Biomarker for diagnosing blood stasis syndrome of ischemic heart failure and application of biomarker

Abstract

The invention belongs to the technical field of biomedicine, and relates to a biomarker for diagnosing blood stasis syndrome of ischemic heart failure and application of the biomarker. The biomarker for diagnosing the blood stasis syndrome of the ischemic heart failure, provided by the invention, is at least one of a gene marker and a protein marker; wherein the gene marker is at least one of CD19, CD22, CD79A, CD79B and CR2, and the gene marker is at least one of CD19, CD22, CD79A, CD79B and CR2; the protein marker is at least one of F2, F8, F9 and FN1. The invention also provides a product for diagnosing the blood stasis syndrome of the ischemic heart failure. The kit contains a reagent for detecting the gene marker or / and the protein marker. By detecting the expression level of the biomarker in the blood of a patient with the ischemic heart failure blood stasis syndrome, the patient with the ischemic heart failure blood stasis syndrome and a healthy control can be effectively diagnosed and distinguished, and auxiliary diagnosis of the ischemic heart failure blood stasis syndrome is achieved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of biomedical technology, and particularly relates to a biomarker for diagnosing blood stasis syndrome of ischemic heart failure and application thereof. BACKGROUND

[0002] Ischemic heart failure is a common cardiovascular disease in clinic, and its morbidity and mortality continue to rise, which seriously threatens human health. In the system of traditional Chinese medicine, blood stasis syndrome is considered to be the most core and common syndrome type of ischemic heart failure, which often coexists with qi deficiency, yang deficiency and water retention syndrome. Traditional Chinese medicine syndrome differentiation and treatment has important guiding significance in the clinical management of ischemic heart failure, but the current traditional Chinese medicine diagnosis still mainly relies on the judgment of physicians on subjective information such as symptoms, tongue and pulse, and lacks unified, objective and quantitative biological indicators, which restricts the standardization and precision of syndrome identification. In recent years, with the rapid development of multi-omics technologies such as transcriptomics, proteomics and metabolomics, important tools have been provided for the objectification of traditional Chinese medicine syndromes. However, there is still a lack of a combination of biomarkers that have been systematically screened and jointly verified to assist in the diagnosis of blood stasis syndrome of ischemic heart failure, and there is also a lack of a standardized application scheme for the combination in the clinic. Therefore, it is urgent to develop a combination of biomarkers with good sensitivity and specificity, combined with modern detection technology, to realize the objective diagnosis and syndrome typing of blood stasis syndrome of ischemic heart failure, and to establish a new method for identifying and verifying blood stasis syndrome treatment targets at the molecular level, so as to improve the scientificity and repeatability of traditional Chinese medicine diagnosis and treatment, and to promote the objectification and precision of traditional Chinese medicine syndrome diagnosis. SUMMARY

[0003] In view of the problems and deficiencies in the prior art, the purpose of the present application is to provide a biomarker for diagnosing blood stasis syndrome of ischemic heart failure and application thereof.

[0004] The first aspect of the present application provides a biomarker for diagnosing blood stasis syndrome of ischemic heart failure, wherein the biomarker is at least one of a gene marker and a protein marker.

[0005] Preferably, the gene marker is at least one of CD19, CD22, CD79A, CD79B and CR2. More preferably, the gene marker is a combination of CD19, CD22, CD79A, CD79B and CR2.

[0006] Preferably, the protein marker is at least one of F2, F8, F9 and FN1. More preferably, the protein marker is a combination of F2, F8, F9 and FN1.

[0007] Preferably, the blood stasis syndrome of ischemic heart failure includes ischemic heart failure with qi deficiency and blood stasis, ischemic heart failure with yang deficiency and blood stasis, and ischemic heart failure with yang deficiency, blood stasis and water retention.

[0008] The second aspect of the present application provides use of a reagent for detecting the biomarker of the first aspect in the preparation of a product for diagnosing blood stasis syndrome of ischemic heart failure.

[0009] According to the use described above, preferably, the genetic marker is at least one of CD19, CD22, CD79A, CD79B and CR2; and the product detects the expression level of the genetic marker in the sample by real-time quantitative PCR, gene chip or high-throughput sequencing platform.

[0010] According to the use described above, preferably, the product contains specific primers for amplifying the genetic marker or / and probes hybridized with the genetic marker.

[0011] According to the use described above, preferably, the genetic marker is a combination of CD19, CD22, CD79A, CD79B and CR2; more preferably, when the genetic marker is a combination of CD19, CD22, CD79A, CD79B and CR2, the product for diagnosing blood stasis syndrome of ischemic heart failure predicts the probability of blood stasis syndrome of ischemic heart failure by the following formula: P(BSS) = 1 / (1+EXP(-(3.718-5.571×CD19+1.661×CD22-0.153×CD79A-1.671×CD79B+1.718×CR2))), wherein P(BSS) represents the probability, CD19 represents the expression level of CD19 gene, CD22 represents the expression level of CD22 gene, CD79A represents the expression level of CD79A gene, CD79B represents the expression level of CD79B gene, and CR2 represents the expression level of CR2 gene.

[0012] According to the use described above, preferably, the protein marker is at least one of F2, F8, F9 and FN1.

[0013] According to the use described above, preferably, the product detects the expression level of the protein marker in the sample by immunohistochemistry, enzyme-linked immunosorbent assay, iPRM, chromatography-mass spectrometry or mass spectrometry. More preferably, the chromatography is liquid chromatography.

[0014] According to the use described above, preferably, the product contains an antibody specifically binding to the protein marker or a standard sample of the protein marker.

[0015] According to the application, preferably, the protein markers are a combination of F2, F8, F9 and FN1; more preferably, when the product is used for diagnosing ischemic heart failure blood stasis syndrome, the probability calculation formula for predicting ischemic heart failure blood stasis syndrome is P(BSS) = 1 / (1+exp(-(-283.338+21.269*log F2+1.393*log F8+57.42*log F9+-26.689*log FN1))), wherein P(BSS) represents the probability, F2 represents the expression level of F2 protein, F8 represents the expression level of F8 protein, F9 represents the expression level of F9 protein, and FN1 represents the expression level of FN1 protein.

[0016] According to the application, preferably, the ischemic heart failure blood stasis syndrome includes ischemic heart failure qi deficiency and blood stasis syndrome, ischemic heart failure yang deficiency and blood stasis syndrome, and ischemic heart failure yang deficiency and blood stasis water retention syndrome.

[0017] The third aspect of the application provides a product for diagnosing ischemic heart failure blood stasis syndrome, which contains reagents for detecting the biomarkers in the first aspect.

[0018] According to the product, preferably, the reagents are reagents for detecting the expression levels of the gene markers in samples by real-time quantitative PCR, gene chip or high-throughput sequencing platform, or reagents for detecting the expression levels of the protein markers in samples by immunohistochemistry, enzyme-linked immunosorbent assay, iPRM, liquid chromatography-mass spectrometry or mass spectrometry.

[0019] According to the product, preferably, the reagents for detecting the expression levels of the gene markers in samples by real-time quantitative PCR, gene chip or high-throughput sequencing platform contain specific primers for amplifying the gene markers or probes for hybridizing with the gene markers.

[0020] According to the product, preferably, the reagents for detecting the expression levels of the protein markers in samples by immunohistochemistry, enzyme-linked immunosorbent assay, iPRM, liquid chromatography-mass spectrometry or mass spectrometry include antibodies specifically binding to the protein markers or standards for detecting the protein markers.

[0021] According to the product, preferably, the ischemic heart failure blood stasis syndrome includes ischemic heart failure qi deficiency and blood stasis syndrome, ischemic heart failure yang deficiency and blood stasis syndrome, and ischemic heart failure yang deficiency and blood stasis water retention syndrome.

[0022] Compared with the prior art, the application has the following effects:

[0023] (1) This invention is the first to discover that the expression levels of CD19, CD22, CD79A, CD79B, and CR2 genes in the blood of patients with blood stasis syndrome of ischemic heart failure are significantly lower than those in healthy controls. By detecting the expression levels of CD19, CD22, CD79A, CD79B, and CR2 genes in human blood, blood stasis syndrome of ischemic heart failure can be effectively detected. It has been verified that when any one of the five gene markers is used alone for the diagnosis of blood stasis syndrome of ischemic heart failure, the AUC of the ROC curve reaches above 0.67. Therefore, the five gene markers can be used for the auxiliary diagnosis of blood stasis syndrome of ischemic heart failure.

[0024] (2) When the present invention combines five gene markers, CD19, CD22, CD79A, CD79B and CR2, for the diagnosis of blood stasis syndrome in ischemic heart failure, the AUC of its ROC curve reaches 0.85, which is significantly higher than the diagnostic effect of the five gene markers alone. Moreover, the diagnostic sensitivity reaches 73% and the specificity reaches 90%, which has strong diagnostic potential and reliability. It provides a new reference for clinicians to diagnose blood stasis syndrome in ischemic heart failure, and also provides a molecular basis for the evaluation of the efficacy of blood stasis syndrome in ischemic heart failure and individualized intervention, and for the precise treatment of syndrome.

[0025] (3) This invention first discovered that the expression levels of F2, F8, and F9 proteins in the blood of patients with ischemic heart failure and blood stasis syndrome were significantly higher than those in healthy controls, while the expression level of FN1 protein in the blood of patients with ischemic heart failure and blood stasis syndrome was significantly lower than that in healthy controls. By detecting the expression levels of F2, F8, F9, and FN1 proteins in human blood, ischemic heart failure and blood stasis syndrome can be effectively detected. It has been verified that when any one of the five protein biomarkers is used alone for the diagnosis of ischemic heart failure and blood stasis syndrome, the AUC of its ROC curve all reached above 0.67. Therefore, the five protein biomarkers can be used for the auxiliary diagnosis of ischemic heart failure and blood stasis syndrome. Furthermore, when this invention combines five protein biomarkers—F2, F8, F9, and FN1—for the diagnosis of blood stasis syndrome in ischemic heart failure, the AUC of its ROC curve reaches 0.997, significantly higher than the diagnostic efficacy of the five protein biomarkers alone. Moreover, the diagnostic sensitivity reaches 97%, and the specificity reaches 99%, demonstrating strong diagnostic potential and reliability. This provides clinicians with a new reference for the diagnosis of blood stasis syndrome in ischemic heart failure, and also provides a widely applicable molecular basis for the evaluation of treatment efficacy and individualized intervention for blood stasis syndrome in ischemic heart failure, as well as for precise treatment of the syndrome.

[0026] (4) The test sample of the kit of the present invention is blood, which can avoid invasive diagnosis. The diagnosis of ischemic heart failure blood stasis syndrome can be achieved by taking blood through minimally invasive means. It requires less blood, causes less pain to the test subjects, and has high compliance. Moreover, it is simple to operate and the test results are obtained in a short time, which has broad market prospects and social benefits.

[0027] (5) Based on the screened gene markers and protein markers, this invention constructs a diagnostic model for ischemic heart failure with good sensitivity and specificity, realizing accurate identification and classification of ischemic heart failure with blood stasis syndrome, and improving the scientificity and standardization of TCM diagnosis. Attached Figure Description

[0028] Figure 1 The results of transcriptome sequencing and DIA proteomics analysis of the QXXYZ group are shown below. A represents the principal component analysis (PCA) plot of the transcriptome analysis of the QXXYZ group; B represents the PCA plot of the proteome analysis of the QXXYZ group; C represents the volcano plot of differentially expressed genes (DEGs) between the QXXYZ and HP groups; D represents the volcano plot of differentially expressed proteins (DEPs) between the QXXYZ and HP groups; E represents the KEGG pathway analysis results of differentially expressed genes in the QXXYZ group; and F represents the KEGG pathway analysis results of enriched differentially expressed proteins in the QXXYZ group.

[0029] Figure 2 The results of transcriptome sequencing and DIA proteomics analysis for the YXXYZ group are shown below. A represents principal component analysis (PCA) plots of transcriptome analysis between the YXXYZ and HP groups; B represents PCA plots of proteome analysis between the YXXYZ and HP groups; C represents a volcano plot of differentially expressed genes between the YXXYZ and HP groups; D represents a volcano plot of differentially expressed proteins between the YXXYZ and HP groups; E represents the KEGG pathway analysis results of differentially expressed gene enrichment in the YXXYZ group; and F represents the KEGG pathway analysis results of differentially expressed protein enrichment in the YXXYZ group.

[0030] Figure 3 The results of transcriptome sequencing and DIA proteomics analysis of the YXXYSYZ group are shown below. A represents principal component analysis (PCA) plots of transcriptome analysis between the YXXYSYZ and HP groups; B represents PCA plots of proteome analysis between the YXXYSYZ and HP groups; C represents a volcano plot of differentially expressed genes between the YXXYSYZ and HP groups; D represents a volcano plot of differentially expressed proteins between the YXXYSYZ and HP groups; E represents the KEGG pathway analysis results of differentially expressed gene enrichment in the YXXYSYZ group; and F represents the KEGG pathway analysis results of differentially expressed protein enrichment in the YXXYSYZ group.

[0031] Figure 4 The results of transcriptome sequencing and DIA proteomics analysis for ischemic heart failure with blood stasis syndrome are shown below. A represents the Venn diagram intersection of differentially expressed genes between the QXXYZ and HP groups, the YXXYZ and HP groups, and the YXXYSYZ and HP groups. B represents the Venn diagram intersection of differentially expressed proteins between the QXXYZ and HP groups, the YXXYZ and HP groups, and the YXXYSYZ and HP groups. C represents the KEGG pathway analysis results related to differentially expressed genes between ischemic heart failure with blood stasis syndrome and the HP group. D represents the KEGG pathway analysis results related to differentially expressed proteins between ischemic heart failure with blood stasis syndrome and the HP group.

[0032] Figure 5 This section presents the proteomic analysis results of patients with ischemic heart failure and blood stasis syndrome after treatment with the Qi-invigorating and blood-activating formula. A shows a volcano plot of differentially expressed proteins between the YQHX group and the healthy control group; B shows the Venn diagram intersection of differentially expressed proteins between the YQHX group and the healthy control group, and between the ischemic heart failure and blood stasis syndrome group and the healthy control group; C shows the KEGG pathway analysis results of differentially expressed protein enrichment; D shows the PPI network of overlapping differentially expressed proteins; E shows the expression changes of key proteins in the YQHX group; F shows the comparison of clinical efficacy indicators between the YQHX group and the QXXYZ group (×p<0.05 vs. QXXYZ group); where BSS Score refers to the BSS score.

[0033] Figure 6 The results show the expression levels of gene and protein markers in the QXXYZ, YXXYZ, and YXXYSYZ groups. Specifically, A represents the RT-qPCR results of gene marker expression levels in the QXXYZ, YXXYZ, and YXXYSYZ groups; B represents the iPRM results of protein marker expression levels in the QXXYZ, YXXYZ, and YXXYSYZ groups.

[0034] Figure 7 ROC curves for differentiating ischemic heart failure with blood stasis syndrome from healthy controls using gene markers and protein markers alone. Detailed Implementation

[0035] Example 1: Screening of biomarkers for the diagnosis of blood stasis syndrome in ischemic heart failure:

[0036] Serum samples were collected from patients with ischemic heart failure and blood stasis syndrome and healthy controls. The serum samples were analyzed separately from those from patients with ischemic heart failure and blood stasis syndrome using a multi-omics data integration algorithm. Differentially expressed genes, proteins and metabolites were screened from the transcriptome, proteome and metabolome. GO functional annotation and KEGG pathway enrichment analysis were performed. The pathway set and corresponding biomarkers that were significantly associated with ischemic heart failure and blood stasis syndrome were determined by statistical analysis.

[0037] 1. Experimental Samples

[0038] Ninety patients with ischemic heart failure complicated by blood stasis syndrome (i.e., ischemic heart failure combined with blood stasis syndrome) from the Cardiology Center of the First Affiliated Hospital of Henan University of Traditional Chinese Medicine (referred to as the blood stasis syndrome group) and 20 healthy subjects from the physical examination center (referred to as the HP group) were collected. Among them, the 90 patients with ischemic heart failure complicated by blood stasis syndrome included 30 patients with ischemic heart failure complicated by qi deficiency and blood stasis syndrome (i.e., ischemic heart failure combined with qi deficiency and blood stasis syndrome) (referred to as the QXXYZ group), 30 patients with ischemic heart failure complicated by yang deficiency and blood stasis syndrome (i.e., ischemic heart failure combined with yang deficiency and blood stasis syndrome) (referred to as the YXXYZ group), and 30 patients with ischemic heart failure complicated by yang deficiency, blood stasis, and water retention syndrome (referred to as the YXXYSYZ group). According to the TCM syndrome differentiation and classification criteria of the "Guidelines for the Diagnosis and Treatment of Chronic Heart Failure in Traditional Chinese Medicine", the ischemic heart failure blood stasis syndrome and its combined syndromes (such as qi deficiency and blood stasis, yang deficiency and blood stasis, yang deficiency and blood stasis combined with water retention, etc.) were identified. The healthy control group was used for comparative analysis. This study was reviewed and approved by the Ethics Committee of the First Affiliated Hospital of Henan University of Traditional Chinese Medicine, and all participants signed written informed consent forms.

[0039] The statistical results of the demographic and clinical biochemical indicators of the above 110 subjects are shown in Table 1.

[0040] Table 1. Baseline demographic and biochemical characteristics of the participants.

[0041]

[0042]

[0043] Table 1 shows that there were no statistically significant differences among the groups in terms of age, sex, BMI, diastolic blood pressure (DBP), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), thrombin time (TT), and fibrinogen (FIB). The HP group had lower systolic blood pressure (SBP) and blood glucose levels than other groups (P<0.05). In addition, the cholesterol (TC) and low-density lipoprotein cholesterol (LDL-C) levels in the QXXYZ, YXXYZ, and YXXYSYZ groups were lower than those in the HP group (P<0.05). Furthermore, the PT and APTT in the QXXYZ, YXXYZ, and YXXYSYZ groups were significantly different from those in the HP group.

[0044] 2. Screening of gene markers and protein markers

[0045] (1) Gene marker screening methods:

[0046] Serum samples were collected from subjects in the QXXYZ, YXXYZ, YXXYSYZ, and HP groups. Total RNA was extracted from each serum sample, and transcriptome sequencing analysis was performed on the extracted RNA using the Illumina NovaSeq 6000 high-throughput platform (Illumina, USA) to obtain differentially expressed genes (DEGs).

[0047] The specific steps are as follows:

[0048] All subjects in the experimental sample had 2.5 ml of fasting venous blood collected in the morning in PAXgene™ tubes (PreAnalytiX, China). The tubes were gently inverted and mixed 10 times, and then cryopreserved according to the manufacturer's instructions. Total RNA was extracted from blood samples collected from the HP, QXXYZ, YXXYZ, and YXXYSYZ groups using the PAXgene Blood miRNA Kit (PreAnalytiX, China). RNA concentration and integrity were accurately detected and assessed using an Agilent 5400 bioanalyzer (Agilent Technologies, USA). Qualified samples were then sequenced using the Illumina NovaSeq 6000 high-throughput sequencing platform (Illumina, USA).

[0049] The number of genes in each sample was standardized using DESeq2 software (1.20.0), and the fold change (FC) was calculated. The Benjamini-Hochberg method was used to adjust the obtained P-values ​​to control the false discovery rate. Differentially expressed genes were screened based on FC > 1.2 or FC < 0.83 and P < 0.05. GO and KEGG pathway enrichment analysis of DEG was performed using clusterProfiler (3.8.1) software in the R package.

[0050] (2) Protein biomarker screening methods:

[0051] Serum samples were collected from subjects in the QXXYZ, YXXYZ, YXXYSYZ, and HP groups. Proteins were extracted from the serum samples, and DIA proteomics technology was used to detect and analyze the extracted proteins to obtain differentially expressed proteins.

[0052] The specific steps are as follows:

[0053] Fasting venous blood was collected from subjects in the morning using 2 ml EDTA anticoagulant tubes. The blood was centrifuged at 3000 × g at 4°C for 15 minutes, and the supernatant was transferred to new Eppendorf tubes, labeled, aliquoted, and stored at -80°C. Serum proteins were extracted and identified according to the manufacturer's instructions. High-abundance proteins were effectively removed using the ProteoMiner Low Abundance Protein Enrichment Kit (Bio-Rad, USA), and protein concentrations were quantified using the Bradford Protein Quantification Kit (Biyuntian, China). Enzymatically digested peptides were desalted and identified using liquid chromatography-tandem mass spectrometry (LC-MS / MS). Differentially expressed proteins were screened based on FC > 1.2 for upregulated proteins and FC < 0.83 for downregulated proteins, with P < 0.05. GO and KEGG pathway enrichment analysis of differentially expressed proteins was performed using clusterProfiler (3.8.1) software in the R package.

[0054] (3) Screening results of gene and protein markers for blood stasis syndrome in ischemic heart failure:

[0055] Based on PCA (Principal Component Analysis), transcriptome sequencing data and proteome analysis data of the QXXYZ, YXXYZ, YXXYSYZ, and HP groups were analyzed. The PCA results are as follows: Figure 1 As shown in A and 1B. From Figure 1 A and Figure 1As shown in Figure B, the transcriptomic and proteomic profiles of the QXXYZ group and the HP group exhibited significant separation. Intergroup analysis of DEGs (differentially regulated genes) using DESeq2 software revealed 1712 differentially regulated genes between the QXXYZ and HP groups; among them, 733 genes were upregulated and 979 genes were downregulated. Figure 1 C). Through DIA proteomics analysis, a total of 375 differentially expressed proteins were identified in the QXXYZ and HP groups; among them, 214 proteins were upregulated and 161 proteins were downregulated. Figure 1 D). Further KEGG pathway enrichment analysis of differentially expressed genes and proteins was performed using clusterProfiler (3.8.1) software. KEGG enrichment analysis indicated that B cell receptor signaling pathway, neutrophil extracellular trap formation, complement and coagulation cascade, focal adhesion, and nf-κB signaling pathway are key pathways in the Qi deficiency and blood stasis syndrome of ischemic heart failure. Figure 1 E, 1F).

[0056] Based on PCA (Principal Component Analysis), the transcriptome sequencing data and proteome detection data of the YXXYZ group and HP group were analyzed respectively. The PCA results are as follows: Figure 2 As shown in A and 2B. From Figure 2 A and Figure 2 As shown in section B, there is a clear separation between the YXXYZ group and the HP group in terms of transcriptome and proteome. Intergroup analysis of DEGs (differentially regulated genes) using DESeq2 software revealed 2380 differentially regulated genes between the YXXYZ and HP groups; among them, 1047 genes were upregulated and 1333 genes were downregulated. Figure 2 C). DIA proteomics analysis revealed 888 differentially expressed proteins between the YXXYZ group and HP; among them, 467 proteins were upregulated and 421 proteins were downregulated. Figure 2 D). Further KEGG pathway enrichment analysis of differentially expressed genes and proteins was performed using clusterProfiler (3.8.1) software. KEGG enrichment analysis suggested that the main biological mechanisms of ischemic heart failure with Yang deficiency and blood stasis syndrome involve B cell receptor signaling pathway, nf-κB signaling pathway, complement and coagulation cascade, and dilated cardiomyopathy pathways. Figure 2 E, 2F).

[0057] Based on PCA (Principal Component Analysis), the transcriptome sequencing data and proteome detection data of the YXXYSYZ group and HP group were analyzed respectively. The PCA results are as follows: Figure 3 As shown in A and 3B. From Figure 3 A andFigure 3 As shown in Figure B, the transcriptomic and proteomic profiles of the QXXYZ group and the HP group exhibited significant separation. Intergroup analysis of DEGs (differentially expressed genes) using DESeq2 software revealed 1704 differentially expressed genes between the YXXYSYZ and HP groups, of which 939 genes were upregulated and 765 genes were downregulated. Figure 3 C). Through DIA proteomics analysis, the YXXYSYZ and HP groups identified a total of 1080 differentially expressed proteins; among them, 717 proteins were upregulated and 363 proteins were downregulated. Figure 3 D). Further KEGG pathway enrichment analysis of differentially expressed genes and proteins was performed using clusterProfiler (3.8.1) software. The differential gene KEGG enrichment analysis suggested that the key biological processes in ischemic heart failure with yang deficiency, blood stasis, and water retention syndrome include B cell receptor signaling pathway, nf-κB signaling pathway, ferroptosis, etc. Figure 3 E); Differential protein KEGG enrichment analysis indicated that the enriched pathways in ischemic heart failure with yang deficiency, blood stasis, and fluid retention syndrome were complement and coagulation cascade, PI3K-Akt signaling pathway, etc. Figure 3 F).

[0058] By using Venn diagrams to extract the intersections of differentially expressed genes and proteins between the QXXYZ and HP groups, the YXXYZ and HP groups, and the YXXYSYZ and HP groups, differentially expressed genes and proteins between ischemic heart failure with blood stasis syndrome and healthy controls were screened. The screening revealed 435 differentially expressed genes associated with ischemic heart failure with blood stasis syndrome. Figure 4 A), there are 176 differentially expressed proteins ( Figure 4 B). Further KEGG pathway enrichment analysis was performed on differentially expressed genes between ischemic heart failure patients with blood stasis syndrome and healthy controls using clusterProfiler (3.8.1) software. The KEGG enrichment analysis showed that the biological mechanism of ischemic heart failure with blood stasis syndrome was significantly enriched in immune and inflammatory regulatory pathways such as the B-cell receptor signaling pathway. Figure 4D); Using the string website (https: / / string-db.org / ), a minimum interaction confidence threshold of 0.4 was set, and a core molecular network was constructed using Cytoscape software. PPI network analysis screened out the core genes CD19 (Cluster of Differentiation 19), CD22 (Cluster of Differentiation 22), CD79A (Cluster of Differentiation 79A), CD79B (Cluster of Differentiation 79B), and CR2 (Complement Receptor 2) related to the B cell receptor signaling pathway as potential gene markers for blood stasis syndrome in ischemic heart failure. KEGG pathway enrichment analysis was performed on differentially expressed proteins between ischemic heart failure patients with blood stasis syndrome and healthy controls using ClusterProfiler (3.8.1) software. The KEGG enrichment analysis of differentially expressed proteins showed that the main biological mechanisms of ischemic heart failure with blood stasis syndrome are complement and coagulation cascade, PI3K-Akt signaling pathway, etc. Using the string website (https: / / string-db.org / ), with a minimum interaction confidence threshold of 0.4, a core molecular network was constructed using Cytoscape software. PPI network analysis screened out FN1 (Fibronectin 1), C3 (Complement Component 3), F2 (Coagulation Factor II), F8 (Coagulation Factor VIII), and F9 (Coagulation Factor IX) proteins related to complement and coagulation cascade reactions as potential protein biomarkers for ischemic heart failure with blood stasis syndrome.

[0059] To conduct cross-validation, this invention further treated 25 patients with ischemic heart failure and qi deficiency and blood stasis syndrome in the QXXYZ group with a qi-tonifying and blood-activating formula (YQHX) on the basis of standard Western medicine treatment. The qi-tonifying and blood-activating formula consists of seven Chinese herbs: Astragalus membranaceus (Gansu, batch number 176231202) 60g, Paeonia lactiflora (Guangxi, batch number 231203) 15g, Ligusticum chuanxiong (Sichuan, batch number 240101) 12g, Angelica sinensis (Gansu, batch number 231202) 20g, Pheretima aspergillum (Gansu, batch number 231203) 12g, Carthamus tinctorius (Xinjiang, batch number 231201) 12g, and Prunus persica (Hebei, batch number 1812312081) 12g. The aforementioned medicinal materials were all provided by the First Affiliated Hospital of Henan University of Traditional Chinese Medicine and prepared into granules (approval number 2204317) by Jiangyin Tianjiang Pharmaceutical Co., Ltd. using modern technology (extraction, concentration, drying, granulation, and sealing), exclusively for clinical research use. The dosage of YQHX granules is 3 sachets each time, twice daily, dissolved in boiling water, for 12 consecutive weeks. Serum samples were collected from 25 patients with ischemic heart failure and qi deficiency and blood stasis syndrome (referred to as the YQHX group) after treatment with Yiqi Huoxue Fang (YQHX) (collected in the morning on an empty stomach). Transcriptome sequencing analysis (using the same method as above) and DIA proteomics analysis (using the same method as above) were performed on the serum samples to screen differentially expressed genes and proteins in the YQHX group before and after treatment with Yiqi Huoxue Fang. Screening revealed significant molecular changes after treatment with the Qi-invigorating and blood-activating formula, according to multi-omics analysis. Transcriptome sequencing analysis identified 289 differentially expressed genes, and DIA proteomics analysis further identified 370 differentially expressed proteins. Figure 5 A); To further explore and verify the potential relationship between key molecules of blood stasis syndrome in ischemic heart failure and the therapeutic targets of the Qi-invigorating and blood-activating formula, the intersection of differentially expressed proteins before and after intervention in the YQHX group and differentially expressed proteins between blood stasis syndrome in ischemic heart failure and healthy controls was taken, and a total of 73 overlapping differentially expressed proteins were identified. Figure 5 B); Further KEGG pathway enrichment analysis was performed on the overlapping differentially expressed proteins (73) using clusterProfiler (3.8.1) software. The KEGG enrichment analysis of the differentially expressed proteins showed that their main enrichment pathways were highly consistent with the pathways related to BSS pathophysiology, including complement and coagulation cascades and ECM receptor interaction signaling pathways. Figure 5 C). Using the string website (https: / / string-db.org / ), with a minimum interaction confidence threshold of 0.4, a core molecular network was constructed using Cytoscape software. The PPI network was then used to analyze and screen 73 overlapping differentially expressed proteins to identify core proteins related to the therapeutic response targets for blood stasis syndrome. The results are as follows: Figure 5 As shown in D.Figure 5 As shown in D, a tightly connected molecular network centered on F2, F8, F9, and FN1 proteins was formed, suggesting that it may be a key pathological node and intervention target for blood stasis syndrome.

[0060] The expression levels of F2, F8, F9, and FN1 proteins in blood samples from the YQHX group were further detected using DIA proteomics technology. The results are as follows: Figure 5 As shown in E. Figure 5 E shows that the expression of F2, F8 and F9 proteins was significantly reduced after treatment with the Qi-invigorating and blood-activating formula, while the expression of FN1 was upregulated, further verifying the potential value of these proteins as key molecules in blood stasis syndrome of ischemic heart failure.

[0061] The comparison of clinical indicators between the YQHX group and the QXXYZ group included LVEF, NT-proBNP, 6MWD (6-minute walk distance), BSS score (Traditional Chinese Medicine syndrome score for blood stasis syndrome in ischemic heart failure, calculated based on clinical symptoms, signs, tongue appearance, and pulse according to the International Criteria for Diagnosis of Blood Stasis Syndrome (SCM68-2021)), and Minnesota Heart Failure Life Questionnaire (MLHFQ) score. Figure 5 As shown in F, by Figure 5 F indicates that after treatment with the Qi-invigorating and blood-activating formula, left ventricular ejection fraction (LVEF) showed an improving trend, NT-proBNP levels decreased, 6-minute walk distance (6MWT) significantly increased, and BSS scores and Minnesota Heart Failure Life Questionnaire (MLHFQ) scores were significantly reduced. Figure 5 F) indicates that the specific manifestations of blood stasis syndrome in ischemic heart failure and the overall quality of life were significantly improved after treatment with the Qi-invigorating and blood-activating formula.

[0062] Example 2: Value assessment of differentially expressed genes in the diagnosis of blood stasis syndrome in ischemic heart failure

[0063] 1. Experimental Samples

[0064] Thirty patients with ischemic heart failure and blood stasis syndrome (referred to as the blood stasis syndrome group) from the Cardiology Center of the First Affiliated Hospital of Henan University of Traditional Chinese Medicine and 10 healthy subjects from the Physical Examination Center (referred to as the HP group) were collected. The 30 patients with ischemic heart failure and blood stasis syndrome included 10 patients with ischemic heart failure and qi deficiency and blood stasis syndrome (referred to as the QXXYZ group), 10 patients with ischemic heart failure and yang deficiency and blood stasis syndrome (referred to as the YXXYZ group), and 10 patients with ischemic heart failure and yang deficiency and blood stasis with fluid retention syndrome (referred to as the YXXYSYZ group). Based on the TCM syndrome differentiation criteria in the "Guidelines for the Diagnosis and Treatment of Chronic Heart Failure in Traditional Chinese Medicine," the ischemic heart failure and blood stasis syndrome and its concurrent syndromes (such as qi deficiency and blood stasis, yang deficiency and blood stasis, yang deficiency and blood stasis with fluid retention, etc.) were identified. The healthy control group was used for comparative analysis. This study was reviewed and approved by the Ethics Committee of the First Affiliated Hospital of Henan University of Traditional Chinese Medicine, and all subjects signed written informed consent forms.

[0065] 2. qRT-PCR detection of expression levels of differentially expressed genes CD19, CD22, CD79A, CD79B, and CR2:

[0066] Serum samples were collected from 40 subjects in the above-mentioned experimental samples on an empty stomach in the morning. The expression levels of differentially expressed genes CD19, CD22, CD79A, CD79B, and CR2, which were initially screened in Example 1, were detected by qRT-PCR. The RT-qPCR experiment was performed using BlazeTaq. TM The RT-qPCR was performed using the Green qPCR Mix 2.0 (GeneCopoeia, USA) system. Primer sequences for RT-qPCR are shown in Table 2. β-actin was used as the endogenous control gene for data normalization. Relative mRNA expression levels were calculated using the 2-ΔΔCt method. All samples were performed in triplicate to ensure the reliability and reproducibility of the results.

[0067] Table 2. Amplification primer sequences for CD19, CD22, CD79A, CD79B, and CR2.

[0068]

[0069] qRT-PCR test results are as follows Figure 6 As shown in Figure A. Figure 6 As shown in A, compared with the HP group, the expression levels of CD19, CD22, CR2, CD79A, and CD79B genes in the QXXYZ, YXXYZ, and YXXYSYZ groups all showed a downward trend, with the expression levels of CR2 in the QXXYZ group and CD19 in the YXXYSYZ group showing a particularly significant decrease.

[0070] 3. Evaluation of the diagnostic value of differentially expressed genes CD19, CD22, CD79A, CD79B, and CR2 alone in ischemic heart failure with blood stasis syndrome:

[0071] Based on the RT-qPCR results of the expression levels of CD19, CD22, CR2, CD79A, and CD79B genes in the ischemic heart failure blood stasis syndrome group and the healthy control group, receiver operating characteristic (ROC) curves were plotted. The ROC curves were used to evaluate the ability of each gene alone to diagnose and distinguish ischemic heart failure blood stasis syndrome from healthy controls.

[0072] The ROC curves of CD19, CD22, CR2, CD79A, and CD79B genes used individually to diagnose and differentiate ischemic heart failure with blood stasis syndrome from healthy controls are shown in the figure. Figure 7 As shown.

[0073] Depend on Figure 7 It can be seen that when using CD19 molecules to diagnose ischemic heart failure with blood stasis syndrome, the AUC value is 0.807 (95% CI: 0.655-0.959), with a cutoff value of 0.398, the sensitivity is 73.3% and the specificity is 80.0%. When using CD22 molecules to diagnose ischemic heart failure with blood stasis syndrome, the AUC value is 0.747 (95% CI: 0.560-0.933), with a cutoff value of 0.927, the sensitivity is 90.0% and the specificity is 60.0%. When using CD79A molecules to diagnose ischemic heart failure with blood stasis syndrome, the AUC value is 0.740 (95% CI: 0.655-0.959). When using CD79B molecules to diagnose blood stasis syndrome in ischemic heart failure, the AUC value was 0.673 (95% CI: 0.466-0.881), with a cutoff value of 0.506, the sensitivity was 50.0% and the specificity was 80.0%. When using CR2 molecules to diagnose blood stasis syndrome in ischemic heart failure, the AUC value was 0.760 (95% CI: 0.572-0.948), with a cutoff value of 0.375, the sensitivity was 66.7% and the specificity was 80.0%. Therefore, the AUC values ​​of the ROC curves of CD19, CD22, CR2, CD79A, and CD79B genes used individually to diagnose and differentiate ischemic heart failure with blood stasis syndrome from healthy controls can all reach above 0.67, indicating that the above five gene markers can all be used for auxiliary diagnosis and differentiation of ischemic heart failure with blood stasis syndrome from healthy controls.

[0074] 4. Value assessment of the combination of differentially expressed genes CD19, CD22, CD79A, CD79B, and CR2 in the diagnosis of blood stasis syndrome in ischemic heart failure:

[0075] Using the expression levels of CD19, CD22, CR2, CD79A, and CD79B genes in blood samples from 30 patients with ischemic heart failure and blood stasis syndrome (detected by RT-qPCR) and 10 healthy controls as independent variables, and whether or not the patient had ischemic heart failure and blood stasis syndrome as the dependent variable, logistic regression analysis was performed on the expression levels of CD19, CD22, CR2, CD79A, and CD79B genes in both groups to construct a diagnostic model to differentiate patients with ischemic heart failure and blood stasis syndrome from healthy controls. The diagnostic model is P(BSS) = 1 / (1+EXP(-(3.718-5.571×CD19+1.661×CD22-0.153×CD79A-1.671×CD79B+1.718×CR2)). In this diagnostic model, EXP represents an exponential function with the natural constant e as the base, P represents the prediction probability, CD19 represents the expression level of the CD19 gene in the subject's blood sample (expression level measured by RT-qPCR), CD22 represents the expression level of the CD22 gene in the subject's blood sample (expression level measured by RT-qPCR), CR2 represents the expression level of the CR2 gene in the subject's blood sample (expression level measured by RT-qPCR), CD79A represents the expression level of the CD79A gene in the subject's blood sample (expression level measured by RT-qPCR), and CD79B represents the expression level of the CD79B gene in the subject's blood sample (expression level measured by RT-qPCR). Substituting the expression levels of CD19, CD22, CR2, CD79A, and CD79B genes in each blood sample into the diagnostic model yields the prediction probability (i.e., the P-value) for each blood sample. ROC curves were plotted based on the predicted probabilities. The Youden index (Youden index = sensitivity + specificity - 1) was then calculated based on the coordinates of the ROC curve. The P-value corresponding to the maximum Youden index was the optimal cutoff value for diagnosing and distinguishing patients with ischemic heart failure and blood stasis syndrome from healthy controls. If the P-value was greater than the optimal cutoff value, the patient was diagnosed with blood stasis syndrome. Specific statistical results are shown in Table 3.

[0076] Table 3. Statistical analysis of the results of using gene markers for the diagnosis of blood stasis syndrome in ischemic heart failure.

[0077]

[0078] As shown in Table 3, compared with single gene markers, the combination of five gene markers, when used to diagnose and differentiate patients with ischemic heart failure and blood stasis syndrome from healthy controls, achieved an AUC of 0.85, a sensitivity of 73.3%, a specificity of 90.0%, and an optimal cutoff value of 0.790 for predictive probability. This indicates that the combined use of five gene markers yields the best diagnostic effect.

[0079] Example 3: Value assessment of differentially expressed proteins in the diagnosis of blood stasis syndrome in ischemic heart failure

[0080] 1. Experimental Samples:

[0081] The experimental samples were the same as those in Example 2.

[0082] 2. iPRM assay for expression levels of differentially expressed proteins C3, F2, F8, F9, and FN1:

[0083] Serum samples were collected from 40 subjects in the above-mentioned experimental samples on an empty stomach in the morning. The expression levels of differentially expressed proteins F2, F8, F9, and FN1, screened in Example 1, were detected using intelligent parallel reaction monitoring (iPRM) technology, and the diagnostic value of these proteins for blood stasis syndrome in ischemic heart failure was evaluated. Protein extraction, quantification, and quality control were strictly performed according to standard operating procedures. After successful extraction, quantification, and quality control, the samples underwent enzymatic digestion and desalting. Peptide information was acquired using data-independent acquisition (DIA) mode, generating raw mass spectrometry data files (.raw). Then, Spectronaut software was used to perform protein identification and quantification analysis on the generated raw data. The chromatographic method was consistent with the DIA data acquisition method. The iPRM List was imported into the mass spectrometry acquisition method settings file, edited to form the iPRM acquisition method, and the acquisition mode was Full MS-PRM.

[0084] The iPRM test results are as follows: Figure 6 As shown in B. Figure 6 As shown in Figure B, compared with the HP group, the expression of F2, F9, and F8 proteins was upregulated in the QXXYZ, YXXYZ, and YXXYSYZ groups. Specifically, the concentrations of F2 and F9 proteins in the QXXYZ, YXXYZ, and YXXYSYZ groups were significantly higher than those in the HP group (p<0.05), while the concentration of F8 in the YXXYZ group was significantly higher than that in the HP group (p<0.05). Compared with the HP group, the expression level of C3 protein was increased in the QXXYZ group, but decreased in the YXXYZ and YXXYSYZ groups. Therefore, differentially expressed proteins F2, F8, F9, and FN1 were selected for the diagnosis of blood stasis syndrome in ischemic heart failure.

[0085] 3. Value assessment of differentially expressed proteins F2, F8, F9, and FN1 alone in the diagnosis of blood stasis syndrome in ischemic heart failure:

[0086] Based on the iPRM detection results of the F2, F8, F9, and FN1 protein expression levels in the ischemic heart failure blood stasis syndrome group and the healthy control group, the receiver operating characteristic (ROC) curves were plotted by performing logarithmic transformation on the protein expression level data. The ROC curves were used to evaluate the ability of each protein to diagnose and distinguish ischemic heart failure blood stasis syndrome from the healthy control group.

[0087] ROC curves of F2, F8, F9, and FN1 proteins used individually to diagnose and differentiate ischemic heart failure with blood stasis syndrome from healthy controls are shown below. Figure 7 As shown. By Figure 7 It can be seen that when using F9 protein to diagnose blood stasis syndrome in ischemic heart failure, the AUC value is 0.913 (95% CI: 0.823-1.000), with a cutoff value of 5.284, the sensitivity is 86.7% and the specificity is 90.0%. When using F2 protein to diagnose blood stasis syndrome in ischemic heart failure, the AUC value is 0.917 (95% CI: 0.826-1.000), with a cutoff value of 6.392, the sensitivity is 90.0% and the specificity is 90.0%. When using F8 protein to diagnose ischemic heart failure with blood stasis syndrome, its AUC value was 0.677 (95% CI: 0.470-0.883), with a cutoff value of 1.658, a sensitivity of 83.3%, and a specificity of 60.0%. When using FN1 protein to diagnose ischemic heart failure with blood stasis syndrome, its AUC value was 0.880 (95% CI: 0.769-0.991), with a cutoff value of 5.180, a sensitivity of 80.0%, and a specificity of 90.0%. Therefore, the AUC values ​​of the ROC curves for F2, F8, F9, and FN1 genes used alone to diagnose and differentiate ischemic heart failure with blood stasis syndrome from healthy controls all reached above 0.67, indicating that the above four protein biomarkers can all be used for auxiliary diagnosis and differentiation of ischemic heart failure with blood stasis syndrome from healthy controls.

[0088] 4. Value assessment of the combination of differentially expressed proteins F2, F8, F9, and FN1 in the diagnosis of blood stasis syndrome in ischemic heart failure:

[0089] Using the expression levels of F2, F8, F9, and FN1 proteins (iPRM detection results) in blood samples from 30 patients with ischemic heart failure and blood stasis syndrome and 10 healthy controls as the dependent variable, logistic regression analysis was performed on the expression levels of F2, F8, F9, and FN1 proteins in the two groups to construct a diagnostic model to differentiate patients with ischemic heart failure and blood stasis syndrome from normal controls. The diagnostic model is P(BSS) = 1 / (1+EXP(-(-283.338+21.269×log F2+1.393×log F8+57.42×logF9+-26.689×log F2)). In this diagnostic model, EXP represents an exponential function with the natural constant e as the base, P represents the prediction probability, F2 represents the expression level of F2 protein in the subject's blood sample (expression level measured by iPRM detection results), F8 represents the expression level of F8 protein in the subject's blood sample (expression level measured by iPRM detection results), F9 represents the expression level of F9 protein in the subject's blood sample (expression level measured by iPRM detection results), and FN1 represents the expression level of FN1 protein in the subject's blood sample (expression level measured by iPRM detection results). Substituting the expression levels of F2, F8, F9, and FN1 proteins in each blood sample into the diagnostic model yields the prediction probability (i.e., the P-value) for each blood sample. ROC curves were plotted based on the predicted probabilities. The Youden index (Youden index = sensitivity + specificity - 1) was then calculated based on the coordinates of the ROC curve. The P-value corresponding to the maximum Youden index was the optimal cutoff value for diagnosing and distinguishing patients with ischemic heart failure and blood stasis syndrome from healthy controls. If the P-value was greater than the optimal cutoff value, the patient was diagnosed with blood stasis syndrome. Specific statistical results are shown in Table 4.

[0090] Table 4. Statistical analysis of the results of using protein biomarkers in the diagnosis of blood stasis syndrome in ischemic heart failure.

[0091] Biomarker AUC Sensitivity Specificity Youden's index Optimal cut-off F9 0.913 0.867 0.90 0.767 5.284 F2 0.917 0.90 0.90 0.80 6.392 F8 0.677 0.83 0.60 0.43 1.658 FN1 0.880 0.80 0.90 0.70 5.810 F9+F2+F8+FN1 0.997 0.97 0.99 0.96 0.778

[0092] As shown in Table 3, compared with single gene markers, the combination of five gene markers, when used to diagnose and differentiate patients with ischemic heart failure and blood stasis syndrome from healthy controls, achieved an AUC of 0.85, a sensitivity of 73.3%, a specificity of 90.0%, and an optimal cutoff value of 0.790 for predictive probability. This indicates that the combined use of five gene markers yields the best diagnostic effect.

[0093] As shown in Table 4, compared with single protein biomarkers, the combination of four protein biomarkers, when used to diagnose and differentiate patients with ischemic heart failure and blood stasis syndrome from healthy controls, achieved an AUC of 0.997, a sensitivity of 97.0%, a specificity of 99.0%, and an optimal cutoff value of 0.778 for predictive probability. This indicates that the combined use of five gene biomarkers yields the best diagnostic effect.

[0094] Example 4:

[0095] A kit for diagnosing blood stasis syndrome in ischemic heart failure, the kit containing reagents for detecting the expression levels of gene markers; wherein the gene markers are at least one of CD19, CD22, CD79A, CD79B, and CR2; the reagents are used to detect the expression levels of the gene markers in samples via real-time quantitative PCR, gene chips, or high-throughput sequencing platforms. Preferably, the kit detects the expression levels of the gene markers via RT-qPCR. The primer nucleotide sequences for detecting CD19, CD22, CD79A, CD79B, and CR2 are shown in Table 2 of Example 2.

[0096] Example 5:

[0097] A kit for diagnosing blood stasis syndrome in ischemic heart failure, the kit containing reagents for detecting the expression level of protein biomarkers; wherein the protein biomarkers are at least one of F2, F8, F9, and FN1; and the reagents are reagents for detecting the expression level of the protein biomarkers in samples by immunohistochemistry, enzyme-linked immunosorbent assay, liquid chromatography-mass spectrometry, mass spectrometry, or iPRM.

[0098] The above embodiments are intended to illustrate the essential content of the present invention, but are not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the essence and scope of protection of the present invention.

Claims

1. The application of reagents for detecting biomarkers in the preparation of products for the diagnosis of blood stasis syndrome in ischemic heart failure; wherein the biomarker is at least one of gene markers and protein markers; wherein the gene marker is at least one of CD19, CD22, CD79A, CD79B, and CR2; and wherein the protein marker is at least one of F2, F8, F9, and FN1.

2. The application according to claim 1, characterized in that, The product detects the expression level of the gene markers in the sample using real-time quantitative PCR, gene chip, or high-throughput sequencing platforms.

3. The application according to claim 2, characterized in that, The product contains specific primers for amplifying the genetic marker and / or probes for hybridizing with the genetic marker.

4. The application according to claim 1, characterized in that, The gene markers are a combination of CD19, CD22, CD79A, CD79B, and CR2. When the product is used to diagnose ischemic heart failure with blood stasis syndrome, the probability calculation formula for predicting ischemic heart failure with blood stasis syndrome is: P(BSS) = 1 / (1 + EXP(-(3.718 - 5.571×CD19 + 1.661×CD22 -0.153×CD79A - 1.671×CD79B + 1.718×CR2 ))), where P(BSS) represents the probability, CD19 represents the expression level of the CD19 gene, CD22 represents the expression level of the CD22 gene, CD79A represents the expression level of the CD79A gene, CD79B represents the expression level of the CD79B gene, and CR2 represents the expression level of the CR2 gene.

5. The application according to claim 1, characterized in that, The expression level of the protein biomarker in the sample was detected by immunohistochemistry, enzyme-linked immunosorbent assay, liquid chromatography-mass spectrometry, mass spectrometry, or iPRM.

6. The application according to claim 5, characterized in that, The product contains an antibody that specifically binds to the protein biomarker or a standard of the protein biomarker.

7. The application according to claim 1, characterized in that, The protein biomarkers are a combination of F2, F8, F9, and FN1. When the product is used to diagnose ischemic heart failure with blood stasis syndrome, the probability calculation formula for predicting ischemic heart failure with blood stasis syndrome is: P(BSS) = 1 / (1 + EXP(-(-283.338 + 21.269×log F2 + 1.393×log F8 + 57.42×log F9 + -26.689×log FN1))) ; where P(BSS) represents the probability, F2 represents the expression level of F2 protein, F8 represents the expression level of F8 protein, F9 represents the expression level of F9 protein, and FN1 represents the expression level of FN1 protein.

8. The application according to claim 1, characterized in that, The blood stasis syndrome in ischemic heart failure includes qi deficiency and blood stasis syndrome, yang deficiency and blood stasis syndrome, and yang deficiency and blood stasis syndrome with water retention syndrome in ischemic heart failure.

9. A product for diagnosing blood stasis syndrome in ischemic heart failure, the product containing a reagent for detecting biomarkers; the biomarkers are at least one of gene biomarkers and protein biomarkers; the gene biomarkers are at least one of CD19, CD22, CD79A, CD79B, and CR2; the protein biomarkers are at least one of F2, F8, F9, and FN1.

10. The product according to claim 9, characterized in that, The reagents are those used to detect the expression levels of the gene markers in the sample by real-time quantitative PCR, gene chips, or high-throughput sequencing platforms, or to detect the expression levels of the protein markers in the sample by immunohistochemistry, enzyme-linked immunosorbent assay, iPRM, liquid chromatography-mass spectrometry, or mass spectrometry.

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