WO2021238085A1 - 构建体外检测癌症的数学模型的方法及其应用 - Google Patents

构建体外检测癌症的数学模型的方法及其应用 Download PDF

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WO2021238085A1
WO2021238085A1 PCT/CN2020/127727 CN2020127727W WO2021238085A1 WO 2021238085 A1 WO2021238085 A1 WO 2021238085A1 CN 2020127727 W CN2020127727 W CN 2020127727W WO 2021238085 A1 WO2021238085 A1 WO 2021238085A1
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cancer
concentration
markers
marker
mathematical model
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高金波
高俊莉
高俊顺
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杭州广科安德生物科技有限公司
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    • 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
    • G16B20/00—ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
    • G16B20/20—Allele or variant detection, e.g. single nucleotide polymorphism [SNP] detection
    • 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
    • G16B30/10—Sequence alignment; Homology search
    • 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
    • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
    • 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
    • G16B5/00—ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks
    • 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

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  • This application relates to the technical field of medical diagnosis, in particular to a method for constructing a mathematical model for detecting cancer in vitro.
  • the mainstream tumor detection methods include the following: tumor markers, imaging examinations, tissue biopsy, liquid biopsy, etc.
  • An ideal marker should have high specificity to ensure that samples from non-cancer patients will not appear false positives. This is the biggest shortcoming of most tumor markers in use.
  • the upper limit reference value needs to be set so low that a large number of healthy people undergoing the test will also be positive.
  • the clinical (diagnostic) sensitivity of tumor marker detection refers to the percentage of cancer patients with elevated tumor marker results. Although a certain test has elevated results in most patients with malignant tumors, if it has elevated results in even a small proportion of non-tumor patients (non-specific), then if it is used for screening, it will There are unacceptably high false positive results (due to the relatively low incidence of cancer in the population).
  • PSA Prostate Specific Antigen
  • tumor markers far exceed the capabilities of current detection items. Although some markers do a good job in the early detection of recurrence and monitoring the efficacy of treatment, there are still serious shortcomings in their sensitivity and specificity. In addition, although the detection process of tumor markers is simple and cost-effective, the limitations of immunoassays are also a major drawback. Although the use of tumor markers is widespread and they do provide a lot of information, you need to be aware of their limitations.
  • the best strategy to deal with tumors is early diagnosis and combined diagnosis.
  • the cure rate can be increased to 83%.
  • the latest and most effective method for early diagnosis of cancer is to look for tumor markers, especially protein markers, through blood tests.
  • protein markers such as CA19-9, TIMP1, and LRG1, supplemented by the detection of mutations in the KRAS gene, can find prostate cancer patients whose tumors are resectable, and the detection rate is higher than that of methods that only use ctDNA.
  • the present invention provides a multi-dimensional combined method for in vitro diagnosis of tumors, which combines tumor-related protein markers, metabolites, cell-free DNA, cell-free non-coding RNA, autoantibodies, inflammatory factors and growth factors, circulating tumor cells, and external Joint detection of exosomes, etc., to improve the sensitivity and specificity of tumor detection.
  • the main purpose of this application is to provide a method for constructing a mathematical model for detecting cancer in vitro to improve the sensitivity and specificity of clinical tumor detection.
  • a marker for tumor detection that can simultaneously diagnose with very high sensitivity and specificity.
  • Tumors most tumors use the joint inspection form, but molecular diagnosis or immunodiagnosis is used to detect one or the same type of several markers, without combining the detection of various dimensions, in order to enhance the accuracy of prediction, it is best It is a combination of horizontal and vertical, internal and external considerations: combining metabolites, exosomes, molecular diagnosis, and immunodiagnosis, which is the purpose of the present invention.
  • This application provides a method for constructing a mathematical model for detecting cancer in vitro.
  • the method includes obtaining the concentration of at least two cancer markers from a sample. First, logistic regression is performed on the measured concentration value of each marker to obtain the regression Model, substituting the detected concentration into the logistic regression model to obtain the analysis result, and use the concentration of each marker and the logistic regression analysis result to perform a comprehensive cancer judgment analysis.
  • the method includes obtaining the concentration of three cancer markers from the sample, performing logistic regression on the measured concentration value of each marker, and substituting the detected concentration into the logistic regression model to obtain the analysis result.
  • the method includes obtaining the concentration of four cancer markers from the sample, performing logistic regression on the measured concentration value of each marker, and substituting the detected concentration into the logistic regression model to obtain the analysis result.
  • the cancer marker includes at least one of the following categories:
  • Cancer protein markers cancer protein markers, cancer metabolite markers, cell-free DNA tumor markers, cell-free non-coding RNA markers, cancer autoantibodies, cancer-related inflammatory factors and/or growth factors, circulating tumor cells, and cancer-related exosomes.
  • the cancer protein marker is one of a lung cancer protein marker, a breast cancer protein marker, and a colorectal cancer protein marker.
  • the cell-free non-coding RNA markers are miR-486-5p, miR-145, miR-150, miR-223, miR-636, miR-122, miR-505.
  • the logistic regression formula is:
  • Logit(P) is the result of logistic regression model of the above-mentioned cancer markers of the same or different types
  • C is the natural constant obtained by regression
  • ⁇ is the coefficient of each marker obtained by regression analysis, which is a natural number
  • the concentration of the marker i is For the concentration of markers in the same category or different categories
  • n is an integer greater than or equal to 2.
  • the sample to be tested includes: any one or more of human or animal body tissues, blood samples, urine, saliva, hair, body fluids, and feces.
  • the detection technology methods include radiation methods, immunological methods, fluorescence methods, flow fluorescence, latex turbidimetric methods, biochemical methods, enzymatic methods, PCR methods, sequencing methods, hybridization methods, gas mass spectrometry, liquid mass spectrometry, and layer One or more of analytical methods, chemiluminescence methods, magnetoelectric and photoelectric conversion methods.
  • the method for constructing a mathematical model for detecting cancer in vitro is provided to obtain the application of the mathematical model in predicting the risk of cancer in a sample subject, when the value of the calculation and analysis result obtained according to the mathematical model is greater than or equal to Or when it is less than or equal to a certain value, the subject of the sample is considered to be at risk of cancer.
  • This application has the following advantages: different dimensions of tumors, different types of combinations, horizontal and vertical, both internal and external detection, overcome the shortcomings of the detection sensitivity and specificity of one marker or one dimension on the market, and greatly improve the accuracy of tumor diagnosis The degree and accuracy can replace traditional CT or biopsy puncture and other invasive diagnosis, and bring good news to patients in the early diagnosis of tumors.
  • the detection methodology used in the examples can be purchased reagent detection kits or self-made kits.
  • chemiluminescence method kits to test the concentration of 3 lung cancer protein markers in blood samples, test 4 lung cancer cell-free DNA tumor markers in blood samples by fluorescence in situ hybridization, and detect blood samples by flow fluorescence method Concentrations of 3 lung cancer-related inflammatory factors were used to detect 1 lung cancer-related metabolite markers in urine by liquid-mass spectrometry.
  • chemiluminescence kits to test the concentration of 3 cancer protein markers in the blood sample, use fluorescence in situ hybridization to test the 4 cancer cancer cell-free DNA tumor markers in the blood sample, and use PCR to test the blood sample 2
  • Two kinds of cancer cell-free non-coding RNA tumor markers using purchased immunofluorescence method to detect the concentration of two kinds of cancer autoantibodies in blood samples. Flow fluorescence method was used to detect the concentration of three cancer-related inflammatory factors in blood samples, and liquid-mass spectrometry was used to detect two cancer-related metabolite markers in urine.
  • chemiluminescence kits to test the concentration of 5 cancer protein markers in blood samples (carcinoembryonic antigen-associated cell adhesion molecule 1 (CEACAM1), osteopontin (Osteopontin, OPN), leucine-rich ⁇ 2- Glycoprotein 1 (LRG1), human matrix metalloproteinase inhibitor 1 (TIMP1), intercellular adhesion molecule-1 (ICAM-1)), 7 kinds of cancer cell-free non-coding RNA tumor markers (miR) in blood samples were tested by PCR -486-5p, miR-145, miR-150, miR-223, miR-636, miR-122, miR-505), use the purchased fluorescence in situ hybridization method to detect the concentration of 4 kinds of cancer exosomes in the blood sample ( miR-1246, miR-4644, miR-3976, miR-4306), the concentration of diacetylspermine (DAS) metabolite markers in urine was detected by liquid-mass
  • colorectal cancer protein markers CEA, CA50, IGFBP2, LRG1, MAPR1, CA24-2, M2-PK
  • 6 kinds of colorectal cancer molecular markers miR-150, miR-130a, miR-195-5p, miR-29a, miR-223, miR-224
  • concentration of colorectal cancer autoantibodies APE1, P53-AAbs, NY-ESO-1AAbs, MAPRI1AAbs
  • was used to detect 13 kinds of colorectal cancer-related exosomes ⁇ Np73, CRNDE-h, CD24, A33, CD147, circ-KLDHC10, circRTN4, CircFAT1, circARHGAP5, MAGEA3, CRNDE-h, miR-193a, miR-17-92a).
  • chemiluminescence kits to test the concentration of six breast cancer protein markers (CA15-3, TIMP-1, OPN, CEACAM6, CEA, IGFBP1) in the blood sample, and test the blood sample by fluorescence in situ hybridization 9 breast cancer molecular markers (miR-21, miR-20a, miR-214, miR-181a, miR-1304, miR-141, miR-200a/c, miR-203, miR-210), use purchased Immunofluorescence method was used to detect the concentration of 13 breast cancer autoantibodies in blood samples (CTAG1B, CTAG2, TP53, RNF216, PPHLN1, PIP4K2C, ZBTB16, TAS2R8, WBP2NL, DOK2, PSRC1, MN1, TRIM21), and flow fluorescence method to detect urine Or 11 types of breast cancer-related exosomes in the blood (miR-27a, miR-451, miR-21-5p, miR-21, miR-221,
  • chemiluminescence detection kit to test the concentration of 7 lung cancer protein markers in the blood sample (Pro-SFTBP, CEA, CA125, SCC-Ag, CYFRA21-1, Pro-GRP, NSE), using fluorescence in situ hybridization Or sequencing method to test the concentration of 9 lung cancer molecular markers in blood samples (EGFR, AKT1, ALK, HER2, KRAS, BRAF, pIK3CA, ROS1, BRCA1/2), and use flow fluorescence method to detect 4 lung cancer-related inflammatory factors in blood samples Concentrations (IL-6, IL-10, S100, IL-13), using standard LC/MS methods to detect the concentration of two lung cancer-related metabolite markers (8-hydroxydeoxyguanosine, diacetyl Spermine (DAS)).
  • DAS diacetyl Spermine
  • multi-dimensional joint and combined tumor detection has higher sensitivity and specificity than single or several types of detection.
  • the sensitivity can reach 99% and the specificity is 100%. It is superior to the cancer diagnostic markers on the market.

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Abstract

一种构建体外检测癌症的数学模型的方法及应用，所述方法包括从样本中获得至少两种癌症标志物的浓度，对测定的每个标志物的浓度值进行logistic回归，将检测得到的浓度代入到logistic回归模型中，得到分析结果，使用每个标志物的浓度和logistic回归分析结果进行综合肿瘤判断分析。

Description

构建体外检测癌症的数学模型的方法及其应用
相关申请的交叉引用
本申请要求于2020年5月29日提交中国专利局，申请号为2020104825386，发明名称为“构建体外检测癌症的数学模型的方法及其应用”的中国专利申请的优先权，其全部内容通过引用结合在本申请中。
技术领域
本申请涉及医疗诊断技术领域，具体地涉及一种构建体外检测癌症的数学模型的方法。
背景技术
近年来，随着人们生活节奏加快，生活压力变大，饮食安全卫生不良，环境污染等原因导致癌症发病率不断攀升，因为肿瘤细胞具有无限繁殖的特点，如果没有及时发现并加以控制，肿瘤不断恶化，最终导致病入膏肓回天乏术。反之，若及时发现肿瘤，进行治疗，那么就能扼住癌症的“咽喉”。
随着科技的发展，肿瘤的检测技术不断提升，方法逐渐多元化，主流的肿瘤检测方法有以下几种：肿瘤标记物、影像学检查、组织活检、液体活检等。
一个理想的标志物应有很高的特异性以保证来自非癌症病人的样品不会出现假阳性。而这一点正是大多数正在使用的肿瘤标志物的最大缺点。为使检测的灵敏度达到足够查出较大比例患者的程度，上限参考值需要设定到低得可能使为数不少接受检测的健康人也出现阳性。肿瘤标志物检测的临床(诊断)灵敏度指的是癌症病人具有升高的肿瘤标志物结果的百分比。虽然某一项检测在多数恶性肿瘤病人中有升高结果，但如果它在即使一个较小比例的非肿瘤病人(非特异性)中也有升高结果的话，那么它若用于筛查，就会有高得无法接受的假阳性结果(由于在人口中相对较低的癌症发生率所致)。
大多数现在使用的标志物的另一个缺点是它们不只是在一种癌症中有升高，因此它们没有被用作确切地认定肿瘤类型。甚至PSA(前列腺特异性抗原)也不像我们从前认为的只在前列腺中表达。许多研究表明其他一些组织也表达这种蛋白，包括尿道周腺、女性乳腺、与性别无关的胰腺和唾液腺。灵敏度和特异性的关键是找到一个分析物在特定浓度下在健康人 和患者之间没有重叠——但这样的肿瘤标志物是不存在的。
理想肿瘤标志物的特征远远超过了现行检测项目的能力。虽然一些标志物在复发的早期检测和监控治疗功效方面做得较好，但它们的灵敏度和特异性仍存在着存在严重不足。另外，尽管肿瘤标志的检测过程简便且符合成本效益，但免疫测定的局限性也是一大缺陷。尽管肿瘤标志物的使用已很普遍而且它们的确提供了许多信息，但需要注意它们的局限性。
对付肿瘤最好的策略是早期诊断和联合诊断。可使治愈率提高到83％。目前，早期诊断癌症的最新，最有效的方法是通过验血寻找肿瘤标志物，特别是其中的蛋白标志。
对于肿瘤检测诊断在更多的情况下，一个指标是远远不够的。在涉及多个指标的案例中，我们还需要考虑参数整合的问题。在最早的一些尝试中，人们主要综合的是ctDNA与蛋白标志物。举例来说，CA19-9、TIMP1、以及LRG1等蛋白标志物，辅以对KRAS基因突变的检测，能够找到肿瘤可切除的前列腺癌患者，且检测率要高于仅使用ctDNA的方法。
本发明提供一种多维度组合的体外诊断肿瘤的方法，把肿瘤相关的蛋白标志物、代谢物、无细胞DNA、无细胞非编码RNA、自身抗体、炎症因子和生长因子、循环肿瘤细胞、外泌体等联合检测，提高肿瘤检测的灵敏度和特异性。
发明内容
本申请的主要目的在于提一种构建体外检测癌症的数学模型的方法，以提高临床检测肿瘤的灵敏度和特异性，目前肿瘤检测没有一种标志物可以同时以非常高的灵敏度和特异性结果诊断肿瘤，大部分肿瘤采用联检形式，但都是采用分子诊断或免疫诊断检测一种或同种类型的几种标志物，没有把各种维度的检测组合起来，为加强预测的精准度，最好是连横合纵、内外兼顾结合：把代谢物、外泌体、分子诊断、免疫诊断结合起来，此为本发明的目的。
本申请提供了一种构建体外检测癌症的数学模型的方法，所述方法包括从样本中获得至少两种癌症标志物的浓度，首先对测定的每个标志物的浓度值进行logistic回归，得到回归模型，将检测得到的浓度代入到logistic回归模型中，得到分析结果，使用每个标志物的浓度和logistic回归分析结果进行综合癌症判断分析。
优选地，所述方法包括从样本中获得三种癌症标志物的浓度，对测定的每个标志物的浓度值进行logistic回归，将检测得到的浓度代入到logistic回归模型中，得到分析结果。
优选地，所述方法包括从样本中获得四种癌症标志物的浓度，对测定的每个标志物的浓度值进行logistic回归，将检测得到的浓度代入到logistic回归模型中，得到分析结果。
优选地，所述癌症标志物包括以下大类中的至少一类：
癌症蛋白标志物、癌症代谢物标志物、无细胞DNA肿瘤标志物、无细胞非编码RNA标志物、癌症自身抗体、癌症相关炎症因子和/或生长因子、循环肿瘤细胞以及癌症相关外泌体。
优选地，所述癌症蛋白标志物为肺癌蛋白标志物、乳腺癌蛋白标志物、结直肠癌蛋白标志物的一种。
优选地，无细胞非编码RNA标志物为miR-486-5p，miR-145，miR-150，miR-223，miR-636，miR-122，miR-505。
优选地，所述logistic回归的公式为:
Logit(P)＝C+∑ n i＝1αi＊标志物浓度i
其中Logit(P)为上述同一类或不同类癌症标志物的logistic回归模型结果，C为回归得到的自然常数，α为回归分析得到的每个标志物的系数，为自然数，标志物浓度i为同一大类或不同大类中的标志物浓度，n为大于等于2的整数。
优选地，检测的样本包括：人体或动物体组织、血样、尿液、唾液、毛发、体液、粪便中的任何一种或多种。
优选地，检测技术方法包括放射方法、免疫方法、荧光方法、流式荧光、胶乳比浊法、生化法、酶法、PCR方法、测序法、杂交法、气质联用法、液质联用法、层析法、化学发光方法、磁电、光电转换方法其中的一种或多种。
根据本申请的另一个方面，提供使用所述的构建体外检测癌症的数学模型的方法得到数学模型在预测样本主体患癌风险中的应用，当根据所述数学模型得到计算分析结果的值大于等于或小于等于某一值时，认为所述样本的主体具有癌症风险。
本申请具有以下优点：肿瘤不同维度，不同种类组合连横合纵，内外兼顾的检测，克服市场上一种标志物或一个维度的检测灵敏度和特异性不高等缺点，极大提高诊断肿瘤的精准度和准确性，可以替代传统的CT或活检穿刺等有创诊断，为患者早期诊断肿瘤带来福音。
具体实施方式
为了使本技术领域的人员更好地理解本申请方案，对本申请实施例中的技术方案进行清楚、完整地描述，显然，所描述的实施例仅仅是本申请一部分的实施例，而不是全部的实施例。基于本申请中的实施例，本领域普通技术人员在没有做出创造性劳动前提下所获得的所有其他实施例，都应当属于本申请保护的范围。
需要说明的是，在不冲突的情况下，本申请中的实施例及实施例中的特征可以相互组合。
实施例中所用的检测方法学，可以是购买的试剂检测试剂盒或自制试剂盒。
实施例1
用购买的化学发光方法检测试剂盒，测试血样中3种肿瘤蛋白标志物浓度，用荧光原位杂交法测试血样中4种无细胞DNA肿瘤标志物，用流式荧光方法检测血样中3种肿瘤相关的炎症因子浓度，用标准的液质联用方法检测尿液中2种肿瘤相关代谢物标志物。
把上述相关标志物的测试浓度先进行logistics回归分析得到Logit(P)＝常数+λ1*P1+λ2*P2+η3*P3+η4*P4……
再测试未知血样各个标志物浓度，代入到回归模型中，根据计算得到的Logit(P)及回归模型Logit(P)值的判断标准，综合诊断是否患癌的情况及癌症的风险。
实施例2
用购买的或自制的化学发光方法试剂盒，测试血样中3种肺癌蛋白标志物浓度，用荧光原位杂交法测试血样中4种肺癌无细胞DNA肿瘤标志物，用流式荧光方法检测血样中3种肺癌相关的炎症因子浓度，用液质联用方法检测尿液中1种肺癌相关代谢物标志物。
把上述相关标志物的测试浓度先进行logistics回归分析得到Logit(P)＝常数+λ1*P1+λ2*P2+η3*P3+η4*P4……
再测试未知血样各个标志物浓度，代入到回归模型中，根据计算得到的Logit(P)及回归模型Logit(P)值的判断标准，综合诊断是否患癌的情况及癌症的风险。
实施例3
用自制的化学发光方法测试血样中3种肿瘤蛋白标志物浓度和2种肿瘤相关代谢物标志物，用PCR法测试血样中2种无细胞非编码RNA肿瘤标志物，用购买的免疫荧光方法检测血样中2种自身抗体的浓度。
把上述相关标志物的测试浓度先进行logistics回归分析得到Logit(P)＝常数+λ1*P1+λ2*P2+η3*P3+η4*P4……
再测试未知血样各个标志物浓度，代入到回归模型中，根据计算得到的Logit(P)及回归模型Logit(P)值的判断标准，综合诊断是否患癌的情况及癌症的风险。
实施例4
用购买的或自制的化学发光方法试剂盒，测试血样中3种癌症蛋白标志物浓度，用荧光原位杂交法测试血样中4种癌症癌无细胞DNA肿瘤标志物，用PCR法测试血样中2种癌症无细胞非编码RNA肿瘤标志物，用购买的免疫荧光方法检测血样中2种癌症自身抗体的浓度。用流式荧光方法检测血样中3种癌症相关的炎症因子浓度，用液质联用方法检测尿液中2种癌症相关代谢物标志物。
把上述相关标志物的测试浓度先进行logistics回归分析得到Logit(P)＝常数+λ1*P1+λ2*P2+η3*P3+η4*P4……
再测试未知血样各个标志物浓度，代入到回归模型中，根据计算得到的Logit(P)及回归模型Logit(P)值的判断标准，综合诊断是否患癌的情况及癌症的风险。
实施例5
用购买的或自制的化学发光方法试剂盒，测试血样中5种癌症蛋白标志物浓度(癌胚抗原相关细胞黏附分子1(CEACAM1)、骨桥蛋白(Osteopontin,OPN)、富亮氨酸α2-糖蛋1(LRG1)、人基质金属蛋白酶抑制剂1(TIMP1)、细胞间黏附分子-1(ICAM-1))，用PCR法测试血样中7种癌症无细胞非编码RNA肿瘤标志物(miR-486-5p，miR-145，miR-150，miR-223，miR-636，miR-122，miR-505)，用购买的荧光原位杂交法检测血样中4种癌症外泌体的浓度(miR-1246，miR-4644，miR-3976，miR-4306)，用液质联用方法检测尿液中二乙酰精胺(DAS)代谢物标志物浓度。
把上述相关标志物的测试浓度先进行logistics回归分析得到Logit(P)＝常数+λ1*P1+λ2*P2+η3*P3+η4*P4……
再测试未知血样各个标志物浓度，代入到回归模型中，根据计算得到的Logit(P)及回归模型Logit(P)值的判断标准，综合诊断是否患癌的情况及癌症的风险。
实施例6
结直肠癌蛋白标志物为CEA、IGFBP2、LRG1、MAPRE1、CA24-2、M2-PK、DKK3，结直肠癌分子诊断标志物为APC、E cadherin、BAT-26、Bcl-2、DAPK1、EGFR、MMP9，结直肠癌相关DNA甲基化标志物为hMLH1、CDKN2A(p16)、CDH4、HLTF、ALX4、HPP1、TPEF、RUNX3、RASSF1A，获得这些标志在样本的浓度值，进行自然对数或常用对数转换，经logistic回归分析，剔除无贡献的标志物后，得到的回归模型为：Logit(P)＝-7.421+1.051*Ln(CEA)+0.821*Ln(IGFBP2)+0.712*Ln(LRG1)+0.621*Ln(MAPRE1)+0.934*Ln(CA24-2)+0.652*Ln(M2-PK)+1.165*Ln(APC)+0.712*Ln(E cadherin)+0.606*Ln(Bcl-2)+0.425*Ln(MMP9)+0.754*Ln(hMLH1)+0.528*Ln(CDKN2A)+0.754*Ln(RUNX3)+0.364*Ln(RASSF1A)，其中Ln为自然对数或常用对数。
再测试未知血样各个标志物浓度，代入到上述回归模型中，根据计算得到的Logit(P)及回归模型Logit(P)值的判断标准，综合诊断是否患癌的情况及癌症的风险。
实施例7
用购买的或自制的化学发光方法试剂盒，测试血样中7种结直肠癌蛋白标志物浓度(CEA、 CA50、IGFBP2，LRG1、MAPRE1、CA24-2、M2-PK)，用荧光原位杂交法测试血样中6种结直肠癌分子标志物(miR-150、miR-130a、miR-195-5p、miR-29a、miR-223、miR-224)，用购买的免疫荧光方法检测血样中4种结直肠癌自身抗体的浓度(APE1、P53-AAbs、NY-ESO-1AAbs、MAPRE1AAbs)，用流式荧光方法检测尿液或血液中13种结直肠癌相关外泌体(ΔNp73、CRNDE-h、CD24、A33、CD147、circ-KLDHC10、circRTN4、CircFAT1、circARHGAP5、MAGEA3、CRNDE-h、miR-193a、miR-17-92a)。
把上述相关标志物的测试浓度先进行logistics回归分析得到Logit(P)＝常数+λ1*P1+λ2*P2+η3*P3+η4*P4……
再测试未知血样各个标志物浓度，代入到回归模型中，根据计算得到的Logit(P)及回归模型Logit(P)值的判断标准，综合诊断是否患结直肠癌的情况及结直肠癌风险。
Figure PCTCN2020127727-appb-000001
实施例8
用购买的或自制的化学发光方法试剂盒，测试血样中6种乳腺癌蛋白标志物浓度(CA15-3、TIMP-1、OPN、CEACAM6、CEA、IGFBP1)，用荧光原位杂交法测试血样中9种乳腺癌分子标志物(miR-21,miR-20a、miR-214、miR-181a、miR-1304、miR-141、miR-200a/c、miR-203、miR-210)，用购买的免疫荧光方法检测血样中13种乳腺癌自身抗体的浓度(CTAG1B、CTAG2、TP53、RNF216、PPHLN1、PIP4K2C、ZBTB16、TAS2R8、WBP2NL、DOK2、PSRC1、MN1、TRIM21)，用流式荧光方法检测尿液或血液中11种乳腺癌相关外泌体(miR-27a、miR-451、miR-21-5p、miR-21、miR-221、TGF-β1、HMGB1、CagA、GKN1、UBR2、TRIM3)，用流式荧光方法检测尿液或血液中7种相关炎症因子和生长因子：(CRP、Ch17CEP、sHER2、MAD1L1、IL-6、TNF、TGF-β1)
把上述相关标志物的测试浓度先进行logistics回归分析得到Logit(P)＝常数+λ1*P1+λ2*P2+η3*P3+η4*P4……
再测试未知血样各个标志物浓度，代入到回归模型中，根据计算得到的Logit(P)及回归 模型Logit(P)值的判断标准，综合诊断是否患乳腺癌的情况及乳腺癌风险。
Figure PCTCN2020127727-appb-000002
实施例9
用购买的化学发光方法检测试剂盒，测试血样中7种肺癌蛋白标志物浓度(Pro-SFTBP、CEA、CA125、SCC-Ag、CYFRA21-1、Pro-GRP、NSE)，用荧光原位杂交法或测序法测试血样中9种肺癌分子标志物浓度(EGFR、AKT1、ALK、HER2、KRAS、BRAF、pIK3CA、ROS1、BRCA1/2)，用流式荧光方法检测血样中4种肺癌相关的炎症因子浓度(IL-6、IL-10、S100、IL-13)，用标准的液质联用方法检测尿液或血液中2种肺癌相关代谢物标志物浓度(8-羟基脱氧鸟苷、二乙酰精胺(DAS))。
把7种肺癌蛋白标志物的浓度、9种肺癌分子标志物浓度、4种肺癌相关的炎症因子浓度、2种肺癌相关代谢物标志物浓度、进行logistics回归分析得到Logit(P)＝常数+λ1*P1+λ2*P2+η3*P3+η4*P4……
测试未知血样各个标志物浓度，代入到回归模型中，根据计算得到的Logit(P)及回归模型Logit(P)值的判断标准，综合诊断是否患肺癌的情况及肺癌风险。
Figure PCTCN2020127727-appb-000003
经过试验研究发现，多维度的联合，组合方式肿瘤检测，比单独的一种或几种种类型的检测，有更高的灵敏度和特异性，灵敏度可以达到99％，特异性100％，远远优于市场上的癌 症诊断标志物。
以上所述仅为本申请的优选实施例而已，并不用于限制本申请，对于本领域的技术人员来说，本申请可以有各种更改和变化。凡在本申请的精神和原则之内，所作的任何修改、等同替换、改进等，均应包含在本申请的保护范围之内。

Claims (10)

  1. 一种构建体外检测癌症的数学模型的方法，其特征在于，所述方法包括从样本中获得至少两种癌症标志物的浓度，对测定的每个标志物的浓度值进行logistic回归，将检测得到的浓度代入到logistic回归模型中，得到分析结果，使用每个标志物的浓度和logistic回归分析结果进行综合癌症判断分析。
  2. 根据权利要求1所述的构建体外检测癌症的数学模型的方法，其特征在于，所述方法包括从样本中获得三种癌症标志物的浓度，对测定的每个标志物的浓度值进行logistic回归，将检测得到的浓度代入到logistic回归模型中，得到分析结果。
  3. 根据权利要求1所述的构建体外检测癌症的数学模型的方法，其特征在于，所述方法包括从样本中获得四种癌症标志物的浓度，对测定的每个标志物的浓度值进行logistic回归，将检测得到的浓度代入到logistic回归模型中，得到分析结果。
  4. 根据权利要求1所述的构建体外检测癌症的数学模型的方法，其特征在于，所述癌症标志物包括以下大类中的至少一类：
    癌症蛋白标志物、癌症代谢物标志物、无细胞DNA肿瘤标志物、无细胞非编码RNA标志物、癌症自身抗体、癌症相关炎症因子和/或生长因子、循环肿瘤细胞以及癌症相关外泌体。
  5. 根据权利要求4所述的构建体外检测癌症的数学模型的方法，其特征在于，所述癌症蛋白标志物为肺癌蛋白标志物、乳腺癌蛋白标志物、结直肠癌蛋白标志物的一种。
  6. 根据权利要求4所述的构建体外检测癌症的数学模型的方法，其特征在于，无细胞非编码RNA标志物为miR-486-5p，miR-145，miR-150，miR-223，miR-636，miR-122，miR-505。
  7. 根据权利要求2所述的构建体外检测癌症的数学模型的方法，其特征在于，所述logistic回归的公式为：
    Logit(P)＝C+∑ n i＝1αi*标志物浓度i
    其中Logit(P)为上述同一类或不同类癌症标志物的logistic回归模型结果，C为回归得到的自然常数，α为回归分析得到的每个标志物的系数，为自然数，标志物浓度i为同一大类或不同大类中的标志物浓度，n为大于等于2的整数。
  8. 根据权利要求1所述的构建体外检测癌症的数学模型的方法，其特征在于检测的样本包括：人体或动物体组织、血液、尿液、唾液、体液、毛发、粪便中的任何一种或多种。
  9. 根据权利要求1所述的构建体外检测癌症的数学模型的方法，其特征在于检测技术方法包括放射方法、免疫方法、荧光方法、流式荧光、胶乳比浊法、生化法、酶法、PCR方法、测序法、杂交法、气质联用法、液质联用法、层析法、化学发光方法、磁电、光电转换方法 其中的一种或多种。
  10. 使用权利要求1-9中任意一项所述的构建体外检测癌症的数学模型的方法得到数学模型在预测样本主体患癌风险中的应用，其特征在于，当根据所述数学模型得到计算分析结果的值大于等于或小于等于参考值或参考区间时，认为所述样本的主体具有患癌症的风险。
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