Key miRNA related to myocardial ischemia reperfusion and screening method and application thereof

Abstract

The invention relates to the technical field of bioinformatics, in particular to a key miRNA (micro Ribonucleic Acid) related to myocardial ischemia reperfusion as well as a screening method and application of the key miRNA, and the key miRNA is selected from one or two or more of mmu-miR-1843a-5p, mmu-miR-5621-3p, mmu-miR-5113, mmu-miR-8116, mmu-miR-144-5p, mmu-miR-3962, mmu-miR-103-3p, mmu-miR-505-5p and mmu-miR-299b-3p. According to the technical scheme provided by the invention, nine key miRNAs participating in the MIR injury are determined, and a miRNA-miRNA-TF-mRNA-mRNA regulation network is established, so that a new thought and a new entry point are provided for further discussing a molecular mechanism and a therapeutic target of the MIR injury.

Description

Technical Field

[0001] The present invention relates to the technical field of bioinformatics, and in particular to a key miRNA related to myocardial ischemia-reperfusion and a screening method and application thereof. Background Art

[0002] Ischemic heart disease is the leading cause of death worldwide. Rapid restoration of blood flow and oxygen supply to myocardial tissue after ischemia is a common method for treating myocardial ischemia. However, blood flow restoration may cause myocardial tissue damage, namely myocardium ischemia-reperfusion (MIR) injury. A series of harmful changes in myocardial ultrastructure, energy metabolism, cardiac function and electrophysiology during myocardial ischemia become more obvious after myocardial blood flow is restored, and may even lead to severe arrhythmias or sudden cardiac death. Therefore, in order to seek better treatment options, it is crucial to elucidate the molecular mechanisms behind MIR injury.

[0003] MicroRNAs (miRNAs) and transcription factors (TFs) are two key gene regulators in numerous genetic variations. They are both involved in various key cellular processes and share the same regulatory mechanism to coordinately control target genes. miRNAs are short, highly conserved noncoding RNA fragments that negatively regulate gene expression through messenger RNA (mRNA) splicing, thereby inhibiting protein translation. In addition, miRNAs are also associated with MIRs, and related studies have shown that miR-734a-6p can protect mice from myocardial ischemia-reperfusion injury by inhibiting the mitogen-activated protein kinase (MAPK) pathway. TFs are involved in the regulation of thousands of mammalian genes. miRNAs and TFs coordinately regulate target genes through the 3' untranslated region (UTR), while TFs regulate gene transcription through the gene promoter region. However, the basic mechanism of how miRNAs and TFs coordinately regulate target gene expression and promote the pathophysiology of MIRs remains unknown.

[0004] Despite great advances in early diagnosis and treatment over the past decade, ischemic heart disease remains one of the leading causes of death and disability. Rapid restoration of myocardial blood flow and oxygen supply after ischemia is a common treatment for patients with ischemic heart disease. However, MIR triggers the release of oxygen free radicals, intracellular calcium overload, a cascade of inflammation, and overexpression of apoptotic factors. [19-22] This not only impairs heart function, but can also cause dysfunction in distant organs

[23] , greatly increasing the morbidity and mortality of patients. Therefore, understanding the molecular mechanisms behind MIR is crucial for developing effective treatment strategies. Summary of the invention

[0005] In view of the deficiencies of the prior art, the present invention provides a key miRNA related to myocardial ischemia-reperfusion and a screening method and application thereof.

[0006] In order to achieve the above-mentioned objectives, in the first aspect, the present invention provides a key miRNA related to myocardial ischemia-reperfusion, and the key miRNA is selected from one or two or more of mmu-miR-1843a-5p, mmu-miR-5621-3p, mmu-miR-5113, mmu-miR-8116, mmu-miR-144-5p, mmu-miR-3962, mmu-miR-103-3p, mmu-miR-505-5p and mmu-miR-299b-3p.

[0007] In a second aspect, the present invention provides a method for screening key miRNAs associated with myocardial ischemia-reperfusion as described in the first aspect, the screening method comprising:

[0008] S1. Acquisition of chip data: Download the experimental group samples and the control group samples from the gene expression database;

[0009] S2. Differential expression analysis: using a differential analysis software package to screen the differentially expressed miRNAs between the experimental group and the control group;

[0010] S3. KEGG enrichment analysis: KEGG enrichment analysis was performed on the differentially expressed miRNA using the DIANA-miRPath tool, and the receiver operating characteristic curve was drawn to evaluate the diagnostic value of the differentially expressed miRNA for myocardial ischemia-reperfusion;

[0011] S4, differentially expressed miRNAs with an area under the curve > 0.85 were identified as key miRNAs in myocardial ischemia-reperfusion;

[0012] Among them, in S1, the dataset is GSE148864;

[0013] In S1, the experimental group consisted of 8 mouse samples subjected to myocardial ischemia-reperfusion, and the control group consisted of 6 mouse samples subjected to sham operation;

[0014] In S2, the differential analysis software package is limma.

[0015] Preferably, in S2, the screening conditions include: |Log2FC|>0.5 and p value<0.05.

[0016] In a third aspect, the present invention provides an application of the key miRNA as described in the first aspect in constructing a myocardial ischemia-reperfusion regulatory network.

[0017] In a fourth aspect, the present invention provides a method for constructing a miRNA-TF-mRNA regulatory network of myocardial ischemia-reperfusion, the construction method comprising:

[0018] 1) Use comprehensive miRNA target gene database software to screen mRNAs regulated by key miRNAs, obtain miRNA-mRNA regulatory relationship pairs, analyze the biological functions of the mRNAs, and construct biological process-gene networks and KEGG pathway-gene networks;

[0019] 2) Overlapping the mRNA with the coding sequence target in step 1) with the TF extracted from the transcriptional regulatory network database to obtain the regulatory relationship pair of miRNA-TF. At the same time, using the TRRUST database to analyze the mRNA of the selected TF to obtain the regulatory relationship pair of TF-mRNA;

[0020] 3) Use the protein-protein interaction database to screen and obtain the regulatory relationship pairs of mRNA;

[0021] 4) Establishing the regulatory relationship between miRNA-miRNA based on the common target mRNA between two miRNAs;

[0022] 5) merging the regulatory relationship pairs of miRNA-mRNA, miRNA-TF, TF-mRNA, mRNA-mRNA and miRNA-miRNA to construct a miRNA-miRNA-TF-mRNA-mRNA regulatory network, and visualizing it using Cytoscape software;

[0023] 6) Based on the miRNA-miRNA-TF-mRNA-mRNA regulatory network, different types of three-node and four-node network models were formed using FANMOD software, all matching network models were screened, and the miRNA-TF-mRNA network was drawn;

[0024] Wherein, in step 1), the key miRNA is the key miRNA described in the first aspect.

[0025] Preferably, in step 1), the screening condition is confidence ≥ 1.

[0026] Preferably, in step 1), the top 5 biological process-gene networks are selected, and the top 10 KEGG pathway-gene networks are selected.

[0027] Preferably, in step 3), the screening condition is a confidence level of 0.4.

[0028] Preferably, in step 4), the number of common targets is ≥2.

[0029] Preferably, in step 6), the screening conditions include: three-node and four-node network modes with Zscore>2 and P value<0.05.

[0030] In the above technical scheme, the inventors first performed differential analysis and KEGG enrichment analysis on 8 MIR group mouse samples and 6 sham operation group mouse samples in the GSE148864 data set of the gene expression database (GEO), and found 12 differentially expressed miRNAs (DEMs), which were enriched into 32 KEGG pathways. Then, the receiver operating characteristic curve (ROC) was drawn to evaluate the diagnostic value of DEMs, and DEMs with an area under the curve (AUC) greater than 0.85 were identified as key microRNAs (miRNAs) in MIR. Nine key miRNAs related to MIR damage were screened out, and the miRNA-miRNA-TF-mRNA-mRNA regulatory network of MIR damage was constructed to understand how miRNA and TF coordinately regulate gene expression and the potential mechanism behind MIR.

[0031] Construction of miRNA-miRNA-TF-mRNA-mRNA regulatory network of MIR damage A miRNA-mRNA network with 1305 nodes and 1396 edges was constructed based on the miRDB database based on the obtained key miRNAs, among which mRNAs were enriched in 486 Gene Ontology (GO) terms and 14 KEGG pathways; then, the predicted mRNAs with coding sequence targets were overlapped with TFs downloaded from the TRRUST database to obtain 35 miRNA-TF regulatory relationship pairs; at the same time, 157 mRNAs were predicted to be regulated by the above 35 TFs through the TRRUST database, and 182 TF-mRNA regulatory relationship pairs were obtained; then, 534 mRNA-mRNA regulatory relationship pairs were obtained using the STRING database, and 15 pairs of miRNA-miRNA were obtained through screening; finally, a miRNA-miRNA-TF-mRNA-mRNA regulatory network with 1,466 nodes and 2,121 edges was constructed, and the network followed a scale-free distribution.

[0032] In addition, using FANMOD software, 7 three-node and 15 four-node network patterns were identified, and NM_ID:2182 with the highest Zscore was selected as the most important network pattern in MIR, and the miRNA-TF-mRNA network was drawn.

[0033] The technical scheme of the present invention identified 9 key miRNAs involved in MIR injury (mmu-miR-1843a-5p, mmu-miR-5621-3p, mmu-miR-5113, mmu-miR-8116, mmu-miR-144-5p, mmu-miR-3962, mmu-miR-103-3p, mmu-miR-505-5p and mmu-miR-299b-3p), and established a miRNA-miRNA-TF-mRNA-mRNA regulatory network, providing new ideas and entry points for further exploring the molecular mechanism and therapeutic targets of MIR injury.

[0034] Other features and advantages of the present invention will be described in detail in the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the following specific embodiments, they are used to explain the present invention but do not constitute a limitation of the present invention. In the accompanying drawings:

[0036] Figure 1 It is a design flow chart of the present invention;

[0037] Figure 2 is a volcano plot of differentially expressed miRNAs obtained in Example 1 of the present invention;

[0038] Figure 3 is a heat map of differentially expressed miRNAs obtained in Example 1 of the present invention;

[0039] Figure 4 is the KEGG pathway enrichment analysis of the differentially expressed miRNAs obtained in Example 1 of the present invention;

[0040] Figure 5 is the single gene receiver operating characteristic curve (ROC) of the differentially expressed miRNA obtained in Example 1 of the present invention;

[0041] Figure 6 is the single gene receiver operating characteristic curve (ROC) of the differentially expressed miRNA obtained in Example 1 of the present invention;

[0042] Figure 7 is the miRNA-mRNA regulatory network of Example 2 of the present invention;

[0043] Figure 8 It is the result of the first 10 terms of the Gene Ontology enrichment analysis of the present invention;

[0044] Fig. 9 is the gene network included in the first five terms of the gene ontology enrichment analysis of the present invention;

[0045] Fig.10 is the KEGG pathway enrichment analysis of the present invention;

[0046] Fig.11 It is the gene network diagram of the first 10 KEGG pathways in Table 1 of the present invention;

[0047] Fig.12 is the screening transcription factor (TF) of the present invention;

[0048] Fig.13 is the miRNA-TF regulatory network of the present invention;

[0049] Fig.14 is the miRNA-miRNA-TF-mRNA-mRNA regulatory network of the present invention;

[0050] Fig.15 It is the topological characteristics of the miRNA-miRNA-TF-mRNA-mRNA regulatory network of the present invention;

[0051] Fig.16 It is the miRNA-miRNA-TF-mRNA-mRNA regulatory network connectivity model fitting of the present invention;

[0052] Fig.17 All sub-network types that conform to the significant network model of the present invention;

[0053] Fig.18 It is the miRNA-TF-mRNA regulatory network of 2182 that is screened and conforms to the network model. DETAILED DESCRIPTION

[0054] The specific embodiments of the present invention are described in detail below. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0055] The endpoints and any values ​​of the ranges disclosed in this article are not limited to the precise ranges or values, and these ranges or values ​​should be understood to include values ​​close to these ranges or values. For numerical ranges, the endpoint values ​​of each range, the endpoint values ​​of each range and the individual point values, and the individual point values ​​can be combined with each other to obtain one or more new numerical ranges, which should be considered as specifically disclosed in this article.

[0056] In the first aspect, the present invention provides a key miRNA related to myocardial ischemia-reperfusion, wherein the key miRNA is selected from one or two or more of mmu-miR-1843a-5p, mmu-miR-5621-3p, mmu-miR-5113, mmu-miR-8116, mmu-miR-144-5p, mmu-miR-3962, mmu-miR-103-3p, mmu-miR-505-5p and mmu-miR-299b-3p.

[0057] In a second aspect, the present invention provides a method for screening key miRNAs associated with myocardial ischemia-reperfusion as described in the first aspect, the screening method comprising:

[0058] S1. Acquisition of chip data: Download the experimental group samples and the control group samples from the gene expression database;

[0059] S2. Differential expression analysis: using a differential analysis software package to screen the differentially expressed miRNAs between the experimental group and the control group;

[0060] S3. KEGG enrichment analysis: KEGG enrichment analysis was performed on the differentially expressed miRNA using the DIANA-miRPath tool, and the receiver operating characteristic curve was drawn to evaluate the diagnostic value of the differentially expressed miRNA for myocardial ischemia-reperfusion;

[0061] S4, differentially expressed miRNAs with an area under the curve > 0.85 were identified as key miRNAs in myocardial ischemia-reperfusion;

[0062] Among them, in S1, the dataset is GSE148864;

[0063] In S1, the experimental group consisted of 8 mouse samples subjected to myocardial ischemia-reperfusion, and the control group consisted of 6 mouse samples subjected to sham operation;

[0064] In S2, the differential analysis software package is limma.

[0065] The inventors first performed differential analysis and KEGG enrichment analysis on 8 MIR group mouse samples and 6 sham operation group mouse samples in the GSE148864 dataset of the Gene Expression Database (GEO), and found 12 differentially expressed miRNAs (DEMs), which were enriched into 32 KEGG pathways. Then, the receiver operating characteristic (ROC) curve was drawn to evaluate the diagnostic value of DEMs, and DEMs with an area under the curve (AUC) greater than 0.85 were identified as key microRNAs (miRNAs) in MIR. Nine key miRNAs related to MIR damage were screened out, and a miRNA-miRNA-TF-mRNA-mRNA regulatory network of MIR damage was constructed to understand how miRNA and TF coordinately regulate gene expression and the potential mechanisms behind MIR.

[0066] In a preferred embodiment of the present invention, in order to screen differentially expressed miRNAs to obtain key miRNAs, in S2, the screening conditions include: |Log2FC|>0.5 and p value<0.05.

[0067] In a third aspect, the present invention provides an application of the key miRNA as described in the first aspect in constructing a myocardial ischemia-reperfusion regulatory network.

[0068] The technical scheme of the present invention identified 9 key miRNAs involved in MIR injury (mmu-miR-1843a-5p, mmu-miR-5621-3p, mmu-miR-5113, mmu-miR-8116, mmu-miR-144-5p, mmu-miR-3962, mmu-miR-103-3p, mmu-miR-505-5p and mmu-miR-299b-3p), and established a miRNA-miRNA-TF-mRNA-mRNA regulatory network, providing new ideas and entry points for further exploring the molecular mechanism and therapeutic targets of MIR injury.

[0069] In a fourth aspect, the present invention provides a method for constructing a miRNA-TF-mRNA regulatory network of myocardial ischemia-reperfusion, the construction method comprising:

[0070] 1) Use comprehensive miRNA target gene database software to screen mRNAs regulated by key miRNAs, obtain miRNA-mRNA regulatory relationship pairs, analyze the biological functions of the mRNAs, and construct biological process-gene networks and KEGG pathway-gene networks;

[0071] 2) Overlapping the mRNA with the coding sequence target in step 1) with the TF extracted from the transcriptional regulatory network database to obtain the regulatory relationship pair of miRNA-TF. At the same time, using the TRRUST database to analyze the mRNA of the selected TF to obtain the regulatory relationship pair of TF-mRNA;

[0072] 3) Use the protein-protein interaction database to screen and obtain the regulatory relationship pairs of mRNA;

[0073] 4) Establishing the regulatory relationship between miRNA-miRNA based on the common target mRNA between two miRNAs;

[0074] 5) merging the regulatory relationship pairs of miRNA-mRNA, miRNA-TF, TF-mRNA, mRNA-mRNA and miRNA-miRNA to construct a miRNA-miRNA-TF-mRNA-mRNA regulatory network, and visualizing it using Cytoscape software;

[0075] 6) Based on the miRNA-miRNA-TF-mRNA-mRNA regulatory network, different types of three-node and four-node network models were formed using FANMOD software, all matching network models were screened, and the miRNA-TF-mRNA network was drawn;

[0076] Wherein, in step 1), the key miRNA is the key miRNA described in the first aspect.

[0077] Construction of miRNA-miRNA-TF-mRNA-mRNA regulatory network of MIR damage A miRNA-mRNA network with 1305 nodes and 1396 edges was constructed based on the miRDB database based on the obtained key miRNAs, among which mRNAs were enriched in 486 Gene Ontology (GO) terms and 14 KEGG pathways; then, the predicted mRNAs with coding sequence targets were overlapped with TFs downloaded from the TRRUST database to obtain 35 miRNA-TF regulatory relationship pairs; at the same time, 157 mRNAs were predicted to be regulated by the above 35 TFs through the TRRUST database, and 182 TF-mRNA regulatory relationship pairs were obtained; then, 534 mRNA-mRNA regulatory relationship pairs were obtained using the STRING database, and 15 pairs of miRNA-miRNA were obtained through screening; finally, a miRNA-miRNA-TF-mRNA-mRNA regulatory network with 1,466 nodes and 2,121 edges was constructed, and the network followed a scale-free distribution.

[0078] In a preferred embodiment of the present invention, in order to screen out mRNA that meets the conditions, in step 1), the screening condition is confidence ≥1.

[0079] In a preferred embodiment of the present invention, in order to make the obtained regulatory network a key regulatory network of MIR damage, in step 1), the top 5 biological process-gene networks are selected, and the top 10 KEGG pathway-gene networks are selected.

[0080] In a preferred embodiment of the present invention, in order to make the obtained regulatory network the key regulatory network of MIR damage, in step 3), the screening condition is a confidence level of 0.4.

[0081] In a preferred embodiment of the present invention, in order to make the obtained regulatory network a key regulatory network for MIR damage, in step 4), the number of common targets is ≥2.

[0082] In a preferred embodiment of the present invention, in order to make the obtained regulatory network a key regulatory network of MIR damage, in step 6), the screening conditions include: three-node and four-node network models with Zscore>2 and P value<0.05.

[0083] The present invention will be described in detail below through examples, but the protection scope of the present invention is not limited thereto.

[0084] Example 1

[0085] This example is used to illustrate the screening method of key miRNAs related to myocardial ischemia-reperfusion:

[0086] (1) A total of 14 samples were downloaded from the GSE148864 dataset of GEO (http: / / www.ncbi.nlm.nih.gov / geo), including 8 samples of mice in the MIR group and 6 samples of mice in the sham operation group;

[0087] (2) Plasma miRNA profiles of 14 samples were downloaded, and the differentially expressed microRNAs (DEMs) between the two groups were screened using the linear models for microarray data (limma) software package, with |Log2FC|>0.5 and p value <0.05 as the screening criteria;

[0088] (3) The DIANA-miRPath tool was used to perform Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis on DEM.

[0089] (4) The key miRNAs with areas under curves (AUC) > 0.85 were identified through the receiver operating characteristic (ROC) curve.

[0090] The results of the difference analysis are as follows: Figure 2-4 The receiver operating characteristic curve (ROC) is shown in Figure 5-6 shown.

[0091] exist Figure 2 In the volcano plot of differentially expressed miRNAs, the horizontal axis represents log2FC, the vertical axis represents -log10 (pvalue), each point in the figure represents a gene, squares and triangles represent significantly differentially expressed genes, and triangles represent up-regulated differential expression, squares represent down-regulated differential expression, black dots represent that these genes have no significant differences, the horizontal reference line represents -log10 (pvalue) = 0.05, and the vertical reference line represents log2FC = ± 0.5. Figure 3 The results of the heat map of differentially expressed miRNAs showed that a total of 12 DEMs were identified between the MIR group mice and the sham-operated group mice, 10 of which were upregulated and 2 were downregulated; Figure 4 In the KEGG pathway enrichment analysis diagram, each bubble represents a KEGG pathway. The horizontal axis represents the number of target genes included in the pathway, the vertical axis represents the -log10 (pvalue) of the enrichment analysis, and the size of the dot represents the miRNA included in the pathway. It can be seen that a total of 32 pathways are enriched.

[0092] Then, draw Figure 5-6 The single gene ROC curves of the 12 differentially expressed miRNAs screened and analyzed their ability to predict sham operation group samples and MIR group samples. The area under the ROC curve is the AUC value. The larger the AUC value, the more accurate the prediction. Figure 5-6It can be seen that miRNAs with AUC>0.85 were screened as key miRNAs for subsequent analysis. It can be seen that the AUC values ​​of mmu-miR-1843a-5p (AUC = 0.99), mmu-miR-299b-3p (AUC = 0.948), mmu-miR-5621-3p (AUC = 0.906), mmu-miR-5113 (AUC = 0.896), mmu-miR-8116 (AUC = 0.865), mmu-miR-144-5p (AUC = 0.896), mmu-miR-3962 (AUC = 0.865), mmu-miR-103-3p (AUC = 0.917) and mmu-miR-505-5p (AUC = 0.875) are all > 0.85, because the above 9 miRNAs were used as key miRNAs for subsequent analysis.

[0093] Example 2

[0094] This example is used to illustrate the construction of miRNA-miRNA-TF-mRNA-mRNA regulatory network:

[0095] (1) The comprehensive miRNA target gene database (miRWalk) software was used to predict the mRNAs that may be regulated by the 9 key miRNAs obtained in Example 1, and the screening condition was set to confidence ≥ 1 to obtain miRNA-mRNA regulatory relationship pairs. The clusterProfiler R software package was then used to perform enrichment analysis based on KEGG and gene ontology to find the common functions and related pathways of a large number of mRNAs in the mRNA set in the network, and analyze and predict the biological functions of mRNAs; the top 5 biological process-gene networks and the top 10 KEGG pathway-gene networks were constructed using the cnetplot function in the enrichplot package (version 1.12.2);

[0096] (2) The predicted mRNAs with coding sequence targets were overlapped with the TFs extracted from the transcriptional regulatory relationships unraveled by sentence-based text mining (TRRUST) database to obtain the regulatory relationship pairs of miRNA-TFs; at the same time, the mRNAs of the selected TFs were predicted using the TRRUST database to obtain the regulatory relationship pairs of TFs and mRNAs;

[0097] (3) Using the protein-protein interaction database (search tool for the retrieval of interacting genes / proteins, STRING) with the confidence level set to 0.4, the regulatory relationship pairs of mRNAs were obtained;

[0098] (4) Establishing miRNA-miRNA regulatory relationship pairs based on the common target mRNA between two miRNAs;

[0099] (5) The regulatory relationship pairs of miRNA-mRNA, miRNA-TF, TF-mRNA, mRNA-mRNA and miRNA-miRNA were merged to construct the miRNA-miRNA-TF-mRNA-mRNA regulatory network, which was visualized using Cytoscape software to further analyze the topological characteristics of the regulatory network (including degree, clustering coefficient, topological coefficient and new neighborhood connectivity).

[0100] Table 1

[0101]

[0102]

[0103] Depend on Figure 7 In the miRNA-mRNA regulatory network diagram, each node represents a gene, each edge represents the interaction between genes, the triangle represents miRNA, and the square represents mRNA. It can be seen that the present invention obtains a miRNA-mRNA network containing 1305 nodes and 1396 edges. The present invention enriches a total of 486 gene ontology entries, including 335 biological process catalogs, 51 molecular function catalogs, and 100 cellular component catalogs.

[0104] The catalogs obtained from the gene ontology enrichment analysis were arranged from low to high according to the p-value, and the top 10 entries were taken for visualization. The results are as follows Figure 8 As shown; at the same time, the gene network contained in the first 5 entries is drawn, as shown Fig. 9 shown by Fig.10 It can be seen that a total of 14 KEGG pathways were enriched, and the specific data are shown in Table 1; the gene network diagram of the first 10 pathways in Table 1 is shown in Fig.11 shown.

[0105] In order to gain an overall perspective on the miRNA-miRNA-TF-mRNA-mRNA regulatory network in MIR, the regulatory relationships including miRNA-TF, TF-gene, mRNA-mRNA and miRNA-miRNA were studied. First, 511 mRNAs with miRNA targeting sites on coding sequence targets were extracted and cross-referenced with 827 TFs downloaded from the TRRUST database ( Fig.12 ). As a result, 35 miRNA-TF regulatory relationship pairs were obtained, including 40 nodes and 35 edges ( Fig.13 ).

[0106] Among the miRNA-TF regulatory relationship pairs, mmu-miR-103-3p was found to regulate the most TFs, including Sox6, Ncor1, Sall1, Ash1l, Satb2, Crem, Bcl11a, Chd1, Hcfc1, Rfx3, and Per2; mmu-miR-5113 regulated 8 TFs, including Apc, Zfp488, Hdac1, Pou2f2, Hnmpk, Vdr, Hr, and Fmr; and mmu-miR-1843a-5p regulated 6 TFs. TF, including Zbtb16, Gtf2ird1, Dach1, Sall4, Clock, Nono; mmu-miR-8116 regulated 5 TF, including Nab2, Cux2, Zfp281, Sall2, Klf8; mmu-miR-505-5p regulated 2 TF, including Mtf1 and Zeb1; at the same time, mmu-miR-299b-3p also regulated 2 TF, Tcf4 and Cux1; while mmu-miR-5621-3p only regulated Cebpe( Fig.13 ).

[0107] Secondly, the TRRUST database was used to predict that 157 mRNAs were regulated by the above 35 TFs, thus obtaining 182 TF-mRNA regulatory relationship pairs. The gene interaction network of 511 mRNAs was constructed using the STRING database, and 534 mRNA-mRNA regulatory relationship pairs were obtained. In addition, 15 pairs of miRNA-miRNA were screened based on the common gene targets between two miRNAs.

[0108] Finally, by merging the regulatory relationships of miRNA-TF, TF-mRNA, mRNA-mRNA, and miRNA-miRNA, a miRNA-miRNA-TF-mRNA-mRNA regulatory network with 1,466 nodes and 2,121 edges was constructed ( Fig.14 ).

[0109] In order to study the global picture of the regulatory network, the topological characteristics of the network were analyzed, including degree, clustering coefficient, topological coefficient and new neighborhood connectivity. Fig.15 As shown, it can be seen that most nodes have low degrees and only a few nodes have high degrees.

[0110] In order to further analyze whether the network conforms to the scale-free network, a regression model was constructed with the connectivity as the independent variable and the number of nodes as the dependent variable:

[0111] f(x)=3.526+990.717x-2.3742, R2 is 0.9965,

[0112] The model fitting results are as follows Fig.16 As shown, it shows that the network follows a scale-free distribution.

[0113] Example 3

[0114] This example is used to illustrate the screening of miRNA-TF-mRNA regulatory network associated with MIR damage:

[0115] In the miRNA-miRNA-TF-mRNA-mRNA regulatory network obtained in Example 2, different types of three-node and four-node network patterns were formed using FANMOD software according to the regulatory relationship. Each network pattern type was evaluated for significance through random network generation, and the Z score (Zscore) of each network pattern type was calculated. The higher the Zscore, the greater the importance of the network pattern. The three-node and four-node network patterns with Zscore>2 and p-value<0.05 were screened, the type with the highest Zscore was selected, all the network patterns that met the requirements were extracted, and the miRNA-TF-mRNA network was drawn.

[0116] The Zscore represents the frequency of network patterns observed in real networks minus their mean occurrence in random networks, divided by the standard deviation.

[0117] like Fig.17 As shown in Figure 1, a total of 7 three-node network patterns and 15 four-node network patterns were screened out. All sub-network types that conform to the network pattern in the miRNA-miRNA-TF-mRNA-mRNA regulatory network were drawn, and the sub-network types containing miRNA-TF-mRNA regulatory relationships were further screened, such as Fig.17 As shown in the red box; Finally, the sub-network type with the highest Zscore is retained, such as Fig.17 The * in the middle indicates NM_ID: 2182. All the relationship pairs matching NM_ID: 2182 are extracted to draw the miRNA-TF-mRNA network, as shown in Fig.18 shown.

[0118] In summary, the present invention screened 9 key miRNAs associated with MIR damage and constructed a miRNA-miRNA-TF-mRNA-mRNA regulatory network of MIR damage to understand how miRNA and TF coordinately regulate gene expression and the potential mechanism behind MIR.

[0119] In the GSE148864 dataset, a total of 12 DEMs were found to be differentially expressed between the MIR group and the sham group. Functional analysis showed that these DEMs were mainly involved in mitochondrial function, lipid metabolism, and pathways related to energy metabolism. Subsequently, the ROC curve of each DEM was plotted to study their ability to distinguish between MIR and sham group samples. The inventors found that 9 miRNAs could more accurately distinguish between MIR and sham group samples and had high diagnostic value.

[0120] Among them, miRNA-144 is a cytoprotective miRNA that can reduce myocardial infarction area and provide effective acute cardiac protection in the MIR model, and can also improve myocardial remodeling after ischemia in the non-MIR model. miRNA144 plays a key role in remote ischemic preconditioning-induced cardiac protection against MIR. In addition, the study also observed that miRNA-144 expression was downregulated in the rat MIR injury model, while overexpression of miRNA-144 significantly reduced myocardial ischemic injury and cell apoptosis. Similar findings in hypoxia / reoxygenation H9c2 cells also confirmed this result.

[0121] miRNA-103-3p is closely related to cardiac cell death, electron transport chain regulation, and various fatty acid and sugar metabolism processes. Previous studies have demonstrated that miRNA-103-3p is negatively correlated with the life span of cardiomyocytes. Increased expression of miRNA-103-3p exacerbates hypoxia-induced cardiomyocyte apoptosis and reduces cardiomyocyte proliferation, while loss of miRNA-103-3p ameliorates hypoxia-induced cardiomyocyte apoptosis and increases cardiomyocyte proliferation. miRNA-103 prevents cardiomyocyte apoptosis under conditions of high oxidative stress by inhibiting Fas-associated protein with death domain (FADD) and subsequently activating pro-apoptotic cysteine ​​aspartic acid specific protease (Caspases). By targeting FADD, miRNA-103 / 107 can control programmed necrosis and MIR injury. miRNA-103-3p is also upregulated in patients with ischemic dilated cardiomyopathy and idiopathic dilated cardiomyopathy. However, anti-phagosomes targeting miRNA-103 / 107 had no effect on calcium flux or the phosphorylation status of proteins regulating contraction in mouse cardiomyocytes. In contrast, inhibition of miRNA-103 / 107 using miRNA blockers resulted in decreased mitochondrial size, glycolytic capacity, and oxygen consumption. Evidence suggests that increased miRNA-103 may be closely related to the mechanism of enhancing energy metabolism and preventing apoptosis in neonatal cardiomyocytes. In addition, overexpression of miRNA-193a promoted autophagy and prevented paraquat-induced pulmonary fibrosis.

[0122] These findings are consistent with previous studies that key miRNAs (such as miRNA-144 and miRNA-103) can serve as potential molecular indicators or therapeutic targets for MIR. Based on the interactions of miRNA-mRNA, mRNA-mRNA, miRNA-TF, TF-mRNA, and miRNA-miRNA relationship pairs, a miRNA-TF-mRNA regulatory network was constructed. By analyzing the biological functions and biological pathways involved in these mRNAs in the network, this study found that key miRNAs may affect the expression of these mRNAs.

[0123] The present invention found that the top 10 biological processes were related to ion transport and neuronal activity, such as metal ion transport regulation, ion transmembrane transport regulation, calcium ion transport regulation, positive regulation of neuronal process development, migration and differentiation. In addition, a total of 14 KEGG pathways were significantly enriched, including endocytosis, circadian rhythm regulation, γ-aminobutyric acid synapses, morphine addiction, dopaminergic synapses, adenosine-5'-monophosphate activated protein kinase signaling pathway, cholinergic synapses, proteoglycans in cancer, hypoxia-inducible factor-1 signaling pathway, circadian rhythm, insulin resistance, secretory glycoprotein signaling pathway, ubiquitin-mediated proteolysis and epidermal growth factor receptor tyrosine kinase inhibitor resistance.

[0124] KEGG enrichment analysis showed that mRNAs in multiple signaling pathways, such as adenosine-5'-monophosphate-activated protein kinase and hypoxia-inducible factor, were upregulated. Adenosine-5'-monophosphate-activated protein kinase is a mitochondrial stress response kinase that regulates cellular metabolism and protects cardiomyocytes from damage during MIR. Excessive mitochondrial reactive oxygen species generation and subsequent stress-activated protein kinase signaling during reperfusion have been shown to be critical for preventing the opening of the mitochondrial membrane permeability transition pore, permanent mitochondrial damage, and myocardial injury. miRNA-451 is a miRNA that acts as an inhibitor of the adenosine-5'-monophosphate-activated protein kinase signaling pathway. Reducing the expression of miRNA-451 can enhance the activity of the adenosine-5'-monophosphate-activated protein kinase signaling pathway, thereby assisting in the reduction of MIR.

[0125] HIF is a family of heterodimeric transcription factors, and downstream genes of HIF activity enhance phenotypes such as oxygen-independent adenine nucleoside triphosphate generation, cell survival, and angiogenesis. Therefore, they play a key role in protecting tissues from IR injury. Autophagy triggered by the HIF-1α / B-cell lymphoma2and adenovirus E1B 19-kilodalton interacting protein 3 (BNIP3) signaling pathway has been shown to have a protective role during MIR injury. Deferoxamine treatment combined with sevoflurane postconditioning can improve MIR injury in diabetic rats by reestablishing HIF-1α / BNIP3-mediated mitophagy.

[0126] At the same time, studies have found that downregulating miRNA-144-5p can promote the apoptosis rate of rats with renal ischemia-reperfusion injury and activate the secretory glycoprotein-β-trypsin signaling pathway. In addition, studies have found that MIR injury reduces the level of miRNA-144 in the mouse myocardium, and systemic treatment of miRNA-144 increases serine / threonine protein kinase B, glycogen synthase kinase 3β and P-p44 / 42 mitogen-activated protein kinase, reduces the level of P-rapamycin target protein, and induces autophagy signals to improve myocardial cell function recovery and reduce infarct size. MiRNA-193a regulates autophagy by affecting the pro-autophagic serine / threonine protein kinase B, autophagy protein-related gene 6 and microtubule-associated protein light chain 3 pathways. Ligustrazine increases the expression of miRNA-193a, inhibits phosphatidylinositol 3-kinase / protein kinase B / rapamycin target protein and hedgehog signaling pathways, thereby increasing lung cell autophagy and improving paraquat-induced pulmonary fibrosis. Furthermore, overexpression of circular RNA sucrose non-fermentation 1-related kinase targeted miRNA-103-3p, inhibited apoptosis and promoted cardiac repair in rats with myocardial infarction via the glycogen synthase kinase-3β / β-catenin pathway.

[0127] Finally, the miRNA-TF-mRNA regulatory network was constructed, the network patterns of three-node and four-node themes were predicted, and the four-node subnetwork with the highest Z-score was screened out, which may be the key regulatory network of MIR injury. Compared with other pattern types, it is worth noting that the four-node NM_ID:2182 network pattern with the highest Z-score score. In the NM_ID:2182 regulatory network, miRNA-299b-3p was observed to reduce the expression of TF (homeobox CUX-like protein 1 and transcription factor 4), both of which target and regulate the expression of myeloproliferative proto-oncogenes; miRNA-5113 inhibited the expression of TF (vitamin D receptor, adenomatous polyposis protein, histone deacetylase 1, and Oct-2 transcription factor), thereby regulating the transcription of inducible nitric oxide synthase 2, β-catenin, and secreted phosphoprotein 1 (SPP1).

[0128] C-myeloproliferative proto-oncogene has been shown to regulate the expression of N-myeloproliferative proto-oncogene downstream regulatory gene 2 in IR-affected cardiomyocytes, which may be one of the causes of apoptosis in rat cardiomyocytes. TF4 has been shown to be a direct target of miRNA-299-3p. Overexpression of miRNA-299-3p can inhibit the growth and invasion of cervical cancer cells, while overexpression of TF4 can reverse the inhibitory effect of miRNA-299-3p on the behavior of cervical cancer cells.

[0129] SPP1 is a highly phosphorylated acidic glycoprotein that has been identified as a key gene in ischemia-reperfusion. A recent study found that the expression of SPP1 was significantly downregulated in ischemia-reperfusion cells after percutaneous coronary intervention compared with normal samples. In addition, it has been found that SPP1 has a cardioprotective effect in the progression of ischemic cardiomyopathy and myocardial infarction in mice. miRNA-21 mediates the protective effect of emulsified isoflurane on MIR injury in mice by targeting SPP1. In a mouse model of MIR injury, silencing SPP1 improved cardiac function, inhibited oxidative stress, myocardial fibrosis, inflammatory response, and cardiomyocyte apoptosis, thereby alleviating MIR injury.

[0130] In summary, the present invention screened and obtained 9 key miRNAs related to MIR damage, constructed the miRNA-miRNA-TF-mRNA-mRNA regulatory network of MIR damage, explored how miRNA and TF coordinately regulate gene expression, and the intrinsic mechanism leading to the pathogenesis of MIR, laying the foundation for future experimental and clinical research.

[0131] The preferred embodiments of the present invention are described in detail above. However, the present invention is not limited to the specific details in the above embodiments. Within the technical concept of the present invention, a variety of simple modifications can be made to the technical solution of the present invention, and these simple modifications all belong to the protection scope of the present invention.

[0132] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present invention will not further describe various possible combinations.

[0133] In addition, various embodiments of the present invention may be arbitrarily combined, and as long as they do not violate the concept of the present invention, they should also be regarded as the contents disclosed by the present invention.

Claims

1. A key miRNA associated with myocardial ischemia-reperfusion, characterized in that: The key miRNA is selected from one or two or more of mmu-miR-1843a-5p, mmu-miR-5621-3p, mmu-miR-5113, mmu-miR-8116, mmu-miR-144-5p, mmu-miR-3962, mmu-miR-103-3p, mmu-miR-505-5p and mmu-miR-299b-3p.

2. A method for screening key miRNAs associated with myocardial ischemia-reperfusion as claimed in claim 1, characterized in that: The screening method comprises: S1. Acquisition of chip data: Download the experimental group samples and the control group samples from the gene expression database; S2. Differential expression analysis: using a differential analysis software package to screen the differentially expressed miRNAs between the experimental group and the control group; S3. KEGG enrichment analysis: KEGG enrichment analysis was performed on the differentially expressed miRNA using the DIANA-miRPath tool, and the receiver operating characteristic curve was drawn to evaluate the diagnostic value of the differentially expressed miRNA for myocardial ischemia-reperfusion; S4, differentially expressed miRNAs with an area under the curve > 0.85 were identified as key miRNAs in myocardial ischemia-reperfusion; in, In S1, the dataset is GSE148864; In S1, the experimental group consisted of 8 mouse samples subjected to myocardial ischemia-reperfusion, and the control group consisted of 6 mouse samples subjected to sham operation; In S2, the differential analysis software package is limma.

3. The screening method according to claim 2, characterized in that In S2, the screening conditions include: |Log2FC|>0.5 and p value<0.

05.

4. An application of the key miRNA as claimed in claim 1 in constructing a myocardial ischemia-reperfusion regulatory network.

5. A method for constructing a miRNA-TF-mRNA regulatory network for myocardial ischemia-reperfusion, characterized in that: The construction method comprises: 1) Use comprehensive miRNA target gene database software to screen mRNAs regulated by key miRNAs, obtain miRNA-mRNA regulatory relationship pairs, analyze the biological functions of the mRNAs, and construct biological process-gene networks and KEGG pathway-gene networks; 2) Overlapping the mRNA with the coding sequence target in step 1) with the TF extracted from the transcriptional regulatory network database to obtain the regulatory relationship pair of miRNA-TF. At the same time, using the TRRUST database to analyze the mRNA of the selected TF to obtain the regulatory relationship pair of TF-mRNA; 3) Use the protein-protein interaction database to screen and obtain the regulatory relationship pairs of mRNA; 4) Establishing the regulatory relationship between miRNA-miRNA based on the common target mRNA between two miRNAs; 5) merging the regulatory relationship pairs of miRNA-mRNA, miRNA-TF, TF-mRNA, mRNA-mRNA and miRNA-miRNA to construct a miRNA-miRNA-TF-mRNA-mRNA regulatory network, and visualizing it using Cytoscape software; 6) Based on the miRNA-miRNA-TF-mRNA-mRNA regulatory network, different types of three-node and four-node network models were formed using FANMOD software, all matching network models were screened, and the miRNA-TF-mRNA network was drawn; Wherein, in step 1), the key miRNA is the key miRNA described in claim 1.

6. The construction method according to claim 5, characterized in that: In step 1), the screening condition is confidence ≥ 1.

7. The construction method according to claim 5 or 6, characterized in that: In step 1), the top 5 biological process-gene networks are selected, and the top 10 KEGG pathway-gene networks are selected.

8. The construction method according to any one of claims 5 to 7, characterized in that: In step 3), the screening condition is that the confidence level is 0.

4.

9. The construction method according to any one of claims 5 to 8, characterized in that: In step 4), the number of common targets is ≥2.

10. The construction method according to any one of claims 5 to 9, characterized in that: In step 6), the screening conditions include: three-node and four-node network models with Zscore>2 and p-value<0.05.

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