WO2023061241A1 - 免疫球蛋白a肾病t细胞诊断标志物 - Google Patents
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- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/48—Biological material, e.g. blood, urine; Haemocytometers
- G01N33/50—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
- G01N33/68—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids
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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
- 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
Definitions
- the present application relates to the technical field of nephropathy detection, in particular to immunoglobulin A nephropathy T cell diagnostic markers.
- Immunoglobulin A (IgA) nephropathy which is the most common primary glomerular disease, is caused by the deposition of IgA complexes in the kidneys, leading to local autoimmune reactions in the kidneys and renal tissue lesions. More than 30% of patients progress to end-stage renal disease (ESRD) 10-20 years after onset, making IgA nephropathy one of the most common causes of uremia.
- ESRD end-stage renal disease
- IgA nephropathy one of the most common causes of uremia.
- the gold standard for the diagnosis of IgA nephropathy is pathological tissue biopsy of renal biopsy.
- invasive renal biopsy has several disadvantages: (1) Renal biopsy cannot be used for early diagnosis and can only detect patients with established renal damage.
- Renal puncture is risky, because many patients have relative contraindications for renal puncture, or the hospital does not have the conditions for pathological diagnosis of renal puncture, so patients cannot obtain a clear diagnosis and receive targeted treatment.
- the medical cost of renal puncture is high, which is equivalent to one operation and needs to be hospitalized for one week. Therefore, there is an urgent need to develop non-invasive biomarkers that can help diagnose or judge the condition of IgA nephropathy.
- the biomarkers for the diagnosis of IgA nephropathy can be roughly divided into two categories: immunodiagnostic markers and gene diagnostic markers.
- Immunodiagnostic markers refer to proteins or antibodies
- genetic diagnostic markers refer to DNA detection, mRNA gene expression, and miRNA that regulate gene expression, including genetic mutations and genotyping of hereditary IgA nephropathy.
- Existing immunodiagnostic markers for IgA nephropathy usually have a specificity of 25%-75% and a sensitivity of 60%-90%.
- This application aims to solve at least one of the technical problems existing in the prior art. Therefore, the present application proposes a marker of immunoglobulin A nephropathy with good diagnostic value.
- the first aspect of the present application provides the application of a reagent for quantitatively detecting at least one of the following markers in the preparation of a diagnostic kit for IgA nephropathy: CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA and VEGFC.
- the pathogenesis of immunoglobulin A nephropathy is related to five gene axes (Axis).
- This application starts from the T cell immunity axis (T Cell Immunity Axis), based on T cell-specific genes or proteins, and from related mRNA gene expression data. After screening, the above-mentioned 8 markers are obtained. Quantitative detection of subjects based on at least one of these 8 markers can efficiently and accurately diagnose IgA nephropathy, and has good specificity and sensitivity.
- CD4 refers to T lymphocyte CD4 membrane glycoprotein (CD4membrane glycoprotein of T lymphocytes), which is a co-receptor of T cell receptors and can recognize antigens displayed by antigen-presenting cells in class II MHC molecules.
- CD8 is a cell surface glycoprotein present on most cytotoxic T lymphocytes that mediates efficient cell-cell interactions within the immune system.
- the CD8 antigen acts as a co-receptor with T cell receptors on T lymphocytes, recognizing antigens displayed by antigen-presenting cells in class I MHC molecules.
- CD8 is composed of a homodimer composed of two ⁇ chains or a heterodimer composed of one ⁇ and one ⁇ chain.
- CD8A encodes the CD8 ⁇ chain.
- GATA3 GATA Binding Protein 3
- GATA binding Protein 3 The protein encoded by GATA3 (GATA Binding Protein 3) belongs to the GATA family, which contains two GATA-type zinc fingers, and is an important regulator of T cell development.
- GATA3 GATA Binding Protein 3
- GZMA GRAnzyme A
- GZMA is a protease in the cytoplasmic granules of cytotoxic T cells and NK cells that activates caspase-independent apoptosis when entering target cells through the immune synapse.
- HDAC7 Histone Deacetylase 7
- FOXP3 histone deacetylase 7
- VEGFC Vascular Endothelial Growth Factor C
- PDGF/VEGF platelet-derived growth factor/vascular endothelial growth factor
- CCR3 (C-C Motif Chemokine Receptor 3) is a C-C type chemokine receptor that can bind and respond to a variety of chemokines, including CCL11, CCL26, MCP-3 (CCL7), MCP-4 (CCL13) and RANTES (CCL5). It is highly expressed in eosinophils and basophils and is also detected in TH1 and TH2 cells and airway epithelial cells.
- RORA RAR Related Orphan Receptor A
- RORA is a RAR-related orphan receptor A and a member of the nuclear hormone receptor NR1 subfamily. Downstream of IL6 and TGFB, and synergistically with RORC subtype 2, it is reflected in the Th differentiation of unidentified CD4+ helper T cells into Th17 and inhibition of Th1 differentiation.
- the reagent detects at the transcript level or protein level.
- the reagents are quantitatively detected by any one of second-generation sequencing, third-generation sequencing, fluorescent quantitative PCR, digital PCR, gene chips, mass spectrometry, electrophoresis, and immunoadsorption.
- the reagent quantitatively detects at least two, at least three, at least four, at least five, at least six of CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA and VEGFC, At least seven, at least eight.
- the reagent quantitatively detects at least one of CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA and VEGFC.
- the reagent quantitatively detects at least one of GATA3 and VEGFC.
- the reagent quantitatively detects any two of CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA and VEGFC. In some preferred embodiments, the reagent quantitatively detects any three of CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA and VEGFC. In some preferred embodiments, the reagent quantitatively detects any four of CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA and VEGFC. In some preferred embodiments, the reagent quantitatively detects any five of CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA and VEGFC.
- the reagent quantitatively detects any six of CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA and VEGFC. In some preferred embodiments, the reagent quantitatively detects any seven of CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA and VEGFC. In some of the preferred embodiments, the reagent quantitatively detects all eight of CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA and VEGFC.
- the second aspect of the present application provides a diagnostic kit for IgA nephropathy
- the diagnostic kit includes reagents for quantitatively detecting at least one of the following markers: CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA and VEGFC.
- the reagent detects at the transcript level or protein level.
- the reagents are quantitatively detected by any one of second-generation sequencing, third-generation sequencing, fluorescent quantitative PCR, digital PCR, gene chips, mass spectrometry, electrophoresis, and immunoadsorption. According to different detection requirements, samples can be quantitatively detected through different detection platforms or detection methods.
- the reagent quantitatively detects at least two, at least three, at least four, at least five, at least six, at least seven, and all eight of the above markers.
- a computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to cause a computer to perform the following operations:
- Step 1 Obtain information from the expression level of at least one of the following markers in a sample from a subject: CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA, and VEGFC.
- Step 2 performing mathematical correlation on the expression levels to obtain a score; the score is used to indicate the risk of IgA nephropathy of the subject.
- the subject refers to the person to be tested whose risk of IgA nephropathy is to be assessed
- the subject sample refers to the sample of the person to be tested that contains the information on the expression levels of the above markers, specifically including but not limited to peripheral blood samples , urine samples, tissue samples (such as puncture samples), etc.
- Performing mathematical correlation to obtain a score refers to obtaining the relationship between the risk of disease and the expression levels of these marker genes through methods such as modeling, and the risk of disease is reflected in the form of a score.
- the expression level is the transcription level or protein level of the marker. Depending on the source of the actual sample, gene expression can be detected at the transcription level or protein level.
- step 1 further includes normalizing the expression level. Standardization is used to further avoid errors in diagnostic results that may be caused.
- the operation further includes step 3: evaluating the subject's risk of IgA nephropathy according to the score.
- the score threshold for distinguishing normal people and patients can be obtained through the score difference between the patient group and normal people, and the risk of IgA nephropathy can be evaluated according to the relationship between the subject's score and the score threshold. For example, if the subject's score reaches or exceeds the set threshold, it is judged that the subject has a greater possibility of suffering from IgA nephropathy.
- an electronic device includes a processor and a memory, the memory stores a computer program that can run on the processor, and the processor implements the following operations when running the computer program :
- Step 1 obtaining information on the expression level of at least one of the following markers in the subject sample: CCR3, CD4, CD8A, GATA3, GZMA, HDAC7, RORA, and VEGFC;
- Step 2 mathematically correlating the expression levels to obtain a score; the score is used to indicate the risk of immunoglobulin A nephropathy of the subject.
- the memory can be used to store non-transitory software programs and non-transitory computer-executable programs, such as the marker screening method described in the embodiment of the present application or the IgA nephropathy of the subject Risks are assessed.
- the processor executes the non-transitory software program and instructions stored in the memory, so as to realize the above marker screening method or evaluate the risk of IgA nephropathy of the subject.
- the memory may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application program required by at least one function; the data storage area may store and execute the above-mentioned marker screening method.
- the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid-state storage devices.
- the memory may optionally include a memory that is remotely located relative to the processor, and these remote memories may be connected to the processor through a network. Examples of the aforementioned networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.
- the non-transitory software programs and instructions required to realize the above-mentioned marker screening method are stored in the memory, and when executed by one or more processors, the above-mentioned marker screening method is executed.
- the device embodiments described above are only illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place, or may be distributed to multiple network units. Part or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.
- Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cartridges, tape, magnetic disk storage or other magnetic storage devices, or can Any other medium used to store desired information and which can be accessed by a computer.
- communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media .
- FIG. 1 is a boxplot of 8 genetic diagnostic markers screened out in Example 1 of the present application against different sample types.
- Fig. 2 is the ROC curve obtained by modeling the 8 genes screened out in Example 1 of the present application as diagnostic markers alone.
- FIG. 3 is the ROC curve obtained by modeling GATA3 and VEGFC screened out in Example 1 of the present application as diagnostic markers.
- Fig. 4 is the ROC curve obtained by modeling VEGFC, CD8A and RORA screened in Example 1 of the present application as diagnostic markers.
- Fig. 5 is the ROC curve obtained by modeling CD8A, RORA, CCR3 and VEGFC screened in Example 1 of the present application as diagnostic markers.
- Fig. 6 is the ROC curve obtained by modeling VEGFC, HDAC7, CD4, RORA and GZMA screened in Example 1 of the present application as diagnostic markers.
- Fig. 7 is the ROC curve obtained by modeling GZMA, CD8A, HDAC7, RORA, VEGF and CCR3 screened in Example 1 of the present application as diagnostic markers.
- Fig. 8 is the ROC curve obtained by modeling GATA3, RORA, VEGFC, CD8A, CCR3, CD4 and GZMA screened in Example 1 of the present application as diagnostic markers.
- the embodiment of the present application relates to the screening of diagnostic markers.
- Previous studies have shown that mRNA gene expression has great potential for molecular diagnosis of nephropathy, and the pathogenesis of immunoglobulin A nephropathy may be related to T cell immune axis ) including some gene axes (Axis) related. Therefore, this protocol utilizes the screening of potential diagnostic markers based on T cell-specific gene or protein expression.
- the preselected genes associated with five types of T cells that may be involved in the pathogenesis of immunoglobulin A nephropathy are as follows:
- Th1 related genes CCL4, CCR2, CCR5, CXCR3, IFNG, IFNGR1, IFNGR2, IL2, IL20RA, JAK2, JAK3, STAT1, STAT4, TBR1, TNF;
- Helper T cell type 2 (Helper T Cell 2) Th2 related genes: BCL2, BCL2L1, CCR3, CCR4, CCR8, CD28, CXCL11, CXCL12, GATA3, IL10, IL13, IL33, IL4, IL4R, IL5, NFATC2IP, STAT6;
- Th17 related genes CCL20, IL17A, IL17B, IL17RA, IL17RB, IL6, IL6R, IL6ST, NOTCH1, NOTCH2, NOTCH3, NOTCH4, RORA, RORB, RORC, STAT3, VEGFA, VEGFB, VEGFC;
- Follicular Helper T Cell Tfh related genes BCL6, CCR6, CD3D, CD3E, CD3G, CD4, CD40, CD40LG, CD80, CD86, CD8A, CXCR5, ICOS, IL21R, PDCD1, PDCD1LG2;
- Regulatory T Cell Regulatory T Cell Treg related genes: GZMA, GZMH, HDAC7, IKZF4, IL23A, IL2RA, IL2RB, IL2RG, KAT5, KITLG, NFATC1, NFATC3, NFATC4, RELA, STAT5A, STAT5B, TGFB1, TNFRSF11A .
- GSE37460 and GSE93798 from Gene Expression Omnibus (GEO).
- GSE37460 contains 27 kidney tissue samples from healthy people and 27 IgA nephropathy patients, and GSE93798 includes 22 healthy people and 20 IgA nephropathy patient kidney tissue samples, each with more than 20,000 gene probes.
- Data standardization Data standardization is divided into two steps: (1) First, calculate the median of all gene expression levels for each sample, and the normalized expression is the original expression level minus the calculated median. This standardization method removes the difference in the amount of sample mRNA input; (2) In order to facilitate the integration of the two data sets, each data set is standardized by quartiles (Interquartile), that is, each sample (or gene) The first and third quartiles are linearly mapped to 0 and 1.
- This embodiment adopts the multiple iterative linear regression method to establish a model (it can be understood that other supervised machine learning nonlinear algorithms can also be used instead, such as classic SVM, PCA, neural network, etc. or deep learning algorithms instead):
- Step 2 Merge all the genes selected in this way. If the total number is greater than S, repeat the first step for the merged genes until the number of genes after merging does not exceed S.
- the above samples were randomly divided into two data subsets, and one of the subsets was used to establish linearity with any two or more of CD4, CD8A, CCR3, GATA3, GZMA, HDAC7, RORA and VEGFC as input variables.
- the regression model use another subset as the verification data set, draw the ROC graph and calculate the AUC, repeat 20 times and then sort. The maximum, median and minimum values are shown in Table 2.
- ROC curves are shown in Figures 3 to 8. From Figures 3 to 8 combined with Table 2, it can be seen that among the above markers, two, three, four, five, Optional six, optional seven as diagnostic markers of IgA nephropathy have good diagnostic value.
- This embodiment provides a device for assessing the risk of IgA nephropathy.
- the device includes a processor and a memory, and the memory stores a computer program that can be executed by the processor.
- the method of using this device to assess the risk of IgA nephropathy for subjects is as follows:
- a detection device such as a standard qPCR platform
- CD4, CD8A, CCR3, GATA3, GZMA, RORA, VEGFC Send the extracted mRNA into a detection device (such as a standard qPCR platform) to perform quantitative data on the expression of 7 gene diagnostic markers provided in Example 1: CD4, CD8A, CCR3, GATA3, GZMA, RORA, VEGFC.
- the device uses clinical observations (e.g., proteinuria, eGFR, pathological grade of renal biopsy, 5- or 10-year risk of uremia, drug effectiveness prediction, drug resistance) as target variables
- clinical observations e.g., proteinuria, eGFR, pathological grade of renal biopsy, 5- or 10-year risk of uremia, drug effectiveness prediction, drug resistance
- This embodiment provides a kit, including reagents capable of quantifying the mRNA levels of CD4, CD8A, CCR3, GATA3, GZMA, HDAC7, RORA, and VEGFC, including reverse transcriptase, primers, Taq enzymes, fluorescent dyes, and the like.
- kits the kit includes a microfluidic chip, the microfluidic chip includes a liquid storage module, and the liquid storage module is respectively equipped with genes capable of quantifying GATA3, VEGFC, HDAC7, CD4, and CD8A.
- mRNA level reagents The reagent kit can be applied to the diagnosis of IgA nephropathy to achieve a more sensitive and accurate diagnosis.
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Abstract
一种免疫球蛋白A肾病T细胞诊断标志物。一种定量检测以下至少一种标志物的试剂在制备IgA肾病的诊断试剂盒中的应用:CCR3、CD4、CD8A、GATA3、GZMA、HDAC7、RORA和VEGFC。具有如下有益效果:免疫球蛋白A肾病的发病机理与五个基因轴(Axis)相关,从T细胞免疫轴(T Cell Immunity Axis)出发,基于T细胞特定的基因或蛋白,从相关mRNA基因表达数据中进行筛选,得到上述8个标志物,基于这8个标志物中至少一种对受试者进行定量检测都能够可以高效准确地诊断出是否患有IgA肾病,并且具有良好的特异性和灵敏度。
Description
本申请涉及肾病检测技术领域,尤其是涉及免疫球蛋白A肾病T细胞诊断标志物。
免疫球蛋白A(IgA)肾病,由IgA复合物在肾脏沉积,导致肾脏局部自身免疫反应,引起肾组织病变,是一种最常见的原发性肾小球疾病。超过30%的患者在发病10-20年后进展至终末期肾脏病(ESRD),使得IgA肾病成为引起尿毒症最常见的病因之一。目前IgA肾病诊断金标准为肾穿刺的病理组织活检,然而有创肾穿刺存在几个缺陷:(1)肾穿刺无法进行早期诊断,只能够检测发病的肾损伤已经形成的病人。(2)肾穿刺存在风险,因为许多病人存在肾穿刺相对禁忌症,或者医院不具备肾穿刺病理诊断的条件,而导致病人无法获得明确诊断并进行针对性的治疗。(3)肾穿刺医疗费用高,相当于一次手术,需要住院一个星期。因此临床上亟需开发有助于IgA肾病诊断或病情判断的无创性生物标志物。
IgA肾病诊断的生物标志物大致可以分为两类:免疫诊断标志物和基因诊断标志物。免疫诊断标志物是指蛋白质或抗体,而基因诊断标志物是指包含遗传性IgA肾病的基因突变和基因分型的DNA检测、mRNA基因表达、调控基因表达的miRNA等。现有的IgA肾病免疫诊断标志物通常特异性介于25%-75%,敏感性介于60%-90%。其中研究较多的有:(1)半乳糖缺陷IgA1(Gd-IgA1)分子;(2)针对Gd-IgA1的抗糖抗体;(3)IgA/C3比例,补体通路的补体C3;(4)所有IgA复合物的总信号。然而,这些免疫诊断标志物的特异性不高,因此,有必要通过新的方法找到更具诊断价值的标志物。
发明内容
本申请旨在至少解决现有技术中存在的技术问题之一。为此,本申请提出一种具有良好诊断价值的免疫球蛋白A肾病的标志物。
本申请的第一方面,提供定量检测以下至少一种标志物的试剂在制备IgA肾病的诊断试剂盒中的应用:CCR3、CD4、CD8A、GATA3、GZMA、HDAC7、RORA和VEGFC。
根据本申请实施例的应用,至少具有如下有益效果:
免疫球蛋白A肾病的发病机理与五个基因轴(Axis)相关,本申请从T细胞免疫轴(T Cell Immunity Axis)出发,基于T细胞特定的基因或蛋白,从相关mRNA基因表达数据中进行筛选,得到上述8个标志物,基于这8个标志物中至少一种对受试者进行定量检测都能 够可以高效准确地诊断出是否患有IgA肾病,并且具有良好的特异性和灵敏度。
其中,CD4是指T淋巴细胞CD4膜糖蛋白(CD4membrane glycoprotein of T lymphocytes),它是T细胞受体的辅助受体,能够识别抗原呈递细胞在II类MHC分子中显示的抗原。
CD8是一种细胞表面糖蛋白,存在于大多数细胞毒性T淋巴细胞上,介导免疫系统内有效的细胞间相互作用。CD8抗原作为与T淋巴细胞上的T细胞受体的共同受体,识别抗原呈递细胞在I类MHC分子中显示的抗原。CD8是由两个α链组成的同源二聚体或由一个α和一个β链组成的异二聚体组成。而CD8A编码CD8α链。
GATA3(GATA Binding Protein 3)所编码的蛋白质属于GATA家族,其含有两个GATA型锌指,是T细胞发育的重要调节因子。同时,作为与T细胞受体α和δ基因增强子结合的转录激活剂,与共有序列5’-AGATAG-3'结合,是免疫和炎症反应后Th2分化过程所必需的。
GZMA(Granzyme A)是细胞毒性T细胞和NK细胞的胞浆颗粒中的蛋白酶,当通过免疫突触进入靶细胞时,激活caspase非依赖性细胞凋亡。
HDAC7(Histone Deacetylase 7)是组蛋白脱乙酰酶7,它能够正向调节FOXP3的转录抑制活性,而FOXP3对于Treg的发育和抑制功能至关重要。
VEGFC(Vascular Endothelial Growth Factor C)是血管内皮生长因子C,该基因编码的蛋白质是血小板衍生的生长因子/血管内皮生长因子(PDGF/VEGF)家族的成员。能够促进胚胎期静脉和淋巴血管系统的血管生成,以及成人分化淋巴内皮的维护。
CCR3(C-C Motif Chemokine Receptor 3)是C-C型趋化因子受体,能够结合多种趋化因子并对其作出反应,包括CCL11、CCL26、MCP-3(CCL7)、MCP-4(CCL13)和RANTES(CCL5)。它在嗜酸性粒细胞和嗜碱性粒细胞中高度表达,也在TH1和TH2细胞以及气道上皮细胞中检测到。
RORA(RAR Related Orphan Receptor A)是RAR相关的孤儿受体A,是核激素受体NR1亚家族成员。在IL6和TGFB的下游,并且与RORC亚型2协同作用,体现在把未确定的CD4+辅助T细胞Th分化为Th17,抑制向Th1分化。
在本申请的一些实施方式中,试剂在转录水平或蛋白水平上进行检测。
在本申请的一些实施方式中,试剂通过二代测序、三代测序、荧光定量PCR、数字PCR、基因芯片、质谱、电泳、免疫吸附等其中的任一种进行定量检测。
在本申请的一些实施方式中,该试剂定量检测CCR3、CD4、CD8A、GATA3、GZMA、 HDAC7、RORA和VEGFC中的至少两种,至少三种,至少四种,至少五种,至少六种,至少七种,至少八种。
在本申请的一些实施方式中,该试剂定量检测CCR3、CD4、CD8A、GATA3、GZMA、HDAC7、RORA和VEGFC中的至少一种。
在本申请的一些实施方式中,该试剂定量检测GATA3、VEGFC中的至少一种。
在其中一些优选的实施方式中,该试剂定量检测CCR3、CD4、CD8A、GATA3、GZMA、HDAC7、RORA和VEGFC中的任意两种。在其中一些优选的实施方式中,该试剂定量检测CCR3、CD4、CD8A、GATA3、GZMA、HDAC7、RORA和VEGFC中的任意三种。在其中一些优选的实施方式中,该试剂定量检测CCR3、CD4、CD8A、GATA3、GZMA、HDAC7、RORA和VEGFC中的任意四种。在其中一些优选的实施方式中,该试剂定量检测CCR3、CD4、CD8A、GATA3、GZMA、HDAC7、RORA和VEGFC中的任意五种。在其中一些优选的实施方式中,该试剂定量检测CCR3、CD4、CD8A、GATA3、GZMA、HDAC7、RORA和VEGFC中的任意六种。在其中一些优选的实施方式中,该试剂定量检测CCR3、CD4、CD8A、GATA3、GZMA、HDAC7、RORA和VEGFC中的任意七种。在其中一些优选的实施方式中,该试剂定量检测CCR3、CD4、CD8A、GATA3、GZMA、HDAC7、RORA和VEGFC中的全部八种。
本申请的第二方面,提供IgA肾病的诊断试剂盒,该诊断试剂盒包括定量检测以下至少一种标志物的试剂:CCR3、CD4、CD8A、GATA3、GZMA、HDAC7、RORA和VEGFC。
在本申请的一些实施方式中,试剂在转录水平或蛋白水平上进行检测。
在本申请的一些实施方式中,试剂通过二代测序、三代测序、荧光定量PCR、数字PCR、基因芯片、质谱、电泳、免疫吸附等其中的任一种进行定量检测。根据不同的检测要求,可以对样本通过不同的检测平台或检测方法进行定量检测。
在本申请的一些实施方式中,试剂定量检测上述标志物中的至少两种,至少三种,至少四种,至少五种,至少六种,至少七种,全部八种。
本申请的第三方面,提供一种计算机可读存储介质,该计算机可读存储介质存储有计算机可执行指令,计算机可执行指令用于使计算机执行以下操作:
步骤1:获取来自来自受试者样本中的以下至少一种标志物的表达水平的信息:CCR3、CD4、CD8A、GATA3、GZMA、HDAC7、RORA和VEGFC。
步骤2:对表达水平进行数学关联以获得评分;评分用于指示受试者的IgA肾病的患病风险。
其中,受试者是指待评估IgA肾病的患病风险的待测人员,受试者样本是指待测人员的包含上述标志物的表达水平的信息的样本,具体包括但不限于外周血样本、尿样、组织样本(如穿刺样本)等。进行数学关联以获得评分是指通过诸如建模的方式得到患病风险与这些标志物基因的表达水平的关系,而患病风险则以评分的方式体现。
在本申请的一些实施方式中,表达水平为标志物的转录水平或蛋白水平。根据实际样本来源的不同,可以在转录水平或蛋白质水平上对基因的表达进行检测。
在本申请的一些实施方式中,步骤1还包括对表达水平进行标准化。通过标准化处理以进一步避免可能引起的诊断结果误差。
在本申请的一些实施方式中,操作还包括步骤3:根据评分对受试者的免疫球蛋白A肾病的患病风险进行评估。具体可以通过患者组与正常人之间评分的差异得到区分正常人和患者的评分阈值,根据受试者的评分与评分阈值之间的关系对IgA肾病的患病风险进行评估。例如,如果受试者的评分达到设定的阈值或比之更高,判断受试者有较大的可能患有IgA肾病。
本申请的第四方面,提供一种电子设备,该电子设备包括处理器和存储器,存储器上存储有可在处理器上运行的计算机程序,所述处理器在运行所述计算机程序时实现以下操作:
步骤1:获取来自受试者样本中的以下至少一种标志物的表达水平的信息:CCR3、CD4、CD8A、GATA3、GZMA、HDAC7、RORA和VEGFC;
步骤2:对所述表达水平进行数学关联以获得评分;所述评分用于指示受试者的免疫球蛋白A肾病的患病风险。
存储器作为一种非暂态计算机可读存储介质,可用于存储非暂态软件程序以及非暂态性计算机可执行程序,如本申请实施例描述的标志物筛选方法或对受试者的IgA肾病风险进行评估。处理器通过运行存储在存储器中的非暂态软件程序以及指令,从而实现上述的标志物筛选方法或对受试者的IgA肾病风险进行评估。
存储器可以包括存储程序区和存储数据区,其中,存储程序区可存储操作系统、至少一个功能所需要的应用程序;存储数据区可存储执行上述标志物筛选方法。此外,存储器可以包括高速随机存取存储器,还可以包括非暂态存储器,比如至少一个磁盘存储器件、闪存器件、或其他非暂态固态存储器件。在其中一些具体的实施方式中,存储器可选包括相对于处理器远程设置的存储器,这些远程存储器可以通过网络连接至该处理器。上述网络的实例包括但不限于互联网、企业内部网、局域网、移动通信网及其组合。
实现上述的标志物筛选方法所需的非暂态软件程序以及指令存储在存储器中,当被一个 或者多个处理器执行时,执行上述的标志物筛选方法。
以上所描述的装置实施例仅仅是示意性的,其中作为分离部件说明的单元可以是或者也可以不是物理上分开的,即可以位于一个地方,或者也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部模块来实现本实施例方案的目的。
本领域普通技术人员可以理解,上文中所公开方法中的全部或某些步骤、系统可以被实施为软件、固件、硬件及其适当的组合。某些物理组件或所有物理组件可以被实施为由处理器,如中央处理器、数字信号处理器或微处理器执行的软件,或者被实施为硬件,或者被实施为集成电路,如专用集成电路。这样的软件可以分布在计算机可读介质上,计算机可读介质可以包括计算机存储介质(或非暂时性介质)和通信介质(或暂时性介质)。如本领域普通技术人员公知的,术语计算机存储介质包括在用于存储信息(诸如计算机可读指令、数据结构、程序模块或其他数据)的任何方法或技术中实施的易失性和非易失性、可移除和不可移除介质。计算机存储介质包括但不限于RAM、ROM、EEPROM、闪存或其他存储器技术、CD-ROM、数字多功能盘(DVD)或其他光盘存储、磁盒、磁带、磁盘存储或其他磁存储装置、或者可以用于存储期望的信息并且可以被计算机访问的任何其他的介质。此外,本领域普通技术人员公知的是,通信介质通常包含计算机可读指令、数据结构、程序模块或者诸如载波或其他传输机制之类的调制数据信号中的其他数据,并且可包括任何信息递送介质。
本申请的附加方面和优点将在下面的描述中部分给出,部分将从下面的描述中变得明显,或通过本申请的实践了解到。
图1是本申请的实施例1筛选出的8个基因诊断标志物对不同样本类型的箱线图。
图2是本申请的实施例1筛选出的8个基因单独作为诊断标志物建模得出的ROC曲线。
图3是本申请的实施例1筛选出的GATA3和VEGFC作为诊断标志物建模得出的ROC曲线。
图4是本申请的实施例1筛选出的VEGFC、CD8A和RORA作为诊断标志物建模得出的ROC曲线。
图5是本申请的实施例1筛选出的CD8A、RORA、CCR3和VEGFC作为诊断标志物建模得出的ROC曲线。
图6是本申请的实施例1筛选出的VEGFC、HDAC7、CD4、RORA和GZMA作为诊断 标志物建模得出的ROC曲线。
图7是本申请的实施例1筛选出的GZMA、CD8A、HDAC7、RORA、VEGF和CCR3作为诊断标志物建模得出的ROC曲线。
图8是本申请的实施例1筛选出的GATA3、RORA、VEGFC、CD8A、CCR3、CD4和GZMA作为诊断标志物建模得出的ROC曲线。
以下将结合实施例对本申请的构思及产生的技术效果进行清楚、完整地描述,以充分地理解本申请的目的、特征和效果。显然,所描述的实施例只是本申请的一部分实施例,而不是全部实施例,基于本申请的实施例,本领域的技术人员在不付出创造性劳动的前提下所获得的其他实施例,均属于本申请保护的范围。
下面详细描述本申请的实施例,描述的实施例是示例性的,仅用于解释本申请,而不能理解为对本申请的限制。
在本申请的描述中,若干的含义是一个以上,多个的含义是两个以上,大于、小于、超过等理解为不包括本数,以上、以下、以内等理解为包括本数。如果有描述到第一、第二只是用于区分技术特征为目的,而不能理解为指示或暗示相对重要性或者隐含指明所指示的技术特征的数量或者隐含指明所指示的技术特征的先后关系。
本申请的描述中,参考术语“一个实施例”、“一些实施例”、“示意性实施例”、“示例”、“具体示例”、或“一些示例”等的描述意指结合该实施例或示例描述的具体特征、结构、材料或者特点包含于本申请的至少一个实施例或示例中。在本说明书中,对上述术语的示意性表述不一定指的是相同的实施例或示例。而且,描述的具体特征、结构、材料或者特点可以在任何的一个或多个实施例或示例中以合适的方式结合。
实施例1:标志物的筛选
本申请实施例涉及诊断标志物的筛选,在先的研究表明,mRNA基因表达对肾病进行分子诊断的巨大潜力,同时免疫球蛋白A肾病的发病机理可能与包括T细胞免疫轴(T Cell Immunity Axis)在内的一些基因轴(Axis)相关。因此,本方案利用基于T细胞特定的基因或蛋白表达情况来筛选潜在的诊断标志物。
预先选定的可能与免疫球蛋白A肾病发病机制相关的五类T细胞的相关基因如下:
1.辅助性T细胞1型(Helper T Cell 1)Th1相关基因:CCL4、CCR2、CCR5、CXCR3、IFNG、IFNGR1、IFNGR2、IL2、IL20RA、JAK2、JAK3、STAT1、STAT4、TBR1、TNF;
2.辅助性T细胞2型(Helper T Cell 2)Th2相关基因:BCL2、BCL2L1、CCR3、CCR4、CCR8、CD28、CXCL11、CXCL12、GATA3、IL10、IL13、IL33、IL4、IL4R、IL5、NFATC2IP、STAT6;
3.辅助性T细胞17型(Helper T Cell 17)Th17相关基因:CCL20、IL17A、IL17B、IL17RA、IL17RB、IL6、IL6R、IL6ST、NOTCH1、NOTCH2、NOTCH3、NOTCH4、RORA、RORB、RORC、STAT3、VEGFA、VEGFB、VEGFC;
4.滤泡辅助性T细胞(Follicular Helper T Cell)Tfh相关基因:BCL6、CCR6、CD3D、CD3E、CD3G、CD4、CD40、CD40LG、CD80、CD86、CD8A、CXCR5、ICOS、IL21R、PDCD1、PDCD1LG2;
5.调节性T细胞(Regulatory T Cell)Treg相关基因:GZMA、GZMH、HDAC7、IKZF4、IL23A、IL2RA、IL2RB、IL2RG、KAT5、KITLG、NFATC1、NFATC3、NFATC4、RELA、STAT5A、STAT5B、TGFB1、TNFRSF11A。
数据集准备
1.从基因表达综合数据库(GEO)下载基因转录组基因芯片数据集GSE37460及GSE93798。GSE37460包含健康人及IgA肾病病人肾组织样本各27例,GSE93798则包含健康人22例及IgA肾病病人肾组织样本20例,均有超过20000多个基因探针。
2.数据标准化(Normalization):数据标准化分两步:(一)先对每个样本,分别计算所有基因表达量的中位数,标准化表达为原表达量减去计算出的中位数,通过这种标准化方式去除样本mRNA输入量的差异;(二)为了便于把两个数据集综合,分别对每个数据集进行四分位数(Interquartile)标准化,即把每个样本(或基因)的第一、第三个四分位数线性映射到0、1。
3.最后选定两者的基因交集把表达数据堆栈起来,构成具有49例健康人及47例IgA肾病病人的综合数据集,交集共有10000多个基因,并包含上面所选的84个T细胞基因。
标志物筛选
本实施例采用多重迭代线性回归方法建立模型(可以理解的是,也可以采用其它监督的机器学习非线性算法替代,比如经典的SVM、PCA、神经网络等或者深度学习算法代替):
第一步:由于线性回归(Linear Regression)模型的建立比较适合于几个至几十个输入参变量,选定模型输入参变量的个数S,把基因组平均分为由S个基因组成的基因子集,对每个子集分别建立线性回归模型,其中的基因为输入参变量,样本类型编码,HC(健康人)=0,IgAN(IgA肾病病人)=1,为目标变量,把模型中p值小于0.10的基因保留。这 里阈值0.10高于传统的0.05,是因为这些基因在下一轮的模型中也可能满足统计意义的p值。
第二步:把所有这样选出的基因合并,如果总个数大于S,对合并基因重复第一步,直到合并后的基因个数不超过S。
在建模过程中,遍历所有合理的模型大小,S=10,11,…,60,进行上述多重迭代线性回归建模步骤,最后,取每个S得出的R平方值(rsq)的最大值作为最优的模型大小,由此选定S=16,得到的最优模型由8个基因组成:CD4、CD8A、CCR3、GATA3、GZMA、HDAC7、RORA和VEGFC,8个基因组成的最优线性回归模型参考表1,从表中可以看出,模型中每个基因对应的p值均小于0.05。
表1. 8个基因组成的最优线性回归模型及功能标注
单独对不同分组下8个基因表达水平进行检验,结果参考图1,为t检验的箱线图,其中,横坐标的0表示正常人的对照组,1表示IgA肾病的患者组,上述箱线图中两组中各个基因的表达均存在显著差异(p<0.05)。该结果表明,这8个基因对IgA肾病都具有较好的分离性。因此,将这8个基因中的至少一种作为IgA肾病的诊断标志物,可以对受试者检测其中至少一种标志物的表达水平,根据其结果对受试者的IgA肾病的患病风险进行评估。
模型交叉验证(Cross Validation)
把上述49例健康人样本及47例IgAN病人样本分别随机平分,组合成两个平衡了HC与IgAN的数据子集,用其一以CD4、CD8A、CCR3、GATA3、GZMA、HDAC7、RORA和VEGFC为输入变量建立线性回归模型,以另一个子集为验证数据集,画出ROC图并计算AUC。重复20次后排序的AUC:0.91、0.92、0.93、0.94、0.94、0.94、0.94、0.94、0.95、0.95、0.96、0.96、0.96、0.97、0.97、0.97、0.97、0.98、0.98、0.98。其中最差的为0.91,最好的为0.98,中值0.96,同时计算得出其特异性及敏感性均超过90%。该结果表明,以CD4、CD8A、CCR3、GATA3、GZMA、HDAC7、RORA、VEGFC这8个标志物为组合进行IgAN的诊断具有出色的结果,并且稳定性良好。
按照同样的方法将上述样本随机分成两个数据子集,用其中一个子集以CD4、CD8A、CCR3、GATA3、GZMA、HDAC7、RORA和VEGFC分别为输入变量建立线性回归模型,以另一个子集为验证数据集,画出ROC图并计算AUC,重复20次后排序,结果如图2所 示,从图中可以看出,8个标志物单基因建模的AUC值都在0.6以上,其中,GATA3、VEGFC、HDAC7、RORA、CD4、CD8A的AUC值都在0.7以上,GATA3、VEGFC的AUC值更是达到0.79。
按照同样的方法将上述样本随机分成两个数据子集,用其中一个子集以CD4、CD8A、CCR3、GATA3、GZMA、HDAC7、RORA和VEGFC中的任意两个或更多个为输入变量建立线性回归模型,以另一个子集为验证数据集,画出ROC图并计算AUC,重复20次后排序,最大值、中间值和最小值如表2所示。
表2.不同数量诊断标志物的AUC值
其中部分ROC曲线如图3~图8所示,从图3~图8结合表2可以看出,上述标志物中任选两个、任选三个、任选四个、任选五个、任选六个、任选七个作为IgA肾病的诊断标志物都具有良好的诊断价值。
实施例2
本实施例提供一种IgA肾病风险评估的设备,该设备包括处理器和存储器,存储器上存储有可被处理器运行的计算机程序。运用该设备对受试者进行IgA肾病风险的评估的方法如下:
1.选择受试者的外周血样本提取外泌体mRNA。
2.将提取到的mRNA送入检测装置(例如标准qPCR平台)进行实施例1中提供的7个基因诊断标志物的表达的定量数据:CD4、CD8A、CCR3、GATA3、GZMA、RORA、VEGFC。
3.采用该设备利用作为目标变量的临床观察结果(如蛋白尿、eGFR、肾穿刺的病理分级、5年或10年尿毒症风险、药物的有效性预测、耐药性)重新训练线性回归模型,根据得出的最优线性回归模型确定针对外周血样本的参数向量w
n(n=0~7),根据参数向量w
n得到风险分数N与各个基因表达水平之间的线性回归模型N=w
0+w
1×CD4+w
2×CD8A+w
3×CCR3+w
4×GATA3+w
5×GATA3+w
6×GZMA+w
7×RORA+w
8×VEGFC,计算得到受试者的风险分数并确定合适的风险分数的门槛值。如果受试者的风险分数大于门槛值,则判断为阳性。
实施例3
本实施例提供一种试剂盒,包括能够定量CD4、CD8A、CCR3、GATA3、GZMA、HDAC7、RORA和VEGFC的mRNA水平的试剂,该试剂包括逆转录酶、引物、Taq酶、荧光染料等。
实施例4
本实施例提供一种试剂盒,该试剂盒包括一个微流控芯片,该微流控芯片包括储液模块,储液模块中分别装设有能够定量GATA3、VEGFC、HDAC7、CD4、CD8A基因的mRNA水平的试剂。利用该试剂盒可以应用到IgA肾病的诊断中,实现较为灵敏准确的诊断。
上面结合实施例对本申请作了详细说明,但是本申请不限于上述实施例,在所属技术领域普通技术人员所具备的知识范围内,还可以在不脱离本申请宗旨的前提下作出各种变化。此外,在不冲突的情况下,本申请的实施例及实施例中的特征可以相互组合。
Claims (6)
- 定量检测以下至少一种标志物的试剂在制备IgA肾病的诊断试剂盒中的应用:CCR3、CD4、CD8A、GATA3、GZMA、HDAC7、RORA和VEGFC。
- 免疫球蛋白A肾病的诊断试剂盒,其特征在于,包括定量检测以下至少一种标志物的试剂:CCR3、CD4、CD8A、GATA3、GZMA、HDAC7、RORA和VEGFC。
- 计算机可读存储介质,其特征在于,所述计算机可读存储介质存储有计算机可执行指令,所述计算机可执行指令用于使计算机执行以下操作:步骤1:获取来自受试者样本中的以下至少一种标志物的表达水平的信息:CCR3、CD4、CD8A、GATA3、GZMA、HDAC7、RORA和VEGFC;步骤2:对所述表达水平进行数学关联以获得评分;所述评分用于指示受试者的免疫球蛋白A肾病的患病风险。
- 根据权利要求3所述的计算机可读存储介质,其特征在于,所述表达水平为所述标志物的转录水平或蛋白水平。
- 根据权利要求3所述的计算机可读存储介质,其特征在于,所述步骤1还包括对所述表达水平进行标准化。
- 电子设备,其特征在于,包括处理器和存储器,所述存储器上存储有可在处理器上运行的计算机程序,所述处理器在运行所述计算机程序时实现以下操作:步骤1:获取来自受试者样本中的以下至少一种标志物的表达水平的信息:CCR3、CD4、CD8A、GATA3、GZMA、HDAC7、RORA和VEGFC;步骤2:对所述表达水平进行数学关联以获得评分;所述评分用于指示受试者的免疫球蛋白A肾病的患病风险。
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