Application of plasma proteomic biomarkers in the long-term prognostic assessment of quality of life in patients with severe novel coronavirus infection

Abstract

The present invention provides an application of plasma proteomic biomarkers in the long-term prognostic assessment of quality of life in patients with severe novel coronavirus infection, relating to the field of biomedical technology. The biomarkers include CAT, ARCN1, KRT5, and CTSC. Plasma proteomic profiles of patients with severe novel coronavirus infection before MSC treatment are screened to obtain potential biomarkers for predicting long-term response to MSC treatment. By identifying high-risk groups for long-term sequelae using protein biomarker regions, the patient's quality of life level can be accurately predicted one year after MSC treatment, identifying patients who are more likely to benefit from MSC treatment, and helping clinicians select appropriate populations for early intervention treatment and before MSC infusion, thereby improving the quality of life and long-term prognosis of patients with severe novel coronavirus infection during the recovery period.

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 prognostic assessment of quality of life in patients with severe novel coronavirus infection. Background Art

[0002] Some patients recovering from COVID-19 continue to experience long-term COVID-19 symptoms, such as persistent fatigue, dyspnea, chest tightness, and cough, as well as reduced quality of life, including anxiety, depression, social disruption, and limited ability to live and work. However, the lack of prognostic biomarkers makes it difficult to identify individuals at high risk for long-term sequelae. The development of biomarkers is crucial for early intervention and patient recovery, helping to improve patients' quality of life and long-term prognosis during recovery.

[0003] Mesenchymal stem cells (MSCs), due to their anti-inflammatory, immunomodulatory, and low immunogenic properties, have been used in the research of lung diseases such as acute respiratory distress syndrome, chronic obstructive pulmonary disease, and pulmonary fibrosis. They offer a new therapeutic approach for addressing the acute damage and long-term sequelae of novel coronavirus infection. To date, over 380 clinical trials have been registered with ClinicalTrials.gov, both domestically and internationally. Many studies have found that MSCs are safe and effective in treating patients infected with the novel coronavirus. Our team's one-year follow-up of a previously conducted RCT revealed that MSC treatment achieved long-term benefits in terms of lung lesions and recovery of activity tolerance. However, efficacy varies from patient to patient, and patients in the MSC group may also have different responses; not all patients respond favorably to MSCs. Currently, the appropriate population, efficacy, potential toxicities, and mechanism of action of MSC treatment are not fully understood, and there are significant economic costs. Therefore, it is crucial to predict patients' responses to MSCs before treatment and select potential responders.

[0004] In recent years, plasma protein profiles have become specific diagnostic indicators for certain diseases, and well-developed protein models offer superior predictive performance compared to models based solely on basic clinical information. High-throughput mass spectrometry-based analysis of the protein composition of plasma samples allows for proteomic comparisons between patients with varying response levels. Machine learning algorithms can then be used to identify biomarkers for prognosis and develop models to predict treatment outcomes, thus providing a basis for physicians' 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 the lack of biomarkers for predicting the prognosis of quality of life in patients with severe novel coronavirus infection based on plasma proteomics.

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

[0008] The third purpose 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 who receive 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 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 application of plasma proteomic biomarkers in the long-term prognostic assessment of quality of life in 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 nomogram 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 who receive mesenchymal stem cell treatment.

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

[0021] The present invention provides plasma proteomics-based prognostic quality of life biomarkers for patients with severe novel coronavirus infection. By screening the plasma proteomic profile of patients with severe novel coronavirus infection before MSC treatment, potential biomarkers for predicting long-term response to MSC treatment are obtained. By identifying high-risk groups for long-term sequelae through protein biomarker regions, the patient's quality of life level can be accurately predicted one year after MSC treatment, and patients who are more likely to benefit from MSC treatment can be identified. This helps clinicians make selections for appropriate populations before early intervention treatment and MSC infusion, and helps 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 embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the specific embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any 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 embodiment of the present invention;

[0025] Figure 3This is the analysis of DEPs (Differentially Expressed Proteins) 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 plot 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 versus Log(λ); B is a graph showing the regression coefficient during Lasso regression protein screening versus Log(λ);

[0028] Figure 6 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; 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 event of any potential ambiguity, the definitions provided herein take precedence over any dictionary or external definitions. In this application, the use of "or" means "and / or" unless otherwise stated. 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] By analyzing the plasma proteomic profiles of patients with severe novel coronavirus infection before MSC treatment, potential biomarkers predicting long-term responses to MSC treatment were screened. Identifying individuals at high risk for long-term sequelae using protein biomarker regions can accurately predict patients' quality of life one year after MSC treatment, identify patients more likely to benefit from MSC treatment, and assist clinicians in selecting appropriate patients for early intervention and prior to MSC infusion, thereby improving the quality of life and long-term prognosis of patients with severe novel coronavirus infection during their 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 prognostic assessment of quality of life in patients with severe novel coronavirus infection, wherein the biomarkers include CAT, ARCN1, KRT5 and CTSC.

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

[0035] In some specific embodiments, 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 that predicts 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 four proteins: CAT, ARCN1, KRT5 and CTSC), which can accurately predict the patient's quality of life one year after MSC treatment. Identifying the population that will benefit from MSC treatment before treatment is conducive to early intervention treatment and patient recovery for 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 who receive mesenchymal stem cell treatment.

[0040] In some specific embodiments, low expression levels of 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 global 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 solutions of the present invention are described clearly and completely below with reference to the embodiments. It is obvious that the embodiments described are only a portion of the embodiments of the present invention, not all of them. All other embodiments derived by persons of ordinary skill in the art based on the embodiments of the present invention without creative effort are also 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 from the MSC group in a previous clinical trial of MSC therapy for patients with severe novel coronavirus infection (NCT04288102). One of these subjects withdrew informed consent before study treatment. Three subjects without pre-MSC plasma samples and 10 subjects without 1-year follow-up or missing GH data were excluded. A total of 52 patients were enrolled in the MSC group. The primary outcome was the general health (GH) domain score on the 36-item Short-Form (SF-36) questionnaire at 1-year follow-up. This score was calculated after collecting questionnaires to assess individual health-related quality of life. Based on the median cutoff value (90.00), patients were divided into an MSC responder group (n=31, GH ≥90.00) and an MSC non-responder 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] Combine Figure 1 and Figure 2 To illustrate, a multi-biomarker panel was developed and validated using plasma proteomic data from two datasets to predict quality of life after mesenchymal stem cell infusion. Fifty-two patients in the MSC group were randomly divided into a training set (n=39) and a validation set (n=13) in a 3:1 ratio. The multi-biomarker panel was then developed using data from the training set and validated using the validation set.

[0050] 3. Sample processing:

[0051] Lysis buffer (1% SDC / 100mM Tris-HCl, pH=8.5 / 10mM TCEP / 40mMCAA) was added to the sample and incubated at 60°C for 30 minutes to complete the reduction and alkylation. The protein concentration was then determined using the Bradford method. Equal amounts of protein were taken and all samples were brought to the same volume using 1% SDC / 100mM Tris-HCl (pH=8.5) solution. An equal volume of ddH2O was added to dilute the SDC concentration to less than 0.5%. Trypsin was added at a mass ratio of 1:50 enzyme to protein, and the cells were incubated at 37°C with shaking overnight for enzymatic digestion. The next day, TFA was added to terminate the enzymatic digestion reaction, the sample was centrifuged at 12,000g, the supernatant was taken and desalted using a homemade SDB desalting column, vacuum-evacuated, and frozen at -20°C.

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

[0053] The samples were detected by mass spectrometry using an UltiMate 3000RSLCnano nanoliter liquid phase (Thermo) coupled to a timsTOFPro mass spectrometer (Bruker). The peptide samples were injected via an autosampler 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). Analytical gradients were established using mobile phase A (0.1% formic acid) and mobile phase B (0.1% formic acid in ACN). The analytical flow rate was set to 300 nL / min. Mass spectrometry was performed in diaPASEF mode. The capillary voltage was set to 1400 V. The scan range for 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 100ms. The diaPASEF acquisition window was set using 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 20eV.

[0054] 4. Database search and data preprocessing:

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

[0056] Contaminating proteins were removed from the quantitative protein matrix and logarithmically transformed. Missing values ​​were then filled with values ​​representing a normal distribution near the detection limit of the mass spectrometer. To this end, this example determined the mean and standard deviation of the actual intensity distribution 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] Continuous variables that conformed to a normal distribution were described using mean ± standard deviation; those that did not conform to a normal distribution were described using quartiles; categorical variables were described using percentages. For testing intergroup differences in quantitative protein data, t-tests were used when both groups conformed to a normal distribution; otherwise, the Wilcoxon rank sum test was used.

[0059] Differentially expressed proteins were defined as those with a fold change (FC) greater than 1.5 or less than 1 / 1.5 between the responder and non-responder groups, and a P value less than 0.05. Upregulated proteins were defined as those with a fold change greater than 1.5 between the MSC responder and non-responder groups, and a P value less than 0.05. Downregulated proteins were defined as those with a fold change less than 1 / 1.5 between the MSC responder and non-responder groups, and a P value less than 0.05. Boldface indicates the five proteins selected in the logistic regression model below.

[0060] Lasso regression and cross-validation were used to screen the final biomarkers from the DEPs and to establish a predictive model. 75% of the total sample data were used as the training set, and 25% as the test set. Multiple diagnostic models were constructed using multivariate logistic regression, randomly selecting protein combinations from the protein biomarkers within the training set. The optimal diagnostic model was selected using the area under the receiver operating characteristic curve (AUC) and Brier score calculated using cross-validation. Finally, the discrimination, calibration, and clinical utility of the model were evaluated using receiver operating characteristic (ROC) curves, Hosmer-Lemeshow goodness-of-fit tests, calibration curves, and decision curves in both the training and test sets, 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 medications were traditional Chinese 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] 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 After removing proteins with a deletion 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 patients with "good prognosis" and "poor prognosis" (|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 based on the 90 differentially expressed proteins (DEPs), with different points representing different samples. The results showed that the MSC good prognosis group and the MSC poor prognosis group could be clearly distinguished by DEPs ( Figure 3 D in the figure). Using the quantitative information of differentially expressed proteins to draw a heat map, it was found that the expression levels of DEPs were different between the two groups ( 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 showed 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 disease and anemia. Figure 4 As shown in C.

[0071] 8. Biomarkers related to the prognosis of patients with severe novel coronavirus infection receiving MSC treatment:

[0072] The total samples were randomly divided into training and test sets at a ratio of 3:1. In the training set, a 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 predicting quality of life in patients with severe novel coronavirus infection one year after MSC treatment. As shown in Table 2, the regression coefficients for CAT, ARCN1, KRT5, and CTSC in the model represent the degree of influence of each independent variable on the dependent variable. Low pre-treatment expression levels of CAT, ARCN1, KRT5, and CTSC proteins were associated with improved quality of life after MSC treatment and serve as biomarkers for predicting response to MSC treatment in patients with severe novel coronavirus infection.

[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 plotted on the same plane in a certain proportion in the form of scaled line segments to form a nomogram.

[0078] For example, proteomic analysis determined that a patient with severe novel coronavirus infection had a CAT relative quantitative value of 21.5, corresponding to a "single score" of 10. Similarly, the corresponding scores for each protein (CAT, ARCN1, KRT5, and CTSC) were determined. Finally, the scores for all variables were added together to obtain a patient's "Total Points" score of approximately 96. A vertical line was then drawn downward from the total score, and the numerical value was read on the scale line. This predicted that the incidence of improvement in GH scores (i.e., improvement in quality of life) for this patient one year after MSC treatment would be approximately 70%.

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

[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 above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above 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. The use of plasma proteomic biomarkers in the preparation of a product for predicting or assisting in predicting the prognosis and quality of life of patients with severe novel coronavirus infection receiving mesenchymal stem cell therapy, characterized in that: The biomarkers include CAT, ARCN1, KRT5 and CTSC.

2. The use according to claim 1, characterized in that The quality of life includes health status related to the overall health dimension in the SF-36 standardized assessment.

3. The use according to claim 1, 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.

4. The use according to claim 1, characterized in that This includes the application of biomarkers CAT, ARCN1, KRT5 and CTSC to construct a logistics regression analysis prediction model and draw a nomogram to predict the probability of outcome events.

5. The use according to claim 4, 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 treatment.

6. The use according to claim 5, characterized in that Low expression levels of the CAT, ARCN1, KRT5, and CTSC proteins were associated with good quality of life.

7. The use according to claim 6, characterized in that Good quality of life includes a score of ≥90 in the overall health dimension of the SF-36 standardized assessment.

8. The use according to claim 5, 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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