Biomarker PMSA7 for evaluating visceral obesity and application thereof

Abstract

The invention discloses a biomarker PSMA7 for evaluating visceral obesity and application of the biomarker PSMA7, and belongs to the technical field of biomedical detection and molecular marker research. The marker is PSMA7 in serum, the biomarker is serum protein PSMA7, only a serum sample is needed in the evaluation process, detection can be completed under conventional laboratory conditions on the basis of serum protein detection, and the kit is high in specificity, easy and convenient to operate, low in cost, non-invasive, suitable for obesity typing screening and evaluation of large-scale people and capable of being applied to obesity typing screening and evaluation of large-scale people. The method has the potential of wide popularization and application, provides a theoretical basis and a practical basis for early diagnosis and accurate intervention of visceral obesity, and effectively overcomes the limitation of an existing method.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of biomedical detection and molecular marker research, and particularly relates to a serum protein biomarker PMSA7 for evaluating visceral obesity and application thereof. BACKGROUND

[0002] Obesity is a major public health problem worldwide, and is closely related to the occurrence and development of cardiovascular diseases, type II diabetes and various tumors. Clinically, obesity is mainly divided into visceral obesity and subcutaneous obesity according to the distribution of fat in the body, and there are significant differences between the two in metabolic characteristics and health risks.

[0003] Visceral adipose tissue is mainly deposited in the abdominal cavity, surrounding important organs such as liver, pancreas and intestinal tract, and has high fat mobilization capacity and endocrine activity, and can secrete various inflammatory factors and metabolic regulatory molecules, thereby significantly increasing the risk of metabolic disorders and related diseases. In contrast, subcutaneous adipose tissue is mainly distributed under the skin, concentrated in the thigh and buttock regions, and has relatively low metabolic activity and inflammatory factor secretion levels, and has less impact on the body's metabolic homeostasis.

[0004] Therefore, compared with subcutaneous adipose tissue (SAT) obesity, visceral adipose tissue (VAT) obesity has more significant harmfulness in metabolic abnormalities, cardiovascular diseases and tumor risks. How to effectively identify and evaluate VAT obesity has become an important demand in clinical diagnosis and disease prevention.

[0005] At present, body mass index (BMI), waist-hip ratio (WHR) (PMID: 39313919) and imaging examination (such as computed tomography (CT, PMID: 34839215) and magnetic resonance imaging (MRI, PMID: 36918706)) are commonly used obesity evaluation indicators. However, these methods have obvious limitations: BMI cannot distinguish the proportion of visceral adipose tissue (VAT) and subcutaneous adipose tissue (SAT); waist / hip ratio is easily affected by individual differences and has insufficient accuracy; CT / MRI is high in cost and complex in operation, and is not suitable for large-scale screening. At the same time, although some obesity-related molecules have been found through metabolomics and proteomics research, there is a lack of specific markers for visceral obesity, and the clinical application value is limited; imaging examination can accurately evaluate fat distribution, but is expensive, requires high equipment, and is complex in operation, and is not suitable for large-scale screening. In recent years, some studies have tried to use metabolomics or proteomics methods to find serum markers related to obesity, but these studies have focused on the whole obesity or type II diabetes population, and lack of exploration of specific markers for visceral obesity.

[0006] Therefore, it is urgent to develop a non-invasive, high-throughput, economical and convenient detection method for early identification of obesity subtypes, especially visceral obesity with higher metabolic risk. SUMMARY

[0007] In order to solve the above problems, the present application provides a biomarker for evaluating visceral obesity and its application.

[0008] In order to achieve the above-mentioned purpose, the present application is realized by the following technical scheme: A biomarker for evaluating visceral obesity, wherein the biomarker is any one or several of serum proteins PMSA7, serum proteins TSC22D1 and serum proteins PZP.

[0009] The present application also discloses the application of biomarkers serum proteins PMSA7, or serum proteins TSC22D1, or serum proteins PZP in preparing products for evaluating visceral obesity or products for regulating visceral obesity.

[0010] Further, the product for evaluating visceral obesity comprises a detection reagent or a detection kit or a diagnostic device, and the product evaluates visceral obesity by detecting the expression level of any one or several of serum proteins PMSA7, serum proteins TSC22D1 and serum proteins PZP in a sample.

[0011] Further, the detection sample is peripheral serum.

[0012] Further, the expression level of serum proteins PMSA7 in the detection sample is positively correlated with the evaluation of visceral obesity; the AUC value of serum proteins PMSA7 in the detection sample is not less than 0.9.

[0013] Further, the expression level of serum proteins TSC22D1 in the detection sample is positively correlated with the evaluation of visceral obesity; the AUC value of serum proteins TSC22D1 in the detection sample is not less than 0.9.

[0014] Further, the expression level of serum proteins PZP in the detection sample is positively correlated with the evaluation of visceral obesity; the AUC value of serum proteins PZP in the detection sample is not less than 0.9. The present application also discloses the application of a product for evaluating visceral obesity containing biomarkers, which is not for diagnostic purposes, comprising (1) an analysis module, which is used to determine the expression level of biomarkers in a sample to be tested of a subject, and; (2) an evaluation module, which is used to determine whether the subject is visceral obesity according to the expression level of the biomarkers determined in (1). wherein the biomarker in (1) is serum protein PMSA7, or serum protein TSC22D1, or serum protein PZP.

[0015] Further, the judgment in the evaluation module is that visceral obesity is positively correlated with the level of serum protein PMSA7, or serum protein TSC22D1, or serum protein PZP in peripheral blood.

[0016] The present application is used for evaluating the biomarker PMSA7 of visceral obesity and its application, which has the beneficial effects in that: (1) The biomarker of the present application can be used as a non-invasive biomarker for early diagnosis and precise intervention of visceral obesity, and in addition, it provides a theoretical basis for the research of obesity-related metabolic abnormality mechanism and potential intervention target.

[0017] (2) The biomarker of the present application is serum protein, and only serum samples are required in the evaluation process. Based on serum protein detection, the detection can be completed under conventional laboratory conditions, and the operation is simple, the cost is low, and it is non-invasive, suitable for obesity typing screening and evaluation of large-scale population, has the potential for wide application, provides a theoretical basis and practical basis for early diagnosis and precise intervention of visceral obesity, and effectively overcomes the limitations of the existing method.

[0018] (3) High specificity: the present application first proposes serum protein PMSA7 as a potential serum protein biomarker for distinguishing visceral obesity and subcutaneous obesity, filling the gap in clinical detection.

[0019] (4) Great potential for clinical application: through ROC analysis, the serum protein PMSA7 screened by the present application shows high sensitivity and specificity (AUC>0.85) in distinguishing individuals with visceral obesity and subcutaneous obesity, showing good potential for clinical application. BRIEF DESCRIPTION OF DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings needed to be used in the embodiments or prior art description. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0021] Figure 1A. Volcano plot of differentially expressed proteins between visceral obesity group and subcutaneous obesity group (screening criteria: |log2FC| > 1 and p < 0.05); B. KEGG pathway enrichment results of differentially expressed proteins; C. Importance evaluation results of 201 differentially expressed proteins by RF model; D. LASSO regression analysis results; Figure 2 Spearman correlation analysis of candidate proteins and obesity-related clinical indicators. Red indicates positive correlation, blue indicates negative correlation, and significance level is marked with asterisk: * indicates p < 0.05, ** indicates p < 0.01, and *** indicates p < 0.001; Figure 3 Correlation analysis of candidate proteins and VAT / SAT ratio. (A-L) are the correlation scatter plots of 12 candidate proteins (MYD88, TSC22D1, GKN2, CTNNA1, MB, OSBPL9, RMDN1, RPS23, SFT2D3, PSMA7, PZP and HNRNPR) and VAT / SAT ratio, respectively. The Spearman correlation coefficient R and p value are marked in the figure; Figure 4 ROC curve of candidate proteins based on DIA proteomics analysis in distinguishing visceral obesity and subcutaneous obesity; Figure 5 Subcellular localization of candidate proteins in obesity-related pathways and Mantel test analysis; A is the subcellular localization of six candidate proteins (MYD88, TSC22D1, GKN2, CTNNA1, PSMA7, and PZP) based on GeneCards database. B is the Mantel correlation analysis of candidate proteins and proteins in KEGG_PPAR_SIGNALING_PATHWAY. C is the Mantel correlation analysis of candidate proteins and proteins in KEGG_INSULIN_SIGNALING_PATHWAY; Figure 6 ELISA verification of the expression level and diagnostic value of candidate serum proteins in visceral obesity and subcutaneous obesity; A is the ELISA quantitative results of serum protein PMSA7 in visceral obesity group (n = 9) and subcutaneous obesity group (n = 9), and the difference between groups is evaluated by t test; B is the ROC curve analysis of serum protein PMSA7, showing its diagnostic performance in distinguishing visceral obesity and subcutaneous obesity. The significance level is marked with asterisk: * indicates p < 0.05, ** indicates p < 0.01, and *** indicates p < 0.001. DETAILED DESCRIPTION

[0022] To enable those skilled in the art to better understand the present invention, the technical solution of the present invention will be clearly and completely described below with reference to embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] A biomarker for assessing visceral obesity is any one or more of the following serum proteins: PMSA7, TSC22D1, and PZP.

[0024] The biomarkers of this invention can be used to prepare products for assessing visceral obesity, such as visceral obesity detection kits.

[0025] Example 1

[0026] The detection method for visceral obesity based on serum proteins includes the following steps: I. Serum proteomics detection Serum proteins were quantitatively detected in individuals with visceral obesity (VAT / SAT>0.9, 8 cases) and subcutaneous obesity (VAT / SAT<0.5, 4 cases) using data-independent acquisition (DIA) mass spectrometry, and serum protein expression profiles were obtained.

[0027] II. Screening of serum protein biomarkers (1) Plasma sample pretreatment and extraction of metabolite molecules Sample pretreatment using analytical grade reagents: ① Take out the plasma sample stored at -80℃, thaw it on ice, take 100μL of plasma into a 1.5mL centrifuge tube for extracting metabolite molecules, add standardized L-2-chlorophenylalanine as an internal reference metabolite (final concentration 0.3 mg / mL; prepared with 10μL methanol), and shake for 10 sec to mix. ② Organic solvent precipitation of proteins: Pre-cool all reagents at -20℃, add protein precipitant (methanol:acetonitrile 200μL:100μL), shake for 1 min; 0℃ water bath, sonicate for 10 min, freeze at -20℃ for 30 min; centrifuge at 13000 rpm, 4℃ for 10 min, the supernatant is the small molecule metabolite, the precipitate is the large molecule protein and cell debris, etc. Take 200μL of supernatant, evaporate and dry at -20℃ to obtain the metabolic molecules; ③ Redissolution and re-extraction: The metabolic molecules obtained in step ② were redissolved in 300 μL of methanol-water (1:4), vortexed for 30 s, sonicated for 3 min, frozen at -20℃ for 2 hours, centrifuged at 13000 rpm and 4℃ for 10 min, and 150 μL of the supernatant was filtered through a 0.22 μm pinhole filter to obtain the metabolite extract, which was stored at -80℃.

[0028] ④ Preparation of quality control samples (QC): Take equal volumes of metabolite extracts from all samples and mix them to prepare QC samples. The total volume of the QC samples is the same as that of the samples to be tested.

[0029] (2) Biomarker screening Bioinformatics methods were used to screen characteristic proteins in the test samples through differential analysis, random forest (RF) and LASSO regression. Spearman correlation analysis was performed on the candidate proteins and obesity-related clinical indicators, including BMI, WHR, VAT / SAT ratio, and blood lipid indicators (such as TG), combined with receiver operating characteristic (ROC) curve analysis, to screen out biomarkers: TSC22D1, PMSA7, and PZP (the AUC values ​​of these three proteins are all >0.9). Figure 1 , Figure 2 and Figure 3 As shown, the specific filtering method is as follows: ① Differential analysis of protein expression profiles using the limma package identified 201 significantly differentially expressed proteins (p < 0.05). These were selected as core differentially expressed proteins with potential diagnostic value. Machine learning methods were then applied to feature selection from these 201 differentially expressed proteins. First, a RF model based on 5-fold cross-validation was constructed to evaluate the contribution of each protein to classification performance. Proteins were ranked according to their importance based on Gini coefficients, and the top 30 proteins with the highest Gini coefficients were selected as preliminary candidate features. Next, to further compress feature dimensions and identify the most predictive variables, LASSO regression analysis was performed on these 30 proteins. The optimal regularization parameter λ was determined using 5-fold cross-validation, ultimately identifying three key proteins: PSMA7, CTNNA1, and GKN2.

[0030] ② Considering the top 10 proteins in terms of Gini coefficient (PZP, HNRNPR, MB, OSBPL9, RMDN1, RPS23, MYD88, SFT2D3, TSC22D1, GKN2) and the LASSO screening results, a total of 12 candidate proteins were included for subsequent analysis. The ROC curve, with false positive rate (FPR) on the x-axis and true positive rate (TPR) on the y-axis, describes the model's classification ability at different decision thresholds. The area under the curve (AUC) quantifies the overall discriminative ability of the candidate proteins.

[0031] III. Investigation of Serum Detectability and Function of Candidate Proteins Serum detectability and functional analysis were performed on the screened candidate proteins (MYD88, TSC22D1, PZP). Results are as follows: Figure 4 and Figure 5 As shown, subcellular localization analysis revealed that GKN2 and PZP were mainly located extracellularly, while CTNNA1, MYD88, and PSMA7 were mainly distributed in the cytoplasm, plasma membrane, and cytoskeleton. TSC22D1 was distributed in the nucleus, cytoplasm, and mitochondria, providing a structural basis for serum detection. Single-gene enrichment analysis and Mantel test results indicated that these candidate proteins are closely related to lipid metabolism, insulin signaling, and chronic inflammation-related pathways, suggesting that they may play a regulatory role in the metabolic differences between visceral and subcutaneous obesity. The above analysis, based on subcellular localization and biological function, supports the rationale for the detectability of candidate proteins in serum and provides a theoretical basis for their potential as biomarkers for differentiating obesity phenotypes.

[0032] IV. ELISA Validation To verify the reliability of the three candidate proteins (TSC22D1, PMSA7, and PZP) screened through bioinformatics analysis as potential biomarkers for visceral obesity, enzyme-linked immunosorbent assay (ELISA) was performed on the three candidate proteins.

[0033] The specific method was as follows: ELISA was performed on 9 serum samples from each of the visceral obesity group and the subcutaneous obesity group. Each sample was subjected to three biological replicates, and the average of the replicate results was taken as the protein expression level of that sample.

[0034] like Figure 6 As shown, the results indicated that PMSA7 expression was significantly higher in the visceral obesity group than in the subcutaneous obesity group, and ROC analysis showed AUC>0.8, consistent with the DIA proteomics results, further supporting the reliability and potential clinical value of serum PMSA7 in the diagnosis of visceral obesity.

[0035] Example 2

[0036] A kit for assessing visceral obesity includes a serum protein PMSA7 and reagents for isolating nucleic acids from a sample. The serum protein PMSA7 is detectably labeled.

[0037] Three serum samples were selected from each of the visceral obesity group and the subcutaneous obesity group. The kit of this invention was used to detect the expression level of serum protein PMSA7 in the serum samples. The expression level of serum protein PMSA7 was positively correlated with visceral obesity.

[0038] The visceral obesity detected by the kit of the present invention was retested by ELISA, and the results were the same as those detected by the kit of the present invention.

[0039] Example 3

[0040] A kit for assessing visceral obesity includes serum protein PZP and reagents for isolating nucleic acids from a sample. Serum protein PZP is detectably labeled.

[0041] Three serum samples were selected from each of the visceral obesity group and the subcutaneous obesity group. The expression level of serum protein PZP in the serum samples was detected using the kit of this invention. The expression level of serum protein PZP was positively correlated with visceral obesity.

[0042] The visceral obesity detected by the kit of the present invention was retested by ELISA, and the results were the same as those detected by the kit of the present invention.

[0043] Example 4

[0044] A kit for assessing visceral obesity includes a serum protein TSC22D1 and reagents for isolating nucleic acids from a sample. The serum protein TSC22D1 is detectably labeled.

[0045] Three serum samples were selected from each of the visceral obesity group and the subcutaneous obesity group. The expression level of serum protein TSC22D1 in the serum samples was detected using the kit of this invention. The expression level of serum protein TSC22D1 was positively correlated with visceral obesity.

[0046] The visceral obesity detected by the kit of the present invention was retested by ELISA, and the results were the same as those detected by the kit of the present invention.

[0047] Example 5

[0048] A kit for assessing visceral obesity includes serum protein PMSA7, serum protein TSC22D1, and reagents for isolating nucleic acids from a sample. Serum proteins PMSA7 and TSC22D1 are detectably labeled.

[0049] Three serum samples were selected from each of the visceral obesity group and the subcutaneous obesity group. The expression levels of serum proteins PMSA7 and TSC22D1 in the serum samples were detected using the kit of this invention. The expression levels of serum proteins PMSA7 and TSC22D1 were positively correlated with visceral obesity.

[0050] The visceral obesity detected by the kit of the present invention was retested by ELISA, and the results were the same as those detected by the kit of the present invention.

[0051] Example 6

[0052] A kit for assessing visceral obesity includes serum proteins PMSA7, TSC22D1, and PZP, and reagents for isolating nucleic acids from a sample. Serum proteins PMSA7, TSC22D1, and PZP are detectably labeled.

[0053] Three serum samples were selected from each of the visceral obesity group and the subcutaneous obesity group. The kit of this invention was used to detect the expression levels of serum protein PMSA7, serum protein TSC22D1 and serum protein PZP in the serum samples. The expression levels of serum protein PMSA7, serum protein TSC22D1 and serum protein PZP were positively correlated with visceral obesity.

[0054] The visceral obesity detected by the kit of the present invention was retested by ELISA, and the results were the same as those detected by the kit of the present invention.

[0055] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0056] Finally, it should be noted that the embodiments disclosed in this invention are merely preferred embodiments of this invention and are only used to illustrate the technical solutions of this invention, not to limit it. Although this invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this invention.

Claims

1. A biomarker for assessing visceral obesity, characterized in that: The biomarker is any one or more of serum protein PMSA7, serum protein TSC22D1, and serum protein PZP.

2. The use of a biomarker according to claim 1 in the preparation of products for evaluating visceral obesity.

3. The application of the biomarker according to claim 2 in the preparation and evaluation of products for visceral obesity, characterized in that: The product for assessing visceral obesity includes a test reagent or test kit or diagnostic device, and the product assesses visceral obesity by detecting the expression levels of any one or more of serum proteins PMSA7, TSC22D1 and PZP in a sample.

4. The application of the biomarker according to claim 3 in the preparation and evaluation of products for visceral obesity, characterized in that: The test sample was peripheral serum.

5. The application of the biomarker according to claim 4 in the preparation and evaluation of products for visceral obesity, characterized in that: The expression level of serum protein PMSA7 in the tested samples was positively correlated with the assessment of visceral obesity.

6. The application of the biomarker according to claim 5 in the preparation and evaluation of products for visceral obesity, characterized in that: The AUC value of serum protein PMSA7 in the test sample is not less than 0.

9.

7. The application of the biomarker according to claim 4 in the preparation and evaluation of products for visceral obesity, characterized in that: The expression level of serum protein TSC22D1 in the test samples was positively correlated with the assessment of visceral obesity; the AUC value of serum protein TSC22D1 in the test samples was not less than 0.

9.

8. The application of the biomarker according to claim 4 in the preparation and evaluation of products for visceral obesity, characterized in that: The expression level of serum protein PZP in the test samples was positively correlated with the assessment of visceral obesity, and the AUC value of serum protein PZP in the test samples was not less than 0.9.

Images