阅读说明：本技术 与视网膜病变相关的生物标志物及其应用 (Biomarker related to retinopathy and application thereof ) 是由 伍海建 王冬国 徐志伟 于 2021-02-20 设计创作，主要内容包括：本发明公开了与视网膜病变相关的生物标志物及其应用,所述生物标志物包括FCGR3A、NAGLU和/或CNTN1,与NDR相比,FCGR3A、NAGLU、CNTN1在DR中呈现显著性差异,且ROC曲线具有较高的AUC值,提示FCGR3A、NAGLU、CNTN1可应用于DR的诊断。(The invention discloses biomarkers related to retinopathy and application thereof, wherein the biomarkers comprise FCGR3A, NAGLU and/or CNTN1, compared with NDR, the FCGR3A, NAGLU and CNTN1 show significant difference in DR, and an ROC curve has a higher AUC value, so that the FCGR3A, NAGLU and CNTN1 can be applied to diagnosis of DR.)

1. The application of a reagent for detecting biomarkers in a sample in preparing a product for diagnosing retinopathy is characterized in that the biomarkers are selected from one or more of FCGR3A, NAGLU or CNTN 1.

2. Use according to claim 1, wherein the product comprises reagents for detecting biomarker levels by sequencing techniques, nucleic acid hybridization techniques, nucleic acid amplification techniques, protein immunization techniques, chromatography techniques, mass spectrometry techniques.

3. Use according to claim 2, wherein said agent is selected from:

a probe that specifically recognizes the biomarker; or

Primers that specifically amplify the biomarkers; or

A binding agent that specifically binds to a protein encoded by the biomarker.

4. The use according to any one of claims 1 to 3, wherein the sample is selected from the group consisting of tissue, blood.

5. The use according to any one of claims 1 to 3, wherein the retinopathy is diabetic retinopathy.

6. A product for diagnosing retinopathy, comprising a reagent for detecting a biomarker selected from one or more of FCGR3A, NAGLU or CNTN1 in a sample;

preferably, the retinopathy is diabetic retinopathy.

7. The product of claim 6, wherein the product comprises a kit, chip, dipstick;

preferably, the kit comprises a qPCR kit, an immunoblotting detection kit, an immunochromatography detection kit, a flow cytometry kit, an immunohistochemical detection kit, an ELISA kit and an electrochemiluminescence detection kit;

preferably, the kit further comprises instructions for assessing whether the subject is suffering from or susceptible to retinopathy.

8. A product according to claim 6 or 7, further comprising reagents for processing the sample.

9. Use of a biomarker for constructing a computational model for predicting retinopathy or a system in which said computational model is embedded, wherein said biomarker is selected from one or more of FCGR3A, NAGLU or CNTN 1;

preferably, the computational model is operated by bioinformatics methods with the level of the biomarker as an input variable;

preferably, the retinopathy is diabetic retinopathy.

10. A system, comprising:

(1) a retinopathy evaluation device including a control unit and a storage unit for evaluating whether a subject has retinopathy; and

(2) information communication terminal devices communicatively connected to each other, which provide data on the level of the biomarker recited in claim 1 in a sample from a subject;

wherein the control unit of the retinopathy evaluation device includes:

1) a data receiving unit that receives data on the level of the biomarker of the sample transmitted from the information communication terminal device;

2) a discrimination value calculation unit that calculates a discrimination value based on discrimination of the level of the biomarker in the sample received by the data reception unit and the level of the biomarker having the explanatory variable stored in the storage unit;

3) a discrimination value criterion evaluation unit that evaluates a condition of retinopathy in the subject based on the discrimination value calculated by the discrimination value calculation unit; and

4) an evaluation result transmitting unit that transmits the evaluation result of the subject obtained by the discrimination value reference evaluation unit to the information communication terminal device;

preferably, the retinopathy is diabetic retinopathy.

Technical Field

The invention relates to the field of biomedicine, in particular to a biomarker related to retinopathy and application thereof.

Background

Retinopathy (retinopathy) refers to changes in the retina that occur under the influence of various factors, such as bleeding, exudation, an abnormal number of cells, or edema. The classification is more and more complex, and the common diseases are retinal detachment, macular degeneration, eye trauma, diabetic retinopathy, endophthalmitis, intrabulbar foreign bodies, congenital eye diseases such as neonatal Retinopathy (ROP) and intraocular parasites.

Diabetic Retinopathy (DR) is one of the major microvascular complications of diabetes, and is also a major blinding disease (Dow C, Mancini F, Rajaobelina K, et al. diet and risk of Diabetic retinopathy: a systematic review [ J ]. Eur J epidemic, 2018,33(2):141-156.doi:10.1007/s 10654-. At present, DR pathogenesis is known to be related to factors such as inflammation, oxidative stress, abnormal cytokine expression, and gene methylation, but specific pathogenesis cannot be clarified. The retina of a diabetic patient can cause the reduction of the number of retinal microvascular pericytes, the thickening of basement membrane, the proliferation of endothelial cells, the increase of retinal vascular leakage caused by abnormal retinal microcirculation, the degenerative change of optic nerve till the PDR is developed, the retinal neovascularization occurs, the vitreous hemorrhage, the formation of fibrous membrane and the retinal detachment due to long-term hyperglycemia, hypoxia and oxidative stress. PDR is hidden, vision loss occurs at the late stage, and many patients lose vision due to late discovery and untimely treatment. Currently, methods for DR treatment such as laser photocoagulation, intravitreal injection of anti-VEGF drugs or corticosteroids, vitreoretinal surgery, and the like are mainly aimed at more advanced patients, but it is difficult to reverse the structural and functional impairment of the retina, and only partial vision can be restored and maintained. Early diagnosis and timely treatment are therefore critical to disease control and prognosis. Although Non-Proliferative diabetic retinopathy (NPDR) and Proliferative Diabetic Retinopathy (PDR) can be distinguished by examination of fundus images, retinal fluorescence imaging, and the like, effective biomarkers as risk factors for DR onset and clinical indicators for prognosis judgment are still lacking.

With the development of biotechnology, proteomic research techniques have been applied to various fields of life sciences, such as cell biology, neurobiology, and the like. Research has covered the fields of prokaryotic microorganisms, eukaryotic microorganisms, plants, and animals, and has been implicated in a variety of important biological phenomena, such as signal transduction, cell differentiation, protein folding, and the like. The field of proteomics research is becoming increasingly widespread. Research on proteomics of DR, and search for noninvasive biomarkers for early diagnosis and prognosis of DR, so as to select appropriate means to effectively intervene on diseases in early stage, improve vision of patients and improve life quality, are important research points of DR.

Disclosure of Invention

The invention researches the biomarkers related to the occurrence and the development of the retinopathy based on the function of the genes in the occurrence and the development of the retinopathy, thereby providing a new means for diagnosing and treating the retinopathy.

The invention provides application of a reagent for detecting a biomarker in a sample in preparing a product for diagnosing retinopathy, wherein the biomarker is selected from one or more of FCGR3A, NAGLU or CNTN 1.

Further, the product comprises reagents for detecting the level of biomarkers by sequencing technology, nucleic acid hybridization technology, nucleic acid amplification technology, protein immunization technology, chromatography technology, mass spectrometry technology.

Further, the agent is selected from:

a probe that specifically recognizes the biomarker; or

Primers that specifically amplify the biomarkers; or

A binding agent that specifically binds to a protein encoded by the biomarker.

Examples of specific binding agents are peptides, peptidomimetics, aptamers, spiegelmers, dappin, ankyrin repeat proteins, Kunitz-type domains, antibodies, single domain antibodies and monovalent antibody fragments.

As a preferred embodiment, the specific binding agent is an antibody.

Further, the sample is selected from tissue or blood.

In a preferred embodiment, the sample is blood.

Further, the retinopathy is diabetic retinopathy.

The invention provides a product for diagnosing retinopathy, which comprises a reagent for detecting a biomarker in a sample, wherein the biomarker is selected from one or more of FCGR3A, NAGLU or CNTN 1.

Further, the retinopathy is diabetic retinopathy.

Further, the product comprises a kit, a chip and test paper.

Further, the kit comprises a qPCR kit, an immunoblotting detection kit, an immunochromatography detection kit, a flow cytometry kit, an immunohistochemical detection kit, an ELISA kit and an electrochemiluminescence detection kit.

Further, the kit also includes instructions for assessing whether the subject is suffering from or susceptible to retinopathy.

Further, the product also includes reagents for processing the sample.

The invention provides application of a biomarker in constructing a calculation model for predicting retinopathy or a system embedded with the calculation model, wherein the biomarker is selected from one or more of FCGR3A, NAGLU or CNTN 1.

Further, the calculation model takes the level of the biomarker as an input variable, and carries out calculation through a bioinformatics method to output the risk probability of the disease.

The present invention provides a system for diagnosing retinopathy, comprising:

(1) a retinopathy evaluation device including a control unit and a storage unit for evaluating whether a subject has retinopathy; and

(2) information communication terminal devices communicatively connected to each other, which provide data on the levels of the aforementioned biomarkers in a sample from a subject;

wherein the control unit of the retinopathy evaluation device includes:

1) a data receiving unit that receives data on the level of the biomarker of the sample, the biomarker being selected from one or several of FCGR3A, NAGLU, or CNTN1, transmitted from the information communication terminal device.

2) A discrimination value calculation unit that calculates a discrimination value based on discrimination of the level of the biomarker in the sample received by the data reception unit and the level of the biomarker having the explanatory variable stored in the storage unit;

3) a discrimination value criterion evaluation unit that evaluates a condition of retinopathy in the subject based on the discrimination value calculated by the discrimination value calculation unit; and

4) an evaluation result transmitting unit that transmits the evaluation result of the subject obtained by the discrimination value reference evaluation unit to the information communication terminal device.

The invention has the advantages and beneficial effects that:

the marker has extremely high association degree with retinopathy, has better diagnosis efficiency when judging the retinopathy, has higher accuracy, sensitivity and specificity, and can be used for early discovery of the retinopathy.

Drawings

FIG. 1 is a graph showing the expression of a differential gene, wherein A is a graph showing the expression of FCGR 3A; panel B is a graph of NAGLU expression; panel C is a graph of the expression of CNTN 1.

FIG. 2 is a ROC plot of genes as the detected variables, wherein, Panel A is a ROC plot of FCGR 3A; FIG. B is a ROC plot for NAGLU; FIG. C is a ROC plot of CNTN 1; FIG. D is a ROC plot of FCGR3A + NAGLU + CNTN 1.

Detailed Description

In order to screen the biomarkers for diagnosing and treating retinopathy, the invention provides a biomarker suitable for diagnosing and treating retinopathy by collecting blood (plasma) samples of patients with diabetic retinopathy and normal control blood (plasma) samples, comprehensively analyzing protein expression profiles of the samples, screening proteins with levels showing significant differences in two groups in a training set and further analyzing the diagnostic efficacy of the different proteins.

In the present invention, the term "biomarker" means a compound, preferably a gene or expression product thereof, which is differentially present (i.e. increased or decreased) in a biological sample from a subject or group of subjects having a first phenotype (e.g. having a disease) compared to a biological sample from a subject or group of subjects having a second phenotype (e.g. no disease). The term "biomarker" generally refers to the presence/concentration/amount of one gene or its expression product or the presence/concentration/amount of two or more genes or their expression products.

Biomarkers can be differentially present at any level, but are typically present at levels that are increased by at least 5%, at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, at least 100%, at least 110%, at least 120%, at least 130%, at least 140%, at least 150%, or more; or generally at a level that is reduced by at least 5%, at least 10%, at least 15%, at least 20%, at least 25%, at least 30%, at least 35%, at least 40%, at least 45%, at least 50%, at least 55%, at least 60%, at least 65%, at least 70%, at least 75%, at least 80%, at least 85%, at least 90%, at least 95%, or 100% (i.e., absent).

Preferably, the biomarkers are differentially present at levels of statistical significance (i.e., p-value less than 0.05 and/or q-value less than 0.10, as determined using the Welch's T-Test or the Wilcoxon rank-sum Test).

In a specific embodiment of the invention, the biomarker comprises FCGR3A, NAGLU or CNTN 1.

In the present invention, FCGR3A (Gene ID: 2214) includes CGR3A gene and its encoded protein and homologs, mutations and isoforms. The term encompasses full-length, unprocessed CGR3A, as well as any form of CGR3A that results from processing in the cell. The term encompasses naturally occurring variants (e.g., splice variants or allelic variants) of CGR 3A.

NAGLU (Gene ID: 4669) includes the NAGLU gene and its encoded protein and homologs, mutations, and isoforms. The term encompasses full-length, unprocessed NAGLU, as well as any form of NAGLU that results from processing in a cell. The term encompasses naturally occurring variants (e.g., splice variants or allelic variants) of NAGLU.

CNTN1 (Gene ID: 1272) includes CNTN1 gene and its encoded protein and homologs, mutations, and isoforms. The term encompasses full-length, unprocessed CNTN1, as well as any form of CNTN1 that results from processing in a cell. The term encompasses naturally occurring variants (e.g., splice variants or allelic variants) of CNTN 1.

In the present invention, any suitable method may be used to analyze a biological sample to determine the level of the biomarker in the sample. These methods include, but are not limited to: nucleic acid sequencing, nucleic acid hybridization, nucleic acid amplification technology, protein immunization technology and mass spectrum technology.

Illustrative, non-limiting examples of the nucleic acid sequencing methods of the present invention include, but are not limited to, chain terminator (Sanger) sequencing and dye terminator sequencing. One of ordinary skill in the art will recognize that RNA is typically reverse transcribed into DNA prior to sequencing because it is less stable in cells and more susceptible to nuclease attack in experiments.

Another illustrative, non-limiting example of a nucleic acid sequencing method of the present invention includes next generation sequencing (deep sequencing/high throughput sequencing), a high throughput sequencing technique that is a unimolecular cluster-based sequencing-by-synthesis technique based on proprietary reversible termination chemical reaction principles. Random fragments of genome DNA are attached to an optically transparent glass surface during sequencing, hundreds of millions of clusters are formed on the glass surface after the DNA fragments are extended and subjected to bridge amplification, each cluster is a monomolecular cluster with thousands of identical templates, and then four kinds of special deoxyribonucleotides with fluorescent groups are utilized to sequence the template DNA to be detected by a reversible edge-to-edge synthesis sequencing technology.

Methods of nucleic acid hybridization in the present invention include, but are not limited to, In Situ Hybridization (ISH), microarrays, and Southern or Northern blots. In Situ Hybridization (ISH) is a hybridization of specific DNA or RNA sequences in a tissue section or section using a labeled complementary DNA or RNA strand as a probe (in situ) or in the entire tissue if the tissue is small enough (whole tissue embedded ISH). DNA ISH can be used to determine the structure of chromosomes. RNA ISH is used to measure and locate mRNA and other transcripts (e.g., ncRNA) within tissue sections or whole tissue embedding. Sample cells and tissues are typically treated to fix the target transcript in situ and to increase probe access. The probe is hybridized to the target sequence at high temperature, and then excess probe is washed away. The localization and quantification of base-labeled probes in tissues labeled with radiation, fluorescence or antigens is performed using autoradiography, fluorescence microscopy or immunohistochemistry, respectively. ISH can also use two or more probes labeled with radioactive or other non-radioactive labels to detect two or more transcripts simultaneously.

Southern and Northern blots were used to detect specific DNA or RNA sequences, respectively. DNA or RNA extracted from the sample is fragmented, separated by electrophoresis on a matrix gel, and then transferred to a membrane filter. The filter-bound DNA or RNA is hybridized to a labeled probe complementary to the sequence of interest. Detecting the hybridization probes bound to the filter. A variation of this procedure is a reverse Northern blot, in which the substrate nucleic acid immobilized to the membrane is a collection of isolated DNA fragments and the probe is RNA extracted from the tissue and labeled.

The nucleic acid amplification method of the present invention is selected from the group consisting of Polymerase Chain Reaction (PCR), reverse transcription polymerase chain reaction (RT-PCR), Transcription Mediated Amplification (TMA), Ligase Chain Reaction (LCR), Strand Displacement Amplification (SDA), and Nucleic Acid Sequence Based Amplification (NASBA). Among them, PCR requires reverse transcription of RNA into DNA before amplification (RT-PCR), TMA and NASBA to directly amplify RNA.

Generally, PCR uses multiple cycles of denaturation, annealing of primer pairs to opposite strands, and primer extension to exponentially increase the copy number of a target nucleic acid sequence; RT-PCR Reverse Transcriptase (RT) is used to prepare complementary DNA (cDNA) from mRNA, and the cDNA is then amplified by PCR to produce multiple copies of the DNA; TMA autocatalytically synthesizes multiple copies of a target nucleic acid sequence under substantially constant conditions of temperature, ionic strength and pH, wherein multiple RNA copies of the target sequence autocatalytically generate additional copies, TMA optionally including the use of blocking, partial, terminating and other modifying moieties to improve the sensitivity and accuracy of the TMA process; LCR with target nucleic acid adjacent region hybridization of two sets of complementary DNA oligonucleotides. The DNA oligonucleotides are covalently linked by DNA ligase in repeated cycles of heat denaturation, hybridization, and ligation to produce a detectable double-stranded ligated oligonucleotide product; the SDA uses multiple cycles of the following steps: primer sequence pairs anneal to opposite strands of the target sequence, primer extension in the presence of dNTP α S to produce double-stranded hemiphosphorothioated (phosphorothioated) primer extension products, endonuclease-mediated nicking of the hemimodified restriction enzyme recognition site, and polymerase-mediated extension from the 3' end of the nick to displace the existing strand and produce a strand for the next round of primer annealing, nicking and strand displacement, thereby causing geometric amplification of the products.

The protein immunization methods of the invention include sandwich immunoassays, such as sandwich ELISA, in which the detection of a biomarker is performed using two antibodies that recognize different epitopes on the biomarker; radioimmunoassay (RIA), direct, indirect or contrast enzyme-linked immunosorbent assay (ELISA), Enzyme Immunoassay (EIA), Fluorescence Immunoassay (FIA), western blot, immunoprecipitation, and any particle-based immunoassay (e.g., using gold, silver or latex particles, magnetic particles, or quantum dots). The immunization can be carried out, for example, in the form of microtiter plates or strips.

The immunization method according to the present invention may be based on, for example, any of the following methods.

Immunoprecipitation is the simplest immunoassay method; this method measures the amount of precipitate that is formed after the reagent antibody has been incubated with the sample and reacted with the target antigen present therein to form insoluble aggregates. The immunoprecipitation can be either qualitative or quantitative.

In a particle immunoassay, multiple antibodies are attached to the particle and the particle is capable of binding many antigenic molecules simultaneously. This greatly accelerates the speed of the visible reaction. This allows for a fast and sensitive detection of the biomarker.

In immunoturbidimetry (immunonephelometry), the interaction of an antibody and a target antigen on a biomarker causes the formation of an immune complex that is too small to precipitate. However, these complexes will scatter incident light, which can be measured using a turbidimeter. The concentration of the antigen (i.e. biomarker) can be determined within a few minutes of the reaction.

Radioimmunoassay (RIA) methods use radioisotopes such as I125 to label antigens or antibodies. The isotope used emits gamma rays, which are usually measured after removal of unbound (free) radiolabel. The main advantages of RIA compared to other immunoassays are higher sensitivity, easy signal detection and confirmation, fast assay. The main disadvantages are the health and safety risks posed by the use of radiation and the time and expense associated with maintaining the licensed radiation safety and disposal procedures. For this reason, RIA has been largely replaced by enzyme immunoassays in routine clinical laboratory practice.

Enzyme Immunoassays (EIAs) have evolved as alternatives to Radioimmunoassays (RIA). These methods use enzymes to label the antibody or target antigen. The sensitivity of EIA is close to that of RIA and there is no risk caused by radioisotopes. One of the most widely used EIA methods for detection is enzyme-linked immunosorbent assay (ELISA). The ELISA method may use two antibodies, one specific for the target antigen and the other coupled to an enzyme, the addition of an enzyme substrate causing the generation of a chemiluminescent or fluorescent signal.

Fluorescence Immunoassay (FIA) refers to an immunoassay that uses a fluorescent label or an enzyme label that acts on a substrate to form a fluorescent product. Fluorescence measurements are inherently more sensitive than chromatic (spectrophotometric) measurements. Thus, the FIA method has higher analytical sensitivity than the EIA method using absorption (optical density) measurement.

Chemiluminescent immunoassays use a chemiluminescent label that produces light when excited by chemical energy; the emission is measured using a photodetector.

Thus, the immunization method according to the present invention can be carried out using well-known methods. Any direct (e.g., using a sensor chip) or indirect method may be used in the detection of the biomarkers of the invention.

Levels of the biomarkers of the invention can also be determined using mass spectrometry techniques including, but not limited to, electrospray ionization mass spectrometry (ESI-MS), ESI-MS/MS, ESI-MS/(MS) n, matrix assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF-MS), surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS), laser desorption/ionization mass spectrometry (DIOS) of silicon surfaces, Secondary Ion Mass Spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), tandem time-of-flight (TOF/TOF) techniques known as ultraflex III TOF/TOF, atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS/MS, APCI- (MS) n APPI-MS/MS and APPI- (MS) sup.N, quadrupole mass spectrometry, Fourier Transform Mass Spectrometry (FTMS), quantitative mass spectrometry and ion trap mass spectrometry.

Sample preparation strategies are used to label and enrich the samples prior to mass spectrometric characterization of protein biomarkers and determination of biomarker values. Labeling methods include, but are not limited to, equivalent ectopic tags (iTRAQ) for relative and absolute quantitation and stable isotopes labeled with amino acids in cell culture (SILAC). Capture reagents used to selectively enrich a sample for candidate biomarker proteins prior to mass spectrometry include, but are not limited to, aptamers, antibodies, nucleic acid probes, chimeras, small molecules, F (ab')2 fragments, single chain antibody fragments, Fv fragments, single chain Fv fragments, nucleic acids, lectins, ligand binding receptors, affybods, nanobodies, ankyrins, domain antibodies, optionally antibody scaffold (e.g., diabodies, etc.) imprinted polymers, avimers, polypeptide mimetics, peptoids, peptide nucleic acids, threose nucleic acids, hormone receptors, cytokine receptors, and synthetic receptors, and modified forms and fragments of these.

The terms "sample" and "sample" are used interchangeably herein to refer to a composition obtained or derived from a subject (e.g., an individual of interest) that comprises cells and/or other molecular entities to be characterized and/or identified based on, for example, physical, biochemical, chemical, and/or physiological characteristics. For example, the phrase "disease sample" or variants thereof refers to any sample obtained from a subject of interest that is expected or known to contain the cells and/or molecular entities to be characterized. Samples include, but are not limited to, tissue samples (e.g., tumor tissue samples), primary or cultured cells or cell lines, cell supernatants, cell lysates, platelets, serum, plasma, vitreous humor, lymph, synovial fluid, follicular fluid, semen, amniotic fluid, milk, whole blood, blood-derived cells, urine, cerebrospinal fluid, saliva, sputum, tears, sweat, mucus, tumor lysates, and tissue culture fluids, tissue extracts such as homogenized tissue, tumor tissue, cell extracts, and combinations thereof.

As a preferred embodiment, the sample is selected from blood, serum, plasma.

The invention provides a product for diagnosing retinopathy, which comprises a reagent for detecting the biomarker in a sample; and instructions for using the kit to assess whether a subject is suffering from or susceptible to retinopathy may be included.

The most reliable results are possible when processing samples in a laboratory environment. For example, a sample may be taken from a subject in a doctor's office and then sent to a hospital or commercial medical laboratory for further testing. However, in many cases, it may be desirable to provide immediate results at the clinician's office or to allow the subject to perform the test at home. In some cases, the need for testing that is portable, prepackaged, disposable, ready to use by the subject without assistance or guidance, etc., is more important than a high degree of accuracy. In many cases, especially in the case of physician visits, it may be sufficient to perform a preliminary test, even a test with reduced sensitivity and/or specificity. Thus, assays provided in product form can involve detecting and measuring relatively small amounts of biomarkers to reduce the complexity and cost of the assay.

Any form of sample assay capable of detecting a sample biomarker described herein may be used. Typically, the assay will quantify the biomarkers in the sample to an extent, for example whether their concentration or amount is above or below a predetermined threshold. Such kits may take the form of test strips, dipsticks, cartridges, chip-based or bead-based arrays, multi-well plates, or a series of containers, and the like. One or more reagents are provided to detect the presence and/or concentration and/or amount of a selected sample biomarker. The sample from the subject may be dispensed directly into the assay or indirectly from a stored or previously obtained sample. The presence or absence of a biomarker above or below a predetermined threshold may be indicated, for example, by chromogenic, fluorogenic, electrochemiluminescent or other output (e.g., in an Enzyme Immunoassay (EIA), such as an enzyme-linked immunoassay (ELISA)).

In one embodiment, the product may comprise a solid substrate such as a chip, slide, array, or the like, having reagents capable of detecting and/or quantifying one or more sample biomarkers immobilized at predetermined locations on the substrate. As an illustrative example, the chip may be provided with reagents immobilized at discrete predetermined locations for detecting and quantifying the presence and/or concentration and/or amount of a biomarker in a sample. As described above, a reduced or increased level of the biomarker is found in a sample of a subject with retinopathy. The chip may be configured such that a detectable output (e.g. a colour change) is provided only when the concentration of one or more of these biomarkers exceeds a threshold value selected or differentiated between the concentration and/or amount of the biomarker indicative of a control subject and the concentration and/or amount of the biomarker indicative of a patient suffering from or susceptible to retinopathy. Thus, the presence of a detectable output (such as a color change) immediately indicates that a significantly reduced level of biomarker is contained in the sample, indicating that the subject is suffering from or susceptible to retinopathy.

In the present invention, biomarkers may be determined individually, or in one embodiment of the invention, they may be determined simultaneously, for example using a chip or bead-based array technology. The concentration of the biomarkers is then interpreted independently, for example using individual retention of each marker, or a combination thereof.

As the skilled artisan will appreciate, the step of associating a marker level with a certain likelihood or risk may be implemented and realized in different ways. Preferably, the measured concentrations of the protein and one or more other markers are mathematically combined and the combined value is correlated with the underlying diagnostic problem. The determination of marker values may be combined by any suitable prior art mathematical method.

Preferably, the mathematical algorithm applied in the marker combination is a logarithmic function. Preferably, the result of applying such a mathematical algorithm or such a logarithmic function is a single value. Such values can be readily correlated, in terms of underlying diagnostic problems, with, for example, individual risk for retinopathy or with other intentional diagnostic uses that help assess retinopathy patients. In a preferred manner, such a logarithmic function is obtained as follows: a) classifying individuals into groups, e.g., normal persons, individuals at risk of retinopathy, patients with retinopathy, etc., b) identifying markers that differ significantly between these groups by univariate analysis, c) logarithmic regression analysis to assess independent difference values of the markers that can be used to assess these different groups, and d) constructing a logarithmic function to combine the independent difference values. In this type of analysis, the markers are no longer independent, but represent a combination of markers.

The logarithmic function used to correlate marker combinations with disease preferably employs algorithms developed and obtained by applying statistical methods. For example, suitable statistical methods are Discriminant Analysis (DA) (i.e., linear, quadratic, regular DA), Kernel methods (i.e., SVM), nonparametric methods (i.e., k-nearest neighbor classifiers), PLS (partial least squares), tree-based methods (i.e., logistic regression, CART, random forest methods, boosting/bagging methods), generalized linear models (i.e., logistic regression), principal component-based methods (i.e., SIMCA), generalized additive models, fuzzy logic-based methods, neural network-and genetic algorithm-based methods. The skilled person will not have problems in selecting a suitable statistical method to evaluate the marker combinations of the invention and thereby obtain a suitable mathematical algorithm. In one embodiment, the statistical method used to obtain the mathematical algorithm used in assessing retinopathy is selected from DA (i.e., linear, quadratic, regular discriminant analysis), Kernel method (i.e., SVM), non-parametric method (i.e., k-nearest neighbor classifier), PLS (partial least squares), tree-based methods (i.e., logistic regression, CART, random forest method, boosting method), or generalized linear models (i.e., logarithmic regression).

The area under the receiver operating curve (AUC) is an indicator of the performance or accuracy of a diagnostic procedure. The accuracy of a diagnostic method is best described by its Receiver Operating Characteristics (ROC). ROC plots are line graphs of all sensitivity/specificity pairs derived from continuously varying decision thresholds across the entire data range observed.

The clinical performance of a laboratory test depends on its diagnostic accuracy, or the ability to correctly classify a subject into a clinically relevant subgroup. Diagnostic accuracy measures the ability to correctly discriminate between two different conditions of the subject under investigation. Such conditions are, for example, health and disease or disease progression versus no disease progression.

In each case, the ROC line graph depicts the overlap between the two distributions by plotting sensitivity versus 1-specificity for the entire range of decision thresholds. On the y-axis is the sensitivity, or true positive score [ defined as (number of true positive test results)/(number of true positives + number of false negative test results) ]. This is also referred to as a positive for the presence of a disease or condition. It is calculated from the affected subgroups only. On the x-axis is the false positive score, or 1-specificity [ defined as (number of false positive results)/(number of true negatives + number of false positive results) ]. It is an indicator of specificity and is calculated entirely from unaffected subgroups. Because the true and false positive scores are calculated completely separately using test results from two different subgroups, the ROC line graph is independent of the prevalence of disease in the sample. Each point on the ROC line graph represents a sensitivity/1-specificity pair corresponding to a particular decision threshold. One test with perfect discrimination (no overlap of the two result distributions) has a ROC line graph that passes through the upper left corner where the true positive score is 1.0, or 100% (perfect sensitivity), and the false positive score is 0 (perfect specificity). A theoretical line graph for an undifferentiated test (the results of the two groups are equally distributed) is a 45 ° diagonal from the lower left to the upper right. Most line graphs fall between these two extremes. (if the ROC line graph falls well below the 45 ° diagonal, this is easily corrected by reversing the criteria for "positive" from "greater to" less than "or vice versa.) qualitatively, the closer the line graph is to the upper left corner, the higher the overall accuracy of the test.

One convenient goal to quantify the diagnostic accuracy of a laboratory test is to express its performance by a single numerical value. The most common global metric is the area under the ROC curve (AUC). Conventionally, this area is always ≧ 0.5 (if not, the decision rule can be reversed to do so). The range of values was between 1.0 (test values that perfectly separated the two groups) and 0.5 (no significant distribution difference between the test values of the two groups). The area depends not only on a particular part of the line graph, such as the point closest to the diagonal or the sensitivity at 90% specificity, but also on the entire line graph. This is a quantitative, descriptive representation of how the ROC plot is close to perfect (area 1.0).

Overall assay sensitivity will depend on the specificity required to carry out the methods disclosed herein. In certain preferred settings, a specificity of 75% may be sufficient, and statistical methods and resulting algorithms may be based on this specificity requirement. In a preferred embodiment, the method for assessing an individual at risk for retinopathy is based on specificity of 80%, 85%, or also preferably 90% or 95%.

The technical solutions of the present invention are further illustrated by the following specific examples, which do not represent limitations to the scope of the present invention. Insubstantial modifications and adaptations of the present invention by others of the concepts fall within the scope of the invention.

Example 1 screening of retinopathy differential proteins

1. Sample collection

Blood samples of 14 patients with diabetic retinopathy were collected as a test group (DR) and blood samples of 6 patients with no retinopathy as normal controls (NDR), wherein 14 patients with diabetic retinopathy included 7 non-proliferative diabetic retinopathy groups (NPDR) and 7 proliferative diabetic retinopathy groups (PDR). All patients had no history of hypertension, cardiovascular and cerebrovascular diseases, etc. The patient profile statistics are shown in table 1.

TABLE 1 patient data

2. Protein sequencing

2.1 protein extraction

1) Taking out a sample in a frozen state;

2) use of Pierce^TMRemoving the high-abundance Protein by using a Top 12 Absundant Protein Depletion Spin Columns high-abundance kit;

3) removing the high-abundance protein and collecting the protein solution according to the kit instruction;

4) concentrating the sample to a proper volume by using a 3KD ultrafiltration tube, and replacing the sample by using 8M urea solution (containing protease inhibitor) for 3 times;

5) BCA quantification, SDS-PAGE electrophoresis.

2.2 Total protein BCA assay

1) Preparing a BCA working solution: preparing a proper amount of BCA working solution by adding 50 volumes of BCA reagent A and 1 volume of BCA reagent B (50:1), and fully and uniformly mixing;

2) adding the standard substance into standard substance wells of 96-well plate in an amount of 0, 4, 8, 12, 16, 20 μ l, adding urea lysis solution to make up to 20 μ l, wherein the concentrations of the standard substance are 0, 0.1, 0.2, 0.3, 0.4, 0.5mg/ml respectively;

3) mu.l of each sample was mixed with 18. mu.l of water, followed by addition of 200. mu.l of BCA working solution. Shaking and mixing evenly, and reacting for 30min at 37 ℃;

4) absorbance at 562nm was read using a SPECTRA MAX microplate reader. The protein concentration of the samples was calculated from the standard curve and the sample volume used, and the sample concentrations are shown in table 2.

TABLE 2 sample concentrations

Serial number	Sample numbering	Concentration (mg/ml)	Total amount (μ g)	Initial volume
					1	4	1.527	99	30
2	5	1.28	83	30
					3	7	1.34	87	30
4	8	1.048	68	30
					5	10	1.277	83	30
6	11	1.263	82	30
					7	13	1.373	89	30
8	15	1.018	66	30
					9	18	1.21	79	30
10	19	1.372	89	30
					11	20	1.182	77	30
12	21	1.229	80	30
					13	22	1.356	88	30
14	23	1.34	87	30
					15	24	1.164	76	30
16	25	1.31	85	30
					17	26	1.328	86	30
18	28	1.01	66	30
					19	29	1.461	95	30
20	41	1.057	69	30

3. Polypeptide sample preparation

1) Taking 100 mu g of protein solution of a sample to be detected respectively;

2) adding TEAB (Triethylammonium bicarbonate buffer) to make TEAB final concentration 100 mM;

3) adding TCEP (tris (2-carboxyethyl) phosphine) to give a final concentration of 10mM of TCEP, and reacting at 37 ℃ for 60 min;

4) adding IAM (Iodoacetamide ), and reacting at room temperature in dark for 40min until the final concentration of IAM is 40 mM;

5) centrifuging at 10000g for 20min, and collecting precipitate;

6) the sample was thoroughly dissolved with 100. mu.l of 100mM TEAB;

7) according to the enzyme: trypsin (Trypsin) was added to the protein (m/m) ═ 1:50, and the mixture was digested at 37 ℃ overnight.

4. Establishing a spectrogram library

4.1 high pH RP-UPLC first dimension separation

Equivalently mixing the polypeptide samples subjected to enzymolysis, performing vacuum centrifugal concentration, and redissolving the polypeptide samples by using UPLC (ultra performance liquid chromatography) sample loading buffer solution;

high PH liquid phase separation was performed using a reverse phase C18 column with the following parameters:

column information: ACQUITY UPLC BEH C18 Column 1.7 μm,2.1mm X150 mm (Waters corporation, USA)

Chromatography apparatus: thermo Scientific Vanqish Flex UHPLC

Phase A: 2% acetonitrile (adjusted to pH10 with Ammonia water)

Phase B: 80% acetonitrile (adjusted to pH10 with Ammonia)

Ultraviolet detection wavelength: 214nm

Flow rate: 200 mul/min

Gradient: time of 47min

TABLE 2 UPLC gradient

A total of 20 fractions were collected according to peak type and time, concentrated by vacuum centrifugation (Christ RVC 2-25, Christ, Germany), solubilized with mass spectrometry loading buffer, scaled to 10 × iRT peptide and mixed for secondary analysis.

4.2 liquid phase tandem Mass Spectrometry detection

Data acquisition software: thermo Xcalibur 4.0(Thermo, USA)

Information of the reversed phase column: c18 column (75 μm. times.25 cm, Thermo, USA)

Chromatography apparatus: easy nLC-1200(Thermo, USA)

Mass spectrometer: q _ active HF-X (Thermo, USA)

Chromatographic separation time: 120min

Phase A: 2% ACN 0.1% formic acid

Phase B: 80% ACN 0.1% formic acid

Flow rate: 300nL/min

Gradient: shown in Table 3

TABLE 3 easy 1200 liquid phase gradient

MS scanning Range (m/z): 350-1500, collection mode: DDA, Top 20;

top 20 (the 20 most intense signals of the parent ions were selected for secondary fragmentation);

first-order mass spectrum resolution: 60000, AGC target: 3e6, maximum injection time: 20ms, fragmentation mode: HCD;

secondary resolution: 15000, AGC target: 5e4, maximum injection time: 45ms, fixed first mass: 100 m/z;

minimum AGC target: 8e3, Intensity threshold: 1.8e5, dynamic exclusion time: for 30 s.

4.3 database search

The library using Software version is ProteMeDiscoverTM Software 2.4. And when searching the database, submitting the raw file to a server where the software is located, selecting the established database, and then searching the database.

5. Individual sample SWATH mass spectrometric detection

5.1 desalting and quantifying of peptide fragment

1) After trypsinization, taking an equivalent sample, and drying the peptide fragment by a vacuum centrifugal concentrator;

2) re-dissolving the peptide segment after enzymolysis and drying by using 0.1% TFA;

3) desalting the peptide segment by using HLB, and drying by using a vacuum concentrator;

4) using Thermo Fisher Scientific (cat #: 23275) And (3) carrying out peptide fragment quantification by using the peptide fragment quantification kit.

5.2 liquid phase tandem Mass Spectrometry detection

Dissolving equivalent peptide fragments by using mass spectrum loading buffer solution, adding 10 multiplied by iRT peptide fragments according to a proportion, uniformly mixing, and carrying out SWATH detection analysis;

data acquisition software: thermo Xcalibur 4.0(Thermo, USA)

Information of the reversed phase column: c18 column (75 μm. times.25 cm, Thermo, USA)

Chromatography apparatus: easy nLC-1200(Thermo, USA)

Mass spectrometer: q _ active HF-X (Thermo, USA)

Chromatographic separation time: 120min

Phase A: 2% ACN 0.1% formic acid

Phase B: 80% ACN 0.1% formic acid

Flow rate: 300nL/min

Gradient: same as above

MS scanning Range (m/z): 350-1500;

fragmentation pattern HCD, Variable Window: 30.

the SWATH window settings are as follows:

TABLE 4 SWATH Window

SWATH Exp Index	Mass[m/z]	Window width[m/z]
			1	372	45
2	406.5	26
			3	430.5	24
4	453.5	24
			5	473	17
6	489	17
			7	505	17
8	521	17
			9	537	17
10	553	17
			11	569	17
12	585	17
			13	601	17
14	617	17
			15	634	19
16	652	19
			17	670.5	20
18	690.5	22
			19	711.5	22
20	733	23
			21	755.5	24
22	779	25
			23	804	27
24	831	29
			25	862	35
26	899	41
			27	942.5	48
28	995	59
			29	1065.5	84
30	1303.5	394

6. SWATH data analysis

Using a proteomediscover filter^TMSoftware 2.4 searches the hierarchical database building data, builds a digital spectrogram database as a qualitative basis of subsequent SWATH quantitative analysis, and imports the spectrogram database into Spectronaut^TMPerforming sub-ion peak extraction on SWATH original data, iRT correcting retention time, selecting 6 Peptide segments for each Protein and 3 sub-ions for each Peptide segment for quantitative analysis, wherein Protein FDR is less than or equal to 0.01, Peptide Confidence is more than or equal to 99%, XIC width is less than or equal to 75ppm, shared Peptide segments and modified Peptide segments are excluded, and peak areas are calculated and added to obtain a quantitative result. iRT most data points are uniformly distributed on the fitting curve, and the experimental result is ideal. Identification of the identified proteinThe quantitative results are normalized by the ratio of the total peak area and then used for screening and counting the differential proteins.

7. Differential protein analysis

Calculating the difference significance p value among samples by using a t.test function in an R language, wherein the screening standard of the protein with the difference expression is as follows: p < 0.05& (FC < 0.83 or FC > 1.20)

8. Results

The results of the analysis are shown in fig. 1, FCGR3A, NAGLU or CNTN1 are significantly upregulated in DR.

Example 2 diagnostic Performance validation

And (3) drawing a Receiver Operating Curve (ROC) by using R, analyzing the AUC value, sensitivity and specificity, and judging the diagnostic efficacy of the indexes alone or in combination.

When the diagnosis efficiency of the index combination is judged, firstly, the genes are subjected to logistic regression, wherein independent variables are corresponding indexes, dependent variables are diseased conditions, the probability of whether each individual is diseased or not can be calculated through a fitted regression curve, and different probability partition threshold values are determined to obtain a prediction result. The optimal probability partition threshold is determined by the point at which the john index is maximum. According to the determined probability division threshold values, the sensitivity, specificity, positive predicted values, negative predicted values and the like of each combined detection scheme in the training set and the verification set can be calculated.

The diagnostic potency of FCGR3A, NAGLU, CNTN1 alone or in combination is shown in table 5 and fig. 2, and the AUC values of FCGR3A, NAGLU, CNTN1 are 0.893, 0.798, 0.833 respectively, suggesting that FCGR3A, NAGLU, CNTN1 have better potency for DR diagnosis.

TABLE 5 AUC values

The above description of the embodiments is only intended to illustrate the method of the invention and its core idea. It should be noted that, for those skilled in the art, without departing from the principle of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications will also fall into the protection scope of the claims of the present invention.