CN113718026A

CN113718026A - Reagent and product for diagnosing diabetic nephropathy disease and application thereof

Info

Publication number: CN113718026A
Application number: CN202111021406.4A
Authority: CN
Inventors: 杨承刚; 李雨晨; 刘乐凯; 李丹
Original assignee: Qingdao Yangshen Biomedical Co Ltd
Current assignee: Qingdao Yangshen Biomedical Co Ltd
Priority date: 2021-09-01
Filing date: 2021-09-01
Publication date: 2021-11-30

Abstract

The invention discloses a reagent for diagnosing diabetic nephropathy, a product and application thereof, and particularly relates to biomarkers TNFAIP6 and RUNX3, wherein the TNFAIP6 and the RUNX3 are both up-regulated in diabetic nephropathy patients, and the combination of the TNFAIP6 and the RUNX3 has better diagnosis efficiency in the diagnosis of diabetic nephropathy, so that the TNFAIP6 and the RUNX3 can be used as diagnosis and/or treatment targets to be applied to clinical diagnosis and treatment of diabetic nephropathy.

Description

Reagent and product for diagnosing diabetic nephropathy disease and application thereof

Technical Field

The invention belongs to the field of gene biological agents, and particularly relates to a reagent and a product for diagnosing diabetic nephropathy and application thereof.

Background

Diabetic Nephropathy (DN) is one of the most serious complications of diabetes, and is the major microvascular complication of diabetes, and DN mainly refers to Diabetic glomerulosclerosis, a glomerular disease mainly caused by vascular damage. Early stage is asymptomatic, and blood pressure may be normal or high. The incidence increases with the course of diabetes. The kidney volume is increased in the early stage of diabetes, the glomerular filtration rate is increased and is in a high filtration state, interstitial proteinuria or micro albuminuria gradually appears, continuous proteinuria, edema, hypertension and glomerular filtration rate reduction appear along with the prolongation of the disease course, and further renal insufficiency and uremia are one of the main death reasons of the diabetes.

DN is a main cause of End-stage renal disease (ESRD), once a patient progresses to ESRD, the patient needs to receive renal replacement and renal transplantation treatment, prognosis outcome is poor, blood sugar fluctuation, non-aryblood pressure, nocturnal systolic pressure, blood uric acid, vitamin D level, thyroid gland dysfunction and the like are the development risks of DN, the life quality of the patient is seriously influenced, and serious economic burden is brought to families, so that the DN generation development mechanism is clarified, and non-traumatic markers for early diagnosis of the DN patient are discovered and verified to have very important significance.

At present, the most common early diagnosis marker in clinical diabetic nephropathy patients is microalbuminuria. However, in practice, microalbuminuria still has many deficiencies and limitations as a diagnostic marker, and the microalbuminuria level of a patient is affected clinically by many other physiological and pathological factors, such as body temperature rise, diet, body weight, and the like. Secondly, the level of microalbuminuria is stable, for example, the use of antihypertensive drugs can reduce the level of microalbuminuria, which affects the judgment of kidney diseases of DN patients; and the level of microalbuminuria is less relevant to diabetic nephropathy in the early stages of DN. In view of this, there is a need in the art to find suitable markers that can be used for diagnosing DN, particularly early diagnosis of DN.

Disclosure of Invention

The invention aims to provide a reagent and a product for early screening and diagnosis of diabetic nephropathy based on machine learning and various statistical analysis methods, wherein the reagent and the product can be used for early screening and diagnosis of diabetic nephropathy to predict occurrence and development conditions of diseases, and provide an auxiliary diagnosis method for early diagnosis of diabetic nephropathy clinically.

In order to achieve the purpose, the invention adopts the following technical scheme:

in one aspect, the invention provides the use of a reagent for detecting a biomarker in a sample in the manufacture of a diagnostic product for diabetic nephropathy.

Further, the biomarkers are TNFAIP6 and RUNX 3.

Still further, the sample is selected from blood or tissue.

Still further, the sample is derived from a subject, preferably a human.

Still further, the diagnostic product comprises a reagent for detecting the expression level of TNFAIP6 and RUNX3 in a sample.

Further, the reagent includes a reagent for detecting the expression level of TNFAIP6 and RUNX3 in a sample by a sequencing technique, a nucleic acid hybridization technique, a nucleic acid amplification technique and a protein immunization technique.

Still further, the sequencing technologies are nucleic acid sequencing technologies, including chain terminator (Sanger) sequencing technology and dye terminator sequencing technology, and 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, and in addition, the sequencing technologies also include next generation sequencing technologies (i.e., deep sequencing/high throughput sequencing technologies), which is a single-molecule cluster-based sequencing-by-synthesis technology based on proprietary principles of reversible termination chemical reactions. 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.

Still further, such nucleic acid hybridization techniques include, but are not limited to, In Situ Hybridization (ISH), microarrays, and Southern or Northern blots.

Still further, the nucleic acid amplification technique 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).

Still further, the protein immunization techniques include sandwich immunoassays, such as sandwich ELISA, wherein the detection of the biomarkers TNFAIP6, RUNX3 is performed using two antibodies recognizing different epitopes on the biomarkers TNFAIP6, RUNX 3; 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).

Still further, the agent is selected from:

primers for specifically amplifying TNFAIP6 and RUNX 3; or

A probe specifically recognizing TNFAIP6 and RUNX 3; or

A binding agent that specifically binds to a protein encoded by TNFAIP6, RUNX 3.

Still further, the binding agent that specifically binds to a protein encoded by TNFAIP6, RUNX3 includes a receptor for the protein, an antibody directed against the protein, a peptide antibody directed against the protein, a lectin that binds the protein, a bispecific dual binding agent, or a bispecific antibody.

Still further, the binding agent that specifically binds to a protein encoded by TNFAIP6, RUNX3 is an antibody directed against said protein.

In another aspect, the present invention provides a diagnostic product for diabetic nephropathy.

Further, the diagnostic product comprises reagents for detecting the biomarkers TNFAIP6 and RUNX 3.

Further, the agent is selected from:

primers for specifically amplifying TNFAIP6 and RUNX 3; or

A probe specifically recognizing TNFAIP6 and RUNX 3; or

Furthermore, the diagnostic product comprises a preparation, a kit, a chip and a test strip.

Further, the preparation is a biological preparation comprising reagents for detecting the biomarkers TNFAIP6, RUNX 3;

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

still further, the kit further comprises instructions for whether the subject has, or is at risk for having, diabetic nephropathy.

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

In yet another aspect, the present invention provides the use of TNFAIP6, RUNX3 in the construction of a computational model or a system in which said computational model is embedded for the prediction of diabetic nephropathy;

wherein the calculation model is operated by a bioinformatics method with the expression levels of TNFAIP6 and RUNX3 as input variables, and outputs the risk probability of diabetic nephropathy.

In yet another aspect, the present invention provides a device for diagnosing diabetic nephropathy.

Further, the device comprises a processor, an input module and an output module;

the processor is used for carrying out logic operation on input information by adopting a bioinformatics method; an input module for inputting an expression level of TNFAIP6, RUNX3 in a sample, a computer readable medium comprising instructions that when executed by the processor perform an algorithm on the input expression level of TNFAIP6, RUNX 3; the output module is used for outputting whether the subject suffers from the diabetic nephropathy or risks suffering from the diabetic nephropathy.

Has the advantages that:

according to the invention, TNFAIP6 and RUNX3 are selected as gene markers for diagnosis, so that rapid and effective diagnosis of diabetic nephropathy can be realized, an auxiliary diagnosis method for early diagnosis of diabetic nephropathy is provided for a clinician, and further warning is provided for a subject so as to realize early intervention.

Drawings

Embodiments of the invention are described in detail below with reference to the attached drawing figures, wherein:

FIG. 1 shows the differential expression profiles of TNFAIP6 and RUNX3 gene mRNA, wherein, Panel A: TNFAIP6, training set, panel B: TNFAIP6, validation set, panel C: RUNX3, training set, graph D: RUNX3, validation set;

FIG. 2 shows ROC profile of TNFAIP6, RUNX3 gene for diagnosing diabetic nephropathy, wherein, panel A: TNFAIP6, training set, panel B: TNFAIP6, validation set, panel C: RUNX3, training set, graph D: RUNX3, validation set;

FIG. 3 shows ROC plots for TNFAIP6+ RUNX3 in combination for the diagnosis of diabetic nephropathy, wherein panel A: training set, graph B: and (5) verifying the set.

Detailed Description

In the context of the present invention, the term "marker", as well as "biomarker", "genetic marker", refers to an indicator of a patient's phenotype, such as a pathological state or a possible reactivity to a therapeutic agent, which can be detected in a biological sample of said patient, biomarkers including, but not limited to, DNA, RNA, proteins, small molecule metabolites, carbohydrates, glycolipid-based molecules, etc.;

in a particular embodiment of the invention, the marker is TNFAIP6 and/or RUNX3, preferably the markers are the genes TNFAIP6(TNF alpha induced protein 6, Gene ID: 7130) and RUNX3(RUNX family transcription factor 3, Gene ID: 864), encompassing full-length, unprocessed genes, as well as genes derived from any form of processing in the cell, which encompasses naturally occurring variants of the genes. Details of the gene TNFAIP6 and the gene RUNX3 are available at h ttps:// www.ncbi.nlm.nih.gov/gene.

In the context of the present invention, the term "sample", as well as "sample", "subject sample", refers to a composition obtained or derived from a subject of interest, which comprises cellular entities and/or other molecular entities to be characterized and/or identified, e.g. on the basis of physical, biochemical, chemical and/or physiological characteristics. The sample may be obtained from the blood of a subject and other fluid samples of biological origin and tissue samples, such as biopsy tissue samples or tissue cultures or cells derived therefrom. The source of the tissue sample may be solid tissue, such as from a fresh, frozen and/or preserved organ or tissue sample, biopsy tissue or aspirate; blood or any blood component; a body fluid; cells from any time of pregnancy or development of the individual; or plasma. The term "sample" includes a biological sample that has been treated in any way after it has been obtained, e.g., by treatment with a reagent, stabilization, or enrichment for certain components (e.g., proteins or polynucleotides), or embedded in a semi-solid or solid matrix for sectioning purposes. Samples described herein include, but are not limited to, whole blood, blood-derived cells, serum, plasma, lymph, synovial fluid, cell extracts, and combinations thereof.

In the context of the present invention, the term "primer", as well as "amplification primer", refers to a nucleic acid fragment comprising 5-100 nucleotides, preferably said primer or amplification primer comprises 15-30 nucleotides capable of initiating an enzymatic reaction (e.g., an enzymatic amplification reaction).

In the context of the present invention, the term "probe" refers to a nucleic acid sequence comprising at least 5 nucleotides, e.g.comprising 5 to 100 nucleotides, which probe is capable of hybridizing under the specified conditions with the expression product of the target gene or with the amplification product of this expression product to form a complex. The hybridization probes may also include labels for detection. Such labels include, but are not limited to, labels for fluorescent quantitative PCR or fluorescent in situ hybridization.

In the context of the present invention, the term "kit" refers to an article of manufacture (e.g., a package or container) comprising probes for specifically detecting the biomarker genes or proteins of the present invention;

in certain embodiments, the article of manufacture is marketed, distributed, or sold as a unit for performing the methods of the present invention. Such kits may comprise carrier means compartmentalized to receive, in close confinement, one or more container means (e.g., vials, tubes, etc.), each container means comprising one of the separate components to be used in the method. For example, one of the container means may comprise a probe that carries or can carry a detectable label. Such probes may be polynucleotides specific for polynucleotides of one or more genes comprising gene expression characteristics. Where the kit utilizes nucleic acid hybridization to detect a target nucleic acid, the kit can also have a container containing one or more nucleic acids for amplifying the target nucleic acid sequence and/or a container containing a reporter means, such as a biotin-binding protein, such as avidin or streptavidin, bound to a reporter molecule, such as an enzymatic, fluorescent, or radioisotope label;

a kit will generally comprise the above-described container and one or more additional containers containing commercially and user-desired materials, including buffers, diluents, filters, needles, syringes, and package inserts containing instructions for use. A label may be present on the container to indicate that the composition is for a particular therapeutic or non-therapeutic application, and may also indicate the direction of in vivo or in vitro use, such as those described above. Other optional components of the kit include one or more buffers (e.g., blocking buffer, wash buffer, substrate buffer, etc.), other reagents (e.g., substrate chemically altered by enzymatic labeling), epitope retrieval solutions, control samples (positive and/or negative controls), control sections, and the like.

In the context of the present invention, the term "diagnosis" or "diagnosis aid" refers to the identification or classification of a molecular or pathological state, disease or disorder. For example, by molecular characterization (e.g., characterized by the expression of a particular gene or one or a combination of the proteins encoded by the gene), the identification of whether or not and the risk of having diabetic nephropathy is identified.

As the skilled person will be familiar with, the step of correlating biomarker levels with a certain likelihood or risk may be carried out and carried out 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 biomarker combinations 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 to, for example, an individual's risk for diabetic nephropathy or to other diagnostic uses of interest that are helpful in assessing diabetic nephropathy patients, based on underlying diagnostic questions. In a preferred manner, such a logarithmic function is obtained as follows: a) classifying the individual into a group, e.g., a normal person, an individual at risk of having diabetic nephropathy, a patient having diabetic nephropathy, etc.; b) identifying markers that differ significantly between these groups by univariate analysis; c) logistic regression analysis to assess independent difference values of the markers that can be used to assess these different sets; d) a logarithmic function is constructed 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 diabetic nephropathy 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 method (i.e., logistic regression, CART, random forest method, boosting method), or generalized linear model (i.e., logarithmic regression).

The area under the subject's working characteristic curve (AUC) is an indicator of the performance or accuracy of the diagnostic protocol. 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 following examples are given to illustrate the present invention but not to limit the scope of the present invention.

Example 1 study of biomarkers associated with diabetic nephropathy

The purpose of this study was to screen out biomarkers associated with diabetic nephropathy based on machine learning analysis and to study their value for diagnosis or prognosis of diabetic nephropathy.

1. Research method

(1) Data used for research and preprocessing method

The method comprises the steps of searching public GENE EXPRESSION data related to diabetic nephropathy and complete annotations thereof in a GENE EXPRESSION comprehensive database (GEO database) by taking the 'diabetic nephropathy' as a key word, and selecting clinical sample information of diabetic nephropathy patients according to clinical information recorded in the public GENE EXPRESSION data. Downloading gene expression data of GSE30122 (normal: case 50: 19) from a GEO database (http:// www.ncbi.nlm.nih.gov/GEO /), downloading gene expression data of GSE142153 (normal: case 10: 23) from a GEO database (http:// www.ncbi.nlm.nih.gov/GEO /), and annotating them with an annotation file;

performing joint treatment and quality control by using fastp software to obtain cleardata, comparing the cleardata to a human reference genome (the version of the reference genome is GRCh38.d1.vd1) by using ICGC software to obtain a bam file, combining htseq software with an annotation file, quantifying the expression quantity of genes of the compared bam file, combining the expression quantities of multiple samples according to the ID of the genes to construct an M N gene expression quantity matrix, taking the average value of the multiple probes corresponding to the same gene as the expression quantity of the gene, storing the expression quantity matrix as an Rdata object file, performing characteristic marking on clinical information according to sample grouping information, naming a healthy control group sample as normal, and naming a diabetic nephropathy group sample as case; grouping data, and analyzing biomarkers related to diabetic nephropathy by using data from GSE30122, Rdata object files as verification sets and data from GSE142153, Rdata object files as training sets.

(2) Differential expression Gene analysis between healthy control group and diabetic nephropathy group

Analyzing the differential expression genes between the healthy control group and the diabetic nephropathy group by using a 'limma' packet in R software according to the grouping in the step (1), wherein the screening standard of the differential expression genes is adj<0.05，|log₂FC|>0.5, screening genes that were significantly differentially expressed between the healthy control group and the diabetic nephropathy group.

2. Results of the study

The results obtained from the analysis study showed that TNFAIP6 and RUNX3 showed significant differential expression between the healthy control group and the diabetic nephropathy group, wherein TNFAIP6 and RUNX3 were both up-regulated in the diabetic nephropathy group compared to the healthy control group, and the expression profiles are shown in fig. 1A-D.

Example 2 evaluation of the diagnostic potency of the biomarkers TNFAIP6, RUNX3

1. Evaluation analysis method

The subject operating curve (ROC) was plotted using the R package "pROC", and AUC values, sensitivities, and specificities of the biomarkers TNFAIP6, RUNX3, TNFAIP6+ RUNX3, which were obtained from the study of example 1 and exhibited significant differential expression between the healthy control group and the diabetic nephropathy group, were analyzed to judge the diagnostic efficacy of the markers. Wherein, when evaluating and analyzing the diagnostic efficacy of TNFAIP6 and RUNX3 on diabetic nephropathy, the expression level (log2 expression level) of the gene is used for evaluating and analyzing, and the point corresponding to the largest Youden index is selected as the cutoff value, namely the optimal division threshold value is determined by the point with the largest Youden index; when the diagnosis efficacy of TNFAIP6+ RUNX3 on diabetic nephropathy is evaluated and analyzed, firstly, Logitics regression analysis is carried out on the TNFAIP6+ RUNX3 genes, in the Logitics regression analysis, the independent variable is TNFAIP6+ RUNX3, the dependent variable is the disease condition of the diabetic nephropathy, the probability of whether each individual suffers from the diabetic nephropathy can be calculated through a fitted regression curve, and different probability division threshold values are determined to obtain the prediction result. The optimal probability partition threshold is determined by the point with the largest Youden index, and according to the determined probability partition threshold, the AUC value, sensitivity, specificity and the like of the TNFAIP6+ RUNX3 combination in the training set and the verification set can be calculated.

2. Evaluating the results of the analysis

The results of evaluation and analysis show that the TNFAIP6+ RUNX3 combination is superior to TNFAIP6 and RUNX3 in diagnosing diabetic nephropathy, and TNFAIP6+ RUNX3 shows higher diagnostic efficacy whether in training set or verification set, the AUC values are 0.843 and 0.849 respectively, and the TNFAIP6+ RUNX3 combination has higher sensitivity and specificity (see Table 1, FIGS. 2A-D and 3A-B), which proves that the TNFAIP6+ RUNX3 combination can be used in the diagnosis of diabetic nephropathy.

TABLE 1 statistical results of AUC values of TNFAIP6 and RUNX3 in training set and validation set

The above embodiments are merely preferred embodiments of the present invention, and are not intended to limit the present invention, it should be noted that, for those skilled in the art, many modifications and variations can be made without departing from the technical principle of the present invention, and these modifications and variations should also be construed as the protection scope of the present invention.

Claims

1. Use of a reagent for detecting a biomarker in a sample for the preparation of a diagnostic product for diabetic nephropathy, wherein the biomarker is TNFAIP6 or RUNX 3.

2. Use according to claim 1, wherein the sample is selected from blood or tissue.

3. The use of claim 1, wherein said diagnostic product comprises a reagent for detecting the level of expression of TNFAIP6 or RUNX3 in a sample.

4. The use of claim 3, wherein the reagent comprises a reagent for detecting the expression level of TNFAIP6 or RUNX3 in the sample by sequencing, nucleic acid hybridization, nucleic acid amplification, or protein immunization.

5. The use according to claim 4, wherein the agent is selected from:

primers for specifically amplifying TNFAIP6 and RUNX 3; or

A probe specifically recognizing TNFAIP6 and RUNX 3; or

6. A diagnostic product for diabetic nephropathy, comprising reagents for the detection of the biomarkers TNFAIP6, RUNX 3.

7. The diagnostic product of claim 6, wherein the agent is selected from the group consisting of:

primers for specifically amplifying TNFAIP6 and RUNX 3; or

A probe specifically recognizing TNFAIP6 and RUNX 3; or

8. The diagnostic product of claim 6 or 7, wherein the diagnostic product comprises a formulation, a kit, a chip, a strip.

Use of TNFAIP6, RUNX3 in the construction of a computational model or a system in which said computational model is embedded for the prediction of diabetic nephropathy;

10. An apparatus for diagnosing diabetic nephropathy, the apparatus comprising a processor, an input module, an output module;