WO2023061240A1 - 免疫球蛋白A肾病RhoGTPase相关诊断标志物 - Google Patents

免疫球蛋白A肾病RhoGTPase相关诊断标志物 Download PDF

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WO2023061240A1
WO2023061240A1 PCT/CN2022/122917 CN2022122917W WO2023061240A1 WO 2023061240 A1 WO2023061240 A1 WO 2023061240A1 CN 2022122917 W CN2022122917 W CN 2022122917W WO 2023061240 A1 WO2023061240 A1 WO 2023061240A1
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sample
marker
nephropathy
computer
rhog
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饶皑炳
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深圳市陆为生物技术有限公司
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    • C—CHEMISTRY; METALLURGY
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • 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
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • 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
    • G01N33/6893—Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing involving proteins, peptides or amino acids related to diseases not provided for elsewhere
    • 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
    • G16B20/30—Detection of binding sites or motifs
    • 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
    • G16B35/00—ICT specially adapted for in silico combinatorial libraries of nucleic acids, proteins or peptides
    • G16B35/20—Screening of libraries
    • 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
    • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
    • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
    • C12Q2600/00—Oligonucleotides characterized by their use
    • C12Q2600/158—Expression markers
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N2800/00—Detection or diagnosis of diseases
    • G01N2800/34—Genitourinary disorders
    • G01N2800/347—Renal failures; Glomerular diseases; Tubulointerstitial diseases, e.g. nephritic syndrome, glomerulonephritis; Renovascular diseases, e.g. renal artery occlusion, nephropathy

Definitions

  • the present application relates to the technical field of nephropathy detection, in particular to a diagnostic marker related to immunoglobulin A nephropathy RhoGTPase.
  • 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 a to b in a sample in the preparation of a diagnostic kit for immunoglobulin A nephropathy:
  • the pathogenesis of immunoglobulin A nephropathy is related to five gene axes (Axis).
  • This application starts from the RhoGTPase pathway axis (RhoGTPase Pathway Axis), based on the expression data of related genes on the RhoGTPase pathway from different samples such as tissues or peripheral blood.
  • RhoGTPase Pathway Axis RhoGTPase Pathway Axis
  • a total of 16 markers were obtained. Quantitative detection of at least one of the 16 markers can efficiently and accurately diagnose whether the subject has IgA nephropathy, and has good specificity. sex and sensitivity.
  • ARFGAP1 ADP Ribosylation Factor GTPase Activating Protein 1
  • ADP Ribosylation Factor GTPase Activating Protein 1 is the GTPase activating protein 1 of ADP ribosylation factor, which participates in membrane transport and/or vesicle transport, and can promote the hydrolysis of ARF1-bound GTP. Necessary for the separation of the coat protein from the vesicle and a prerequisite for the fusion of the vesicle with the target region.
  • ARHGEF5 (Rho Guanine Nucleotide Exchange Factor 5) is a Rho guanine nucleotide exchange factor 5, which can strongly activate RhoA/B, weakly activate RhoC/G, and participate in the regulation of cell shape and actin cytoskeleton organization. Loss of actin stress fibers and formation of membrane ruffles and filopodia play a role in actin organization.
  • ARHGEF6 (Rac/Cdc42Guanine Nucleotide Exchange Factor 6) is RAC/CDC42 Guanine Nucleotide Exchange Factor 6.
  • DOCK10 (Dedicator Of Cytokinesis 10) is a member of the cytokine protein family involved in the intracellular signal transduction network, belonging to the D (or Zizimin) subfamily of the DOCK family.
  • NUP62CL Nucleoporin 62C-Terminal Like
  • NUP62CL Nucleoporin 62C-terminal protein, which is a protein containing a domain of nucleoporin, a glycoprotein found in the nuclear pore complex.
  • RAB6B is a RAS-related protein of the Rasoncogene family, and its related pathways include TBC/RabGaps and COPI-independent Golgi-ER retrograde.
  • RAP2A is also a RAS-related protein of the Rasoncogene family, which is involved in the regulation of cytoskeleton rearrangement, cell migration, cell adhesion and cell spreading.
  • RASGRP2 RAS Guanyl Releasing Protein 2
  • RAS guanylate releasing protein 2 RAS guanylate releasing protein 2, which can activate small GTPases, including RAS and RAP1/RAS3, and can stimulate the nucleotide exchange activity of this protein by calcium and diacylglycerol.
  • RHOBTB1 Rho Related BTB Domain Containing 1
  • Rho-related BTB domain containing 1 the protein encoded by this gene belongs to the Rho family of the small GTPase superfamily, and is involved in the signal transduction mediated by small GTPases and the organization and construction of actin filaments in effect.
  • RHOBTB2 is a homologous gene of RHOBTB1.
  • ARFGAP3 (ADP Ribosylation Factor GTPase Activating Protein 3) is an ADP ribosylation factor GTPase activating protein (GAP) 3, which is associated with the Golgi apparatus and regulates the early secretion pathway of proteins, which can promote ADP-ribosylation factor 1 (ARF1) Bound GTP is hydrolyzed.
  • GAP GTPase activating protein
  • CDC42 Cell Division Cycle 42
  • Rho subfamily that regulates the guidance transduction pathway, which controls different cellular functions, including cell morphology, migration, endocytosis, and cell cycle progression.
  • DOCK3 (Dedicator Of Cytokinesis 3) is also a member of the DOCK family. DOCK3 and DOCK1, DOCK2 and DOCK4 share several conserved amino acids required for GEF activity in their homology domains, and through their DHR-1 domains Binds directly to WAVE proteins. Also induces axon outgrowth in the central nervous system by stimulating membrane recruitment of the wave complex and activating the small G protein Rac1.
  • NUP153 (Nucleoporin 153) is the core pore complex protein 153, which is a component of the nuclear pore complex (NPC) required for transport across the nuclear membrane.
  • RASA1 (RAS P21 Protein Activator 1) is a RAS P21 protein activator 1, located in the cytoplasm, and a part of the GTPase activating protein GAP1 family. As a RAS function inhibitor, it can enhance the weak internal GTPase activity of RAS protein.
  • RHOG Ras Homolog Family Member G
  • RAS homologous family G which cycles between the inactive GDP-bound state and the active GTP-bound state, and functions as a molecular switch in the signal transduction cascade. In addition, it can promote the reorganization of the actin cytoskeleton and regulate cell shape, attachment and motility.
  • the encoded protein facilitates the transfer of functional guanine nucleotide exchange factor (GEF) complexes from the cytoplasm to the plasma membrane, where it activates ras-associated C3 botulinum toxin substrate 1 to promote lamella formation and cell migration .
  • GEF functional guanine nucleotide exchange factor
  • the quantitative detection of at least one marker in a to b in the sample by the reagent means that the reagent can quantitatively detect at least one marker in the group consisting of 10 markers in a, or that the reagent can quantitatively detect the marker in b. At least one marker in the group consisting of 6 markers, or means that the reagent can quantitatively detect at least one marker in the group consisting of a total of 16 markers in a and b.
  • the sample is at least one of tissue or blood
  • the reagent quantitatively detects ARFGAP1, ARHGEF5, ARHGEF6, DOCK10, NUP62CL, RAB6B, RAP2A in at least one of the materials to be tested in the tissue sample or blood sample , RASGRP2, RHOBTB1 and RHOBTB2 at least one marker.
  • the sample is tissue
  • the reagent quantitatively detects at least one marker among ARFGAP1, ARHGEF5, ARHGEF6, DOCK10, NUP62CL, RAB6B, RAP2A, RASGRP2, RHOBTB1 and RHOBTB2 in the tissue sample.
  • the reagent quantitatively detects at least two, at least three, at least four, at least five of ARFGAP1, ARHGEF5, ARHGEF6, DOCK10, NUP62CL, RAB6B, RAP2A, RASGRP2, RHOBTB1 and RHOBTB2, At least six, at least seven, at least eight, at least nine, all ten markers.
  • the sample is blood
  • the reagent quantitatively detects at least one marker among ARFGAP3, CDC42, DOCK3, NUP153, RASA1 and RHOG in the blood sample.
  • the reagent quantitatively detects at least two, at least three, at least four, at least five, and all six markers of ARFGAP3, CDC42, DOCK3, NUP153, RASAl and RHOG.
  • the reagent quantitatively detects at least two of ARFGAP1, ARHGEF5, ARHGEF6, DOCK10, NUP62CL, RAB6B, RAP2A, RASGRP2, RHOBTB1, RHOBTB2, ARFGAP3, CDC42, DOCK3, NUP153, RASA1 and RHOG, At least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen species, at least fifteen, at least sixteen, at least seventeen, all eighteen markers.
  • 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.
  • a diagnostic kit for IgA nephropathy includes a reagent for quantitatively detecting at least one marker in the following a to b:
  • 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 of ARFGAP1, ARHGEF5, ARHGEF6, DOCK10, NUP62CL, RAB6B, RAP2A, RASGRP2, RHOBTB1 and RHOBTB2, At least six, at least seven, at least eight, at least nine, all ten markers.
  • the reagent quantitatively detects at least two, at least three, at least four, at least five, and all six markers of ARFGAP3, CDC42, DOCK3, NUP153, RASAl and RHOG.
  • the reagent quantitatively detects at least two of ARFGAP1, ARHGEF5, ARHGEF6, DOCK10, NUP62CL, RAB6B, RAP2A, RASGRP2, RHOBTB1, RHOBTB2, ARFGAP3, CDC42, DOCK3, NUP153, RASA1 and RHOG, At least three, at least four, at least five, at least six, at least seven, at least eight, at least nine, at least ten, at least eleven, at least twelve, at least thirteen, at least fourteen species, at least fifteen, all sixteen 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 marker in the following a ⁇ b in the sample from the subject:
  • 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 immunoglobulin A nephropathy is to be assessed
  • the sample of the subject 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 Blood samples (such as 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 immunoglobulin A 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 Obtain information on the expression level of at least one marker in the following a ⁇ b in the subject sample:
  • 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 memory as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs, such as the marker screening method described in the embodiments of the present application or the immune globulin of the subject The risk of protein A nephropathy was assessed.
  • the processor executes the non-transitory software program and instructions stored in the memory, so as to realize the above-mentioned marker screening method or evaluate the risk of immunoglobulin A 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 .
  • Figure 1 is a box plot of the expression levels of 10 genetic diagnostic markers screened in Example 1 of the present application in tissue samples.
  • FIG. 2 is a boxplot of the expression levels of six genetic diagnostic markers screened in Example 1 of the present application in peripheral blood mononuclear cell samples.
  • Fig. 3 is the ROC curve obtained by modeling the tissue samples with the combination of 10 genes screened in Example 1 of the present application as diagnostic markers.
  • Fig. 4 is the ROC curve obtained by modeling the peripheral blood samples with the combinations of the six genes screened in Example 1 of the present application as diagnostic markers.
  • Fig. 5 is the ROC curve obtained by modeling the peripheral blood samples with the combinations of 10 genes screened in Example 1 of the present application as diagnostic markers.
  • Fig. 6 is the ROC curve obtained by modeling the tissue samples with the combination of six genes screened out in Example 1 of the present application as diagnostic markers.
  • Fig. 7 is the ROC curve obtained by modeling tissue samples with 10 genes screened out in Example 1 of the present application as diagnostic markers alone.
  • Fig. 8 is the ROC curve obtained by modeling the peripheral blood samples using the six genes screened out in Example 1 of the present application as diagnostic markers alone.
  • Fig. 9 is the ROC curve obtained by modeling multiple different genes among the 10 gene combinations screened out in Example 1 of the present application as diagnostic markers.
  • Figure 10 is the ROC curve obtained by modeling multiple different genes in the 6 gene combinations screened out 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 the RhoGTPase axis (RhoGTPase Pathway Axis). Some gene axes are correlated.
  • the genes on the RhoGTPase pathway that may be related to the pathogenesis of IgAN are pre-selected as shown in Table 1 below, a total of 241. It should be noted that the genes in the table are only based on the function of the pathway. The general classification does not constitute a unique limitation.
  • Samples are divided into tissue samples (Tissue) and peripheral blood samples (PBMC) according to different sources, of which:
  • GSE37460 includes 27 cases of kidney tissue samples from healthy people (HC, Health Control) and IgAN, IgA Nephropathy (IgAN, IgA Nephropathy) patients, and 15 cases of hypertensive nephropathy (HN, Hypertension Nephropathy); GSE93798 includes 22 cases of healthy people and In 20 renal tissue samples from patients with IgA nephropathy, there were more than 20,000 gene probes. HC and IgAN samples were selected for subsequent modeling data, and HN samples were reserved for use when exploring the diagnostic value of the model for other renal diseases.
  • HC Health Control
  • IgAN IgA Nephropathy
  • HN Hypertension Nephropathy
  • 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. The standardized method removes the difference in the input amount of sample mRNA; (2) In order to facilitate the synthesis of different data sets, each data set is separately standardized by quartile (Interquartile), that is, the first value of each sample (or gene) , the third quartile is linearly mapped to 0, 1.
  • RhoGTPase enzyme pathway For the preselected 241 genes of the RhoGTPase enzyme pathway. The t-test was used to compare the expression levels between healthy people and IgAN nephropathy patients, and the part with statistically significant difference between the two expression levels was selected.
  • kidney tissue comprehensive dataset ARFGAP1, ARHGAP10, ARHGAP15, ARHGAP19, ARHGAP25, ARHGAP26, ARHGAP4, ARHGDIB, ARHGEF1, ARHGEF15, ARHGEF16, ARHGEF18, ARHGEF5, ARHGEF6, ARHGEF9, CDC42EP2, CDC42EP3, CDC42EP4, DOCK1, DOCK10, DOCK2, DOCK4, DOCK5, DOCK6, EZR, FAT2, FERMT1, KANK1, KANK3, MAGI2, NUP107, NUP214, NUP50, NUP62, NUP62CL, NUP85, NUP88, NUP93, NUPR1, PAK4, RAB11A, RAB11B, RAB11FIP2, RAB11FIP3, RAB17, RAB22A, RAB2A, RAB31, RAB35, RAB3GAP1, RAB5A, RAB6B, RABEP2, RABG
  • multiple iterative linear regression methods are used to establish models for the kidney tissue comprehensive data set and the peripheral blood mononuclear cell comprehensive data set respectively (it can be understood that other supervised machine learning nonlinear algorithms can also be used instead, such as the classic SVM , PCA, neural network, etc. or deep learning algorithm 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.
  • kidney tissue data were ARFGAP1, ARHGEF5, ARHGEF6, DOCK10, NUP62CL, RAB6B, RAP2A, RASGRP2, RHOBTB1 and RHOBTB2.
  • the optimal linear regression models are shown in Table 2 and Table 3 respectively. It can be seen from the table that the p values corresponding to each gene in the model are all less than 0.05.
  • At least one of a total of 16 genes can be used as a diagnostic marker for IgA nephropathy, and the expression level of at least one of the markers can be detected for the subject. Risks are assessed.
  • the data of the tissue samples of the above 49 cases of healthy people and 47 cases of IgAN patients were randomly divided equally, and combined into two data subsets that balanced HC and IgAN.
  • RAP2A, RASGRP2, RHOBTB1, and RHOBTB2 were used to establish linear regression models for the input variables, and another subset was used as the validation data set to draw the ROC graph and calculate the AUC.
  • the results are shown in Figure 3, where the maximum AUC is 1 and the median AUC is 0.968. This result shows that the combination of 10 markers, ARFGAP1, ARHGEF5, ARHGEF6, DOCK10, NUP62CL, RAB6B, RAP2A, RASGRP2, RHOBTB1, and RHOBTB2, has excellent results in the diagnosis of IgAN.
  • the data of peripheral blood samples of 19 cases of healthy people and 35 cases of IgAN patients were randomly divided equally, and combined into two data subsets that balanced HC and IgAN.
  • RASA1 and RHOG established a linear regression model for the input variables, and another subset was used as the validation data set to draw the ROC graph and calculate the AUC.
  • the results are shown in Figure 4, the minimum AUC is 0.6, the maximum AUC is 0.989, and the median AUC is 0.911. It can be seen that using this group of genes to distinguish the peripheral blood samples of healthy people and patients has a higher accuracy, but it is slightly less than the 10-gene combination in tissue samples.
  • the data of peripheral blood samples of 19 cases of healthy people and 35 cases of IgAN patients were randomly divided equally, and combined into two data subsets that balanced HC and IgAN.
  • NUP62CL, RAB6B, RAP2A, RASGRP2, RHOBTB1, and RHOBTB2 were used as input variables to establish linear regression models, and another subset was used as a validation data set to draw ROC graphs and calculate AUC.
  • the results are shown in Figure 5, where the maximum AUC is 0.672, the median AUC is 0.506, and the minimum AUC is 0.389.
  • the model using the 10-gene combination was applied to peripheral blood mononuclear cell samples, with a median AUC of 0.506, and the diagnostic value was limited.
  • the above tissue samples were randomly divided into two data subsets, and one of the subsets was used to establish a linear regression model with ARFGAP1, ARHGEF5, ARHGEF6, DOCK10, NUP62CL, RAB6B, RAP2A, RASGRP2, RHOBTB1 and RHOBTB2 as input variables, respectively.
  • ARFGAP1, ARHGEF5, ARHGEF6, DOCK10, NUP62CL, RAB6B, RAP2A, RASGRP2, RHOBTB1 and RHOBTB2 as input variables, respectively.
  • the AUC values of the 10 genes are all above 0.6, and ARFGAP1 , ARHGEF5, ARHGEF6, DOCK10, RAP2A, RASGRP2, RHOBTB1 and RHOBTB2 have AUC values above 0.7, and the AUC values of RASGRP2 and RHOBTB2 are above 0.8.
  • peripheral blood samples were randomly divided into two data subsets, and a linear regression model was established with ARFGAP3, CDC42, DOCK3, NUP153, RASA1 and RHOG as input variables in one of the subsets, and the other subset was used for verification
  • ARFGAP3, CDC42, DOCK3, NUP153, RASA1 and RHOG as input variables in one of the subsets
  • RASA1 and RHOG as input variables in one of the subsets
  • the other subset was used for verification
  • the above tissue samples were randomly divided into two data subsets, and any two or more of ARFGAP1, ARHGEF5, ARHGEF6, DOCK10, NUP62CL, RAB6B, RAP2A, RASGRP2, RHOBTB1 and RHOBTB2 were used in one of the subsets.
  • Establish a linear regression model for the input variables 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 of some results are shown in Table 4.
  • ROC curves are shown in Figure 9, from a to h are the ROC of the two-gene combination, three-gene combination, four-gene combination, five-gene combination, six-gene combination, seven-gene combination, eight-gene combination, and nine-gene combination Curve, as can be seen from Figure 9 combined with the results in Table 4, any two, optional three, optional four, optional five, optional six, optional seven, optional Selecting eight or optional nine as diagnostic markers for IgA nephropathy has good diagnostic value.
  • peripheral blood samples were randomly divided into two data subsets according to the same method, and a linear regression model was established with any two or more of ARFGAP3, CDC42, DOCK3, NUP153, RASA1 and RHOG as input variables in one of the subsets , take another subset as the verification data set, draw the ROC diagram and calculate the AUC, repeat 20 times and then sort.
  • the maximum, median and minimum values of some results are shown in Table 5.
  • ROC curves are shown in Figure 10. From a to d are the ROC curves of the two-gene combination, three-gene combination, four-gene combination, and five-gene combination. From Figure 10 combined with the results in Table 5, it can be seen that the above Any two, three, four, or five of the markers have good diagnostic value as diagnostic markers for IgA nephropathy.
  • 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:
  • 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 ARFGAP1, ARHGEF5, ARHGEF6, DOCK10, NUP62CL, RAB6B, RAP2A, RASGRP2, RHOBTB1 and RHOBTB2, the reagents include reverse transcriptase, primers, Taq enzymes, fluorescent Dyes etc.
  • kits the kit includes a microfluidic chip, the microfluidic chip includes a liquid storage module, the liquid storage module is respectively equipped with quantitative ARFGAP1, ARHGEF5, ARHGEF6, DOCK10, NUP62CL, RAB6B , RAP2A, RHOBTB1, RHOBTB2 gene 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

免疫球蛋白A肾病RhoGTPase相关诊断标志物。提供定量检测样本中以下a～b中至少一种标志物的试剂在制备免疫球蛋白A肾病的诊断试剂盒中的应用：a.ARFGAP1、ARHGEF5、ARHGEF6、DOCK10、NUP62CL、RAB6B、RAP2A、RASGRP2、RHOBTB1和RHOBTB2；b.ARFGAP3、CDC42、DOCK3、NUP153、RASA1和RHOG。基于这16个标志物中至少一种对受试者进行定量检测都能够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

免疫球蛋白A肾病RhoGTPase相关诊断标志物 技术领域
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RhoGTPase相关诊断标志物。
背景技术
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，提供定量检测样本中以下a～b中至少一种标志物的试剂在制备疫球蛋白A肾病的诊断试剂盒中的应用：
a.ARFGAP1、ARHGEF5、ARHGEF6、DOCK10、NUP62CL、RAB6B、RAP2A、RASGRP2、RHOBTB1和RHOBTB2；
b.ARFGAP3、CDC42、DOCK3、NUP153、RASA1和RHOG。
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RhoGTPase通路轴(RhoGTPase Pathway Axis)出发，基于RhoGTPase通路上相关基因，从组织或外周血等不同样本来源的表达数据中进行筛选，得到上述两组共16个标志物，基于这16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，ARFGAP1(ADP Ribosylation Factor GTPase Activating Protein 1)是ADP核糖基化因子的GTP酶激活蛋白1，它参与膜运输和/或囊泡运输，能够促进ARF1结合GTP的水解， 是从高尔基体衍生膜和囊泡分离外壳蛋白所必需的条件，也是囊泡与靶区融合的先决条件。
ARHGEF5(Rho Guanine Nucleotide Exchange Factor 5)是Rho鸟嘌呤核苷酸交换因子5，它能够强激活RhoA/B，弱激活RhoC/G，同时参与调节细胞形状和肌动蛋白细胞骨架组织，通过产生肌动蛋白应力纤维的损失和膜皱褶和丝足的形成，在肌动蛋白组织中发挥作用。
ARHGEF6(Rac/Cdc42Guanine Nucleotide Exchange Factor 6)是RAC/CDC42鸟嘌呤核苷酸交换因子6。
DOCK10(Dedicator Of Cytokinesis 10)是一种涉及细胞内信号转导网络的细胞因子蛋白质家族成员，属于DOCK家族中的D(或Zizimin)亚家族。
NUP62CL(Nucleoporin 62C-Terminal Like)是核致核蛋白62C末端蛋白，其是一种含有核孔蛋白结构域的蛋白质，该核孔蛋白是在核孔复合体中发现的糖蛋白。
RAB6B是Ras oncogene家族的RAS相关蛋白，其相关通路包括TBC/RabGaps和COPI独立的高尔基体-ER逆行。
RAP2A同样是Ras oncogene家族的RAS相关蛋白，参与调节细胞骨架重排、细胞迁移、细胞粘附和细胞扩散。
RASGRP2(RAS Guanyl Releasing Protein 2)是RAS鸟苷酸释放蛋白2，该蛋白质可以激活小GTP酶，包括RAS和RAP1/RAS3，并且可以通过钙和二酰基甘油刺激该蛋白质的核苷酸交换活性。
RHOBTB1(Rho Related BTB Domain Containing 1)是Rho相关BTB结构域1，该基因编码的蛋白属于小GTP酶超家族的Rho家族，在小GTP酶介导的信号转导和肌动蛋白丝的组织构建中起作用。而RHOBTB2是RHOBTB1的同源基因。
ARFGAP3(ADP Ribosylation Factor GTPase Activating Protein 3)是ADP核糖基化因子GTP酶激活蛋白(GAP)3，与高尔基体相关并调节蛋白质的早期分泌途径，其能够促进ADP-核糖基化因子1(ARF1)结合的GTP水解。
CDC42(Cell Division Cycle 42)是Rho亚家族的小GTP酶，调节指导传导通路，而该信号通路控制不同细胞功能，包括细胞形态、迁移、内吞作用和细胞周期进展等。
DOCK3(Dedicator Of Cytokinesis 3)同样是DOCK家族的成员之一，DOCK3和DOCK1、DOCK2和DOCK4在其同源区结构域中共享GEF活性所需的几个保守氨基酸，并通过其DHR-1结构域直接与WAVE蛋白结合。此外，还通过刺激波复合体的膜募集和激活小G蛋白Rac1，诱导中枢神经系统的轴突生长。
NUP153(Nucleoporin 153)是核心孔复合蛋白153，它是穿过核膜运输所需的核孔复合体(NPC)的组成部分。
RASA1(RAS P21 Protein Activator 1)是RAS P21蛋白激活剂1，位于细胞质中，是GTP酶激活蛋白GAP1家族的一部分。作为RAS功能抑制剂，能够增强RAS蛋白微弱的内在GTPase活性。
RHOG(Ras Homolog Family Member G)是RAS同源家族成员G，在非活性GDP结合态和活性GTP结合态之间循环，并在信号转导级联中作为分子开关发挥作用。另外能够促进肌动蛋白细胞骨架的重组并调节细胞形状、附着和运动。而且编码的蛋白质能够促进功能性鸟嘌呤核苷酸交换因子(GEF)复合物从细胞质转移到质膜，在质膜上激活ras相关C3肉毒毒素底物1，以促进板层形成和细胞迁移。
其中，试剂定量检测样本中a～b中至少一种标志物是指，试剂能够定量检测a中10个标志物所组成的组中的至少一种标志物，或是指试剂能够定量检测b中6个标志物所组成的组中的至少一种标志物，或是指试剂能够定量检测a和b中共计16个标志物所组成的组中的至少一种标志物。
在本申请的一些实施方式中，样本为组织或血液中的至少一种，试剂定量检测组织样本或血液样本中的至少一种待测材料中ARFGAP1、ARHGEF5、ARHGEF6、DOCK10、NUP62CL、RAB6B、RAP2A、RASGRP2、RHOBTB1和RHOBTB2中至少一种标志物。
在本申请的一些实施方式中，样本为组织，试剂定量检测组织样本中ARFGAP1、ARHGEF5、ARHGEF6、DOCK10、NUP62CL、RAB6B、RAP2A、RASGRP2、RHOBTB1和RHOBTB2中至少一种标志物。
在本申请的一些实施方式中，该试剂定量检测ARFGAP1、ARHGEF5、ARHGEF6、DOCK10、NUP62CL、RAB6B、RAP2A、RASGRP2、RHOBTB1和RHOBTB2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，至少九种，全部十种标志物。
在本申请的一些实施方式中，样本为血液，试剂定量检测血液样本中ARFGAP3、CDC42、DOCK3、NUP153、RASA1和RHOG中至少一种标志物。
在本申请的一些实施方式中，该试剂定量检测ARFGAP3、CDC42、DOCK3、NUP153、RASA1和RHOG中的至少两种，至少三种，至少四种，至少五种，全部六种标志物。
可以理解的是，也可以采用a中的标志物组和b中的标志物组中任选多种联合得到新的标志物组合用于检测。
在本申请的一些实施方式中，该试剂定量检测ARFGAP1、ARHGEF5、ARHGEF6、DOCK10、NUP62CL、RAB6B、RAP2A、RASGRP2、RHOBTB1、RHOBTB2、ARFGAP3、CDC42、DOCK3、NUP153、RASA1和RHOG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，至少九种，至少十种，至少十一种，至少十二种，至少十三种，至少十四种，至少十五种，至少十六种，至少十七种，全部十八种标志物。
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。
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a～b中至少一种标志物的试剂：
a.ARFGAP1、ARHGEF5、ARHGEF6、DOCK10、NUP62CL、RAB6B、RAP2A、RASGRP2、RHOBTB1和RHOBTB2；
b.ARFGAP3、CDC42、DOCK3、NUP153、RASA1和RHOG。
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，该试剂定量检测ARFGAP1、ARHGEF5、ARHGEF6、DOCK10、NUP62CL、RAB6B、RAP2A、RASGRP2、RHOBTB1和RHOBTB2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，至少九种，全部十种标志物。
在本申请的一些实施方式中，该试剂定量检测ARFGAP3、CDC42、DOCK3、NUP153、RASA1和RHOG中的至少两种，至少三种，至少四种，至少五种，全部六种标志物。
在本申请的一些实施方式中，该试剂定量检测ARFGAP1、ARHGEF5、ARHGEF6、DOCK10、NUP62CL、RAB6B、RAP2A、RASGRP2、RHOBTB1、RHOBTB2、ARFGAP3、CDC42、DOCK3、NUP153、RASA1和RHOG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，至少九种，至少十种，至少十一种，至少十二种，至少十三种，至少十四种，至少十五种，全部十六种标志物。
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以下a～b中至少一种标志物的表达水平的信息：
a.ARFGAP1、ARHGEF5、ARHGEF6、DOCK10、NUP62CL、RAB6B、RAP2A、RASGRP2、RHOBTB1和RHOBTB2；
b.ARFGAP3、CDC42、DOCK3、NUP153、RASA1和RHOG；
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免疫球蛋白A肾病的患病风险。
其中，受试者是指待评估免疫球蛋白A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，具体包括但不限于血液样本(如外周血样本)、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免疫球蛋白A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以下a～b中至少一种标志物的表达水平的信息：
a.ARFGAP1、ARHGEF5、ARHGEF6、DOCK10、NUP62CL、RAB6B、RAP2A、RASGRP2、RHOBTB1和RHOBTB2；
b.ARFGAP3、CDC42、DOCK3、NUP153、RASA1和RHOG；
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免疫球蛋白A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免疫球蛋白A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。
附图说明
图1是本申请的实施例1筛选出的10个基因诊断标志物在组织样本中表达水平的箱线图。
图2是本申请的实施例1筛选出的6个基因诊断标志物在外周血单核细胞样本中表达水平的箱线图。
图3是本申请的实施例1筛选出的10个基因的组合作为诊断标志物对组织样本建模得出的ROC曲线。
图4是本申请的实施例1筛选出的6个基因的组合作为诊断标志物对外周血样本建模得出的ROC曲线。
图5是本申请的实施例1筛选出的10个基因的组合作为诊断标志物对外周血样本建模得出的ROC曲线。
图6是本申请的实施例1筛选出的6个基因的组合作为诊断标志物对组织样本建模得出的ROC曲线。
图7是本申请的实施例1筛选出的10个基因单独作为诊断标志物对组织样本建模得出的ROC曲线。
图8是本申请的实施例1筛选出的6个基因单独作为诊断标志物对外周血样本建模得出的ROC曲线。
图9是本申请的实施例1筛选出的10个基因组合中的多个不同基因作为诊断标志物建模得出的ROC曲线。
图10是本申请的实施例1筛选出的6个基因组合中的多个不同基因作为诊断标志物建模得出的ROC曲线。
具体实施方式
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RhoGTPase轴(RhoGTPase Pathway Axis)在内的一些基因轴相关。
因此，根据文献及公开基因数据库，预先选定可能与IgAN发病机制相关的RhoGTPase通路上的基因如下表1所示，共计241个，需要说明的是，表中基因仅仅是根据通路上的功能的大致分类，并不对其构成唯一性的限定。
表1.RhoGTPase轴相关基因
Figure PCTCN2022122917-appb-000001
Figure PCTCN2022122917-appb-000002
数据集准备
1.样本按照不同的来源分为组织样本(Tissue)和外周血样本(PBMC)，其中：
A.组织样本，从基因表达综合数据库(GEO)下载肾组织基因转录组基因芯片数据集GSE37460及GSE93798。GSE37460包含健康人(HC，Health Control)及IgA肾病(IgAN，IgA Nephropathy)病人肾组织样本各27例，此外还包含15例高血压肾病(HN，Hypertension Nephropathy)；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。后续建模数据选用HC及IgAN样本，HN样本留待探索模型对于其它肾病的诊断价值时使用。
B.外周血单核细胞样本，从基因表达综合数据库(GEO)下载外周血单细胞基因转录组基因芯片数据集GSE14795(健康8例，肾病12例)，数据集GSE58539(健康9例，肾病8例)，及数据集GSE73953(健康2例，肾病15例)。
2.数据标准化(Normalization)：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)为了便于把不同的数据集综合，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肾病病人的肾组织综合数据集；以及包含19例健康人及35例IgA肾病病人的外周血单核细胞综合数据集。
标志物筛选
对于预先选定的241个RhoGTPase酶通路的基因。利用t-检验进行健康人与IgAN肾病病人之间表达水平的对比，选出两者表达水平之间的区别具有统计意义的部分。
其中，利用肾组织综合数据集经过t-检验筛选出的基因有89个：ARFGAP1、ARHGAP10、ARHGAP15、ARHGAP19、ARHGAP25、ARHGAP26、ARHGAP4、 ARHGDIB、ARHGEF1、ARHGEF15、ARHGEF16、ARHGEF18、ARHGEF5、ARHGEF6、ARHGEF9、CDC42EP2、CDC42EP3、CDC42EP4、DOCK1、DOCK10、DOCK2、DOCK4、DOCK5、DOCK6、EZR、FAT2、FERMT1、KANK1、KANK3、MAGI2、NUP107、NUP214、NUP50、NUP62、NUP62CL、NUP85、NUP88、NUP93、NUPR1、PAK4、RAB11A、RAB11B、RAB11FIP2、RAB11FIP3、RAB17、RAB22A、RAB2A、RAB31、RAB35、RAB3GAP1、RAB5A、RAB6B、RABEP2、RABGEF1、RABGGTA、RAC1、RALGPS2、RAP2A、RAPGEF4、RAPGEF6、RASA3、RASAL2、RASGRF1、RASGRP1、RASGRP2、RASGRP3、RASIP1、RASL11B、RASL12、RASSF2、RASSF8、RGL2、RHOA、RHOB、RHOBTB1、RHOBTB2、RHOC、RHOF、RHOG、RHOT1、SNUPN、SOS2、SOSTDC1、SRGAP2、TIAM1、TNS1、TRIOBP、TRPC6、VAV1。将其按照p值从小到大排序。
同样，利用外周血单核细胞综合数据集经过t-检验筛选出的基因有31个：ARFGAP3、ARHGAP26、ARHGDIB、ARHGEF11、CDC42、DOCK1、DOCK3、DOCK9、EZR、GDI1、GDI2、NUP153、NUP188、PAK4、RAB11A、RAB11FIP2、RAB1A、RAB21、RAB35、RAB5A、RAB5B、RABGAP1L、RAP2B、RAP2C、RAPGEF1、RASA1、RASGRF1、RASGRP3、RHOG、SOSTDC1、TRIOBP。同样将其按照p值从小到大排序。
两者交集，即同时在组织和外周血中的表达均有统计意义上区别的基因共14个：ARHGAP26、ARHGDIB、DOCK1、EZR、PAK4、RAB11A、RAB11FIP2、RAB35、RAB5A、RASGRF1、RASGRP3、RHOG、SOSTDC1、TRIOBP。
本实施例采用多重迭代线性回归方法分别对肾组织综合数据集和外周血单核细胞综合数据集建立模型(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。
最终得到肾组织数据的最优模型S＝10，分别是ARFGAP1、ARHGEF5、ARHGEF6、DOCK10、NUP62CL、RAB6B、RAP2A、RASGRP2、RHOBTB1和RHOBTB2。
外周血数据的最优模型S＝6，分别是ARFGAP3、CDC42、DOCK3、NUP153、RASA1和RHOG。最优线性回归模型分别如表2和表3，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2. 10个基因组成的组织数据最优线性回归模型及功能标注
Figure PCTCN2022122917-appb-000003
Figure PCTCN2022122917-appb-000004
表3. 6个基因组成的外周血数据最优线性回归模型及功能标注
Figure PCTCN2022122917-appb-000005
Figure PCTCN2022122917-appb-000006
从表2和表3的结果可以看出，两者建模得出的最优模型中，各个基因的p值均小于0.05。
单独对不同分组下的10个和6个基因表达水平的t检验结果的箱线图分别如图1和图2所示，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，上述箱线图中对照组 和患者组的组织和外周血样本中各个基因的表达均存在显著差异(p<0.05)。该结果表明，对于各自的组织或外周血的样本类型，这10个和6个基因对IgA肾病都具有较好的分离性，表达水平在HC与IgAN之间具有统计意义上的差异。
综合上述结果，总计16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)
1、全组合验证
1.1 10基因组织样本验证
把上述49例健康人及47例IgAN病人的组织样本的数据分别随机平分，组合成两个平衡了HC与IgAN的数据子集，用其一以ARFGAP1、ARHGEF5、ARHGEF6、DOCK10、NUP62CL、RAB6B、RAP2A、RASGRP2、RHOBTB1和RHOBTB2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。结果如图3所示，其中最大AUC为1，中值AUC为0.968。该结果表明，以ARFGAP1、ARHGEF5、ARHGEF6、DOCK10、NUP62CL、RAB6B、RAP2A、RASGRP2、RHOBTB1和RHOBTB2这10个标志物为组合进行IgAN的诊断具有出色的结果。
1.2 6基因外周血样本验证
按照同样的方法将19例健康人及35例IgAN病人的外周血样本的数据分别随机平分，组合成两个平衡了HC与IgAN的数据子集，用其一以ARFGAP3、CDC42、DOCK3、NUP153、RASA1和RHOG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。结果如图4所示，最小AUC为0.6，最大AUC为0.989，中值AUC为0.911。由此可见，用这组基因来区分健康人与病人的外周血样本，其结果同样有较高的准确性，但比组织样本中的10基因组合稍有不足。
1.3 10基因外周血样本验证
按照同样的方法将19例健康人及35例IgAN病人的外周血样本的数据分别随机平分，组合成两个平衡了HC与IgAN的数据子集，用其一以ARFGAP1、ARHGEF5、ARHGEF6、DOCK10、NUP62CL、RAB6B、RAP2A、RASGRP2、RHOBTB1和RHOBTB2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。结果如图5所示，其中最大AUC为0.672，中值AUC为0.506，最小AUC为0.389。结合上述结果，采用10基因组合的模型应用在外周血单核细胞样本，其中值AUC为0.506，诊断价值有限。
1.4 6基因组织样本验证
把上述49例健康人及47例IgAN病人的组织样本的数据分别随机平分，组合成两个平衡了HC与IgAN的数据子集，用其一以ARFGAP3、CDC42、DOCK3、NUP153、RASA1和RHOG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。结果如图6所示，其中最大AUC为0.742，中值AUC为0.667，最小AUC为0.463。由此可见，用这组基因来区分健康人与病人样本，中值AUC为0.667，也有一定的诊断价值，但与组织样本建立的10基因组合相比，其中值AUC为0.968，6基因模型用于组织稍有不足。
2、单基因验证
按照同样的方法将上述组织样本随机分成两个数据子集，用其中一个子集以ARFGAP1、ARHGEF5、ARHGEF6、DOCK10、NUP62CL、RAB6B、RAP2A、RASGRP2、RHOBTB1和 RHOBTB2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7所示，从图中可以看出，10个基因的AUC值都在0.6以上，ARFGAP1、ARHGEF5、ARHGEF6、DOCK10、RAP2A、RASGRP2、RHOBTB1和RHOBTB2共8个基因的AUC值在0.7以上，RASGRP2和RHOBTB2的AUC值更是达到0.8以上。
按照同样的方法将上述外周血样本随机分成两个数据子集，用其中一个子集以ARFGAP3、CDC42、DOCK3、NUP153、RASA1和RHOG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8所示，从图中可以看出，6个基因的AUC值都在0.6以上，而ARFGAP3、DOCK3、NUP153的AUC值都在0.7以上，DOCK3的AUC值更是达到0.81。
3、多基因验证
按照同样的方法将上述组织样本随机分成两个数据子集，用其中一个子集以ARFGAP1、ARHGEF5、ARHGEF6、DOCK10、NUP62CL、RAB6B、RAP2A、RASGRP2、RHOBTB1和RHOBTB2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，部分结果的最大值、中间值和最小值如表4所示。
表4.不同数量诊断标志物应用于组织样本的AUC值
Figure PCTCN2022122917-appb-000007
Figure PCTCN2022122917-appb-000008
Figure PCTCN2022122917-appb-000009
其中，部分ROC曲线如图9所示，从a～h分别是二基因组合、三基因组合、四基因组合、五基因组合、六基因组合、七基因组合、八基因组合、九基因组合的ROC曲线，从图9结合表4中的结果可以看出，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、任选八个、任选九个作为IgA肾病的诊断标志物都具有良好的诊断价值。
按照同样的方法将上述外周血样本随机分成两个数据子集，用其中一个子集以ARFGAP3、CDC42、DOCK3、NUP153、RASA1和RHOG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，部分结果的最大值、中间值和最小值如表5所示。
表5.不同数量标志物应用于外周血样本的AUC值
诊断标志物 最大AUC 中值AUC 最小AUC
ARFGAP3、CDC42 0.883 0.706 0.617
ARFGAP3、RASA1 0.811 0.65 0.383
ARFGAP3、RHOG 0.906 0.806 0.594
DOCK3、CDC42 0.944 0.883 0.733
DOCK3、RASA1、RHOG 0.978 0.906 0.839
DOCK3、NUP153、CDC42 0.944 0.861 0.667
ARFGAP3、DOCK3、RHOG 0.994 0.867 0.667
ARFGAP3、CDC42、RASA1、RHOG 0.933 0.844 0.572
NUP153、RHOG、CDC42、DOCK3 0.967 0.894 0.789
RASA1、RHOG、DOCK3、NUP153 0.989 0.883 0.817
DOCK3、RHOG、ARFGAP3、RASA1 0.989 0.906 0.75
ARFGAP3、DOCK3、CDC42、NUP153、RHOG 0.95 0.828 0.761
RASA1、RHOG、NUP153、ARFGAP3、CDC42 0.956 0.856 0.694
其中，部分ROC曲线如图10所示，从a～d分别是二基因组合、三基因组合、四基因组合、五基因组合的ROC曲线，从图10结合表5中的结果可以看出，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6个基因诊断标志物的表达的定量数据：ARFGAP3、CDC42、DOCK3、NUP153、RASA1和RHOG。
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＝0～6)，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×ARFGAP3+w 2×CDC42+w 3×DOCK3+w 4×NUP153+w 5×RASA1+w 6×RHOG，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ARFGAP1、ARHGEF5、ARHGEF6、DOCK10、NUP62CL、RAB6B、RAP2A、RASGRP2、RHOBTB1和RHOBTB2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ARFGAP1、ARHGEF5、ARHGEF6、DOCK10、NUP62CL、RAB6B、RAP2A、RHOBTB1、RHOBTB2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 (9)

  1. 定量检测样本中以下a～b中至少一种标志物的试剂在制备免疫球蛋白A肾病的诊断试剂盒中的应用：
    a.ARFGAP1、ARHGEF5、ARHGEF6、DOCK10、NUP62CL、RAB6B、RAP2A、RASGRP2、RHOBTB1和RHOBTB2；
    b.ARFGAP3、CDC42、DOCK3、NUP153、RASA1和RHOG。
  2. 根据权利要求1所述的应用，其特征在于，所述样本为血液或组织中的至少一种，所述试剂定量检测所述样本中a中至少一种标志物。
  3. 根据权利要求2所述的应用，其特征在于，所述样本为组织，所述试剂定量检测所述样本中a中至少一种标志物。
  4. 根据权利要求1所述的应用，其特征在于，所述样本为血液，所述试剂定量检测所述样本中b中至少一种标志物。
  5. 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a～b中至少一种标志物的试剂：
    a.ARFGAP1、ARHGEF5、ARHGEF6、DOCK10、NUP62CL、RAB6B、RAP2A、RASGRP2、RHOBTB1和RHOBTB2；
    b.ARFGAP3、CDC42、DOCK3、NUP153、RASA1和RHOG。
  6. 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：获取来自样本中以下a～b中至少一种标志物的表达水平的信息：
    a.ARFGAP1、ARHGEF5、ARHGEF6、DOCK10、NUP62CL、RAB6B、RAP2A、RASGRP2、RHOBTB1和RHOBTB2；
    b.ARFGAP3、CDC42、DOCK3、NUP153、RASA1和RHOG；
    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。
  7. 根据权利要求5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。
  8. 根据权利要求5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。
  9. 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以下a～b中至少一种标志物的表达水平的信息：
    a.ARFGAP1、ARHGEF5、ARHGEF6、DOCK10、NUP62CL、RAB6B、RAP2A、RASGRP2、RHOBTB1和RHOBTB2；
    b.ARFGAP3、CDC42、DOCK3、NUP153、RASA1和RHOG；
    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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US20140141449A1 (en) * 2011-04-06 2014-05-22 The Board Of Trustees Of The Leland Stanford Junior University Autoantibody Biomarkers for IGA Nephropathy
CN110108889A (zh) * 2019-05-23 2019-08-09 大连医科大学 一种用于诊断IgA肾病的试剂盒及其应用
CN113652478A (zh) * 2021-08-16 2021-11-16 深圳市华启生物科技有限公司 IgA肾病诊断标志物组合及其应用
CN113981063A (zh) * 2021-10-14 2022-01-28 深圳市华启生物科技有限公司 免疫球蛋白A肾病RhoGTPase相关诊断标志物

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CN110108889A (zh) * 2019-05-23 2019-08-09 大连医科大学 一种用于诊断IgA肾病的试剂盒及其应用
CN113652478A (zh) * 2021-08-16 2021-11-16 深圳市华启生物科技有限公司 IgA肾病诊断标志物组合及其应用
CN113981063A (zh) * 2021-10-14 2022-01-28 深圳市华启生物科技有限公司 免疫球蛋白A肾病RhoGTPase相关诊断标志物

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