Application of plasma proteome biomarker in prognosis evaluation related to long-term life quality of severe novel coronavirus infected patient

Abstract

The invention provides an application of plasma proteomic biomarkers in prognosis evaluation related to long-term life quality of severe novel coronavirus infected patients, and relates to the technical field of biomedicines.The biomarkers comprise CAT, ARCN1, KRT5 and CTSC, and the prognosis evaluation related to the long-term life quality of the severe novel coronavirus infected patients is evaluated through plasma proteomic spectra of the severe novel coronavirus infected patients before MSC treatment. And screening to obtain the potential biomarker for predicting the long-term response of MSC treatment. High-risk crowds with long-term sequelae are identified through the protein biomarker region, the life quality level of a patient after MSC treatment for one year can be accurately predicted, patients more likely to benefit from MSC treatment can be identified, clinical doctors are helped to select applicable crowds before early intervention treatment and MSC infusion, and the clinical experience is improved. And improvement of life quality and long-term prognosis of patients suffering from severe novel coronavirus infection in the rehabilitation period is facilitated.

Description

Technical Field

[0001] The present invention relates to the field of biomedical technology, and in particular to the application of a plasma proteomic biomarker in the long-term quality of life-related prognosis assessment of patients with severe novel coronavirus infection. Background Art

[0002] Some patients infected with the novel coronavirus still have long-term COVID-19 symptoms such as persistent fatigue, dyspnea, chest tightness, and cough during the recovery period, as well as reduced quality of life such as anxiety, depression, social relationship obstruction, and limited living and working ability. However, due to the lack of prognostic biomarkers, it is difficult to identify people at high risk of long-term sequelae. The development of biomarkers is of great significance for early intervention treatment and patient rehabilitation, and helps to improve the quality of life and long-term prognosis of patients during the recovery period.

[0003] Mesenchymal stem cells (MSC) have been used in the study of lung diseases such as acute respiratory distress syndrome, chronic obstructive pulmonary disease, and pulmonary fibrosis due to their anti-inflammatory, immunomodulatory, and low immunogenicity properties, providing a new therapeutic approach to deal with acute damage and long-term sequelae of novel coronavirus infection. To date, more than 380 related clinical trials have been registered on ClinicalTrials.gov at home and abroad. Many studies have found that MSC treatment of patients with novel coronavirus infection is safe and effective. Our team's one-year follow-up of the previously conducted RCT clinical study found that MSC treatment achieved long-term benefits in lung lesions and activity endurance recovery. However, the efficacy varies from person to person, and there may be differences in the response of patients in the MSC group. Not all patients respond well to MSC. At present, the applicable population, efficacy, potential toxicity, and mechanism of action of MSC treatment have not been fully clarified, and there are significant economic costs. It is very important to predict the patient's response to MSC before treatment and select potential responders.

[0004] In recent years, plasma protein characteristics have become specific diagnostic indicators for certain diseases, and the predictive performance of good protein models is better than that of models that only use basic clinical information. By detecting the protein composition in plasma samples by high-throughput mass spectrometry, it is possible to compare the differences in proteomics between patients with different response levels, use machine learning algorithms to screen biomarkers for predicting prognosis, and establish models to predict treatment effects, thereby providing a basis for doctors' clinical decision-making.

[0005] In view of this, the present invention is proposed. Summary of the invention

[0006] One of the purposes of the present invention is to provide biomarkers related to the long-term quality of life of patients with severe novel coronavirus infection to solve the technical problem in the prior art of lack of biomarkers for predicting the prognosis of quality of life in patients with severe novel coronavirus infection based on plasma proteomics.

[0007] The second purpose of the present invention is to provide the application of plasma proteomic biomarkers in the long-term quality of life prognosis assessment of patients with severe novel coronavirus infection.

[0008] The third object of the present invention is to provide the application of plasma proteomic biomarkers in predicting or assisting in predicting the prognosis and quality of life of patients with severe novel coronavirus infection receiving mesenchymal stem cell treatment.

[0009] The fourth purpose of the present invention is to provide a prediction model for the prognosis and quality of life of patients with severe novel coronavirus infection who receive mesenchymal stem cell treatment.

[0010] In order to achieve the above-mentioned purpose of the present invention, the following technical solutions are particularly adopted:

[0011] In a first aspect, the present invention provides biomarkers related to the long-term quality of life of patients with severe novel coronavirus infection, characterized in that the biomarkers include CAT, ARCN1, KRT5 and CTSC.

[0012] In a second aspect, the present invention provides the use of plasma proteomic biomarkers in the long-term quality of life prognosis assessment of patients with severe novel coronavirus infection, wherein the biomarkers include CAT, ARCN1, KRT5 and CTSC.

[0013] Furthermore, the quality of life includes health status related to the overall health dimension in the SF-36 standardized assessment.

[0014] Furthermore, CAT, ARCN1, KRT5 and CTSC are independent factors for assessing the long-term quality of life of patients with severe novel coronavirus infection.

[0015] In a third aspect, the present invention provides the use of plasma proteomic biomarkers in predicting or assisting in predicting the prognosis and quality of life of patients with severe novel coronavirus infection receiving mesenchymal stem cell treatment, wherein the biomarkers include CAT, ARCN1, KRT5 and CTSC.

[0016] In a fourth aspect, the present invention provides a prediction model for the prognosis of quality of life of patients with severe novel coronavirus infection who receive mesenchymal stem cell treatment, including a Logistics regression analysis prediction model constructed using the above-mentioned biomarkers, and a Nomograph drawn to predict the probability of occurrence of outcome events.

[0017] Furthermore, CAT, ARCN1, KRT5 and CTSC are independent factors for predicting the prognosis and quality of life of patients with severe novel coronavirus infection receiving mesenchymal stem cell treatment.

[0018] Furthermore, low expression levels of the CAT, ARCN1, KRT5 and CTSC proteins were associated with a good quality of life.

[0019] Furthermore, the good quality of life includes an overall health dimension score of ≥90 in the SF-36 standardized assessment.

[0020] Furthermore, the regression coefficient of CAT is -3.05, the regression coefficient of ARCN1 is -0.63, the regression coefficient of KRT5 is -0.18, and the regression coefficient of CTSC is -0.42.

[0021] The present invention provides a prognostic quality of life-related biomarker based on plasma proteomics in patients with severe novel coronavirus infection. Through the plasma proteomics spectrum of patients with severe novel coronavirus infection before MSC treatment, potential biomarkers for predicting long-term response to MSC treatment are screened. The high-risk population for long-term sequelae can be identified by protein biomarker regions, which can accurately predict the quality of life level of patients after MSC treatment for one year, identify patients who are more likely to benefit from MSC treatment, and help clinicians make selections for applicable populations before early intervention treatment and MSC infusion, which is helpful to improve the quality of life and long-term prognosis of patients with severe novel coronavirus infection during the recovery period. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0023] Figure 1 A flow chart for grouping participants in an embodiment of the present invention;

[0024] Figure 2 The research design and flow chart of the embodiments of the present invention;

[0025] Figure 3The DEPs (Differentially Expressed Proteins) analysis between the MSC treatment response and non-response groups provided by the present invention; wherein A is a quantitative protein quantity distribution diagram; B is a quantitative peptide quantity distribution diagram; C is a volcano diagram of DEPs; D is a sample PCA diagram characterized by DEPs; E is a quantitative heat map of DEPs between samples;

[0026] Figure 4 The results of the enrichment analysis of prognosis-related DEPs provided by the present invention are as follows; A is the DEP enrichment analysis based on GO; B is the DEP enrichment analysis based on KEGG; C is the DEP enrichment analysis based on DO;

[0027] Figure 5 The LASSO binary logistic regression model provided by the present invention; wherein A is a graph showing the mean square error during Lasso regression protein screening as a function of Log(λ); and B is a graph showing the regression coefficient during Lasso regression protein screening as a function of Log(λ);

[0028] Figure 6 The evaluation results of the prognosis prediction model provided by the present invention; wherein A is the ROC curve of the training set and the test set; B is the confusion matrix of the prediction results in the test set; C is the calibration curve of the prognosis prediction model in the test set; D is the decision curve of the prognosis prediction model in the test set; and E is the nomogram display form of the Logistic regression model. DETAILED DESCRIPTION

[0029] Unless otherwise defined herein, scientific and technical terms used in conjunction with the present invention shall have the meanings commonly understood by those of ordinary skill in the art. The meaning and scope of the terms should be clear, however, in the case of any potential ambiguity, the definitions provided herein take precedence over any dictionary or external definitions. In this application, unless otherwise stated, the use of "or" means "and / or". In addition, the use of the term "including" and other forms is non-limiting.

[0030] The methods and techniques of the present invention are generally performed according to conventional methods well known in the art and as described in various general and more specific references that are cited and discussed throughout the present specification unless otherwise indicated.

[0031] On the one hand, the present invention provides biomarkers related to the long-term quality of life of patients with severe novel coronavirus infection, characterized in that the biomarkers include CAT, ARCN1, KRT5 and CTSC.

[0032] Through the plasma proteomic profile of patients with severe novel coronavirus infection before MSC treatment, potential biomarkers predicting long-term response to MSC treatment were screened. Identifying high-risk groups for long-term sequelae through protein biomarker regions can accurately predict the quality of life of patients one year after MSC treatment, identify patients who are more likely to benefit from MSC treatment, and help clinicians make selections for the appropriate population before early intervention treatment and MSC infusion, which will help improve the quality of life and long-term prognosis of patients with severe novel coronavirus infection during the recovery period.

[0033] According to another aspect of the present invention, there is also provided the use of plasma proteomic biomarkers or substances for detecting the biomarkers in the long-term quality of life prognosis assessment of patients with severe novel coronavirus infection, wherein the biomarkers include CAT, ARCN1, KRT5 and CTSC.

[0034] In some specific embodiments, the quality of life includes health status related to the global health dimension of the SF-36 standardized assessment.

[0035] In some specific embodiments, the CAT, ARCN1, KRT5 and CTSC are independent factors for assessing the long-term quality of life of patients with severe novel coronavirus infection.

[0036] According to another aspect of the present invention, there is also provided the use of plasma proteomic biomarkers in predicting or assisting in predicting the prognosis and quality of life of patients with severe novel coronavirus infection receiving mesenchymal stem cell treatment, wherein the biomarkers include CAT, ARCN1, KRT5 and CTSC.

[0037] According to another aspect of the present invention, a prediction model for the prognosis of quality of life of patients with severe novel coronavirus infection who receive mesenchymal stem cell treatment is also provided, including a Logistics regression analysis prediction model constructed using the above-mentioned biomarkers, and a nomogram for predicting the probability of occurrence of outcome events; the biomarkers include CAT, ARCN1, KRT5 and CTSC.

[0038] Through long-term follow-up of a randomized controlled trial of MSC treatment for patients with severe novel coronavirus infection, plasma proteomics technology was used to find a prognostic prediction model for long-term response to MSC treatment (containing 4 proteins: CAT, ARCN1, KRT5 and CTSC), which can accurately predict the patient's quality of life level one year after MSC treatment. Identifying the population that benefits from MSC treatment before treatment is conducive to early intervention treatment and patient rehabilitation of patients with severe novel coronavirus infection, and helps to improve the patient's quality of life during the recovery period and long-term prognosis.

[0039] In some specific embodiments, CAT, ARCN1, KRT5 and CTSC are independent factors for predicting the prognosis and quality of life of patients with severe novel coronavirus infection receiving mesenchymal stem cell treatment.

[0040] In some specific embodiments, low expression levels of the CAT, ARCN1, KRT5 and CTSC proteins are associated with good quality of life.

[0041] In some specific embodiments, the good quality of life comprises a score of ≥90 in the overall health dimension of the SF-36 standardized assessment.

[0042] In some specific embodiments, the regression coefficient of CAT is -3.05, the regression coefficient of ARCN1 is -0.63, the regression coefficient of KRT5 is -0.18, and the regression coefficient of CTSC is -0.42.

[0043] The technical solution of the present invention will be clearly and completely described below in conjunction with the embodiments. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] Example 1: Screening of biomarkers related to quality of life in patients with severe novel coronavirus infection based on plasma proteomics

[0045] 1. Patients and groups:

[0046] This study screened 66 subjects in the MSC group of the clinical trial of MSC treatment for patients with severe new coronavirus infection (NCT04288102) conducted by the team in the early stage. Among them, one withdrew the informed consent before the study treatment. Three patients without plasma samples before MSC treatment and 10 patients without follow-up or missing GH data at 1 year were excluded. Finally, 52 patients in the MSC group were included. The primary outcome was the general health (GH) dimension score in the health status questionnaire (36-item Short-Form, SF-36) at the 1-year follow-up. The score was calculated after collecting the questionnaire to assess the individual health-related quality of life. According to the median cutoff value (90.00), the patients were divided into the MSC response group (n=31, GH≥90.00) and the MSC non-response group (n=21, GH<90.00).

[0047] Patient enrollment and sample collection before MSC treatment were performed between March 5 and March 28, 2020. Demographic information, clinical characteristics, laboratory tests, and quality of life GH score results were collected from the EDC system.

[0048] 2. Research design:

[0049] Combination Figure 1 and Figure 2 To illustrate, a multi-biomarker combination was developed and validated using plasma proteomic data from two datasets to predict the level of quality of life after mesenchymal stem cell infusion. The 52 patients in the MSC group were randomly divided into a training set (n=39) and a validation set (n=13) in a ratio of 3:1. Subsequently, a multi-biomarker combination was developed using the data from the training set and validated by the validation set.

[0050] 3. Sample processing:

[0051] Lysis buffer (1% SDC / 100mM Tris-HCl, pH=8.5 / 10mM TCEP / 40mM CAA) was added to the samples and incubated at 60°C for 30 min to complete reduction and alkylation. The protein concentration was then determined using the Bradford method. Equal amounts of protein were taken and all samples were filled to the same volume using 1% SDC / 100mM Tris-HCl (pH=8.5) solution. An equal volume of ddHO was added. 2 O, dilute the SDC concentration to less than 0.5%, add trypsin at a mass ratio of 1:50 between enzyme and protein, and incubate at 37°C with shaking overnight for enzymatic digestion. The next day, add TFA to terminate the enzymatic digestion reaction, centrifuge the sample at 12000g, take the supernatant and desalt it with a homemade SDB desalting column, vacuum dry it, and freeze it at -20°C.

[0052] Liquid chromatography with tandem mass spectrometry (LC-MS / MS):

[0053] The mass spectrometry of the samples was performed using an UltiMate 3000RSLCnano nanoliter liquid phase (Thermo) coupled to a timsTOFPro mass spectrometer (Bruker). The peptide samples were injected through an automatic sampler and bound to a C18 trapping column (75 μm*2 cm, 3 μm particle size, pore size, Thermo), and then enter the analytical column (75μm*25cm, 1.6μm particlesize, pore size, IonOpticks). The analytical gradient was established using mobile phase A (0.1% formic acid) and mobile phase B (0.1% formic acid in ACN). The flow rate of the analysis was set to 300 nL / min. The mass spectrometer was used for data acquisition in diaPASEF mode. The capillary voltage was set to 1400 V. The scan range of MS1 and MS2 spectra was set to 100-1700 m / z. The ion mobility range was set to 0.6-1.6 Vs / cm 2 The accumulation time and ramp time were set to 100 ms. The diaPASEF acquisition window was set using the timsControl software according to the distribution law of mass-to-charge ratio-ion mobility. The collision energy was set from 1 / K0 = 1.6 Vs / cm 2 The linear increase from 59eV to 1 / K0 = 0.6Vs / cm 2 of 20eV.

[0054] 4. Database search and data preprocessing:

[0055] DIA raw data were analyzed using the library-free mode of DIA-NN (1.8.1) software. The database used for the search was the HUMAN protein sequence database (20230619) downloaded from Uniprot. The search parameters were mainly set by default, and the key parameters are described as follows: the Precursor ion generation option was enabled for theoretical spectrum library prediction; Trypsin / P was used, and up to 2 missed cleavage sites were allowed; Carbamidomethyl (C) modification was set as fixed modification; Oxidaton (M) and Acetylation (protein N-terminal) were set as variable modifications; MS1 and MS2 mass tolerances were set to 15ppm; MBR was enabled; Heuristic protein inference was enabled; FDR was set to 1%. Protein quantitative information was normalized using the MaxLFQ algorithm.

[0056] The contaminating proteins in the quantitative protein matrix were removed and logarithmically transformed. Then, the missing values ​​were filled with values ​​representing the normal distribution near the detection limit of the mass spectrometer. To this end, the mean and standard deviation of the actual intensity distribution were determined in this embodiment to create a new distribution with a downward shift of 1.8 standard deviations and a width of 0.25 standard deviations. These values ​​were used to estimate the missing values ​​in the protein matrix, excluding proteins with a missing ratio of more than 50% in both groups, and retaining the remaining proteins for subsequent analysis.

[0057] 5. Statistical analysis:

[0058] For continuous variables that conform to normal distribution, mean ± standard deviation was used for description; for continuous variables that do not conform to normal distribution, quartiles were used for description; categorical variables were described as percentages. For quantitative protein data, inter-group differences were tested. If both groups of data conformed to normal distribution, t-test was used; otherwise, Wilcoxon rank sum test was used.

[0059] Differentially expressed proteins were defined as proteins with a fold change (FC) greater than 1.5 or less than 1 / 1.5 between the response group and the non-response group, and a P value less than 0.05. Upregulated proteins were defined as proteins with a fold change greater than 1.5 between the MSC response group and the MSC non-response group, and a P value less than 0.05. Downregulated proteins were defined as proteins with a fold change less than 1 / 1.5 between the MSC response group and the MSC non-response group, and a P value less than 0.05. Bold words represent the five proteins selected in the Logistic regression model below.

[0060] Lasso regression and cross-validation were used to screen the final biomarkers from DEPs and used to establish a prediction model. 75% of the data in the total sample were used as the training set, and 25% of the data were used as the test set. In the training set, multivariate logistic regression was used to randomly select protein combinations from protein biomarkers to construct multiple diagnostic models, and the optimal diagnostic model was selected by the area under receiver operating characteristic curve (AUC) and Brier score calculated by cross-validation. Finally, the receiver operating characteristic (ROC) curve, Hosmer-Lemeshow goodness of fit test and calibration curve, and decision curve were used to evaluate the discrimination, calibration and clinical utility performance of the model in the training set and test set, respectively.

[0061] All statistical analyses were performed using R (version 4.4.0) and Python (version 3.10.12).

[0062] 6. Baseline clinical characteristics:

[0063] The median age of the 52 patients in this study was 61.50 years, of whom 30 (57.69%) were male. The median time from symptom onset to MSC treatment was 46.00 days (41.00, 52.00). The most common symptoms were fever (28 / 52, 53.85%), cough (28 / 52, 53.85%), fatigue (23 / 28, 82.14%), shortness of breath (17 / 52, 32.69%), and chest tightness (9 / 52, 17.31). The most common comorbidities were hypertension, diabetes, hyperuricemia or gout, and hyperlipidemia. The most commonly used drugs were Chinese patent medicine preparations, antibiotics, corticosteroids, and antiviral drugs. The "responder" and "non-responder" groups were comparable in terms of baseline levels of gender, age, and comorbidities. No patient died 1 year after MSC treatment.

[0064] Table 1. Baseline demographic and clinical characteristics of patients in the MSCs group

[0065]

[0066]

[0067] The data are expressed as median (interquartile range), n (%), or mean (standard deviation). IL-6, interleukin-6; MSCs, mesenchymal stem cells.

[0068] 7. Differentially expressed proteins between the responder and non-responder groups:

[0069] In order to fully understand the baseline plasma protein levels of the selected patients, proteomic analysis was performed, where baseline plasma refers to plasma samples collected before the patients received any treatment (such as mesenchymal stem cell treatment). A total of 1589 proteins ( Figure 3 A) and 17028 peptides ( Figure 3 B), after removing proteins with a missing ratio of more than 50% in both groups, a total of 1168 proteins were analyzed. The distribution and quality control results of the total peptides and proteins detected showed that the proteomics data had high quality and reproducibility. Figure 3As shown in Figure C, 90 proteins with different expression levels were screened out in the baseline plasma proteomes of the "good prognosis" and "poor prognosis" groups of patients (|log2FC|≥0.26, p<0.05; FC, fold change), of which 31 proteins were upregulated and 59 proteins were downregulated. A principal component analysis (PCA) diagram was drawn from the 90 differentially expressed proteins (DEPs, differentially expressed proteins) found, and different points represent different samples. The results showed that the MSC Good prognosis group and the MSC Poor prognosis group can be clearly distinguished by DEPs ( Figure 3 D in Figure 2). Using the quantitative information of differentially expressed proteins to draw a heat map, it was found that the expression levels of DEPs between the two groups were different ( Figure 3 E).

[0070] GO BP functional enrichment analysis was performed on these 90 proteins, and the results were sorted according to gene proportion and p-value. Their functions were mainly enriched in biological processes such as DNA repair regulation, gas transport, and pigment deposition; GO CC analysis showed that they were mainly enriched in cellular components such as specific protein complexes; GO MF analysis showed that they were mainly enriched in molecular functions such as integrin binding, transmembrane transporter activity, and protein heterodimer activity. Figure 4 As shown in A. KEGG enrichment results show enrichment in PI3K / Akt signaling pathway and diabetic cardiomyopathy pathway, such as Figure 4 As shown in B. The results of DO enrichment analysis showed that it was enriched in diseases such as lung diseases and anemia. Figure 4 As shown in C.

[0071] 8. Biomarkers related to quality of life in patients with severe novel coronavirus infection receiving MSC treatment:

[0072] The total samples were randomly divided into training set and test set in a ratio of 3:1. In the training set, the Lasso regression model was used for 5-fold cross validation to screen out 8 important proteins from 90 prognosis-related DEPs, namely CAT, ARCN1, KRT5, CTSC, KIT, BLMH, ATP5F1B and PCBP1. Figure 5 As shown in Table 2, these 8 important proteins were randomly combined for logistic regression analysis to construct a prognosis prediction model, and the optimal prognosis prediction model was screened by the discrimination index AUC and calibration index Brier score calculated by 5-fold cross validation. The results are shown in Table 2.

[0073] Table 2 Results of multivariate logistic regression analysis of training set

[0074] protein Estimate Std.Error z value Pr(>|z|) (intercept) 76.39 31.83 2.4 0.02 CAT -3.05 1.48 -2.06 0.04 ARCN1 -0.63 0.36 -1.73 0.08 KRT5 -0.18 0.34 -0.54 0.59 CTSC -0.42 0.26 -1.59 0.11

[0075] Among them, "Estimate" is the regression coefficient, "Std.Error" is the standard error of the regression coefficient, "z value" is the test statistic of the Z test on the regression coefficient, and "Pr(>|z|)" is the P value of the Z test on the regression coefficient.

[0076] CAT, ARCN1, KRT5 and CTSC are independent factors for predicting the quality of life level of patients with severe novel coronavirus infection after MSC treatment for 1 year. As shown in Table 2, the regression coefficients of CAT, ARCN1, KRT5 and CTSC in the model indicate the degree of influence of each independent variable on the dependent variable. Low expression levels of CAT, ARCN1, KRT5 and CTSC proteins before treatment are associated with improved quality of life after MSC treatment, and are biomarkers for predicting the response of patients with severe novel coronavirus infection to MSC treatment.

[0077] like Figure 6 As shown in E, a nomogram is drawn to predict the probability of occurrence of outcome events. According to the regression coefficients of the four independent variables CAT, ARCN1, KRT5 and CTSC in the multivariate logistic regression results, a certain "single score" is assigned to the different relative quantitative values ​​of each protein, reflecting the relative importance of each independent variable to the result. The "single score" corresponding to each protein is added together to obtain a "total score". The "Risk" corresponding to the total score reflects the predicted incidence of improvement in the quality of life of patients with severe new coronavirus infection after MSC treatment (0.01-0.9×100%). Finally, the single score and the total score are drawn on the same plane in a certain proportion in the form of a line segment with a scale to form a nomogram.

[0078] For example, the relative quantitative value of CAT in a patient with severe novel coronavirus infection was 21.5 through proteomics analysis, and the corresponding "single score" was 10 points. Similarly, the scores of each protein (CAT, ARCN1, KRT5 and CTSC) were found. Finally, the scores of all variables were added together to obtain the patient's "total score" (Total Points) of about 96 points. Then a vertical line was drawn downward at the total score, and the value was read on the scale line. It was predicted that the incidence of improvement in GH score (i.e., improvement in quality of life) in this patient one year after MSC treatment was about 70%.

[0079] The AUC values ​​of the training set and test set were 0.886 (0.781-0.991) and 0.825 (0.574-1.000), respectively, with accuracy rates of 85% and 69%, respectively, and the Hosmer-Lemeshow goodness-of-fit test P values ​​were 0.267 and 0.204, respectively. It can be seen that the prognostic prediction model constructed using the above four important proteins CAT, ARCN1, KRT5 and CTSC has good discrimination ability, accuracy and calibration, and can accurately predict the GH-related quality of life level of patients after MSC treatment for one year.

[0080] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. Biomarkers related to long-term quality of life in patients with severe novel coronavirus infection, characterized by: The biomarkers include CAT, ARCN1, KRT5 and CTSC.

2. Application of plasma proteomic biomarkers in the long-term quality of life prognosis assessment of patients with severe novel coronavirus infection, characterized in that: The biomarkers include CAT, ARCN1, KRT5 and CTSC.

3. The use according to claim 2, characterized in that: The quality of life includes health status related to the overall health dimension in the SF-36 standardized assessment.

4. The use according to claim 2, characterized in that: The CAT, ARCN1, KRT5 and CTSC are independent factors for assessing the long-term quality of life in patients with severe novel coronavirus infection.

5. The application of plasma proteomic biomarkers in predicting or assisting in predicting the prognosis and quality of life of patients with severe novel coronavirus infection receiving mesenchymal stem cell treatment, characterized in that: The biomarkers include CAT, ARCN1, KRT5 and CTSC.

6. A prediction model for the prognosis and quality of life of patients with severe novel coronavirus infection who receive mesenchymal stem cell treatment, characterized in that: It includes a Logistics regression analysis prediction model constructed using the biomarker described in claim 1, and a Nomograph for predicting the probability of occurrence of an outcome event.

7. The prediction model according to claim 6, characterized in that The CAT, ARCN1, KRT5 and CTSC are independent factors for predicting the prognosis and quality of life of patients with severe novel coronavirus infection receiving mesenchymal stem cell therapy.

8. The prediction model according to claim 7, characterized in that Low expression levels of the CAT, ARCN1, KRT5 and CTSC proteins were associated with a good quality of life.

9. The prediction model according to claim 8, characterized in that The good quality of life includes a score of ≥90 in the overall health dimension of the SF-36 standardized assessment.

10. The prediction model according to claim 7, characterized in that The regression coefficient of CAT was -3.05, the regression coefficient of ARCN1 was -0.63, the regression coefficient of KRT5 was -0.18, and the regression coefficient of CTSC was -0.42.

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