Hypertension yin deficiency syndrome blood Micro RNA biomarker and application thereof
Through high-throughput sequencing technology and machine learning algorithms, 14 serum miRNAs were screened as biomarkers, and a support vector machine learning model was constructed, which solved the problem of diagnosis of yin deficiency syndrome in hypertension, and achieved high sensitivity and specific early diagnosis , simplifies the process and reduces the risk of trauma.
Patent Information
- Application Number
- CN202411392070.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-08
- Publication Date
- 2024-12-27
AI Technical Summary
The existing technology lacks effective diagnostic methods to distinguish between yin deficiency syndrome and non-yin deficiency syndrome in hypertension, which affects the development of traditional Chinese medicine clinical and scientific research.
High-throughput sequencing technology combined with machine learning algorithms were used to determine the serum differential MicroRNAs in patients with Yin deficiency and non-yin deficiency syndrome of hypertension, build a support vector machine learning model, and screen 14 miRNAs as biomarkers for early stages Auxiliary diagnosis.
It has achieved early diagnosis with high sensitivity and specificity, simplified the diagnosis process, reduced the risk of trauma, and is suitable for the distinction between Yin deficiency syndrome and non-yin deficiency syndrome in hypertension, and has high clinical application value.
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Figure CN119193805A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the application field of clinical testing and diagnosis, and in particular to a blood MicroRNA biomarker for Yin deficiency syndrome of hypertension and application thereof. Background Art
[0002] Epigenetics, also known as epigenetic genetics, is a branch of genetics. Its core lies in the interaction between DNA and the environment, and the corresponding different phenotypes are produced. Different from permanent changes caused by changes in amino acid sequences or DNA sequences, epigenetic regulation is a dynamic response made through factors such as the environment and lifestyle. It is a transient change in gene expression or independent of DNA-related cell regulation. Epigenetics mainly includes four aspects, namely DNA methylation, histone modification, non-coding RNA, and chromatin conformation. Among non-coding RNAs, miRNA is the most widely studied. The expression level of miRNA in serum is stable and reproducible. Therefore, circulating miRNA occupies an important position in the cardiovascular field, such as the research field of biological markers of coronary heart disease, hypertension, atrial fibrillation, heart failure and other diseases. There is increasing evidence to support the important role of miRNA expression profiles and their target genes in the development of essential hypertension, and it is a new biomarker and therapeutic target for essential hypertension.
[0003] Traditional Chinese medicine focuses on the holistic concept and emphasizes the influence of multiple factors such as human body, society, environment, diet, and spirit on the formation of diseases and different syndromes. Traditional Chinese medicine believes that hypertension can be affected by multiple factors such as improper diet, emotional disorder, physical weakness, and aging, and eventually form related different syndromes, which is highly consistent with the concept of epigenetics. Epigenetics may be one of the key links in the formation of TCM syndromes. Therefore, it is necessary to use this as a starting point to deeply reveal the biological basis of TCM syndromes with the help of modern omics technology. Although more and more scholars have realized that miRNA plays an important role in the identification and diagnosis of TCM syndromes, the current research data is limited, and no scholars have paid attention to the intrinsic connection between circulating miRNA and Yin deficiency syndrome of hypertension. This study is the first to start from the perspective of epigenetics and use circulating miRNA to preliminarily explore the miRNA expression profile and related target genes unique to Yin deficiency syndrome of hypertension. Summary of the invention
[0004] The purpose of the present invention is to provide a blood Micro RNA biomarker for Yin deficiency syndrome of hypertension and its application. The present invention uses high-throughput sequencing technology to determine the serum differential Micro RNA of patients with Yin deficiency syndrome of hypertension and non-Yin deficiency syndrome, and combines machine learning algorithms to build a diagnostic model to determine the blood Micro RNA biomarker for Yin deficiency syndrome of hypertension.
[0005] In order to achieve the above-mentioned object of the invention, the present invention provides the following technical solutions.
[0006] The invention discloses an early auxiliary diagnostic marker for Yin deficiency syndrome of hypertension, characterized in that the marker is a combination of hsa-miR-1193, hsa-miR-1281, hsa-miR-192-3p, hsa-miR-372-3p, hsa-miR-4763-5p, hsa-miR-548aj-3p, hsa-miR-6125, hsa-miR-6724-5p, hsa-miR-6784-3p, hsa-miR-6821-5p, hsa-miR-708-5p, hsa-miR-7151-3p, hsa-miR-874-3p and hsa-miR-874-5p.
[0007] Furthermore, the above-mentioned early auxiliary diagnosis marker of Yin deficiency syndrome in hypertension is used in the preparation of early auxiliary diagnosis products of Yin deficiency syndrome in hypertension.
[0008] The present invention also discloses the use of a substance for detecting the above-mentioned early auxiliary diagnosis marker of hypertension Yin deficiency syndrome in the preparation of an early auxiliary diagnosis product of hypertension Yin deficiency syndrome.
[0009] Furthermore, the substance is a substance used to detect the content of a diagnostic marker selected from serum, plasma, and blood.
[0010] The present invention also discloses a kit for early auxiliary diagnosis of Yin deficiency syndrome of hypertension, which is characterized in that the kit contains substances for detecting the markers for early auxiliary diagnosis of Yin deficiency syndrome of hypertension mentioned above.
[0011] Furthermore, the kit also includes a carrier recording the model or a device loaded with the model.
[0012] The present invention also discloses a method for constructing a diagnosis model of Yin deficiency syndrome of hypertension, which is characterized by comprising the following steps:
[0013] Serum miRNA sequencing was used to detect the difference in the levels of fourteen miRNA markers hsa-miR-1193, hsa-miR-1281, hsa-miR-192-3p, hsa-miR-372-3p, hsa-miR-4763-5p, hsa-miR-548aj-3p, hsa-miR-6125, hsa-miR-6724-5p, hsa-miR-6784-3p, hsa-miR-6821-5p, hsa-miR-708-5p, hsa-miR-7151-3p, hsa-miR-874-3p and the combination of hsa-miR-874-5p, which were determined as predictors in Yin deficiency syndrome and non-Yin deficiency syndrome of hypertension, and the support vector machine learning model was used to construct a prediction model for the differential miRNA marker data of the discovery set.
[0014] Compared with the prior art, the beneficial effects of the present invention are as follows.
[0015] The prior art does not have a standardized diagnostic technology for Yin deficiency syndrome in hypertension, and there are few studies on evaluation methods, which seriously affects the development of clinical and scientific research work in traditional Chinese medicine. The present invention combines high-throughput sequencing technology with machine learning algorithms for the first time for patients with Yin deficiency syndrome and non-Yin deficiency syndrome in hypertension, determines blood Micro RNA biomarkers, and establishes a blood sample diagnostic model. The biomarker screening method of the present invention is highly operational, the model construction method is simple, the obtained diagnostic model has excellent effect, high sensitivity, and excellent specificity, and is suitable for the early diagnosis of Yin deficiency syndrome and non-Yin deficiency syndrome in hypertension. The present invention can complete the diagnosis only by blood sampling and testing, without the need for additional tissue sample collection, which greatly reduces the risk of trauma. The model disclosed in the present invention is for patients with Yin deficiency syndrome and non-Yin deficiency syndrome in hypertension, and the diagnostic process is simple and rapid, which is conducive to timely treatment and has high clinical application and promotion value. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] Figure 1 It is a Small RNA feature analysis. A: Small RNA classification statistics stacked bar chart; B, C: are stacked bar charts of miRNA first base and base preference of each site, respectively.
[0017] Figure 2 Volcano diagram of differentially expressed miRNAs between Yin deficiency syndrome and non-Yin deficiency syndrome in hypertension. Compared with non-Yin deficiency syndrome, orange dots indicate upregulation and blue dots indicate downregulation.
[0018] Figure 3 Cluster heat map and trend analysis of differentially expressed miRNAs in hypertension with Yin deficiency syndrome. A: Cluster heat map of differential miRNAs in two groups; B: Trend analysis of differential miRNAs in two groups.
[0019] Figure 4 Heat map of Spearman correlation analysis of differentially expressed miRNAs in hypertension with Yin deficiency syndrome.
[0020] Figure 5 GO enrichment analysis of differentially expressed miRNAs between Yin deficiency syndrome and non-Yin syndrome in hypertension. (A) GO enrichment analysis bar chart, where MF represents molecular function, BP represents biological process, and CC represents cellular component; (B) GO enrichment analysis bubble chart.
[0021] Figure 6 Bar graph of KEGG Pathway enrichment results of differentially expressed miRNAs between Yin deficiency syndrome and non-Yin syndrome in hypertension.
[0022] Figure 7 KEGG enrichment analysis bubble chart of differentially expressed miRNAs between Yin deficiency syndrome and non-Yin syndrome in hypertension. Rich represents the number of differentially expressed miRNA target genes / number of target genes.
[0023] Figure 8 ROC curve of the support vector machine learning model for constructing a diagnostic model for differential MicroRNA.
[0024] Fig. 9 ROC curve of the support vector machine learning model on the validation set. DETAILED DESCRIPTION
[0025] The present invention is further described in detail below with specific embodiments, but it should not be understood that the scope of the above subject matter of the present invention is limited to the following embodiments, and all technologies realized based on the content of the present invention belong to the scope of the present invention.
[0026] Unless otherwise specified, all reagents and materials used in the present invention are commercially available.
[0027] 1. Methods and reagents.
[0028] 1. Sample collection.
[0029] A total of 118 patients with hypertension were included, including 63 patients with Yin deficiency syndrome of hypertension (abbreviated as HY) and 55 patients with non-Yin deficiency syndrome of hypertension (abbreviated as HNY). The independent validation team included 14 patients, including 9 HY and 5 HNY. Fasting venous blood was collected from the patients and centrifuged to obtain serum for Micro RNA sequencing. Venous blood was drawn from the subjects in the fasting state into an inert separation gel coagulation tube to collect blood samples. After the blood collection tube was left to stand at 20℃-25℃ for 20-30min, it was centrifuged at 1800Xg at room temperature for 10min to obtain serum samples, which were stored at -80℃. Serum samples were used for MicroRNA sequencing research.
[0030] 2. Main reagents and instruments.
[0031]
[0032] 3. Serum miRNA sequencing.
[0033] The experimental process of serum miRNA sequencing includes: total RNA extraction → total RNA quality detection → small RNA library construction → PCR amplification enrichment library → library purification → library quality inspection → library quantification → Illumina platform on-board detection. In short, serum total RNA was first extracted using Triziol reagent, and then its concentration and purity were detected using Agilent2100Bioanalyzer. Then a small RNA library (TruSeq Small RNA Sample prep kit) was constructed. The library was enriched by PCR amplification to increase the predicted linker and index part, and the library was further purified by gel electrophoresis selection. Then the library was quality checked by Agilent 2100Bioanalyzer using Agilent High Sensitivity DNA kit, and the library was quantified using Quant-iTPicoGreen dsDNA kit. Finally, the optimal loading amount was selected for sequencing on the Illumina platform. The sequencing work of this study was commissioned to Shanghai Paisonno Biotechnology Co., Ltd.
[0034] 4. Bioinformatics analysis.
[0035] (1) Data quality control and filtering
[0036] The quality of the raw data is controlled through data filtering, collation and comparative analysis. First, the Clean Reads (Total Reads) between 18nt and 36nt are selected and deduplication is performed by filtering the sequence length statistics. Considering that sequencing may produce errors such as substitution, insertion, and deletion, the obtained sequence is compared with the reference genome through comparative analysis, and further filtering is completed to ensure the quality and accuracy of the sequencing data.
[0037] (2) Small RNA classification annotation
[0038] Considering that miRNA sequences with short sequences are very easy to match different fragments and produce multiple annotation results, it is necessary to summarize the comparative annotation of all small RNAs and various types of RNA. The Small RNA classification annotations in this experiment include known miRNA analysis, known piRNA annotation, known ncRNA analysis, and new miRNA prediction. At the same time, the database of repetitive sequences in eukaryotic DNA (Repbase) was used to statistically analyze the results to remove possible repeat sequences in the sequence results. Species with annotation information in the Repbase database can directly annotate the measured sRNA. For species without annotation information in the Repbase database, the Total sequence and Unique sequence are compared and annotated in the Repbase database respectively, and the annotation results are sorted according to the priority of knownmiRNA>piRNA>rRNA>tRNA>snRNA>snoRNA>repeat>gene>novel miRNA to ensure that each small RNA has a unique annotation.
[0039] (3) Characterization of known miRNAs
[0040] miRNA has a preference for base U and is resistant to G. Statistical analysis of miRNA base usage preferences is also a method and basis for evaluating data quality control.
[0041] (4) Analysis of differential miRNA expression levels
[0042] First, the expression of miRNA was quantified, and then DESeq (v1.30.0) was used to analyze the differentially expressed miRNAs, and the differentially expressed miRNAs with |log2FoldChange|>1 and P-value<0.05 were screened. The differential miRNAs between the two groups were compared, clustered and trended.
[0043] (5) miRNA target gene analysis
[0044] Using 3'UTR' sequence as target sequence, MiRanda (v3.3a) was used to predict target genes of the screened differentially expressed miRNA sequences. Then, GO and KEGG enrichment analysis was performed on the target genes of differentially expressed miRNAs. The hypergeometric distribution method was used to calculate the P value to determine the main biological functions of the differentially expressed genes. Cluster analysis software was used to perform enrichment analysis on the KEGG pathways of differentially expressed miRNA target genes, focusing on the significant enrichment pathways with a P value < 0.05.
[0045] 5. Data and results.
[0046] 5.1Small RNA feature analysis.
[0047] After sorting and comparing the offline data and annotating small RNA, we first statistically analyzed the proportion of various small RNAs ( Figure 1 A). miRNA has a preference for base U (T) and is resistant to G, so the frequency of occurrence of different bases at the first position of miRNAs of different lengths and the frequency of occurrence of different bases at various positions of all miRNAs is a way to control data quality ( Figure 1 B, C).
[0048] 5.2 Identification of differentially expressed miRNA profiles in patients with Yin deficiency syndrome in hypertension.
[0049] Differential expression analysis of miRNA is the most critical step in the analysis of miRNA sequencing results. This experiment analyzed the differentially expressed miRNA spectra of the two groups of hypertension Yin deficiency syndrome and non-Yin deficiency syndrome according to the following screening conditions: ①|log2FoldChange|>1, ②P-value<0.05. The experiment finally screened out 14 hypertensive Yin deficiency syndrome group-specific miRNAs, including 4 up-regulated miRNAs and 10 down-regulated miRNAs, as shown in Table 1 and Volcano Figure 2 .
[0050] Table 1 Differentially expressed miRNAs between Yin deficiency syndrome and non-Yin deficiency syndrome in hypertension
[0051]
[0052] Note: Trend: miRNA change trend of HY group compared with HNY group; baseMean HNY: standardized mean expression of hypertension non-Yin deficiency group; baseMean HY: standardized mean expression of hypertension Yin deficiency group; FC: expression difference multiple of hypertension Yin deficiency group / hypertension non-Yin deficiency group; log2FC: log2 value of FC; p: statistical test significance.
[0053] Based on the 14 specific miRNAs screened above, this experiment further clustered them ( Figure 3 A) and correlation ( Figure 4) analysis and displayed in the form of heat map. The results of correlation analysis showed that hsa-miR-192-3p was significantly positively correlated with hsa-miR-847-5p and hsa-miR-6125 (P<0.05), and was significantly negatively correlated with hsa-miR-847-3p (P<0.05); hsa-miR-372-3p was significantly positively correlated with hsa-miR-708-5p (P<0.05); hsa-miR-708-5p was significantly positively correlated with hsa-mi hsa-miR-4763-5p was significantly positively correlated with hsa-miR-548aj-3p (P<0.05), and significantly negatively correlated with hsa-miR-7151-3p (P<0.05); hsa-miR-6724-5p was significantly positively correlated with hsa-miR-6821-5p and hsa-miR-847-3p (P<0.05), suggesting that these differentially expressed miRNAs may have related biological functions.
[0054] Based on the cluster analysis results, trend analysis was performed on 14 specific miRNAs ( Figure 3 B) to explore the similarities and differences in their expression trends. Finally, based on trend analysis, this experiment divided these 14 specific miRNAs into 9 different clusters. The gene expression trends in the same cluster were similar, including Cluster 1 including hsa-miR-6784-3p; Cluster 2 including hsa-miR-7151-3p; Cluster 3 including hsa-miR-372-3p and hsa-miR-708-5p; Cluster 4 including hsa-miR-4763-5p and hsa-miR-548aj-3p; Cluster 5 including hsa-miR-192-3p and hsa-miR-874-5p; Clusterr 6 including hsa-miR-6125; Cluster 7 including hsa-miR-6821-5p, hsa-miR-6724-5p and hsa-miR-874-3p; Cluster Cluster 8 includes hsa-miR-1193; Cluster 9 includes hsa-miR-1281.
[0055] 5.3 GO enrichment analysis of differentially expressed miRNAs in patients with Yin deficiency syndrome of hypertension.
[0056] First, we predicted the target genes of the above 14 differential miRNAs by pairing their complementary sequences to find their possible target gene sites. In the end, a total of 10,277 target genes and 24,813 target gene sites were predicted. Then GO enrichment analysis was performed on these predicted target genes. GO enrichment analysis focuses on functional groups, mainly observing and understanding the enrichment of the target genes predicted by the above 14 specific miRNAs in molecular functions, biological processes and cellular localization. The corresponding P value was calculated based on the hypergeometric distribution method, and P-value<0.05 was finally screened out. According to the GO enrichment results, the degree of enrichment was measured by Rich factor, FDR value and the number of miRNA target genes enriched in this GO entry. The GO terms of the top 20 differential miRNA target genes with the most significant GO enrichment are respectively shown in bar graphs ( Figure 5 A, showing the top 30) and bubble chart display ( Figure 5 B, showing the top 20). The results suggest that the most significant enrichments in biological processes include regulation of RNA polymerase II transcription, molecular function regulation, primary metabolic process regulation, cell surface receptor signaling pathways, etc. The most significant enrichments in molecular functions include DNA binding transcription factor activity, RNA polymerase II specific, sequence-specific DNA binding and transcriptional regulation activity.
[0057] 5.4 KEGG enrichment analysis of differential miRNAs in Yin deficiency syndrome of hypertension.
[0058] Then, KEGG enrichment analysis was further performed. Finally, the top 30 pathways with the smallest p-value, i.e., the most significant enrichment, were selected for display. KEGG enrichment bar chart display ( Figure 6 ), and the top 20 KEGG enrichment bubble maps ( Figure 7 ). Among them, the most significantly enriched signaling pathway in the first-level category of "cellular process" was autophagy. A total of 8 signaling pathways were most significantly enriched in the first-level category of "environmental information processing", including calcium signaling pathway, MAPK signaling pathway, NF-kappa B signaling pathway, cell adhesion molecules, FoxO signaling pathway, Wnt signaling pathway, phosphatidylinositol signaling system, and TNF signaling pathway.
[0059] The above-mentioned differential MicroRNAs were modeled using multiple machine learning models, including Naive Bayes, Random Forest, Support Vector Machine, Xgboost and Logistic Regression. The results showed that the AUC of the ROC curve of the support vector machine model was the highest, reaching 0.799. It was also better than the AUC of a single differential MicroRNA. We independently verified the optimal support vector machine model on the validation set, and the results showed that the AUC of the ROC curve reached 0.9962, indicating that serum differential miRNAs have a good distinguishing effect on HNY and HY. ROC analysis of the validation set of 14 differential MicroRNAs showed that the use of differential metabolites alone also had a good diagnostic effect on ADNY and ADY, among which hsa-miR-874-3p reached the highest of 0.8667.
[0060] Results: PCA and OPLS-DA analysis showed that the serum MicroRNA composition of HNY and HNY patients was significantly different. By clustering the MicroRNA function, it was found that there were 8 most significant pathways, including calcium signaling pathway, MAPK signaling pathway, NF-kappa B signaling pathway, cell adhesion molecule, FoxO signaling pathway, Wnt signaling pathway, phosphatidylinositol signaling system, and TNF signaling pathway. The blood MicroRNA diagnostic model of Yin deficiency syndrome in hypertension was constructed by support vector machine model. It was found that the support vector machine model constructed by 14 differential MicroRNAs had the best performance, which was better than any diagnostic model composed of a single MicroRNA, with an average AUC of 0.799, and had a good diagnostic effect.
[0061] Conclusion: The serum Micro RNA compositions of patients with Yin deficiency syndrome and those without Yin deficiency syndrome are significantly different. The support vector machine learning model constructed by 14 differential Micro RNAs can effectively distinguish patients with Yin deficiency syndrome and those without Yin deficiency syndrome, and can be used as a blood Micro RNA biomarker for Yin deficiency syndrome in hypertension.
[0062] The above description is only a preferred embodiment of the present invention and is not intended to limit the scope of the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.
Claims
1. An early auxiliary diagnostic marker for Yin deficiency syndrome of hypertension, characterized by: The marker is a combination of: hsa-miR-1193, hsa-miR-1281, hsa-miR-192-3p, hsa-miR-372-3p, hsa-miR-4763-5p, hsa-miR-548aj-3p, hsa-miR-6125, hsa-miR-6724-5p, hsa-miR-6784-3p, hsa-miR-6821-5p, hsa-miR-708-5p, hsa-miR-7151-3p, hsa-miR-874-3p and hsa-miR-874-5p.
2. Use of the marker for early auxiliary diagnosis of Yin deficiency syndrome in hypertension according to claim 1 in the preparation of a product for early auxiliary diagnosis of Yin deficiency syndrome in hypertension.
3. Use of a substance for detecting the marker for early auxiliary diagnosis of Yin deficiency syndrome in hypertension as claimed in claim 1 in the preparation of a product for early auxiliary diagnosis of Yin deficiency syndrome in hypertension.
4. The use according to claim 3, characterized in that: The substance is a substance used to detect the content of diagnostic markers in serum, plasma and blood.
5. A kit for early auxiliary diagnosis of Yin deficiency syndrome in hypertension, characterized by: The kit contains substances for detecting the early auxiliary diagnosis marker of Yin deficiency syndrome of hypertension as claimed in claim 1.
6. The early auxiliary diagnosis kit for Yin deficiency syndrome of Alzheimer's disease according to claim 5, characterized in that: The kit also includes a carrier recording the model or a device loaded with the model.
7. A method for constructing a diagnostic model for Yin deficiency syndrome of hypertension, characterized by: The following steps are involved: Serum miRNA sequencing was used to detect the difference in the levels of fourteen miRNA markers hsa-miR-1193, hsa-miR-1281, hsa-miR-192-3p, hsa-miR-372-3p, hsa-miR-4763-5p, hsa-miR-548aj-3p, hsa-miR-6125, hsa-miR-6724-5p, hsa-miR-6784-3p, hsa-miR-6821-5p, hsa-miR-708-5p, hsa-miR-7151-3p, hsa-miR-874-3p and the combination of hsa-miR-874-5p, which were determined as predictors in Yin deficiency syndrome and non-Yin deficiency syndrome of hypertension, and the support vector machine learning model was used to construct a prediction model for the differential miRNA marker data of the discovery set.