WO2020135422A1 - 健康风险评估方法 - Google Patents

健康风险评估方法 Download PDF

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WO2020135422A1
WO2020135422A1 PCT/CN2019/127934 CN2019127934W WO2020135422A1 WO 2020135422 A1 WO2020135422 A1 WO 2020135422A1 CN 2019127934 W CN2019127934 W CN 2019127934W WO 2020135422 A1 WO2020135422 A1 WO 2020135422A1
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micro
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PCT/CN2019/127934
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陈伟铭
康诗婷
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奎克生技光电股份有限公司
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Priority to EP19905657.3A priority Critical patent/EP3904535A4/en
Priority to US17/417,116 priority patent/US20220076840A1/en
Priority to JP2021534795A priority patent/JP2022514838A/ja
Publication of WO2020135422A1 publication Critical patent/WO2020135422A1/zh

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
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    • 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
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    • G—PHYSICS
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    • 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
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    • 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
    • G16H50/30—ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics for calculating health indices; for individual health risk assessment
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    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
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    • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
    • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
    • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
    • C12Q1/6883—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material
    • C12Q1/6886—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for diseases caused by alterations of genetic material for cancer
    • 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
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G16B25/10—Gene or protein expression profiling; Expression-ratio estimation or normalisation
    • 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
    • C—CHEMISTRY; METALLURGY
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    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/112—Disease subtyping, staging or classification
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    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers

Definitions

  • the invention relates to a health risk assessment method, in particular to a health risk assessment method through microRNA (miRNA) expression analysis.
  • miRNA microRNA
  • MicroRNA is a non-coding ribonucleic acid (non-coding RNA) with a length of about 18 to 25 nucleotides. It is highly retained during evolution and plays a role in intracellular regulation. Very important role. In 1993, miniature RNA was first discovered in C. elegans. One after another, more and more miniature ribonucleic acids have been found in humans or other species. At present, there are approximately 2,500 known mini-ribonucleic acids in human cells. These mini-ribonucleic acids have been shown to regulate mRNA expression of more than fifty percent of informational ribonucleic acids. In addition, abnormal microRNA expression has been shown to be closely related to the generation of many diseases, including cancer lesions, chronic diseases, and autoimmune diseases.
  • microribonucleic acid has been widely respected and is regarded as a new molecular detection target.
  • microribonucleic acid has also been proven to be secreted from cells into the blood and form protein-RNA complexes to ensure that it will not be degraded by RNase.
  • Such characteristics also become very valuable, making free microribonucleic acid in blood relatively easy to obtain, and can be used as a basis for early diagnosis of disease by detecting free microRNA expression (cell free miRNA profiling).
  • cell free miRNA profiling for example, different types of cancer have been proven to have unique free microRNA expression levels, or miRNA signatures, which can be used as a basis for early diagnosis of cancer.
  • cancer screening refers to the process of using tests, tests, or other methods to distinguish between cancers that may or may not have cancer.
  • patients can detect whether they are suffering from cancer through many symptoms or test results, but the most certain way to diagnose malignant tumors is to confirm the existence of cancer cells through pathological examination of biopsies or tissues obtained by surgery by pathologists. It is an intrusive detection method.
  • the detection of tumor markers refers to the detection of cancer by detecting changes in special proteins associated with malignant tumor cells.
  • the sensitivity and specificity of tumor marker detection is poor, and it is often detected when the tumor has grown to a considerable size or has metastasized to other organs.
  • the invention provides a health risk assessment method, which analyzes the expression level of micro ribonucleic acid to monitor and assess health risks in real time.
  • the health risk assessment method of the present invention includes the following steps. First, the microRNA expression database of healthy ethnic groups is established, and then, the microRNA expression in the plasma samples of the subjects is analyzed. Then, compare the microRNA expression data of the subject with the microRNA expression of the microRNA database of the healthy population, and find out the microRNA that is too high or too low in the plasma of the subject, To assess the health risks of the examinee.
  • the determination of health risk includes cancer or diabetes.
  • the miniature ribonucleic acid is classified into H, M, L, or Cn categories according to the expression level.
  • H is represented in the healthy population.
  • the frequency of ribonucleic acid expression is detected to be higher than 60%
  • the M class is represented in healthy groups
  • the frequency of such micro ribonucleic acid expression is detected from 20% to 60%
  • the L class is represented in healthy groups
  • the frequency of detection of such microribonucleic acid is less than 20%
  • the Cn class is represented in the healthy population. No expression of such microribonucleic acid is detected.
  • the microRNA is classified into U, D, N, or En according to the expression, and U represents the subject's The expression level of such micro-ribonucleic acid is higher than that of the healthy population, and the expression level of this type of micro-ribonucleic acid on behalf of the subject is lower than that of the healthy ethnic group.
  • the reference level of expression level, N type represents the expression level of this type of microribonucleic acid of the subject is between the reference level of expression level of such microribonucleic acid of the healthy population
  • the En type represents the type of microribonucleic acid of the subject.
  • each microribonucleic acid for the subject is classified into a first group, a second group, a third group, a fourth group, or a fifth group.
  • the second group, the third group, the fourth group, or the fifth group represents a red dot.
  • the first group represents that such microRNAs belong to both H and U categories
  • the second group represents such microRNAs belong to both.
  • Class M and U the third group represents this type of microRNAs belong to both Cn and U categories
  • the fourth group represents this type of microRNAs belong to both H and D categories
  • the fifth group represents this type of microRNAs Belongs to M category and D category.
  • the present invention provides an improved method for measuring nucleic acid samples, including the following steps. First, establish a micro-RNA expression database for healthy ethnic groups. Thereafter, the amount of microRNA expression in the plasma sample of the subject is analyzed. Then, compare the microRNA expression data of the subject with the microRNA expression of the microRNA database of the healthy population, and find out the microRNA that is too high or too low in the plasma of the subject, To assess the health risks of the examinee.
  • the method of establishing a miniature ribonucleic acid database of a healthy ethnic group is as follows. First, based on more than 30,000 articles, a mini-RNA information database related to diseases was established, and 167 mini-ribonucleic acids highly related to diseases were selected. Recruit more than 300 healthy subjects (not yet determined by the doctor to have cancer/diabetes/or other major diseases). After the doctor has assessed that there is no tumor risk, collect plasma samples to detect the expression level of 167 microRNAs in the plasma samples , Calculate the average and standard deviation of the expression level of each microRNA in the healthy group, and then calculate the normal range of the expression level of each microRNA in the healthy group to establish the expression level of the 167 microRNAs in the healthy group Database.
  • the 167 mini-ribonucleic acids that were highly correlated with the disease were selected as shown in Table 1 below.
  • the method for detecting micro ribonucleic acid in plasma includes the following steps:
  • 19G to 22G needle to 10ml of whole blood drawn into K 2 EDTA Vacutainer (K 2 EDTA BD Vacutainer tube) when the blood flows into the blood collection tube, immediately release the tourniquet.
  • the blood collection tube is gently mixed upside down for 5 to 8 times to ensure that the anticoagulant is fully effective. Keep the blood collection tube at room temperature, and the plasma separation step must be completed within one hour after blood collection.
  • Plasma samples were taken in the refrigerator at -80 degrees Celsius, placed on ice and thawed. After thawing, the experiment was carried out according to the operation manual provided by Qiagen miRNeasy Serum/Plasma Kit, and was reconstituted with 30 ⁇ l of Nuclease-free water.
  • the judgment of health risk may include cancer or diabetes, but the present invention is not limited to this, and may also include other diseases or risk factors that may adversely affect health.
  • the micro-ribonucleic acid can be classified into H, M, L, or Cn according to the expression level, and H is represented in the healthy group.
  • the frequency of expression is detected to be higher than about 60%
  • the M class is represented in healthy groups
  • the frequency of such micro RNA expression is detected is about 20% to 60%
  • the L class is represented in healthy groups
  • the frequency of detection of the expression level of such mini-ribonucleic acid is less than about 20%
  • the Cn class is represented in the healthy population, and no expression of such mini-ribonucleic acid is detected.
  • the microRNA can be classified into U, D, N, or En according to the expression
  • U represents the subject's
  • the expression level of mini RNA is higher than the reference interval of the expression level of such micro ribonucleic acid in healthy group, and the expression level of this type of micro ribonucleic acid on behalf of the subject is lower than that of the healthy group Reference interval
  • type N represents the expression level of this type of microribonucleic acid in the subject is between the reference level of the expression level of this type of microribonucleic acid in the healthy population
  • En type represents the expression level of this type of microribonucleic acid in the subject .
  • each microribonucleic acid for the subject is classified as a first ethnic group, a second ethnic group, a third ethnic group, a fourth ethnic group, or a fifth ethnic group.
  • each microribonucleic acid belongs to the first ethnic group, the second ethnic group , The third ethnic group, the fourth ethnic group or the fifth ethnic group, it represents a red dot
  • the first ethnic group represents this type of microRNAs belong to both H and U categories
  • the second ethnic group represents this type of microRNAs also belong to M Class III and Class U
  • the third group represents this type of microRNAs belong to both Cn and U categories
  • the fourth group represents this type of microRNAs belong to both categories H and D
  • the fifth group represents this type of microRNAs belong to both Class M and D.
  • the number of red dots is greater than or equal to 5 it means that the subject may have a health risk.
  • Example 1 For cancer risk assessment
  • the low-risk group means that the subject has not been diagnosed with any major disease.
  • the accuracy rate of high-risk groups successfully identified as having potential health risks is about 70%.
  • the present invention provides a non-invasive, early-assessment health risk assessment method, which analyzes the expression level of miniature ribonucleic acid in the plasma of the subject related to health risk factors, and then conducts it with the miniature ribonucleic acid database of the healthy population
  • health risks can be assessed immediately and efficiently, the convenience and diagnosis rate of existing cancer screening can be improved, and personalized professional health risk monitoring can be provided.

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Abstract

本发明提供一种健康风险评估方法，包括以下步骤。建立健康族群的微型核糖核酸表达量数据库。之后，分析受检者的血浆样品中的微型核糖核酸表达量。然后，将受检者的微型核糖核酸表达量数据与健康族群的微型核糖核酸数据库的微型核糖核酸表达量进行比较，并找出受检者血浆中表达量过高或过低的微型核糖核酸，以评估受检者的健康风险。

Description

健康风险评估方法 技术领域
本发明涉及一种健康风险评估方法，尤其涉及一种通过微型核糖核酸(microRNA，miRNA)表达分析的健康风险评估方法。
背景技术
微型核醣核酸(microRNA)是一个非编码的核醣核酸(non-coding RNA)，其长度约为18至25核苷酸，于演化过程中，高度地被保留下来，并且于细胞内的调控中扮演非常重要的角色。于西元1993年，微型核醣核酸首度于线虫(C.elegans)中被发现。陆陆续续，于人类或其它的物种中也发现了越来越多的微型核醣核酸。目前，人类细胞内中约有2,500个已知的微型核醣核酸，这些微型核醣核酸被证实能调控大于百分之五十信息核醣核酸表达量(mRNA expression)。此外，不正常的微型核醣核酸表达量(microRNA expression)已被证实与许多疾病的生成息息相关，其中也包括了癌症病变、慢性疾病、自体免疫疾病等。
于过去的数年中，微型核醣核酸已获得广大的推崇，且被看好能当作新型分子检测标靶。目前微型核醣核酸也已被证实能从细胞中分泌到血液，并形成蛋白核醣核酸复合物(protein-RNA complexes)，确保不会被核糖核酸酶(RNase)降解。这样的特征也变得非常有价值，使得血液中游离微型核醣核酸变得相当容易被取得，并可通过检测游离微型核醣核酸表达量(cell free miRNA profiling)来当作疾病初期诊断的依据。举例来说，不同类型的癌症已被证实各自拥有独特的游离微型核醣核酸表达量，或者被称为微型核醣核酸特征(miRNA signature)，可被利用来当作癌症初期诊断的依据。
一直以来，具有便利性及高诊断率的非侵入性疾病检测方法为医学界不断追求的目标。以癌症为例，为了提早找出潜在未发现与早期无症状的癌症，可进行癌症筛检(cancer screening)来达到此目的。癌症筛检是指利用检查、检验或其他方法，辨别可能罹患癌症或可能未罹患癌症的过程。
目前，病患可经由许多症状或检验结果来检测是否罹患癌症，但诊断恶性肿瘤最确定的方式就是经由病理医师对活体组织进行切片或经手术取得的组织做病理检测来证实癌细胞的存在，属于侵入式的检测方式。
此外，肿瘤标记检测是指通过检测与恶性肿瘤细胞相关的特殊蛋白质的变化来判断是否罹患癌症。然而，肿瘤标记检测的灵敏度及专一性不佳，往往在肿瘤已发展到相当大小或已经转移到其他器官时才能检测到。
基于上述，开发出一种非侵入性、早期评估的健康风险评估方法，对罹患疾病的健康风险进行及早的即时监控，为目前所需研究的重要课题。
发明内容
本发明提供一种健康风险评估方法，通过分析微型核糖核酸的表达量，以即时监控评估健康风险。
本发明的健康风险评估方法包括以下步骤。首先，建立健康族群的微型核糖核酸表达量数据库，之后，分析受检者的血浆样品中的微型核糖核酸表达量。然后，将受检者的微型核糖核酸表达量数据与健康族群的微型核糖核酸数据库的微型核糖核酸表达量进行比较，并找出受检者血浆中表达量过高或过低的微型核糖核酸，以评估受检者的健康风险。
在本发明的一实施例中，健康风险的判别包括癌症或糖尿病。
在本发明的一实施例中，针对健康族群的微型核糖核酸数据库，依据表达量将微型核糖核酸分为H类、M类、L类或Cn类，H类代表于健康族群中，此类微型核糖核酸表达量被检测出的频率为高于60％，M类代表于健康族群中，此类微型核糖核酸表达量被检测出的频率为20％至60％，L类代表于健康族群中，此类微型核糖核酸表达量被检测出的频率为低于20％，Cn类代表于健康族群中，此类微型核糖核酸没有检测到表达。
在本发明的一实施例中，针对受检者的微型核糖核酸表达量数据，依据其表达量将微型核糖核酸分为U类、D类、N类或En类，U类代表受检者的此类微型核糖核酸的表达量高于健康族群的此类微型核糖核酸的表达量参考区间，D类代表受检者的此类微型核糖核酸的表达量低于健康族群的此类微型核糖核酸的表达量参考区间，N类代表受检者的此类微型核糖核酸的表达量介于健康族群的此类微型核糖核酸的表达量参考区间，En类代表受检者 的此类微型核糖核酸没有检测到表达。
在本发明的一实施例中，针对受检者的各个微型核醣核酸分类为第一族群、第二族群、第三族群、第四族群或第五族群，当各个微型核醣核酸属于第一族群、第二族群、第三族群、第四族群或第五族群，则代表一个红点，第一族群代表此类微型核醣核酸同时属于H类及U类，第二族群代表此类微型核醣核酸同时属于M类及U类，第三族群代表此类微型核醣核酸同时属于Cn类及U类，第四族群代表此类微型核醣核酸同时属于H类及D类，第五族群代表此类微型核醣核酸同时属于M类及D类。
在本发明的一实施例中，当红点的数量大于或等于5时，代表受检者可能存在健康风险。
基于上述，提供一种非侵入性、早期评估的健康风险评估方法，分析受检者血浆中的微型核糖核酸表达量，再与健康族群的微型核糖核酸数据库进行比较，因此，能够即时且有效率地对健康风险进行评估，更可改善现有健康风险筛检的便利性及诊出率。
为让本发明的上述特征和优点能更明显易懂，下文特举实施例，并作详细说明如下。
具体实施方式
本发明提供一种改良的核酸样品测量方法，包括以下步骤。首先，建立健康族群的微型核糖核酸表达量数据库。之后，分析受检者的血浆样品中的微型核糖核酸表达量。然后，将受检者的微型核糖核酸表达量数据与健康族群的微型核糖核酸数据库的微型核糖核酸表达量进行比较，并找出受检者血浆中表达量过高或过低的微型核糖核酸，以评估受检者的健康风险。
在本实施例中，建立健康族群的微型核糖核酸数据库的方法如下。首先，依据三万多篇文献建立与疾病相关的微型核醣核酸信息库，并筛选出与疾病高度相关的167个微型核醣核酸。招收大于300位健康的受试者(尚未被医师判定已罹患癌症/糖尿病/或其它重大疾病)，经过医师评估没有肿瘤风险后，收集血浆检体，检测血浆检体内167个微型核醣核酸表达量，计算健康族群中每一个微型核醣核酸表达量的平均值和标准差，并据此统计健康族群中每一个微型核醣核酸表达量的正常范围，以建立健康族群对此167个微型核醣 核酸表达量的数据库。
在本实施例中，所筛选出的与疾病高度相关的167个微型核醣核酸如下方表1中所示。
表1
Figure PCTCN2019127934-appb-000001
Figure PCTCN2019127934-appb-000002
在本实施例中，检测血浆中微型核糖核酸的方法包括以下步骤：
1.采集血液样本
将抽血者皮肤以酒精擦拭采血部位，使用止血带用活结方式绑在采血部位上方5公分至15公分处。以19G至22G针头抽取10ml全血至K 2EDTA真空采血管(K 2EDTA BD Vacutainer tube)，当血液流入采血管后，应立即松开止血带。待抽血完成，立即将采血管轻轻上下颠倒混合5至8次，以确保抗凝剂完全发挥作用。将采血管置于室温下保存，在采血后一小时内须完成血浆分离步骤。
2.血浆分离方法
将采血管置于旋翼式转子(Swinging-Bucket Rotor)，以1200xg于室温下离心10分钟。离心完成后，将上清液取出至新的15ml离心管。将15ml离心管以pipette吸放5次确保混匀，再均分至1.5ml DNase/RNase-free eppendorf，以12,000xg于室温下离心10分钟。离心完成后，取出上清液至新的15ml离心管，避免取到1.5ml eppendorf底部的白色沉淀物。将上清液pipette吸放5次确保混匀，分装至1.5ml DNA LoBind Tubes(Eppendorf,22431021)，立即置于-80摄氏度冰箱保存。
3.微型核糖核酸萃取方法
于-80摄氏度冰箱取出血浆样本，置于冰上解冻，解冻后依照Qiagen miRNeasy Serum/Plasma Kit所提供的操作手册进行实验，以30μl Nuclease-free water进行回溶。
4.cDNA合成
取适量miRNA以Quarkbio microRNA Universal RT kit进行逆转录反应合成cDNA。
5.qPCR实验
取适量cDNA以Quarkbio miRSCAN
Figure PCTCN2019127934-appb-000003
所提供的操作手册进行qPCR实验。
在本实施例中，健康风险的判别可包括癌症或糖尿病，但本发明并不以 此为限，也可包含其他疾病或可能对健康造成不良影响的风险因子。
在本实施例中，针对健康族群的微型核糖核酸数据库，依据表达量可将微型核糖核酸分为H类、M类、L类或Cn类，H类代表于健康族群中，此类微型核糖核酸表达量被检测出的频率为高于约60％，M类代表于健康族群中，此类微型核糖核酸表达量被检测出的频率为约20％至60％，L类代表于健康族群中，此类微型核糖核酸表达量被检测出的频率为低于约20％，Cn类代表于健康族群中，此类微型核糖核酸没有检测到表达。
在本实施例中，针对受检者的微型核糖核酸表达量数据，依据其表达量可将微型核糖核酸分为U类、D类、N类或En类，U类代表受检者的此类微型核糖核酸的表达量高于健康族群的此类微型核糖核酸的表达量参考区间，D类代表受检者的此类微型核糖核酸的表达量低于健康族群的此类微型核糖核酸的表达量参考区间，N类代表受检者的此类微型核糖核酸的表达量介于健康族群的此类微型核糖核酸的表达量参考区间，En类代表受检者的此类微型核糖核酸没有检测到表达。
在本实施例中，针对受检者的各个微型核醣核酸分类为第一族群、第二族群、第三族群、第四族群或第五族群，当各个微型核醣核酸属于第一族群、第二族群、第三族群、第四族群或所述第五族群，则代表一个红点，第一族群代表此类微型核醣核酸同时属于H类及U类，第二族群代表此类微型核醣核酸同时属于M类及U类，第三族群代表此类微型核醣核酸同时属于Cn类及U类，第四族群代表此类微型核醣核酸同时属于H类及D类，第五族群代表此类微型核醣核酸同时属于M类及D类。当红点的数量大于或等于5时，代表受检者可能存在健康风险。
以下，通过实验例来详细说明上述实施例的健康风险评估方法。然而，下述实验例并非用以限制本发明。
实验例
为了证明本发明所提出的健康风险评估方法能够即时且有效率地对健康风险进行评估，以下特别作此实验例。
实例1：针对癌症风险评估
分析198例已知为低风险族群及癌症族群(经医师判定)的受试者，依上述实施例提到的判别方式判定每位受试者的红点数，得到以下表2所列出 的结果。在表2中，低风险族群是指受检者尚未确诊有任何重大疾病，癌症族群皆未经过治疗。在198个测试样品中，成功被认定具有潜在健康风险的高风险族群的准确率为约73％。
表2
Figure PCTCN2019127934-appb-000004
实例2：针对糖尿病风险评估
分析56例已知为低风险族群及糖尿病族群(经医师判定)的受试者，依上述实施例提到的判别方式判定每位受试者的红点数，得到以下表3所列出的结果。在表3中，低风险族群是指受检者尚未确诊有任何重大疾病。在56个测试样品中，成功被认定具有潜在健康风险的高风险族群的准确率为约70％。
表3
Figure PCTCN2019127934-appb-000005
综上所述，本发明提供一种非侵入性、早期评估的健康风险评估方法，分析受检者血浆中与健康风险因子相关的微型核糖核酸表达量，再与健康族群的微型核糖核酸数据库进行比较，因此，能够即时且有效率地对健康风险进行评估，更可改善现有癌症筛检的便利性及诊出率，提供个人化的专业健康风险监控。
虽然本发明已以实施例揭示如上，然其并非用以限定本发明，任何所属技术领域中技术人员，在不脱离本发明的精神和范围内，当可作些许的更改与润饰，故本发明的保护范围当视所附的权利要求所界定者为准。

Claims (6)

  1. 一种健康风险评估方法，包括：
    建立健康族群的微型核糖核酸表达量数据库；
    分析受检者的血浆样品中的微型核糖核酸表达量；以及
    将所述受检者的微型核糖核酸表达量数据与所述健康族群的微型核糖核酸数据库的微型核糖核酸表达量进行比较，并找出所述受检者血浆中表达量过高或过低的微型核糖核酸，以评估所述受检者的健康风险。
  2. 根据权利要求1所述的健康风险评估方法，其中所述健康风险的判别包括癌症或糖尿病。
  3. 根据权利要求1所述的健康风险评估方法，其中针对所述健康族群的微型核糖核酸数据库，依据各微型核糖核酸表达量被检测出的频率将各微型核糖核酸分为H类、M类、L类或Cn类，H类代表检测出的频率为高于60％，M类代表检测出的频率为20％至60％，L类代表检测出的频率为低于20％，Cn类代表此微型核糖核酸没有检测到表达。
  4. 根据权利要求3所述的健康风险评估方法，其中针对所述受检者的微型核糖核酸表达量数据，依据表达量将微型核糖核酸分为U类、D类、N类或En类，U类代表所述受检者的此类微型核糖核酸的表达量高于所述健康族群的此类微型核糖核酸的表达量参考区间，D类代表所述受检者的此类微型核糖核酸的表达量低于所述健康族群的此类微型核糖核酸的表达量参考区间，N类代表所述受检者的此类微型核糖核酸的表达量介于所述健康族群的此类微型核糖核酸的表达量参考区间，En类代表所述受检者的此类微型核糖核酸没有检测到表达。
  5. 根据权利要求4所述的健康风险评估方法，其中针对所述受检者的各个微型核醣核酸分类为第一族群、第二族群、第三族群、第四族群或第五族群，当各个微型核醣核酸属于所述第一族群、所述第二族群、所述第三族群、所述第四族群或所述第五族群，则代表一个红点，所述第一族群代表此类微型核醣核酸同时属于H类及U类，所述第二族群代表此类微型核醣核酸同时属于M类及U类，所述第三族群代表此类微型核醣核酸同时属于Cn类及U类，所述第四族群代表此类微型核醣核酸同时属于H类及D类，所述第五族群代表此类微型核醣核酸同时属于M类及D类。
  6. 根据权利要求5所述的健康风险评估方法，其中当所述红点的数量大于或等于5时，所述受检者存在健康风险。
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HAN ZE-PING ET AL: "Role of Circulating MicroRNA in Diabetes Mellitus", JOURNAL OF MODERN LABORATORY MEDICINE, vol. 29, no. 6, 30 November 2014 (2014-11-30), pages 1 - 5, XP055714453, ISSN: 1671-7414, DOI: :10.3969/j.issn.1671-7414.2014.06.001 *
HAN, ZE-PING ET AL: "Role of Circulating MicroRNA in Diabetes Mellitus", JOURNAL OF MODERN LABORATORY MEDICINE, vol. 29, no. 6, 30 November 2014 (2014-11-30), pages 1 - 5, XP055714453 *
See also references of EP3904535A4 *
XI CHEN ET AL: "Characterization of MicroRNAs in Serum: a Novel Class of Biomarkers for Diagnosis of Cancer and Other Diseases", CELL RESEARCH, vol. 18, no. 10, 2 September 2008 (2008-09-02), pages 997 - 1006, XP002714220, ISSN: 1001-0602, DOI: 10.1038/cr.2008.282 *

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TWI758670B (zh) 2022-03-21
EP3904535A1 (en) 2021-11-03
CN111354421A (zh) 2020-06-30
US20220076840A1 (en) 2022-03-10
TW202025176A (zh) 2020-07-01
JP2022514838A (ja) 2022-02-16

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