New crown growing diagnostic biomarker group and application thereof, diagnostic product and diagnostic method

Abstract

The invention relates to a new crown growing diagnosis biomarker group and application thereof, a diagnosis product and a diagnosis method, and relates to the fields of new crown growing diagnosis technology and precision medical treatment. The biomarker group comprises a PSME4 protein, an ABHD17A protein, a CSNK1D protein and an SYVN1 protein, or mRNA (messenger ribonucleic acid) and cDNA (complementary deoxyribonucleic acid) corresponding to the above proteins. The diagnostic product comprises a reagent for detecting the level of each marker in the biomarker group. The diagnosis method comprises the following steps: acquiring expression levels of PSME4 protein, ABHD17A protein, CSNK1D protein and SYVN1 protein in a to-be-detected sample, inputting the expression levels into a long new crown multi-element neural network language diagnosis model, and judging a diagnosis result of the to-be-detected sample according to an output result of the long new crown multi-element neural network language diagnosis model.

Description

Technical Field

[0001] The present invention relates to the field of long-COVID-19 diagnostic technology, and specifically, to a long-COVID-19 diagnostic biomarker group and its application, a long-COVID-19 diagnostic product and a diagnostic method. Background Art

[0002] According to the definition of the World Health Organization, long COVID refers to the continuation or emergence of new symptoms 3 months after COVID-19 infection, including fatigue, shortness of breath and cognitive dysfunction, which lasts for at least 2 months. According to surveys, the incidence of long COVID ranges from 1.9% to 20%. Long COVID is a multi-manifestation disease that can affect multiple systems such as the respiratory, nervous, and circulatory systems of the body. According to statistics, there are more than 200 symptoms of long COVID.

[0003] The first step in alleviating and treating long-term COVID-19 patients is to correctly diagnose long-term COVID-19, but there is currently no specific diagnostic method for long-term COVID-19. Clinicians may use blood tests, electrocardiograms, imaging tests, psychological assessments and other methods to comprehensively evaluate the body state of long-term COVID-19 patients to find possible causes, but the routine test results of long-term COVID-19 patients are usually within the normal range, and abnormal test results may not be related to long-term COVID-19 symptoms.

[0004] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present disclosure, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0005] The purpose of the present disclosure is to overcome the deficiencies of the above-mentioned prior art, and to provide a long-COVID-19 diagnostic biomarker group and its applications, diagnostic products and diagnostic methods, so as to achieve long-COVID-19 diagnosis of the sample to be tested through the corresponding biomarker detection results of the sample to be tested.

[0006] According to one aspect of the present disclosure, a long-term new crown diagnostic biomarker group is provided, and the biomarker group includes PSME4 protein, ABHD17A protein, CSNK1D protein and SYVN1 protein.

[0007] According to another aspect of the present disclosure, a long-term new crown diagnostic biomarker group is provided, and the biomarker group includes mRNA of PSME4 protein, mRNA of ABHD17A protein, mRNA of CSNK1D protein and mRNA of SYVN1 protein.

[0008] According to another aspect of the present disclosure, a long-term new crown diagnostic biomarker group is provided, and the biomarker group includes cDNA of PSME4 protein, cDNA of ABHD17A protein, cDNA of CSNK1D protein and cDNA of SYVN1 protein.

[0009] According to another aspect of the present disclosure, a biomarker group for use in the diagnosis of long-term COVID-19 is provided, wherein the biomarker group includes PSME4 protein, ABHD17A protein, CSNK1D protein and SYVN1 protein.

[0010] According to another aspect of the present disclosure, a biomarker group for use in the diagnosis of long-term COVID-19 is provided, wherein the biomarker group includes mRNA of PSME4 protein, mRNA of ABHD17A protein, mRNA of CSNK1D protein, and mRNA of SYVN1 protein.

[0011] According to another aspect of the present disclosure, a biomarker group is provided for use in the diagnosis of long-term COVID-19, wherein the biomarker group includes cDNA of PSME4 protein, cDNA of ABHD17A protein, cDNA of CSNK1D protein and cDNA of SYVN1 protein.

[0012] According to another aspect of the present disclosure, a long-term new crown diagnostic product is provided, which includes a reagent for detecting the expression level of each protein in the above-mentioned biomarker group. The biomarker group here is PSME4 protein, ABHD17A protein, CSNK1D protein and SYVN1 protein.

[0013] According to another aspect of the present disclosure, a long new crown diagnostic product is provided, which includes a reagent for detecting the level of mRNA of each protein in the above-mentioned biomarker group. The biomarker group here is the mRNA of PSME4 protein, the mRNA of ABHD17A protein, the mRNA of CSNK1D protein, and the mRNA of SYVN1 protein.

[0014] According to another aspect of the present disclosure, a long new crown diagnostic product is provided, which includes a reagent for detecting the level of cDNA of each protein in the above-mentioned biomarker group. The biomarker group here includes cDNA of PSME4 protein, cDNA of ABHD17A protein, cDNA of CSNK1D protein and cDNA of SYVN1 protein.

[0015] In an exemplary embodiment of the present disclosure, the product is a kit, a detection reagent or a biochip.

[0016] In an exemplary embodiment of the present disclosure, the kit, detection reagent or biochip also includes a label or instructions, and the label or instructions indicate that the kit, detection reagent or biochip is used for the diagnosis of long-term COVID-19.

[0017] According to another aspect of the present disclosure, a method for diagnosing long COVID-19 is provided, the method comprising the following steps:

[0018] S1. Obtain a sample to be tested, wherein the sample to be tested is taken from a COVID-19 patient;

[0019] S2. Use the above-mentioned long new crown diagnostic product to detect the expression levels of PSME4 protein, ABHD17A protein, CSNK1D protein and SYVN1 protein in the sample to be tested, and obtain the protein expression results of the sample to be tested;

[0020] S3. Input the protein expression results of the sample to be tested obtained in step S2 into the long COVID-19 multivariate neural network language diagnosis model to obtain the output result of the long COVID-19 multivariate neural network language diagnosis model; if the output result is 1, the sample to be tested is long COVID-19; if the output result is 0, the sample to be tested is not long COVID-19.

[0021] In an exemplary embodiment of the present disclosure, in step S1, the sample to be tested is a peripheral blood sample.

[0022] In an exemplary embodiment of the present disclosure, the construction process of the long-term new crown multi-neural network language diagnosis model in step S3 is:

[0023] Samples of COVID-19 subjects were obtained, and the COVID-19 subjects were divided into two experimental groups, including: a group with long COVID-19 symptoms (LC group) and a group without long COVID-19 symptoms (NLC group);

[0024] The expression levels of PSME4 protein, ABHD17A protein, CSNK1D protein and SYVN1 protein of each sample in each experimental group are obtained; the expression levels of PSME4 protein, ABHD17A protein, CSNK1D protein and SYVN1 protein of each sample in each experimental group constitute a data set;

[0025] 70% to 80% of the data in the data set were randomly selected as the training set, and the training set was input into the multivariate neural network language diagnosis model for iterative training to obtain the long-term new crown multivariate neural network language diagnosis model.

[0026] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The accompanying drawings herein are incorporated into the specification and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the specification are used to explain the principles of the present disclosure. Obviously, the accompanying drawings described below are only some embodiments of the present disclosure, and for ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without creative work.

[0028] Figure 1In the figure, A is a volcano plot of differential expression of proteins and genes between the long-coronavirus group and the non-long-coronavirus group in one embodiment of the present disclosure; B is a schematic diagram of screening genes with high expression at both transcriptional and protein levels in the long-coronavirus group in one embodiment of the present disclosure; C is a schematic diagram of analyzing the biological functions involved in proteins with high expression at both transcriptional and protein levels in the long-coronavirus group in one embodiment of the present disclosure; D is a schematic diagram of a joint network showing protein-biological pathway interactions in one embodiment of the present disclosure.

[0029] Figure 2 In the figure, A is a schematic diagram of significant marker proteins screened out in the long coronavirus (LC) group in one embodiment of the present disclosure; B is a schematic diagram of ROC curve results of PSME4 protein, ABHD17A protein, CSNK1D protein, and SYVN1 protein as biomarker groups in one embodiment of the present disclosure; C is a schematic diagram of the difference in expression levels of PSME4 protein, ABHD17A protein, CSNK1D protein, and SYVN1 protein as biomarkers in the validation cohort in one embodiment of the present disclosure. DETAILED DESCRIPTION

[0030] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be comprehensive and complete and will fully convey the concept of the example embodiments to those skilled in the art.

[0031] Unless otherwise defined, all terms and phrases used herein include the meanings that the terms and phrases have in the art, unless explicitly stated to the contrary or clearly apparent from the context in which the terms and phrases are used. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, specific methods and materials are now described.

[0032] the term

[0033] Long-term new crown multi-neural network language diagnosis model

[0034] 70% to 80% of the data in the data set were randomly selected as the training set, and the training set was input into the multivariate neural network language diagnosis model for iterative training to obtain the long-term new crown multivariate neural network language diagnosis model.

[0035] The long-term COVID-19 symptoms that last for several months after COVID-19 infection have a significant impact on the patient's physical health and quality of life, while also increasing the burden on society and the medical and health systems. The different symptoms of long COVID-19 also increase the difficulty of intervention and treatment of long COVID-19.

[0036] To solve the above problems, the embodiments of the present disclosure provide a biomarker group for the diagnosis of long new crown, and the biomarker group includes PSME4 protein, ABHD17A protein, CSNK1D protein and SYVN1 protein. Based on the above four protein markers, the diagnosis of new crown patients can be performed. For example, the above four protein markers in the test sample are detected. At a given dilution concentration, compared with the background reference, if the test result is positive, the test sample is a long new crown. If the test result is negative, the test sample is not a long new crown.

[0037] In this way, targeted intervention can be carried out on COVID-19 patients according to different long COVID-19 / non-long COVID-19 diagnosis results. At the same time, four protein markers are provided for the study of the pathological mechanism of long COVID-19, in order to provide research ideas for the early intervention of long COVID-19 through the pathological mechanism.

[0038] The disclosed embodiment also provides another biomarker group for the diagnosis of long-term new crown, which includes the mRNA of PSME4 protein, the mRNA of ABHD17A protein, the mRNA of CSNK1D protein and the mRNA of SYVN1 protein. In this way, the new crown patient can be diagnosed by detecting the level of messenger RNA in the gene transcription process.

[0039] The disclosed embodiment also provides another biomarker group for the diagnosis of long-term new crown, which includes cDNA of PSME4 protein, cDNA of ABHD17A protein, cDNA of CSNK1D protein and cDNA of SYVN1 protein. In this way, the new crown patient can be diagnosed by detecting the cDNA levels of the above four proteins in the sample.

[0040] That is to say, when diagnosing long COVID-19, four protein markers can be detected in the sample to be tested to confirm whether the sample to be tested is long COVID-19; four mRNA markers can be detected in the sample to be tested to confirm whether the sample to be tested is long COVID-19; four cDNA markers can also be detected in the sample to be tested to confirm whether the sample to be tested is long COVID-19.

[0041] An embodiment of the present disclosure provides an application of a biomarker group in the diagnosis of long-term COVID-19, wherein the biomarker group includes PSME4 protein, ABHD17A protein, CSNK1D protein and SYVN1 protein.

[0042] The disclosed embodiments also provide an application of a biomarker group in the diagnosis of long-term COVID-19, wherein the biomarker group includes mRNA of PSME4 protein, mRNA of ABHD17A protein, mRNA of CSNK1D protein, and mRNA of SYVN1 protein.

[0043] The disclosed embodiments also provide an application of a biomarker group in the diagnosis of long-term COVID-19, wherein the biomarker group includes cDNA of PSME4 protein, cDNA of ABHD17A protein, cDNA of CSNK1D protein, and cDNA of SYVN1 protein.

[0044] The present disclosure provides a long new crown diagnostic product, which includes a reagent for detecting the expression level of each protein in the biomarker group. Wherein, the biomarker group is PSME4 protein, ABHD17A protein, CSNK1D protein and SYVN1 protein.

[0045] Furthermore, the long-term new crown diagnostic product includes but is not limited to a kit, a detection reagent or a biochip. Exemplarily, the kit can be an enzyme-linked immunosorbent assay kit (ELISA kit), which contains capture antibodies and detection antibodies for PSME4 protein, ABHD17A protein, CSNK1D protein and SYVN1 protein, and can be used to specifically detect the expression level of the corresponding protein.

[0046] The disclosed embodiment also provides a long new crown diagnostic product, which includes a reagent for detecting the level of mRNA of each protein in the biomarker group. Wherein, the biomarker group includes mRNA of PSME4 protein, mRNA of ABHD17A protein, mRNA of CSNK1D protein and mRNA of SYVN1 protein.

[0047] Further, the long-term new crown diagnostic product includes but is not limited to a kit, a detection reagent or a biochip. Exemplary, the reagent used for PCR detection of the levels of mRNA of PSME4 protein, mRNA of ABHD17A protein, mRNA of CSNK1D protein and mRNA of SYVN1 protein. Further, the PCR detection can use reverse transcription quantitative PCR (qRT-PCR).

[0048] The disclosed embodiment also provides a long new crown diagnostic product, which includes a reagent for detecting the level of cDNA of each protein in the biomarker group. Wherein, the biomarker group includes cDNA of PSME4 protein, cDNA of ABHD17A protein, cDNA of CSNK1D protein and cDNA of SYVN1 protein.

[0049] Furthermore, the long-term new crown diagnostic product includes but is not limited to a kit, a detection reagent or a biochip. Exemplary, a reagent used for PCR detection of the levels of cDNA of PSME4 protein, cDNA of ABHD17A protein, cDNA of CSNK1D protein and cDNA of SYVN1 protein.

[0050] Furthermore, the kit, detection reagent or biochip also includes a label or instructions, and the label or instructions indicate that the kit, detection reagent or biochip is used for the diagnosis of long-term COVID-19.

[0051] The present disclosure also provides a method for diagnosing long COVID-19, which comprises the following steps:

[0052] S1. Obtain a sample to be tested, wherein the sample to be tested is taken from a COVID-19 patient. Furthermore, for the convenience of detection, the sample to be tested may be a serum sample of peripheral blood;

[0053] S2. Use the above-mentioned long new crown diagnostic product to detect the expression levels of PSME4 protein, ABHD17A protein, CSNK1D protein and SYVN1 protein in the sample to be tested, and obtain the protein expression results of the sample to be tested;

[0054] S3. Input the protein expression results of the sample to be tested obtained in step S2 into the new crown multivariate neural network language diagnosis model to obtain the output result of the new crown multivariate neural network language diagnosis model. If the output result is 1, the sample to be tested is long-term new crown; if the output result is 0, the sample to be tested is not long-term new crown.

[0055] In one embodiment of the present disclosure, the construction process of the new crown multi-neural network language diagnosis model includes:

[0056] Samples were obtained from COVID-19 subjects, and the COVID-19 subjects were divided into two experimental groups; the experimental groups included: a group with long-term COVID-19 symptoms (LC group) and a group without long-term COVID-19 symptoms (NLC group).

[0057] The expression levels of PSME4 protein, ABHD17A protein, CSNK1D protein and SYVN1 protein of each sample in each experimental group are obtained. The expression levels of PSME4 protein, ABHD17A protein, CSNK1D protein and SYVN1 protein of each sample in each experimental group constitute a data set. It should be noted that each group corresponds to the expression levels of PSME4 protein, ABHD17A protein, CSNK1D protein and SYVN1 protein of the sample in the group. For example, the expression levels of PSME4 protein, ABHD17A protein, CSNK1D protein and SYVN1 protein corresponding to each sample in the LC group constitute a first data combination. Similarly, the expression levels of PSME4 protein, ABHD17A protein, CSNK1D protein and SYVN1 protein corresponding to each sample in the NLC group constitute a second data combination; the first data combination and the second data combination constitute a data set.

[0058] Randomly extract 70% to 80% of the data in the data set as the training set, and the other 20% to 30% of the data as the validation set. For example, part of the data can be extracted from the first data combination and the second data combination as the training set, and the remaining data is used as the validation set. The training set is input into the multivariate neural network language diagnosis model for iterative training to obtain the long new crown multivariate neural network language diagnosis model. The validation set can be used to verify the accuracy and specificity of the long new crown multivariate neural network language diagnosis model. The validation set verifies that the accuracy and specificity of the long new crown multivariate neural network language diagnosis model meet the preset standards, and the long new crown multivariate neural network language diagnosis model is successfully constructed.

[0059] The main advantages of the present invention include:

[0060] (1) The biomarker group provided by the present disclosure has high sensitivity and specificity, and can accurately detect and distinguish long-term COVID-19 from non-long-term COVID-19, so as to improve the accuracy of long-term COVID-19 diagnosis and screening; at the same time, it is convenient for targeted intervention of long-term COVID-19;

[0061] (2) The biomarker group provided by the present disclosure is potential and can be used as a reference indicator to predict whether a patient will develop long COVID-19 after COVID-19 infection. It can predict whether a patient is likely to develop long COVID-19 at an early stage, and conduct timely intervention and management to reduce long-term health effects;

[0062] (3) The biomarker detection method provided by the present disclosure requires a small amount of sample, is simple to obtain, and has a low cost for obtaining samples, which is convenient for large-scale population screening, conducive to clinical screening, and improves the efficiency of public health response.

[0063] The long-term new coronary disease diagnostic biomarker group provided by the present invention is further described below in conjunction with specific embodiments.

[0064] It should be understood that these embodiments are only used to illustrate the present invention and are not used to limit the scope of the present invention. The experimental methods in the following examples that do not specify detailed conditions are usually based on conventional conditions such as the conditions described in "Molecular Cloning Laboratory Guide" by Sambrook.J, USA (Huang Peitang, etc., Beijing: Science Press, 2002), or according to the conditions recommended by the manufacturer (e.g., product instructions). Unless otherwise specified, percentages and parts are calculated by weight. The experimental materials and reagents used in the following examples can be obtained from commercial channels or can be prepared according to literature methods unless otherwise specified.

[0065] Example 1 Screening of detection markers (groups), model construction and effect verification of the present invention 1. Experimental samples and experimental groups

[0066] A total of 257 subjects were selected for the study based on the registration of clinical information such as the COVID-19 nucleic acid / antigen positive records of all subjects, the inclusion criteria of COVID-19 patients, and COVID-19-related questionnaires (COVID-19 symptom questionnaire, EQ-5D-5L questionnaire, GAD-7 questionnaire, PHQ-9 questionnaire, PTSD questionnaire). In the EQ-5D-5L questionnaire, the inventor assigned a value to each option, ranging from 1 point (no problem) to 4 points (extreme problem), to quantify the problems that the subjects had in each dimension.

[0067] Peripheral blood samples were collected from the subjects. Among them, the basis for the diagnosis of long-term COVID-19 in the subjects was: persistent or new symptoms appeared 3 months after COVID-19 infection, including fatigue, shortness of breath and cognitive dysfunction, which lasted for at least 2 months and could not be explained by other reasons.

[0068] The subjects were classified as follows: subjects who showed symptoms of both the cardiovascular and respiratory systems were classified as the cardiovascular and respiratory complex long new crown group (LC-1 group); subjects who showed symptoms of both the cardiovascular and nervous systems were classified as the cardiovascular and nervous system complex long new crown group (LC-2 group); subjects who mainly showed symptoms of the nervous system were classified as the nervous system type long new crown group (LC-3 group); subjects who showed symptoms of the skeletal muscle system and systemic nonspecific system were classified as the skeletal muscle system and systemic nonspecific system complex long new crown group (LC-4 group); subjects who showed symptoms of four or more systems were classified as the multi-system complex long new crown group (LC-5 group); LC-1 group, LC-2 group, LC-3 group, LC-4 group and LC-5 group together constituted the long new crown symptom group (LC group), and subjects who did not have long new crown symptoms were classified as the non-long new crown symptom group (NLC group). Peripheral blood samples were collected from each subject and could be used directly in the experiment or stored at -80°C for future use.

[0069] The inventors summarized the characteristics of the human body systems involved in different symptoms and classified each subject mainly based on the clinical symptoms. For example, pharyngeal discomfort, nasal congestion and runny nose, cough and sputum, and dyspnea are classified as respiratory system type; chest tightness, chest pain, and palpitations are classified as cardiovascular system type; fatigue, weakness, sleep disorders, dizziness, impaired vision, headache, taste disorders, olfactory disorders, cognitive disorders, and brain fog are classified as nervous system type; muscle pain and joint pain are classified as skeletal muscle system type; hair loss and rash are classified as systemic nonspecific system type; loss of appetite is classified as a digestive system symptom.

[0070] In this study, the inventors included a total of 255 COVID-19 infected patients for follow-up and research. According to the follow-up information of the 255 patients 3 months after COVID-19 infection, they were divided into a group with long COVID-19 symptoms (LC group, n=201) and a group without long COVID-19 symptoms (NLC group, n=54).

[0071] Blood samples were collected from 255 subjects 3 months after infection with the new coronavirus. The whole blood was used for transcriptome sequencing, and the plasma was used for proteome sequencing. After obtaining the sequencing results, the gene expression differences (using transcriptome data analysis) and protein level differences (using proteome sequencing data analysis) were analyzed between the long-term new coronavirus (LC group) and the non-long-term new coronavirus patients (NLC group).

[0072] The specific method is as follows:

[0073] 2. Transcriptome Analysis

[0074] Step 1. RNA isolation: Extract total RNA from each peripheral blood sample in the LC group and the NLC group. Total RNA was extracted using the method provided in the instruction manual of TRIzol reagent (Invitrogen, California, USA).

[0075] Step 2: Preparation of RNA library: Based on the total RNA of each sample, the VAHTS Universal V6 RNA-seq library preparation kit was used to construct the RNA library corresponding to each sample.

[0076] Step 3, RNA sequencing: The RNA libraries corresponding to each sample in the LC group and the NLC group were sequenced on the Illumina Novasek 6000 platform, and each sample generated approximately 150 bp of paired-end reads, which were the original sequencing data. The fastq format raw sequencing data were processed using the default parameters of the fastp software, and the sequence alignment was performed using HISAT after removing low-quality reads. The reference genome was GRCh38.p13. Finally, the expression level of each gene in each sample was counted and quantified by HTSeq-count in FPKM to obtain the gene expression level of each sample.

[0077] Step 4: Differential gene expression analysis: The DESeq2 R software package was used to perform differential expression analysis on the gene expression levels of each sample obtained by RNA sequencing based on default parameters. The p value < 0.05 and fold change > 2 or fold change < -2 were used as the thresholds for significantly differentially expressed genes (DEGs).

[0078] For example, according to the threshold of significantly differentially expressed genes (DEGs) set above, the significantly differentially expressed genes obtained by comparing the LC group with the NLC group are the significantly differentially expressed genes (DEGs) of the LC group; thus, the significantly differentially expressed genes (DEGs) of the LC group are obtained through differential gene expression analysis. Figure 1 As shown in A.

[0079] 3. Proteomic Analysis

[0080] Step S10, plasma protein extraction: the collected peripheral blood samples are centrifuged to obtain plasma samples of each subject in the LC group and the NLC group, and plasma proteins are extracted.

[0081] Step S20, enzymatic hydrolysis: a portion of the plasma protein obtained in step S10 is enzymatically hydrolyzed (for example, using trypsin for enzymatic hydrolysis) to obtain a mixed first polypeptide solution.

[0082] Step S30, enriching low-abundance plasma proteins: The other part of the plasma proteins obtained in step S10 was enriched with the EasyPeptide DeepP low-abundance protein enrichment and pretreatment preparation kit (OSFP0002) (Omicsolution, Shanghai, China) to obtain low-abundance proteins in the plasma proteins. The enriched proteins were reduced and alkylated, enzymatically hydrolyzed into peptides, and desalted to obtain a second polypeptide solution.

[0083] It is understandable that the order of step S20 and step S30 can be swapped, and the second polypeptide solution can be obtained first, and then the first polypeptide solution.

[0084] Step S40, mass spectrometry analysis: The first polypeptide solution corresponding to the same sample obtained in step S20 and the second polypeptide solution corresponding to the same sample obtained in step S30 are mixed to obtain polypeptide solutions of each sample. Liquid chromatography-mass spectrometry is used to perform mass spectrometry analysis on the polypeptide segments in the polypeptide solution, and the signal intensities of the polypeptide segments in different samples are compared to relatively quantify the proteins corresponding to the polypeptide segments. The abundance (amount) of each polypeptide segment is proportional to the peak area or signal intensity of the polypeptide segment in the mass spectrum; and the retention time (RT) of the same polypeptide segment in different samples on liquid chromatography is consistent. Therefore, by comparing the information in the spectral library and integrating it into the corresponding protein, a quantitative analysis of the relative expression level of the protein in different samples can be achieved. In this way, the protein expression level of each sample in each group is obtained.

[0085] Step S50, differential protein analysis: The protein expression level of each sample in each group is analyzed using the limma R software package. Trend parameters are selected to improve the stability of the analysis, and FDR<0.05 and Foldchange>1.5, or other thresholds are used as the thresholds of differential proteins (significant differences).

[0086] For example, according to the above threshold of differential proteins, the significantly differential proteins obtained by comparing the LC group with the NLC group are the differential proteins of the LC group; thus, the differential proteins of the LC group are obtained through differential protein analysis.

[0087] 4. Genomic and proteomic data analysis

[0088] (1) Data integration analysis based on similarity network fusion

[0089] Similarity Network Fusion (SNF) was used to perform multi-omics data joint analysis of proteomics and transcriptomics in the LC group and NLC group. The results showed that the protein expression level characteristics of the subjects had a high positive correlation with the clinical-based grouping, that is, the proteomic characteristics of the subjects were highly consistent with the clinical classification defined by each group, suggesting that there are protein markers that can be used as a basis for clinical diagnosis.

[0090] (2) Joint proteome-transcriptome analysis

[0091] Step 100, using the GSEA function in the cluster Profiler R software package to perform gene function enrichment analysis and protein function enrichment analysis on the highly expressed significantly differentially expressed genes (DEGs) and differentially expressed proteins in each group, respectively, and the enrichment analysis uses the Reactome pathway database. In each group, screen for enriched signal pathways with a protein function enrichment analysis score (score represented by NES) greater than 0 and a gene function enrichment analysis score (score represented by NES) greater than 0.

[0092] Intersection analysis of genes and proteins upregulated in long-term COVID-19 compared with non-long-term COVID-19 identified 17 markers with high gene expression and protein levels. The results are as follows Figure 1 As shown in B.

[0093] The functional analysis of 17 up-regulated proteins was performed by GSEA enrichment analysis, and the pathways enriched by the 17 up-regulated proteins mainly included: M phage, growth factor receptor and second messenger signal transduction diseases, asparagine N-glycosylation pathway, MAPK family signal cascade and other four signaling pathways ( Figure 1 C), the 8 related proteins include: SYVN1, ABHD17A, NAPG, KSR1, EMD, CSNK1D, EML4, PSME4. Through the protein + pathway interaction network diagram, the inventors showed the relationship between the 4 enriched pathways and the 8 proteins ( Figure 1 (middle D).

[0094] Step 200, obtain the enriched proteins corresponding to the four signal pathways, and compare the expression level differences and ROC curves of different enriched proteins between the groups according to the protein expression levels of each sample measured in the proteome analysis step S40, and set the screening criteria as follows: the statistical difference in the expression level of the enriched proteins between the groups satisfies P < 0.001 and the area under the ROC curve AUC > 0.85, such as Figure 2 As shown in middle A, significant marker proteins of the LC group were screened.

[0095] Comparing the relative protein level differences of 8 core proteins between the long-term new crown group and the non-long-term new crown group, it was found that four proteins, PSME4, ABHD17A, CSNK1D, and SYVN1, were significantly enriched in the long-term new crown group ( Figure 2 Middle A)

[0096] Step 300, according to the protein expression level in each sample in each group measured in step S40 in the proteomic analysis, the expression level of PSME4 protein, ABHD17A protein, CSNK1D protein, and SYVN1 protein corresponding to each sample in the LC group is used as a data set. 70% of the data set is randomly selected as a training set, and the remaining 30% is used as a test set. The training set is input into the multivariate neural network language diagnosis model, and the multivariate neural network language diagnosis model is trained to obtain a long new crown multivariate neural network language diagnosis model. The test set is used to verify the long new crown multivariate neural network language diagnosis model, and the accuracy and specificity of the long new crown multivariate neural network language diagnosis model are judged according to the area under the curve of the ROC curve corresponding to the measured PSME4 protein, ABHD17A protein, CSNK1D protein, and SYVN1 protein.

[0097] Result analysis:

[0098] like Figure 2 As shown in B, a multivariate neural network predictive diagnosis model was established using the four diagnostic proteins PSME4, ABHD17A, CSNK1D, and SYVN1. The ROC curve showed that the AUC value of the model was as high as 0.901 (the AUC close to 1 indicates a higher model accuracy).

[0099] In this way, PSME4 protein, ABHD17A protein, CSNK1D protein and SYVN1 protein were combined to form a biomarker group, which further verified the accuracy of the selection of protein markers for the diagnosis of long-term COVID-19 / non-long-term COVID-19.

[0100] use Figure 2 The diagnostic model established in Figure B shows the prediction accuracy curve using a new validation cohort (59 LCs, 15 NLCs). Figure 2Figure C is a statistical box plot of the difference in protein expression between LC and NLC for the four diagnostic proteins in the validation cohort. The results show that based on the data of the aforementioned experimental cohort, the long-term new crown diagnostic model established with the four proteins PSME4, ABHD17A, CSNK1D, and SYVN1 can accurately determine whether the subjects in the new validation cohort are long-term new crown patients, with an AUC value of 0.896. At the same time, in the new validation cohort, the expression levels of the three proteins PSME4, ABHD17A, and CSNK1D in the plasma of long-term new crown patients were significantly higher than those in the non-long-term new crown group.

[0101] Those skilled in the art will readily appreciate other embodiments of the present disclosure after considering the specification and practicing the invention disclosed herein. This application is intended to cover any modification, use or adaptation of the present disclosure, which follows the general principles of the present disclosure and includes common knowledge or customary techniques in the art that are not disclosed in the present disclosure. The specification and examples are intended to be exemplary only, and the true scope and spirit of the present disclosure are indicated by the appended claims.

Claims

1. A biomarker panel for the diagnosis of long-term COVID-19, characterized in that: The biomarker panel includes PSME4 protein, ABHD17A protein, CSNK1D protein and SYVN1 protein.

2. A biomarker panel for the diagnosis of long-term COVID-19, characterized in that: The biomarker panel includes mRNA of PSME4 protein, mRNA of ABHD17A protein, mRNA of CSNK1D protein and mRNA of SYVN1 protein.

3. Application of a biomarker panel in the diagnosis of long-term COVID-19, characterized in that: The biomarker panel includes PSME4 protein, ABHD17A protein, CSNK1D protein and SYVN1 protein.

4. Application of a biomarker panel in the diagnosis of long-term COVID-19, characterized in that: The biomarker panel includes mRNA of PSME4 protein, mRNA of ABHD17A protein, mRNA of CSNK1D protein and mRNA of SYVN1 protein.

5. A long-term COVID-19 diagnostic product, characterized in that: Comprising reagents for detecting the expression level of each protein in the biomarker group according to claim 1.

6. A long-term COVID-19 diagnostic product, characterized in that: Comprising reagents for detecting the level of mRNA of each protein in the biomarker panel of claim 2.

7. The long COVID-19 diagnostic product according to claim 5 or 6, characterized in that: The product is a test kit, a detection reagent or a biochip.

8. A method for diagnosing long COVID-19, characterized in that: The following steps are involved: S1. Obtain a sample to be tested, wherein the sample to be tested is taken from a COVID-19 patient; S2. Use the long new crown diagnostic product described in claim 5 or 7 to detect the expression levels of PSME4 protein, ABHD17A protein, CSNK1D protein and SYVN1 protein in the sample to be tested to obtain the protein expression results of the sample to be tested; S3. Input the protein expression results of the sample to be tested obtained in step S2 into the long COVID-19 multivariate neural network language diagnosis model to obtain the output result of the long COVID-19 multivariate neural network language diagnosis model; if the output result is 1, the sample to be tested is long COVID-19; if the output result is 0, the sample to be tested is not long COVID-19.

9. The method for diagnosing long COVID-19 according to claim 8, characterized in that: In step S1, the sample to be tested is a peripheral blood sample.

10. The method for diagnosing long COVID-19 according to claim 8, characterized in that: The construction process of the long-term new crown multi-neural network language diagnosis model in step S3 is: Samples of COVID-19 subjects were obtained, and the COVID-19 subjects were divided into two experimental groups, including: a group with long COVID-19 symptoms (LC group) and a group without long COVID-19 symptoms (NLC group); The expression levels of PSME4 protein, ABHD17A protein, CSNK1D protein and SYVN1 protein of each sample in each experimental group are obtained; the expression levels of PSME4 protein, ABHD17A protein, CSNK1D protein and SYVN1 protein of each sample in each experimental group constitute a data set; 70% to 80% of the data in the data set were randomly selected as the training set, and the training set was input into the multivariate neural network language diagnosis model for iterative training to obtain the long-term new crown multivariate neural network language diagnosis model.

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