Marker composition and application thereof as well as retention type heart failure differential diagnosis kit

Abstract

The invention provides a marker composition and application thereof and a retention type heart failure differential diagnosis kit, the marker composition is composed of IFNG and GATA3, the levels of the markers IFNG and GATA3 in blood of an HFpEF patient with normal BNP are remarkably increased relative to those of the HFpEF patient with increased BNP and a healthy person (P is smaller than 0.001), and the marker composition is used for detecting the retention type heart failure of the HFpEF patient with normal BNP and the level of the markers GATA3 in blood of the HFpEF patient with normal BNP are remarkably increased relative to those of the HFpEF patient with increased BNP and healthy people. The aptamer can be used as a biomarker for predicting the retention type heart failure to be applied to preparation of a retention type heart failure detection reagent or medical equipment, and has important clinical application value.

Description

Technical Field

[0001] This invention relates to the field of biomedicine, and in particular to biomarker compositions and their applications in diagnostic kits for preservative heart failure. Background Technology

[0002] Currently, heart failure accounts for approximately 0.3% (8.9 million / 330 million) of cardiovascular diseases in my country, and its incidence is increasing year by year. Among these, the number of patients with heart filure with preserved ejection fraction (HFpEF) is increasing, accounting for about 56% of all heart failure patients. Most patients with HFpEF have elevated BNP levels, while 20-35% of HFpEF patients have normal BNP levels, which may lead to clinical exclusion of heart failure (HF) diagnosis and miss the optimal treatment window. However, traditional heart failure medications are not very effective in treating HFpEF. Due to the increased incidence, prolonged phenotype, and lack of effective treatment, HFpEF is described as the disease with the greatest need for treatment in modern cardiology. Therefore, exploring precise and effective diagnostic and treatment methods for HFpEF is crucial.

[0003] Studies have shown that when myocardial wall pressure increases, cardiomyocytes secrete BNP and N-terminal pro-BNP (NT-proBNP) to provide vasodilatory protection. BNP and NT-proBNP are plasma biomarkers for hemodynamic cardiac stress and the presence and severity of heart failure. The ESC recommends using BNP <35 ​​ng / L and NT-proBNP <125 ng / L to exclude chronic heart failure, and using BNP >150 ng / L and NT-proBNP >600 ng / L as cutoff values ​​to aid in the diagnosis of HF.

[0004] However, international research has found that 20-35% of HFpEF patients have normal BNP levels. In these patients, low BNP levels may lead to clinical exclusion of HF diagnosis, missing the optimal treatment window. Furthermore, these cases are often obese, and known causes of BNP deficiency include genetics (such as NPPB gene polymorphism), increased androgen levels in women, insulin resistance, and the effects of certain medications (such as spironolactone). Low BNP levels may further contribute to hypertension, fluid retention, and increased metabolically unfavorable adipose tissue in HFpEF patients, resulting in a worse prognosis compared to HFpEF patients with high BNP levels.

[0005] Transcriptome-level analysis may help reveal the underlying mechanisms of HFpEF in patients with normal BNP levels. RNA sequencing (RNA-Seq) is an important tool in transcriptomics research. This technology can combine bioinformatics analysis of transcriptome structure and expression levels to discover disease-related molecular pathways and pathophysiological mechanisms, and explore new therapeutic targets. Researchers using HF myocardial tissue sequencing have found that pathways related to cardiomyocyte hypertrophy, interstitial fibrosis, oxidative stress, and abnormal NO signaling are enriched in both HFpEF and HFrEF. They have also observed that HFpEF has unique transcriptome characteristics and molecular subsets, associated with oxidative phosphorylation, endoplasmic reticulum stress, autophagy, and angiogenesis. In whole blood RNA sequencing studies of HF patients, researchers have noted differential expression of many inflammation-regulating mRNAs and complement components in HFpEF and HFrEF, promoting endothelial cell activation, stimulating cytokine release, promoting plaque rupture, platelet activation, and promoting fibrin formation, thereby affecting the pathological progression of vascular endothelial inflammation, atherosclerosis, and thrombosis. These RNA sequencing studies revealed the pathological mechanisms of HFpEF development and the signaling targets that need to be considered in treatment, laying the foundation for subsequent whole blood RNA sequencing and bioinformatics analysis in patients with normal BNP levels in HFpEF to study its pathophysiological mechanisms.

[0006] Currently, there are no studies on whole blood RNA-Seq for HFpEF with normal BNNP and HFpEF with elevated BNP. Clinically, the diagnosis of HFpEF with normal BNP is unclear, and conventional HFpEF treatment has little effect. Therefore, using transcriptomics to reveal the potential mechanisms of HFpEF with normal BNP is of great significance for finding new early diagnostic molecular markers and therapeutic targets for precision treatment of heart failure. Summary of the Invention

[0007] The technical problem to be solved by the present invention is to provide a marker composition.

[0008] Another technical problem to be solved by the present invention is to provide the application of the above-mentioned marker composition.

[0009] Another technical problem to be solved by the present invention is to provide a preservative heart failure differential diagnostic kit.

[0010] The technical solution adopted in this invention is:

[0011] A marker composition comprising IFNG and GATA3.

[0012] The above-mentioned biomarker composition is used as a biomarker for predicting preservative heart failure in the preparation of preservative heart failure detection reagents or medical devices.

[0013] Preferably, in the application of the above-mentioned biomarker composition, the medical device is a preservative heart failure differential diagnostic kit.

[0014] Preferably, with the application of the above-mentioned biomarker composition, the biomarkers IFNG and GATA3 in the kit are significantly upregulated in the blood of HFpEF patients with normal BNP and in healthy individuals with elevated BNP (P < 0.001).

[0015] A preservative diagnostic kit for heart failure using the above-mentioned biomarker composition.

[0016] Preferably, the primers for detecting the biomarkers IFNG and GATA3 in the above-mentioned preservative heart failure differential diagnostic kit are as follows: GATA3 primers: front primer GCCCCTCATTAAGCCCAAG, back primer...

[0017] TTGTGGTGGTCTGACAGTTCG; IFNG primers: front primer TCGGTAACTGACTTGAATGTCCA, back primer TCGCTTCCCTGTTTTAGCTGC.

[0018] The beneficial effects of this invention are:

[0019] Transcriptomics was used to reveal differentially expressed mRNAs in HFpEF with normal BNP and HFpEF with elevated BNP, and a biomarker composition consisting of IFNG and GATA3 was screened out. This study aims to identify new early diagnostic molecular biomarkers and therapeutic targets for precision treatment of preservative heart failure, which has significant clinical application value. Attached Figure Description

[0020] Figure 1 These are quantitative gene expression plots for the HFpEF group with normal BNP, the HFpEF group with elevated BNP, and the healthy control group. (a) Principal component analysis plot; (b) Venn diagram of co-expression.

[0021] Figure 2 This study screened differentially expressed genes in the HFpEF group with normal BNP, the HFpEF group with elevated BNP, and the healthy control group. (a) Volcano plot of differentially expressed genes; (b) Venn diagram of differentially expressed genes; (c) Clustering among differentially expressed genomes; (d) Clustering among differentially expressed gene samples.

[0022] Figure 3 This is a pathway enrichment map of differentially expressed genes GO and KEGG in the BNP normal group, relative to the BNP elevated group, and the healthy control group. (a) GO enrichment scatter plot (b) GO enrichment bar chart (c) KEGG enrichment scatter plot (d) KEGG enrichment bar chart.

[0023] Figure 4 It is a DIFFERENTIATI0N (GO: 0046632) and T-HELPER 17CELLDIFFERENTIATION (GO: 0072539) GSEA diagram.

[0024] Figure 5 Box plots / violin diagrams showing differential gene expression among the BNP-normal group, the BNP-elevated group, and the healthy control group. Note: *** indicates P-value <0.05; *** indicates P-value <0.001.

[0025] Figure 6 This is a ROC curve analysis graph of patients in the HFpEF group with normal BNP levels. Detailed Implementation

[0026] To enable those skilled in the art to better understand the technical solution of the present invention, the technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.

[0027] Materials and methods

[0028] Study subject enrollment and data collection

[0029] Transcriptome sequencing was performed on EDTA-anticoagulated whole blood samples from HFpEF patients hospitalized at TEDA International Cardiovascular Hospital from November 2021 to August 2022. EDTA-anticoagulated whole blood samples from HFpEF patients hospitalized from November 2021 to August 2023 were collected for differential gene validation.

[0030] (1) Inclusion criteria:

[0031] According to the ESC-HFA 2021 ESC Guidelines for the Diagnosis and Treatment of Acute and Chronic Heart Failure (McDonagh TA, Metra M, Adamo M, et al. 2021 ESC Guidelines for the Diagnosis and Treatment of Acute and Chronic Heart Failure. Eur Heart J. 2021 Sep 21; 42(36): 3599-3726.) and the HFA-PEFF diagnostic algorithm (Pieske B, C, de Boer RA, et al. How to diagnose heart failure with preserved ejection fraction: the HFA-PEFF diagnostic algorithm: consensus recommendation from the Heart Failure Association (HFA) of the European Society of Cardiology (ESC). Eur Heart J. 2019 Oct 21;40(40):3297-3317.):

[0032] 1) Symptoms and signs: dyspnea, orthopnea, paroxysmal nocturnal dyspnea, lower extremity edema, decreased exercise tolerance, etc.

[0033] 2) Left ventricular ejection fraction (LVEF) ≥ 50%

[0034] 3) HFA-PEFF score ≥ 5 points

[0035] (2) Exclusion criteria:

[0036] Case groups meeting any of the following criteria will not be included:

[0037] 1) Patients with congenital heart disease or who have previously undergone coronary artery bypass surgery;

[0038] 2) Patients with concurrent infections, hereditary diseases, rheumatic immune system diseases, severe liver and kidney dysfunction, or malignant tumors;

[0039] 3) Patients with congenital heart disease, valvular heart disease, lung disease, myocardial infarction / cerebral infarction within 90 days, or who have undergone surgery within 30 days.

[0040] The healthy control group excluded patients with cardiovascular and cerebrovascular diseases, tumors, hematological diseases, rheumatic and immune system diseases, trauma, and infections.

[0041] According to the inclusion and exclusion criteria, a total of 30 patients were included in the study. HFpEF with BNP < 150 pg / ml was classified as the normal BNP group, consisting of 10 patients (5 males, 5 females), with an average age of 70.20 ± 9.37 years; HFpEF with BNP > 150 pg / ml was classified as the elevated BNP group, consisting of 10 patients (5 males, 5 females), with an average age of 70.90 ± 4.61 years. Ten healthy individuals who underwent physical examinations during the same period were included as a healthy control group, with an average age of 66.50 ± 8.92 years. All three groups were matched for gender and age.

[0042] Clinical data including patient age, sex, height, weight, present medical history, past medical history, echocardiography, electrocardiogram, and BNP levels were collected. Two tubes (2 ml / tube) of fasting EDTA-anticoagulated venous blood were collected from each of the three groups of subjects on the day of admission. One tube was centrifuged at 3500 rpm for 10 minutes within 2 hours of collection using a Thermo Scientific Multifug X1 high-performance benchtop centrifuge, and the supernatant plasma was separated for BNP concentration detection. The other tube was mixed by inverting, and Invitrogen TRIzol reagent was added. The TRIzol reagent and EDTA-anticoagulated whole blood were mixed at a volume ratio of 3:1, vigorously shaken for 1 minute, and allowed to stand for 5 minutes. After collection, the samples were stored at -80°C. Plasma BNP levels in the three groups of subjects were detected using a BECKMAN COULTER UniCel Dxl 800Access fully automated chemiluminescence immunoassay analyzer. The kit used was the BECKMAN COULTER B-type natriuretic peptide assay kit (chemiluminescence method), and the results are expressed in pg / ml.

[0043] Example 1

[0044] I. Transcriptome sequencing and bioinformatics analysis

[0045] RNA was extracted from a mixture of TRlzol and EDTA-anticoagulated whole blood using chloroform and isopropanol. Ultra TM RNA Library Prep Kit for As a library construction kit, different libraries were pooled and then synthesized and sequenced simultaneously using Novaseq 6000 Illumina.

[0046] HISAT2 software was used to quickly and accurately align clean reads with the reference genome to obtain the location information of reads on the reference genome. Subread software was used to count the number of reads within the start and end ranges covered by each gene, based on the alignment location information on the reference genome. Reads with alignment quality scores below 10, reads on non-paired alignments, and reads aligned to multiple regions of the genome were filtered. The FeatureCounts tool of Subread software was used in this analysis. Gene expression values ​​(FPKM) were used to statistically analyze the gene expression distribution of RNA-seq, and the squared Pearson correlation coefficient (R²) was calculated. 2The correlation coefficients between samples were detected and plotted as a heatmap to visually display the differences between samples and the replication of samples within each group. Principal component analysis (PCA) used linear algebra to perform dimensionality reduction and principal component extraction on tens of thousands of gene variables in the FPKM of all samples. A Venn diagram was created to show the number of genes co-expressed in two or more groups / samples.

[0047] The original readcount values ​​were normalized using TMM standardization with edgeR (Robinson et al., 2010), and the hypothesis test probability (P-value) was calculated using a negative binomial distribution. The FDR value was obtained using BH calculation. Differentially expressed genes were analyzed using DESeq, with selection criteria of |FoldChange| ≥ 1.5 and padj ≤ 0.05. The union of differentially expressed genes across all comparison groups was taken as the differentially expressed gene set. The FPKM values ​​of the genes were clustered using mainstream hierarchical clustering, and the rows were normalized (Z-score).

[0048] Based on the principle of hypergeometric distribution, GO (Gene Ontology) functional enrichment analysis was performed on the differentially expressed gene set using clusterProfiler software. KEGG (Kyoto Encyclopedia of Genes and Genomes), a comprehensive database integrating genomic, chemical, and systemic functional information, was used for KEGG (Kanehisa et al., 2000) pathway enrichment, with a padj value less than 0.05 as the threshold for significant enrichment. Without specifying an explicit differential gene threshold, gene setenrichment analysis (GSEA) ranked genes according to their differential expression levels in the two groups of samples, and then statistical methods were used to test the pre-defined gene set to verify whether enrichment occurred at the top or bottom of the ranking list.

[0049] Additional cohort differential gene PCR validation

[0050] According to the inclusion and exclusion criteria of the transcriptome sequencing study cohort, a total of 180 patients were ultimately included in the study. The BNP-normal group consisted of 60 patients (19 males and 41 females) aged 70 (63, 74) years; the BNP-elevated group consisted of 61 patients (19 males and 42 females) aged 71 (64, 76) years; and 59 healthy individuals (21 males and 38 females) who underwent physical examinations during the same period were collected to form a healthy control group, aged 70 (68, 74) years. All three groups were matched for sex and age.

[0051] RNA was extracted using the TRlzol method, and cDNA was obtained using the TRANTransScript All-in-One First-Strand cDNASynthesis SuperMix for qPCR (One-Step gDNA Removal) kit and an ABI Veriti 96-Well Thermal Cycler gradient PCR instrument.

[0052] rt-qPCR was performed using the TRAN PerfectStartGreen qPCR Super Mix kit and a Roche 480 high-throughput real-time PCR instrument. Each sample was tested in triplicate, and the mean value was calculated. The relative quantification value was calculated using the Livak equation: RQ = 2. -ΔΔCt Primer sequences are shown in Table 1.

[0053] Table 1 qPCR primer sequences

[0054]

[0055] Statistical analysis was performed using SPSS 26.0 and Graphpad Prism 8.0 software. Normally distributed count data were analyzed using... This indicates that an independent samples t-test was used for comparisons between the two groups. For non-normally distributed count data, a p-value was used. 50 (P 25 P 75 The KW rank-sum test was used for comparisons between the two groups. Receiver operating characteristic (ROC) curves were used to assess the diagnostic ability of relevant indicators. A p-value < 0.05 was considered statistically significant.

[0056] III Results

[0057] The clinical baseline data of the case group and the healthy control group are compared in Table 2.

[0058] Table 3 shows the comparison of baseline clinical data between the HFpEF case group with normal BNP, the HFpEF case group with elevated BNP, and the healthy control group in the sequencing cohort. There were no statistically significant differences in gender and age among the three groups (P > 0.05). The BMI, BNP level, and LVMI of the normal BNP group and the elevated BNP group were significantly higher than those of the healthy control group (P < 0.05). BMI =0.003, 0.006; P BNP All <0.001; P LVMI=0.007, 0.003), and the BNP level in the elevated group was significantly higher than that in the normal group (P<0.001); there were no statistically significant differences in the history of hypertension, history of diabetes, LVEF, Septale', Laterale', and E / e' between the BNP normal group and the BNP elevated group (P>0.05).

[0059] Table 2 shows the comparison of baseline clinical data between the HFpEF case group with normal BNP, the HFpEF case group with elevated BNP, and the healthy control group in the differential gene validation cohort. There were no statistically significant differences in gender and age among the three groups (P > 0.05). The BMI, BNP, and LVMI levels in the normal BNP group and the elevated BNP group were significantly higher than those in the healthy control group (P < 0.05). BMI =0.001, 0.019; P BNP All <0.001; P LVMI All values ​​<0.001), and the LVEF in the BNP normal group was significantly higher than that in the BNP elevated group (P < 0.001). LVEF <0.001), the diabetes rate, BNP level, and E / e' in the normal BNP group were significantly lower than those in the elevated BNP group (P < 0.001). diabetes =0.038, P BNP <0.001, P E / e’ =0.032). There were no statistically significant differences in the history of hypertension, Septal', Laterale', and LVMI levels between the BNP normal group and the BNP elevated group (all P > 0.05).

[0060] Table 2 Comparison of baseline clinical data among the BNP-normal group, the BNP-elevated group, and the healthy control group.

[0061]

[0062] Note: a Compared with the healthy control group, P < 0.05; b Compared with the HFpEF case group with normal BNP (P < 0.05), the transcriptome results are summarized below:

[0063] mRNA sequencing was performed on each sample, yielding a total of 1.38 billion reads, averaging 46 million paired-end reads per sample. After data filtering, a total of 1.37 billion reads were obtained, averaging 45 million clean reads per sample. A total of 1.32 billion reads were mapped to the reference genome (Mortazavi et al., 2008), with an average alignment rate of 96%. On average, 87% of reads per sample aligned to exon regions, 11% to intron regions, and 1.5% to intergenic regions.

[0064] The correlation coefficients within and between groups were calculated based on the FPKM values ​​of all genes in each sample. The correlation between the BNP normal group, the BNP elevated group, and the healthy control group was >0.98, and the correlation among all samples was >0.86. PCA analysis was performed on the gene expression values ​​(FPKM) of all samples, and the results are displayed as a scatter plot, where each point represents one sample. The difference in mRNA expression between the BNP elevated group and the healthy control group was 12.35%, and the difference was 7.36% between the BNP normal group and the healthy control group. A total of 11,132 genes were expressed in the BNP normal HFpEF group, the BNP elevated HFpEF group, and the healthy control group; 11,321 genes were expressed in the BNP normal group and the BNP elevated group; 11,327 genes were expressed in the BNP normal group and the healthy control group; and 11,492 genes were expressed in the BNP elevated group and the healthy control group. (See [link to relevant documentation]). Figure 1 .

[0065] Differential gene expression in the BNP-normal group, the BNP-elevated group, and the healthy control group

[0066] RNA from the BNP-normal group, BNP-elevated group, and healthy control group was analyzed using DESeq2 (Anders et al., 2014). The resulting DEG values ​​were then filtered for differentially expressed RNAs between the HFpEF of the BNP-normal group and the other two groups, with padj < 0.05 and |FoldChange| ≥ 1.5 (i.e., |log2Foldchange| ≥ 0.58). A total of 12 significant DERNAs were identified in the HFpEF of the BNP-normal group compared to the BNP-elevated group (9 upregulated and 3 downregulated). A total of 201 significant DERNAs were identified in the HFpEF of the BNP-normal group compared to the healthy control group (156 upregulated and 45 downregulated). A total of 241 significant DERNAs were identified in the HFpEF of the BNP-elevated group compared to the healthy control group (159 upregulated and 82 downregulated). MRNAs encoding proteins were screened from differentially expressed genes and Venn diagrams were plotted. GATA3 (ENSG00000107485) was found to be upregulated in the BNP-normal group relative to the BNP-elevated group and the healthy control group (log2foldchange = 0.85, padj = 0.03; log2foldchange = 0.81, padj = 0.005). GATA3 can be used as a differentially expressed gene for further validation in a larger clinical cohort sample. The union of differentially expressed genes was taken, and cluster analysis was performed on the FPKM values ​​of the genes. See details. Figure 2 And Table 3.

[0067] Table 3. Differential mRNA expression in the BNP-normal group relative to the BNP-elevated group and the healthy control group.

[0068]

[0069]

[0070] Differential gene functional enrichment analysis between the BNP-normal group and the BNP-elevated group

[0071] Functional enrichment of DEGs and significant modules

[0072] Enrichment analysis revealed differentially expressed gene sets (DEG, 213 genes) in the HFpEF group with normal BNP compared to the HFpEF group with elevated BNP, and in the HFpEF group with normal BNP compared to the healthy control group. Through GO classification and enrichment analysis, such as biological processes (BP), cellular components (CC), and molecular functions (MF), key biological pathways playing crucial roles in the disease process of HFpEF patients with normal BNP were identified, thus revealing the fundamental molecular mechanisms of these biological processes. KEGG enrichment analysis, integrating genomic, chemical, and systemic functional information, identified 20 significantly enriched pathways. Figure 3 GO and KEGG analyses were used to create differentially expressed pathway scatter plots. Results showed that, compared to HFpEF with elevated BNP and healthy controls, differentially expressed genes in HFpEF with normal BNP were enriched in multiple inflammation-related pathways, such as G protein-coupled chemokine receptor activity (GO: 0001637), G protein-coupled peptide receptor activity (GO: 0008528), Th17 cell differentiation (hsa04659), NF-κB signaling pathway (hsa04064), cytokine-cytokine receptor interaction (hsa04060), chemokine receptor activity (GO: 0004950), and CC chemokine receptor activity (GO: 0016493). IFNG and GPR84 genes were screened for further validation with a larger sample size.

[0073] GSEA analysis of differentially expressed genes in the BNP-normal and BNP-elevated groups revealed that HFpEF in the BNP-normal group was upregulated in pathways such as DEOXYRIBONUCLEOTIDE BIOSYNTHETIC PROCESS (GO: 0009263) deoxyribonucleotide biosynthesis and T-HELPER 17CELL DIFFERENTIATION (GO: 0072539) T-helper 17 cell differentiation, while downregulated in pathways such as STEROID CATABOLIC PROCESS (GO: 0006706) steroid catabolism. GATA3 and IFNG were upregulated in the ALPHA-BETA T CELL DIFFERENTIATION (GO: 0046632) α-βT cell differentiation pathway (Rank metric scores were 0.935 and 0.542, respectively, Running ES = 0.0362 and 0.235, respectively). See Table 4 for details. Figure 4 .

[0074] Table 4. Normal BNP vs. Elevated BNP (GSEA)

[0075]

[0076] Example 2

[0077] Validate the selected differentially expressed molecules in additional clinical cohorts.

[0078] Following the inclusion and exclusion criteria of the transcriptome cohort, the sample size of the BNP-normal HFpEF group, the BNP-elevated HFpEF group, and the healthy control group was further expanded for differentially expressed gene qPCR validation. The GATA3 expression level in the BNP-normal group was significantly higher than that in the BNP-elevated group and the healthy control group (χ²). 2 = 5.669, P < 0.001; χ² 2 =4.443, P<0.001), there was no significant difference in GATA3 expression levels between the BNP-eluting group and the healthy control group (χ² = 4.443, P<0.001). 2 =-1.195, P value = 0.697); the expression level of IFNG in the BNP normal group was significantly higher than that in the BNP elevated group and the healthy control group (χ² = -1.195, P value = 0.697); 2 =6.699, P<0.001; χ² 2 =5.105, P<0.001), there was no significant difference in IFG expression levels between the elevated BNP group and the healthy control group (χ² = 5.105, P<0.001). 2 1.574, P = 0.346); GPR84 expression levels in the BNP normal group and the BNP elevated group were significantly lower than those in the healthy control group (χ²). 2 = -2.678, P = 0.022; χ² 2=-3.160, P=0.005), there was no significant difference in GPR84 expression levels between the BNP normal group and the BNP elevated group (χ²). 2 =0.533, P-value =1.00). See details. Figure 5 See Table 5.

[0079] The diagnostic ability of whole blood GATA3 and IFNG levels for HFpEF in patients with normal BNP was analyzed using receiver operating characteristic (ROC) curve analysis. The results are as follows: Figure 6 As shown, GATA3 and IFNG levels have good auxiliary diagnostic value in differentiating preservative heart failure with normal BNP levels (GATA3 area under the curve AUC: 0.77, 95% CI: 0.70-0.85, P < 0.001; IFNG area under the curve AUC: 0.81, 95% CI: 0.74-0.89, P < 0.001).

[0080] Table 5. Comparison of differentially expressed genes between the BNP-normal group, the BNP-elevated group, and the healthy control group.

[0081]

[0082] Note: a Compared with the Healthy group, the p-value was <0.05; b For comparison with the BNP_elevated group, the p-value was <0.05.

[0083] Conclusion: In summary, this study, using mRNA transcriptomics, showed that IFNG and GATA3 were significantly upregulated in HFpEF patients with normal BNP compared to those with elevated BNP and in healthy controls, suggesting that inflammation-related mRNAs (IFNG and GATA3) could serve as potential molecular biomarkers for BNP-preserving heart failure patients. In BNP-normal HFpEF, the inflammatory response is enhanced, with upregulation of inflammatory pathways such as chemokine pathways, Th17 differentiation, and related cellular signaling.

[0084] Example 3

[0085] Following the inclusion and exclusion criteria of the transcriptome cohort, the sample sizes of the BNP-normal HFpEF group, the BNP-elevated HFpEF group, and the healthy control group were further expanded for differentially expressed genes validation by qPCR. Receiver operating characteristic (ROC) curves were used to assess the diagnostic ability of relevant indicators. The diagnostic ability of whole blood GATA3 and IFNG levels for BNP-normal HFpEF was analyzed using ROC curves, and the results are as follows: Figure 6As shown in Table 6 (GATA3 area under the curve AUC: 0.77, 95% CI: 0.70–0.85, P < 0.001; IFNG area under the curve AUC: 0.81, 95% CI: 0.74–0.89, P < 0.001), the results indicate that GATA3 and IFNG levels have good auxiliary diagnostic value in differentiating preservative heart failure with normal BNP levels and can be used as diagnostic markers to differentiate between preservative heart failure patients with normal BNP and those with elevated BNP.

[0086] Table 6. Genetic ROCs showing differences between the BNP-normal group, the BNP-elevated group, and the healthy control group.

[0087]

[0088] Example 4

[0089] A preservative heart failure differential diagnostic kit includes the molecular markers IFNG and GATA3. The primers for the molecular markers IFNG and GATA3 are designed as follows: using the TRANTransScript All-in-One First-Strand cDNA Synthesis SuperMix for qPCR (One-Step gDNA Removal) kit and the TRAN PerfectStartGreen qPCR SuperMix kit from Beijing TransGen Biotech Co., Ltd.

[0090] 1) TRAN TransScript All-in-One First-Strand cDNA Synthesis SuperMix for qPCR (One-Step gDNA Removal) Kit Components: RNase-free water, gDNA Remover (genomic RNA removal reagent), RT (reverse transcriptase), RNase inhibitor, Anchored Oligo(dT)18 Primer (18-mer oligonucleotide single strands with 5′ and 3′ hydroxyl ends), Random Primer (N9), dNTPs (deoxynucleotides), and Buffer.

[0091] 2) Components of the TRAN PerfectStartGreen qPCR SuperMix kit (TRANPerfectStartGreen qPCR super mix): Taq hot-start enzyme (utilizing three monoclonal antibodies to efficiently bind to Taq DNA Polymerase, effectively blocking DNA polymerase activity and preventing non-specific amplification at low temperatures), dual cation buffer, SYBR Green I fluorescent dye, dNTPs, PCR enhancer, PCR stabilizer, and RNase-free water.

[0092] 3) GATA3 primers: front primer GCCCCTCATTAAGCCCAAG, back primer TTGTGGTGGTCTGACAGTTCG;

[0093] IFNG primers: front primer TCGGTAACTGACTTGAATGTCCA, back primer TCGCTTCCCTGTTTTAGCTGC.

[0094] The above-described embodiments are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made to the technical solutions of the present invention by those skilled in the art without departing from the spirit of the present invention should fall within the protection scope of the present invention.

Claims

1. A marker composition, characterized in that: It consists of IFNG and GATA3.

2. The use of the biomarker composition of claim 1 as a biomarker for predicting preservative heart failure in the preparation of preservative heart failure detection reagents or medical devices.

3. The application of the marker composition according to claim 2, characterized in that: The medical device is a preservative heart failure differential diagnostic kit.

4. The application of the marker composition according to claim 3, characterized in that: The biomarkers IFNG and GATA3 in the kit were significantly upregulated in the blood of HFpEF patients with normal BNP levels, as well as in HFpEF patients with elevated BNP levels and healthy individuals (P < 0.001).

5. A preservative heart failure differential diagnostic kit using the biomarker composition of claim 1.

6. The preservative-type heart failure differential diagnostic kit according to claim 5, characterized in that: The primers for detecting the biomarkers IFNG and GATA3 are as follows: GATA3 primers: front primer GCCCTCATTAAGCCCAAG, back primer TTTGTGGTGGTCTGACAGTTCG; IFNG primers: front primer TCGGTAACTGACTTGAATGTCCA, back primer TCGCTTCCCTGTTTTAGCTGC.

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