Heart failure wet syndrome specific biomarker and application thereof

Abstract

The invention belongs to the technical field of biological medicines, and particularly relates to a heart failure wet syndrome specific biomarker, which is at least one selected from the group consisting of metalloproteinase tissue inhibition factor-3 (Metalloproteinase inhibitor 3, TIMP3), apolipoprotein C-II (Apolipoprotein C-II, APOC2), and kininogen-1 (Kininogen-1, KNG1). Peripheral serum is used as a sample, a 4D-DIA proteomics technology is adopted for screening differential proteins, a targeted proteomics technology is adopted for verification, and three heart failure dampness syndrome specific markers (TIMP3, APOC2 and KNG1) are screened out, have high sensitivity and specificity, can be used for accurate identification of heart failure dampness syndrome patients, and can be used for clinical application. The blank of specific biological markers of the heart failure dampness syndrome at present is filled, the newly found biological marker can provide reliable basis and practical guidance for clinical diagnosis and drug treatment of the heart failure dampness syndrome in the future, and realization of individuation and precision targets of diagnosis and treatment of chronic heart failure is promoted.

Description

Technical Field

[0001] The present invention belongs to the field of biomedicine technology, and in particular relates to a heart failure dampness-syndrome-specific biomarker and its application. Background Art

[0002] Chronic heart failure is a severe manifestation and advanced stage of various heart diseases. It is a complex clinical syndrome caused by abnormal changes in cardiac structure and function, leading to ventricular dysfunction and other complications. Heart failure has become a major public health issue that severely endangers national health and imposes a heavy economic burden on society. Elucidating its pathophysiological mechanisms and seeking more effective treatment strategies have become current research priorities.

[0003] Traditional Chinese Medicine (TCM) considers heart failure to be a condition characterized by both underlying deficiency and superficial excess. The underlying deficiency is often characterized by deficiency of the heart, lungs, spleen, and kidneys; the superficial excess is characterized by a combination of blood stasis, dampness, and phlegm. Researchers have explored the distribution patterns of TCM syndromes associated with heart failure. While the specific results vary across studies, they demonstrate an underlying consistency: Qi deficiency, dampness, phlegm, and blood stasis are the most common syndromes associated with heart failure. A survey of patients with diastolic heart failure revealed that the disease is primarily located in the heart, lungs, and kidneys, with the primary syndromes being Qi deficiency, Yang deficiency, dampness, and phlegm, accounting for 64.5%, 63.1%, 43.3%, and 62.4%, respectively. An analysis of the TCM syndromes of chronic heart failure over the past decade revealed that the number of TCM syndromes increases with worsening cardiac function, but Qi deficiency and blood stasis remain the predominant syndromes. The proportion of patients with Yang deficiency and dampness syndromes in heart function class IV increases. This suggests that TCM syndrome differentiation for heart failure follows a consistent pattern. Dampness, as a primary symptom of superficial symptoms, is often the primary clinical manifestation and key to progression in heart failure. It can be difficult to eliminate over time, leading to prolonged illness. Accurately identifying dampness-related factors in heart failure and providing targeted treatment will significantly improve the accuracy of heart failure syndrome differentiation and enhance the efficacy of clinical treatment.

[0004] "Syndrome" is the core of the Traditional Chinese Medicine (TCM) theory of "differentiation and treatment based on syndrome differentiation." However, the lack of objective, quantitative indicators for syndrome identification and efficacy evaluation has long been a bottleneck hindering the modernization and objectification of TCM syndromes. The rapid development of systems biology has established a broader technical platform for the objective study of traditional Chinese medicine. In recent years, multi-omics technologies such as genomics, transcriptomics, proteomics, and metabolomics have been widely applied in cardiovascular disease, enabling the identification of specific biomarkers, the improvement of existing treatment options, and the exploration of new therapeutic targets. Currently, objective research on damp-type heart failure (HF) syndrome, a common disease component, is still lacking. No biological markers for HF damp-type heart failure have been reported, and diagnostic strategies and key technologies for combining HF damp-type heart failure with disease-syndrome integration have yet to be established. Summary of the Invention

[0005] In view of the current lack of biological markers for dampness syndrome of heart failure that can guide clinical practice, insufficient objective research on dampness syndrome of heart failure, and the lack of established diagnostic strategies and key technologies for the combination of disease and syndrome of dampness syndrome of heart failure, the present invention provides a biomarker for dampness syndrome of heart failure and its application to solve the aforementioned problems.

[0006] According to one aspect of the present invention, a heart failure damp-syndrome-specific biomarker is provided, wherein the heart failure damp-syndrome-specific biomarker is selected from at least one of tissue inhibitor of metalloproteinase-3 (TIMP3), apolipoprotein C-II (APOC2), and kininogen-1 (KNG1).

[0007] This study reveals that three biomarkers, TIMP3, APOC2, and KNG1, possess high sensitivity and specificity and can be used to accurately identify patients with damp-type heart failure. These newly discovered biomarkers can provide a reliable basis for objective and precise diagnosis of damp-type heart failure, advancing the goal of personalized and precise treatment for chronic heart failure.

[0008] According to another aspect of the present invention, there is provided the use of the above-mentioned heart failure dampness syndrome-specific biomarker in the preparation of a product for diagnosing or assisting in the diagnosis of heart failure dampness syndrome, which product may be a diagnostic reagent or a kit.

[0009] In some embodiments, when diagnosing or assisting in the diagnosis of heart failure dampness syndrome, the heart failure dampness syndrome-specific biomarkers are used to identify heart failure dampness syndrome by analyzing the level of at least one of TIMP3, APOC2, and KNG1 in a sample from a patient with heart failure, wherein the sample is serum.

[0010] According to another aspect of the present invention, there is provided a use of a product for detecting the content of a heart failure dampness syndrome-specific biomarker in a sample in the preparation of a product for diagnosing or assisting in the diagnosis of heart failure dampness syndrome, wherein the sample is serum.

[0011] The present invention uses peripheral serum as a sample, adopts 4D-DIA proteomics technology to screen differential proteins, and uses targeted proteomics technology for verification, and screens out three specific markers of heart failure dampness syndrome (TIMP3, APOC2, KNG1), which have high sensitivity and specificity and can be used to accurately identify patients with heart failure dampness syndrome, filling the current gap in specific biological markers of heart failure dampness syndrome. The newly discovered biomarkers can provide a reliable basis and practical guidance for the clinical diagnosis and drug treatment of heart failure dampness syndrome in the future, and promote the realization of the personalized and precise goals of diagnosis and treatment of chronic heart failure. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1-2 This is the technical roadmap of the present invention.

[0013] Figure 3 This is the PCA diagram of the proteins in the heart failure with dampness syndrome group (HF-D), heart failure without dampness syndrome group (HF-ND), and healthy control group (Con).

[0014] Figure 4-6 The volcano plots of differentially expressed proteins between HF-D group and Con group, HF-ND group and Con group, and HF-D group and HF-ND group are shown respectively.

[0015] Figure 7-9 The heat maps of the top 50 differentially expressed proteins between the HF-D group and the Con group, the HF-ND group and the Con group, and the HF-D group and the HF-ND group, respectively.

[0016] Figure 10-11 These are the Venn analysis results of the significantly up-regulated and down-regulated differential proteins among the three groups.

[0017] Figure 12-14 These are the GO enrichment terms of the significantly different proteins between the HF-D group and the Con group, the HF-ND group and the Con group, and the HF-D group and the HF-ND group.

[0018] Figure 15-17 The KEGG pathway enrichment P<0.05 pathways of significantly different proteins between HF-D group and Con group, HF-ND group and Con group, and HF-D group and HF-ND group are shown.

[0019] Figure 18-20 The top 20 rankings of the KEGG pathways of significantly different proteins between the HF-D group and the Con group, the HF-ND group and the Con group, and the HF-D group and the HF-ND group are shown respectively.

[0020] Figure 21-23 The bubble charts show the top 20 rankings of KEGG pathway enrichment of significantly different proteins between the HF-D group and the Con group, the HF-ND group and the Con group, and the HF-D group and the HF-ND group.

[0021] Figures 24-26 ROC curves of TIMP3, APOC2, and KNG1 were compared between the HF-D group and the Con group.

[0022] Figures 27-29 ROC curves of TIMP3, APOC2, and KNG1 were compared between the HF-D group and the HF-ND group.

[0023] Figure 30The ROC curve of the combined diagnostic model factors was constructed by logistic regression to compare the HF-D group with the Con group.

[0024] Figure 31 The ROC curve of the combined diagnostic model factors was constructed by logistic regression to compare the HF-D group with the HF-ND group. DETAILED DESCRIPTION

[0025] The present invention will be further described in detail below with reference to specific embodiments. Unless otherwise specified, the following raw materials are commercially available. Room temperature refers to 15°C to 25°C.

[0026] The present invention selected healthy subjects, as well as those with and without dampness syndrome in heart failure patients, collected serum samples, and screened for differentially expressed proteins using 4D-DIA proteomics. These differentially expressed proteins were then verified using PRM targeted proteomics. Three heart failure dampness syndrome-specific markers (TIMP3, APOC2, and KNG1) were identified, demonstrating high sensitivity and specificity, and can be used to accurately identify patients with heart failure dampness syndrome. The newly discovered biomarkers can provide a reliable basis for the objective and precise diagnosis of heart failure dampness syndrome, thereby improving the accuracy of clinical diagnosis of heart failure, advancing the personalized and precise treatment of chronic heart failure, and further enhancing the clinical efficacy of traditional Chinese medicine in treating heart failure.

[0027] 1. Experimental Content like Figure 1-2 As shown, peripheral venous blood and serum samples were collected from healthy community residents and outpatients or inpatients at Guangdong Provincial Hospital of Traditional Chinese Medicine according to standard operating procedures. Eight samples were collected from healthy individuals, nine samples were collected from patients with heart failure (non-wet syndrome), and 13 samples were collected from patients with heart failure (wet syndrome). Proteomics 4D-DIA technology was used to construct DDA libraries and perform DIA mass spectrometry analysis on the serum samples. Differential proteins were screened, and bioinformatics GO and KEGG enrichment analysis was combined to identify candidate proteins associated with heart failure (wet syndrome).

[0028] Based on the initial screening results of DIA, samples were further collected and 61 samples were included, including 22 samples from patients with heart failure with dampness syndrome, 12 samples from patients with heart failure without dampness syndrome, and 27 samples from healthy people. Targeted proteomics (PRM) was used to verify the candidate proteins, and finally specific markers for heart failure with dampness syndrome were screened out.

[0029] 2. Experimental Plan (1) Source of cases The cases were selected from healthy people in the community and outpatient or inpatient patients of Guangdong Provincial Hospital of Traditional Chinese Medicine, including healthy people who underwent physical examinations and people who met the diagnosis requirements of relevant diseases.

[0030] (2) Disease diagnostic criteria The diagnostic criteria for dampness syndrome in traditional Chinese medicine can refer to the "Diagnostic Criteria for Dampness Syndrome V2.0" issued by the National Key Laboratory of Dampness Syndrome in Traditional Chinese Medicine jointly built by the province and the ministry. This standard can be used as the basis for the diagnosis of dampness syndrome and typical dampness syndrome. Specifically, If any of the following conditions are met, dampness syndrome can be diagnosed: 1. Possessing Class 1 moderate or severe specificity indicators; 2. It has three sensitivity indicators.

[0031] Typical dampness syndrome can be diagnosed if any of the following conditions are met: 1. Possess one specificity index and two sensitivity indexes; 2. Possess two types of specific indicators; 3. It has 4 sensitivity indicators.

[0032] Specific indicators include: Category 1: (1) greasy tongue coating; (2) slippery tongue coating; (3) thick tongue coating; Category 2: (1) Heaviness in the head; (2) Heaviness throughout the body; (3) Heaviness in the limbs; (4) Heaviness and discomfort in the joints; Category 3: (1) sticky and uncomfortable stool; (2) sticky feeling in the mouth; (3) wet scrotum / excessive vaginal discharge; (4) greasy hair.

[0033] Sensitivity indicators include: (1) obesity; (2) drowsiness; (3) laziness; (4) poor sweating; (5) dirty face; (6) dizziness; (7) chest fullness; (8) excessive phlegm; (9) abdominal distension; (10) lower abdominal distension; (11) soreness and fatigue in the waist and knees; (12) soreness / soreness in joints and muscles; (13) no thirst; (14) poor appetite; (15) loose stools; (16) bad breath; (17) enlarged tongue; (18) soft pulse; (19) slippery pulse.

[0034] Specificity is measured in "classes," while sensitivity is measured in "items." For the diagnostic criteria for dampness syndrome, specificity indicators belonging to the same class are considered to have that class's specificity if any one of them meets the criteria of moderate or severe severity. For the diagnostic criteria for typical dampness syndrome, specificity indicators belonging to the same class, regardless of whether one or more are present, are counted as a class's specificity.

[0035] Diagnostic criteria for heart failure: According to the 2018 "Guidelines for the Diagnosis and Treatment of Heart Failure in China" issued by the Chinese Society of Cardiovascular Diseases of the Chinese Medical Association, diagnosis is mainly based on comprehensive clinical manifestations (symptoms and signs) and auxiliary examinations (cardiac ultrasound, BNP / NT-proBNP).

[0036] (3) Inclusion criteria 1. Heart failure (1) Sign the informed consent form.

[0037] (2) Aged 18-80.

[0038] (3) Meet the diagnostic criteria of the guidelines.

[0039] 2. Healthy people (1) Sign the informed consent form.

[0040] (2) Aged 18-80.

[0041] (3) Physical examinations should be performed to exclude major modern diseases, including those of the circulatory system, respiratory system, urinary system, endocrine system, digestive system, and nervous system.

[0042] (IV) Exclusion criteria 1. Heart failure (1) Those who meet the diagnostic criteria but cannot provide specific information, or whose previous information does not meet the relevant diagnostic criteria.

[0043] (2) Patients with complications such as severe arrhythmia (ventricular rate > 160 beats / min, rapid atrial fibrillation, atrial flutter) that may affect the diagnosis of the disease and the collection of data.

[0044] (3) Those who have used gastric motility regulators, microecological drugs, antibiotics or traditional Chinese medicine preparations in the past 2 weeks.

[0045] (4) Those with a history of long-term chronic enteritis, such as Crohn's disease, ulcerative colitis, etc., those with a history of constipation, chronic diarrhea, and those with a history of hereditary intestinal diseases.

[0046] (5) Those who have difficulty eating and need intravenous nutrition support; those who suffer from diseases that affect gastrointestinal absorption, such as malabsorption syndrome.

[0047] (6) Abnormal liver and kidney function (serum Cr>265 μmol / L, AST and ALT 2 times higher than the upper limit).

[0048] (7) Patients who are expected to have poor compliance and cannot undergo follow-up examinations on time.

[0049] (8) Those who are unable to understand the subject or cooperate with the diagnosis due to various reasons.

[0050] (9) Pregnant or lactating women.

[0051] 2. Healthy people (1) Abnormal liver and kidney function (ALT, AST 2 times higher than the upper limit, or serum Cr> 265 μmol / L).

[0052] (2) Obese people (BMI ≥ 28 kg / m2 ).

[0053] (3) Those who have used gastric motility regulators, antibiotics, probiotics or traditional Chinese medicine preparations in the past 2 weeks.

[0054] (4) Patients who are expected to have poor compliance and cannot undergo follow-up examinations on time.

[0055] (V) DIA proteomics initial screening 1. Sample preparation Grouping is done according to the following grouping criteria.

[0056] Heart failure-dampness syndrome group (HF-D): meets the diagnosis of heart failure and dampness syndrome.

[0057] Heart failure non-dampness syndrome group (HF-ND): meets the diagnosis of heart failure but does not meet the diagnosis of dampness syndrome; Healthy control group (Con): meets the modern medical standards of healthy people; Serum samples were collected from 5 ml of venous blood, allowed to stand at room temperature for 2 hours, and then centrifuged at 1500 g for 10 minutes at 4°C. The supernatant was aliquoted and stored in a -80°C refrigerator for later use.

[0058] 2. DIA proteomics detection (1) Protein extraction and determination After the samples were taken out, they were thawed at room temperature. 100 μL of each sample was taken and low-abundance protein enrichment was performed using the magnetic bead method. 100 μL of serum was added to 4 μL of magnetic beads (washed twice with wash buffer) and mixed and incubated for 2 hours. The beads were placed on a magnetic stand, and the serum was removed and then washed with a cleaning reagent. The beads were inverted and washed for 5 minutes each time, and repeated 5 times. All samples were digested with trypsin using the in-solution method. The peptides of all samples were desalted using a C18 Cartridge. After the peptides were freeze-dried, 20 μL of 0.1% formic acid solution was added to re-dissolve them. The OD 280 Determine the peptide concentration of each sample. Equal amounts of peptides from each sample are mixed into a pool of peptide samples for RP fractionation. Appropriate amounts of iRT standard peptides are added to the RP fractionated samples and the enzymatically digested peptides from each sample for DDA and DIA mass spectrometry detection, respectively.

[0059] (2) DDA database construction DDA analysis was performed using a nanoflow Evosep one system (Denmark). After nano-HPLC separation, samples were analyzed by DDA (data-dependent mass spectrometry) using a timsTOF mass spectrometer (Bruker). Detection mode: positive ionization. Mass spectrometry method: voltage 1500 V, ion source temperature 180°C, drying gas 3 L / min, PASEF cycles: 8, TIMS accumulation time: 100 ms, TIMS range (ion mobility): 0.75-1.35 Vs / cm 2 , MS and MSMS range: 100-1700Da, dynamic exclusion time: 24s.

[0060] (3) DIA detection DIA analysis uses the nanoliter flow rate Evosep one system for chromatographic separation, and the samples after nanoliter HPLC separation are subjected to DIA (data-independent) mass spectrometry analysis using a timsTOF mass spectrometer (Bruker). Detection mode: positive ion, MS and MSMS mass spectrometry scan range: 100-1700 m / z. MS2 uses DIA data acquisition mode, setting 4 TIMS Scan acquisition windows, and the accumulation time of each window is 100ms. In PASEF mode, the collision energy varies linearly with the ion mobility 1 / K0 (fragmentation energy from 20 to 59 eV corresponds to ion mobility 1 / K0 = 0.85 to 1.30 Vs / cm 2 ).

[0061] (4) Qualitative, quantitative and functional analysis of proteins Raw DDA data were directly imported into Spectronaut software (Spectronaut™ 16) to construct the SpectralLibrary. The corresponding database was downloaded based on the species. The search parameters were set as follows: trypsin as the enzyme, 1 as the maximum misscleavage site, carbamidomethyl (C) as the fixed modification, and oxidation (M) and acetyl (Protein N-term) as the dynamic modification. Proteins identified by database search had to pass the specified filter parameter, FDR < 1%.

[0062] Raw DIA data were processed using Spectronaut™ 16 software, using the same database used for database construction. Software parameters were set as follows: retention time prediction type was set to dynamic iRT, interference on MS2 level correction was enabled, cross-run normalization was enabled, and all results had to pass the filtering parameter Q value cutoff of 0.01 (equivalent to an FDR < 1%).

[0063] Differentially expressed proteins were considered to be significantly upregulated if P < 0.05 and FC > 1.2 or FC < 1 / 1.2. Proteins were considered significantly upregulated if P < 0.05 and FC > 1.2, and significantly downregulated if P < 0.05 and FC < 1 / 1.2. The Gene Ontology (GO) database was used to analyze the biological processes (BP), cellular components (CC), and molecular functions (MF) of the differentially expressed proteins according to their biological functions and classifications. The Kyoto encyclopedia of genes and genomes (KEGG) database was used to analyze the main pathways involved in the differentially expressed proteins.

[0064] (VI) Targeted quantitative proteomics validation Collect and expand samples. The grouping criteria and sample collection and preparation methods are the same as those in “(V) DIA proteomics initial screening”.

[0065] Thaw serum samples at room temperature, pipette an appropriate amount of sample, add an appropriate amount of SDT lysis buffer, and boil in a water bath for 10 minutes. Centrifuge at 14,000 g for 10 minutes, and collect the supernatant. Quantify protein using the BCA assay and aliquot the sample.

[0066] For each sample, approximately 100 μg of protein was added with DTT to a final concentration of 100 mM. The mixture was incubated in a boiling water bath for 15 minutes, cooled to room temperature, and mixed with 200 μL of UA buffer (8 M Urea, 150 mM Tris-HCl, pH 8.0). The mixture was transferred to a 10KD ultrafiltration tube and centrifuged at 14,000 g for 30 minutes. 200 μL of UA buffer was added and the mixture was centrifuged at 14,000 g for 30 minutes. The filtrate was discarded. 100 μL of IAA (50 mM IAA in UA) was added, the mixture was shaken at 600 rpm for 1 minute, protected from light and kept at room temperature for 30 minutes, and centrifuged at 14,000 g for 20 minutes. 100 μL of UA buffer was added and the mixture was centrifuged at 14,000 g for 20 minutes, repeated three times. 100 μL of 50 mM NH4HCO3 buffer was added and the mixture was centrifuged at 14,000 g for 20 minutes, repeated two times. Add 40 μL NH4HCO3 buffer (containing Trypsin, enzyme ratio 1:50), shake at 600 rpm for 1 minute, and incubate at 37°C for 16 hours. Replace the collection tube and centrifuge at 14000g for 15 minutes. Add 40 μL NH4HCO3 buffer (50mM) and centrifuge at 14000g for 30 minutes. Collect the filtrate. Desalt and freeze-dry the enzymatically digested peptides, then reconstitute with 0.1% FA. OD 280 Determine the peptide concentration.

[0067] 3. Experimental Results (1) DIA proteomics initial screening Healthy individuals from the community and outpatients or inpatients at Guangdong Provincial Hospital of Traditional Chinese Medicine were selected. Peripheral venous blood and serum samples were collected according to standard operating procedures. Eight samples were collected from healthy individuals, nine samples were collected from patients with heart failure (non-wet syndrome), and 13 samples were collected from patients with heart failure (wet syndrome). Serum samples were subjected to DDA library construction and DIA mass spectrometry analysis using proteomics 4D-DIA technology. Data with at least half of the data in each group containing non-null values ​​were analyzed for significant differences. Proteins with expression differences greater than 1.2-fold (up- and down-regulation) and a P value (T test) less than 0.05 were selected as differentially expressed proteins. Bioinformatics GO and KEGG enrichment analysis were combined to identify candidate proteins for heart failure (wet syndrome).

[0068] PCA principal component analysis was performed on the heart failure dampness syndrome (HF-D) group, heart failure non-dampness syndrome (HF-ND) group and healthy control (Con) group. Figure 3As shown, the results showed that samples from the heart failure dampness syndrome group and the healthy control group showed a significant spatial separation trend, while samples from the heart failure non-dampness syndrome group exhibited a unique distribution pattern: some of their data points overlapped with those of the healthy control group, while other individual data clustered with the heart failure dampness syndrome group. This distribution pattern suggests that the non-dampness syndrome group retains biological characteristics similar to those of the healthy population in terms of protein expression patterns, while also displaying an intermediate phenotype transitioning towards the dampness syndrome pathological state.

[0069] Volcano pictures Figure 4-6 As shown in the results, using FC>1.2 or <0.83 and p-value <0.05 as screening criteria, there were 332 differentially expressed proteins between the HF-D group and the Con group, 92 of which showed an upward trend and 240 showed a downward trend compared with the Con group; there were 146 differentially expressed proteins between the HF-ND group and the Con group, 94 of which showed an upward trend and 52 showed a downward trend compared with the Con group; there were 172 differentially expressed proteins between the HF-D group and the HF-ND group, 6 of which showed an upward trend and 166 showed a downward trend. In addition, the HF-D group showed a mainly downward trend compared with the other two groups. Figure 10-11 The results showed that compared with the HF-ND group and the Con group, there were 0 up-regulated differential proteins and 65 down-regulated differential proteins in the HF-D group.

[0070] GO and KEGG analysis were performed on the differentially expressed proteins between the HF-D group and the con group. The results of GO functional analysis were as follows: Figure 12 、 Figure 15 As shown in the figure, the differentially expressed proteins between the HF-D group and the Con group were mainly enriched in the extracellular region, lipoprotein particles and plasma lipoprotein particles; their biological processes played an important role in cell lysis, blood coagulation, hemostasis and wound healing through negative regulation of blood coagulation, regulation of hemostasis and negative regulation of hemostasis; their molecular functions were mainly related to heparin binding, serine peptidase activity and glycosaminoglycan binding. Figure 13 、 Figure 16 As shown, the differential proteins between the HF-ND and Con groups were mainly enriched in ribosomes, ribosomal subunits, ribonucleoprotein complexes, small ribosomal subunits, cytoplasmic ribosomes, etc. in terms of cellular components; their biological processes play an important role in gene expression, viral gene expression, and macromolecular metabolism through the negative regulation of gene expression, regulation of gene expression, and negative regulation of macromolecular metabolic processes; their molecular functions are mainly related to the structural components of RNA binding, nucleic acid binding, and DNA binding ribosomes.

[0071] There were 24 metabolic pathways that were differentially expressed between the HF-D group and the Con group, as shown in the figure, including complement and coagulation cascades, cholesterol metabolism, extracellular matrix-receptor interaction, ribosomes, neuroactive ligand-receptor interaction, glycosphingolipid biosynthesis-ganglion series, etc. There were 14 metabolic pathways that were differentially expressed between the HF-D group and the HF-ND group, such as Figure 15 As shown, it mainly includes metabolic pathways such as complement and coagulation cascade reactions, cholesterol metabolism, neuroactive ligand-receptor interaction, regulation of actin cytoskeleton, and nucleocytoplasmic transport.

[0072] (II) Targeted proteomics (PRM) verification A total of 61 samples were collected, including 22 samples from patients with heart failure with dampness syndrome, 12 samples from patients with heart failure without dampness syndrome, and 27 samples from healthy people. Based on the initial screening results of DIA, combined with bioinformatics GO and KEGG enrichment analysis, 65 candidate proteins were further screened, and targeted proteomics (PRM) was used to verify the candidate proteins, and finally, the differentially expressed proteins with dampness syndrome-specific characteristics of heart failure were screened.

[0073] The results show: Using FC>1.2 or FC<0.67 and p<0.05 as screening criteria, PRM verified a total of 33 differential proteins, as shown in Table 1. Among them, 3 differential proteins (TIMP3, APOC2, and KNG1) showed significant differences between patients with dampness syndrome and non-dampness syndrome of heart failure and healthy people, but there was no significant difference between the non-dampness group and healthy people.

[0074] The ROC curve was used to evaluate the sensitivity and specificity of the three candidate proteins in predicting heart failure in dampness syndrome. Figure 24-29 As shown in the results, the three candidate proteins can clearly distinguish patients with heart failure dampness syndrome from non-dampness syndrome and healthy controls. In the comparison of HF-D group with Con group and HF-ND group, the AUC values ​​of TIMP3 were 0.721 and 0.712, the AUC values ​​of APOC2 were 0.845 and 0.739, and the AUC values ​​of KNG1 were 0.631 and 0.731. The results suggest that TIMP3 and APOC2 have moderate to good discriminatory abilities between the HF-D group and the other two groups, while the discriminatory ability of KNG1 is relatively low. Based on logistic regression, the factors of the joint diagnostic model were constructed, and the AUC was used to evaluate the effectiveness of the model factors. The results are shown in the figure. Figure 30-31As shown. In the comparison of the combined diagnostic factors between the HF-D group and the Con and HF-ND groups, the AUC values ​​for TIMP3 and APOC2 were 0.907 and 0.867, respectively; the AUC values ​​for TIMP3 and KNG1 were 0.801 and 0.814, respectively; and the AUC values ​​for TIMP3 and KNG1 were 0.843 and 0.788, respectively. The AUC values ​​for the three target proteins were 0.907 and 0.864, respectively. These results indicate that any combination of two target proteins has good discriminatory power between the HF-D group and the other two groups. Overall, these results suggest that TIMP3, APOC2, and KNG1, with reasonable sensitivity and specificity, could serve as potential biomarkers for patients with heart failure characterized by dampness.

[0075] Table 1 Differential protein content and comparison between groups

[0076] The above are only some embodiments of the present invention. For those skilled in the art, several modifications and improvements can be made without departing from the inventive concept of the present invention, which all fall within the scope of protection of the present invention.

Claims

1. Use of a heart failure dampness syndrome-specific biomarker in the preparation of a product for diagnosing or assisting in the diagnosis of heart failure dampness syndrome, characterized in that: The heart failure dampness-syndrome specific biomarker is selected from at least one of TIMP3, APOC2, and KNG1.

2. The use according to claim 1, characterized in that The application is to identify heart failure dampness syndrome by analyzing the content of at least one biomarker among TIMP3, APOC2, and KNG1 in samples of heart failure patients.

3. The use according to claim 2, characterized in that The sample is serum.

4. The use according to any one of claims 1 to 3, characterized in that The product is a reagent or a kit.

5. Use of a product for detecting the content of a specific biomarker of heart failure dampness syndrome in a sample in the preparation of a product for diagnosing or assisting in the diagnosis of heart failure dampness syndrome, characterized in that: The heart failure dampness-syndrome specific biomarker is selected from at least one of TIMP3, APOC2, and KNG1.

6. The use according to claim 5, characterized in that The sample is serum.

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