1, Pro-GRP, NSE;
the lung cancer metabolite marker is selected from any one or more of 8-hydroxydeoxyguanosine, diacetyl spermine, N-acetylated glycoprotein, beta-hydroxybutyric acid, leucine, lysine, tyrosine, threonine, glutamine, valine and aspartic acid, preferably one or more of 8-hydroxydeoxyguanosine, diacetyl spermine and N-acetylated glycoprotein;
the lung cancer molecular diagnostic marker is selected from any one or more of EGFR, AKT1, ALK, HER2, MEK1, KRAS, BRAF, DKK-1, pIK3CA, ROS1, NRAS, RET, MET, BRCA1/2, cap43, miR-21, miR-20a, miR-24, miR-25, miR-145, miR-183, miR-205, miR-196b, miR-203, miR-429 and miR-200 b;
the lung cancer autoantibody is selected from any one or more of IGFBP1, PGAM1, TP53, UBQLN1, ANXA1, ANXA2, CDK2, CTAG1B, MAGE A1, SOX2, p53, GAGE7, PGP9.5, CAGE and GBU 4-5;
the lung cancer related inflammatory factors and growth factors are selected from any one or more of IL-6, IL-10, S100, IL-13, CRP and SAA;
the lung cancer related exosomes are selected from any one or more of LRG1, KIT, CD91, miR-30B, miR-30C, miR-122, miR-195, miR-203, miR-221 and miR-222.
The lung cancer related DNA methylation marker is selected from any one or more of SHOX2, RASSF1A, EGFR, E18, E19, E20, E21, BRAF, PIK3CA, KRAS, p16, CDH1, CDH13, APC, RARP, DAPK, MGMT, FHIT, HIC-1, AKAPl2, ESRl, CYGB, OPCML, ADAMTl, TGFBI, RUNX3, UMDl, hSRBC, CADM1, p14ARF, p16INK4a, DAPK, GSTP1, MGMT, MLH1, FBN2, DAL-1, ASC, preferably one or more of SHOX2, RASSF1A, EGFR, BRAF, PIK3CA, KRAS, p 16.
Preferably, the formula of the logistic regression is:
wherein, Logit (P) is the logistic regression model result of the lung cancer markers of the same or different types, C is a natural constant obtained by regression, alpha is the coefficient of each marker obtained by regression analysis and is a natural number, the concentration i of the marker is the concentration of the marker in the same or different types, and n is an integer which is more 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 different lung cancer class markers are lung cancer protein markers, lung cancer molecular diagnostic markers and lung cancer related DNA methylation markers, wherein the lung cancer protein markers are Pro-SFTBP, CEA, CA125, SCC-Ag, CYFRA21-1 and Pro-GRP, the lung cancer molecular diagnostic markers are EGFR, AKT1, ALK, HER2, BRAF, pIK3CA and BRCA1/2, the lung cancer related DNA methylation markers are SHOX2, RASSF1A and AKAPl2, the concentration values of the markers in a sample are obtained, natural logarithm conversion is carried out, and after a logistic regression analysis is carried out to remove the non-contributing markers, a regression model is obtained as follows: logit (p) ═ 8.536+1.852 × Ln (Pro-SFTBP) +0.741 × Ln (cea) +0.689 × Ln (CA125) +0.521 × Ln (SCC-Ag) +0.534 × Ln (CYFRA21-1) +1.245 × Ln (egfr) +0.872 × Ln (HER2) +0.316 × Ln (SHOX2) +0.258 × Ln (RASSF1A) +0.698 (AKAPl 2).
The preferred marker combination can comprehensively and comprehensively judge early diagnosis, early screening, auxiliary diagnosis of lung cancer, subtype of lung cancer (small cell lung cancer or non-small cell lung cancer), concomitant diagnosis of medicaments and prognostic treatment observation of lung cancer. Can effectively replace the inaccuracy of CT detection, the specificity of judging the benign and the malignant lung nodules within less than 6mm is more than 90 percent, and the sensitivity is more than 95 percent (no judgment method exists in the market at present).
In another aspect of the present application, there is provided a use of the mathematical model obtained by the method for constructing a mathematical model for in vitro lung cancer detection for predicting the risk of cancer in a subject sample, wherein the subject sample is considered to have a cancer risk when the value of the result of computational analysis obtained from the mathematical model is ≥ 2.156
Wherein Ln is a natural logarithm, and when the value of the calculation analysis result logit (P) obtained according to the model formula is more than or equal to-2.156, the subject of the sample is considered to have cancer risk.
The above model is only an example, different models have different functions of diagnosing lung cancer, and the analysis results have different values, so that the early diagnosis, early screening, auxiliary diagnosis or prognosis of lung cancer can be performed.
The application has the following advantages:
the method for constructing the mathematical model starts from different dimensionalities of the lung cancer, different types of marker concentration data are combined for use, and the obtained mathematical model is more complete. The mathematical model embodies the detection of horizontal and vertical connection and internal and external consideration in application, can overcome the defects of low prediction sensitivity and specificity and the like when cancer risk prediction is carried out by using a marker or one dimension in the market, and greatly improves the accuracy and the precision of predicting the lung cancer risk of a sample subject.
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 7 lung cancer protein marker concentrations (Pro-SFTBP, CEA, CA125, SCC-Ag, CYFRA21-1, Pro-GRP, NSE) using a purchased chemiluminescence method detection kit, for 9 lung cancer molecular marker concentrations (EGFR, AKT1, ALK, HER2, KRAS, BRAF, pIK3CA, ROS1, BRCA1/2) using fluorescence in situ hybridization or sequencing methods, for 4 lung cancer-related inflammatory factor concentrations (IL-6, IL-10, S100, IL-13) using flow fluorescence methods, and for 2 lung cancer-related metabolite marker concentrations (8-hydroxydeoxyguanosine, Diacetylspermine (DAS)) using standard LC methods.
Performing logistic regression analysis on the concentrations of 7 lung cancer protein markers, 9 lung cancer molecular markers, 4 lung cancer-related inflammatory factor concentrations and 2 lung cancer-related metabolite markers to obtain the Logit (P) ═ constant + lambda 1P 1+ lambda 2P 2+ eta 3P 3+ eta 4P 4 … …
And testing the concentration of each marker of the unknown blood sample, substituting the concentration into the regression model, and comprehensively diagnosing whether the lung cancer suffers from the condition and the risk of the lung cancer according to the judgment standard of the calculated logit (P) and the value of the logit (P) of the regression model.
Example 2
The concentration of 4 lung cancer protein markers (Pro-SFTBP, CEA, CA125, CYFRA21-1) in a blood sample is tested by using a purchased or self-made chemiluminescence method kit, 6 lung cancer molecular markers (EGFR, KRAS, BRAF, PIK3CA, ALK, ROS1) in the blood sample are tested by using a fluorescence in situ hybridization method or a flow fluorescence method, and 1 lung cancer related metabolite marker (diacetyl spermine (DAS)) in urine is detected by using a flow fluorescence method or a liquid-mass combination method.
Performing logistic regression analysis on the concentrations of 4 lung cancer protein markers, 7 lung cancer molecular markers and the concentration of lung cancer related metabolite marker DAS to obtain Logit (P) ═ constant + lambda 1 × P1+ lambda 2 × P2+ eta 3 × P3+ eta 4 × P4 … …
And testing the concentration of each marker of the unknown blood sample, substituting the concentration into the regression model, and comprehensively diagnosing whether the lung cancer suffers from the condition and the risk of the lung cancer according to the judgment standard of the calculated logit (P) and the value of the logit (P) of the regression model.
Example 3
The blood samples were tested for 4 lung cancer protein markers (Pro-SFTBP, CEA, CA125, CYFRA21-1) using a purchased or self-made chemiluminescence assay kit, for 6 lung cancer molecular markers (EGFR, KRAS, BRAF, PIK3CA, ALK, ROS1) using fluorescence in situ hybridization, and for 7 lung cancer autoantibodies (MAGE A1, SOX2, p53, GAGE7, PGP9.5, CAGE, GBU4-5) using a purchased immunofluorescence assay. Flow-type fluorescence method is used for detecting the concentrations of 4 lung cancer-related inflammatory factors (IL-6, IL-10, S100 and IL-13) in blood samples, and liquid chromatography-mass spectrometry method is used for detecting 3 lung cancer-related exosomes (LRG1, KIT and CD91) in urine.
Constructing a regression model, and performing logistic regression analysis on the concentration of the marker to obtain Logit (P) ═ constant + lambda 1 × P1+ lambda 2 × P2+ eta 3 × P3+ eta 4 × P4 … …
And testing the concentration of each marker of the unknown blood sample, substituting the concentration into the regression model, and comprehensively diagnosing whether the lung cancer suffers from the condition and the risk of the lung cancer according to the judgment standard of the calculated logit (P) and the value of the logit (P) of the regression model.
Example 4
The lung cancer molecular diagnosis marker and the lung cancer related DNA methylation marker combination, wherein the lung cancer protein markers are Pro-SFTBP, CEA, CA125, SCC-Ag, CYFRA21-1 and Pro-GRP, the lung cancer molecular diagnosis markers are EGFR, AKT1, ALK, HER2, BRAF, pIK3CA and BRCA1/2, the lung cancer related DNA methylation markers are SHOX2, RASSF1A and AKAPl2, concentration values of the markers in a sample are obtained, natural logarithm conversion is carried out, and after logistic regression analysis is carried out, non-contributing markers are removed, a regression model is obtained and is as follows: logit (p) ═ 8.536+1.852 × Ln (Pro-SFTBP) +0.741 × Ln (cea) +0.689 × Ln (CA125) +0.521 × Ln (SCC-Ag) +0.534 × Ln (CYFRA21-1) +1.245 × Ln (egfr) +0.872 × Ln (HER2) +0.316 × Ln (SHOX2) +0.258 × Ln (RASSF1A) +0.698 (AKAPl 2).
And testing the concentration of each marker of the unknown blood sample, substituting the concentration into the regression model, and comprehensively diagnosing whether the lung cancer suffers from the condition and the risk of the lung cancer according to the judgment standard of the calculated logit (P) and the value of the logit (P) of the regression model.
Compared with single detection of one or more types, the combined lung cancer detection has higher sensitivity and specificity, the sensitivity can reach 99 percent, and the specificity is 100 percent and is far better than lung 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 in vitro detection of lung cancer, the method comprising obtaining the concentrations of at least two lung cancer markers from a sample, performing logistic regression on the concentration values of each marker determined, substituting the detected marker concentrations into a logistic regression model to obtain an analysis result, and performing an integrated lung cancer analysis using the concentration of each marker and the logistic regression analysis result.
2. The method of constructing a mathematical model for in vitro detection of lung cancer according to claim 1, wherein said lung cancer markers comprise at least one of the following categories:
lung cancer protein markers, lung cancer metabolite markers, lung cancer molecular diagnostic markers, lung cancer autoantibodies, lung cancer-associated DNA methylation markers, lung cancer-associated inflammatory factors and/or growth factors, and lung cancer-associated exosomes.
3. The method for constructing a mathematical model for in vitro detection of lung cancer according to claim 1, wherein the lung cancer protein marker is selected from any one or more of Pro-SFTBP, CEA, CA125, SCC-Ag, CYFRA21-1, Pro-GRP, NSE, CA15-3, CA19-9, CA242, HSPG, NCAM, preferably any one or more of Pro-SFTBP, CEA, CA125, SCC-Ag, CYFRA21-1, Pro-GRP, NSE;
the lung cancer metabolite marker is selected from any one or more of 8-hydroxydeoxyguanosine, diacetyl spermine, N-acetylated glycoprotein, beta-hydroxybutyric acid, leucine, lysine, tyrosine, threonine, glutamine, valine and aspartic acid, preferably one or more of 8-hydroxydeoxyguanosine, diacetyl spermine and N-acetylated glycoprotein;
the lung cancer molecular diagnostic marker is selected from any one or more of EGFR, AKT1, ALK, HER2, MEK1, KRAS, BRAF, DKK-1, pIK3CA, ROS1, NRAS, RET, MET, BRCA1/2, cap43, miR-21, miR-20a, miR-24, miR-25, miR-145, miR-183, miR-205, miR-196b, miR-203, miR-429 and miR-200 b;
the lung cancer autoantibody is selected from any one or more of IGFBP1, PGAM1, TP53, UBQLN1, ANXA1, ANXA2, CDK2, CTAG1B, MAGEA1, SOX2, p53, GAGE7, PGP9.5, CAGE, GBU 4-5;
the lung cancer related inflammatory factors and growth factors are selected from any one or more of IL-6, IL-10, S100, IL-13, CRP and SAA;
the lung cancer-related exosomes are selected from any one or more of LRG1, KIT, CD91, miR-30B, miR-30C, miR-122, miR-195, miR-203, miR-221 and miR-222;
the lung cancer related DNA methylation marker is selected from any one or more of SHOX2, RASSF1A, EGFR, E18, E19, E20, E21, BRAF, PIK3CA, KRAS, p16, CDH1, CDH13, APC, RARP, DAPK, MGMT, FHIT, HIC-1, AKAPl2, ESRl, CYGB, OPCML, ADAMTl, TGFBI, RUNX3, UMDl, hSRBC, CADM1, p14ARF, p16INK4a, DAPK, GSTP1, MGMT, MLH1, FBN2, DAL-1, ASC, preferably one or more of SHOX2, RASSF1A, EGFR, BRAF, PIK3CA, KRAS, p 16.
4. The method for constructing a mathematical model for in vitro lung cancer detection according to claim 3, wherein the logistic regression is formulated as:
wherein, Logit (P) is the logistic regression model result of the lung cancer markers of the same or different types, C is a natural constant obtained by regression, alpha is the coefficient of each marker obtained by regression analysis and is a natural number, the concentration i of the marker is the concentration of the marker in the same or different types, and n is an integer which is more than or equal to 2.
5. The method for constructing a mathematical model for in vitro lung cancer detection according to claim 1, wherein the sample to be tested comprises: human or animal tissue, a blood sample, urine, saliva, body fluid, feces.
6. The method for constructing the mathematical model for in vitro lung 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 lung cancer detection according to claim 1, wherein the lung cancer markers are lung cancer protein markers, lung cancer molecular diagnostic markers and lung cancer-related DNA methylation markers, wherein the lung cancer protein markers are Pro-SFTBP, CEA, CA125, SCC-Ag, CYFRA21-1, Pro-GRP, the lung cancer molecular diagnostic markers are EGFR, AKT1, ALK, HER2, BRAF, pIK3CA, BRCA1/2, the lung cancer-related DNA methylation markers are SHOX2, RASSF1A, and AKAPl2, the concentration values of the markers in the sample are obtained, natural logarithm conversion is performed, and the regression model obtained after removing the non-contributing markers through logistic regression analysis is: logit (p) ═ 8.536+1.852 × Ln (Pro-SFTBP) +0.741 × Ln (cea) +0.689 × Ln (CA125) +0.521 × Ln (SCC-Ag) +0.534 × Ln (CYFRA21-1) +1.245 × Ln (egfr) +0.872 × Ln (HER2) +0.316 × Ln (braf) +0.872 × Ln (pIK3CA) +0.352 × Ln (BRCA1/2) +0.821 × Ln (SHOX2) +0.258 × Ln (RASSF1A) +0.698 (AKAPl2), where Ln is the natural logarithm.
8. Use of a mathematical model obtained by the method for constructing a mathematical model for the in vitro detection of lung cancer according to any one of claims 1 to 7 for predicting the risk of cancer in a subject sample, wherein the subject sample is considered to be at risk for cancer when the value of the calculated analysis result obtained from the mathematical model is ≥ 2.156.
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| 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 |
| CN113960235A (en) * | 2020-10-10 | 2022-01-21 | 中山大学肿瘤防治中心（中山大学附属肿瘤医院、中山大学肿瘤研究所） | Application and method of biomarker in preparation of lung cancer detection reagent |
| WO2022073502A1 (en) * | 2020-10-10 | 2022-04-14 | 中山大学肿瘤防治中心（中山大学附属肿瘤医院、中山大学肿瘤研究所） | Use of biomarker in preparation of lung cancer detection reagent and related method |
| CN113960235B (en) * | 2020-10-10 | 2023-03-14 | 中山大学肿瘤防治中心（中山大学附属肿瘤医院、中山大学肿瘤研究所） | Application and method of biomarker in preparation of lung cancer detection reagent |
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