Construction method and application of coronary heart disease autophagy related ceRNA regulation and control network

Abstract

The invention discloses a construction method and application of a coronary heart disease autophagy related ceRNA regulation and control network. A human coronary artery endothelial cell oxidative stress model is constructed in vitro to simulate the pathogenesis of the coronary heart disease, through transcriptome high-throughput sequencing, by combining and integrating bioinformatics analysis means, differential expression, PPI network, ceRNA network and co-expression network construction are included, coronary heart disease autophagy-related mRNA and lncRNA are screened, a coronary heart disease autophagy-related ceRNA regulation network is constructed, and the coronary heart disease autophagy-related ceRNA regulation network is constructed. A novel molecular marker and a research target are provided for the pathogenesis and diagnosis and treatment of the coronary heart disease.

Description

Technical Field

[0001] The present invention belongs to the technical field of molecular biology, and in particular relates to a construction method and application of a ceRNA regulatory network related to coronary heart disease autophagy. Background Art

[0002] Coronary artery disease (CAD) is one of the common chronic diseases that seriously endangers human health worldwide. It is characterized by irreversible progression of coronary atherosclerotic lesions. Most patients eventually suffer from adverse cardiovascular events such as acute myocardial infarction and heart failure based on the unstable progression of coronary atherosclerotic plaques. In the early stage of coronary AS, it is mainly a cascade inflammatory response of functional cells, which has no significant effect on hemodynamics, and the symptoms are hidden and difficult to detect. In addition, there is a lack of reliable and effective non-invasive early diagnostic measures in clinical practice, resulting in little effect on the early diagnosis and treatment of coronary heart disease. It is still a difficult problem in clinical practice to reduce the morbidity and mortality of coronary heart disease.

[0003] Autophagy is a dynamic process in which cells actively and orderly degrade harmful intracellular components, achieving the circulation of intracellular small molecules and maintaining their own homeostasis. Studies have found that moderate autophagy has a cardioprotective effect, while impaired or abnormal mobilization of autophagy is closely related to a variety of heart diseases. Rapid activation of autophagy helps protect myocardial cells from ischemia-reperfusion injury, but highly active autophagy levels accelerate the progression of heart failure and myocardial fibrosis. After myocardial infarction, blocked autophagic degradation leads to progressive heart failure and adverse ventricular remodeling.

[0004] One of the hot topics in the study of lncRNA mechanisms is the competing endogenous RNA mechanism (ceRNA). Because the long chain of lncRNA contains multiple incomplete binding sites for multiple miRNAs, it acts as a molecular sponge to absorb miRNAs, resulting in a decrease in the binding competitiveness of miRNAs to their target genes, which reduces the degradation of target genes in the RNA-induced silencing complex (RISC) and increases the expression level of target genes. In the study of coronary heart disease, lncRNA affects the progression of atherosclerosis through multiple mechanisms and plays a clinical role as a potential risk marker. However, there are still a large number of lncRNA functions that have not been studied by sequencing. Summary of the invention

[0005] The purpose of the present invention is to address the defects of the prior art and provide a method for constructing and applying a ceRNA regulatory network related to autophagy in coronary heart disease, which will help explore the pathogenesis of coronary heart disease and study diagnostic and therapeutic targets.

[0006] In order to achieve the above object, the present invention adopts the following technical scheme: a method for constructing a coronary heart disease autophagy-related ceRNA regulatory network as claimed in claim 1, comprising the following steps:

[0007] (1) Establish an in vitro human coronary artery endothelial cell oxidative stress model and set up a control group and a model group;

[0008] (2) Transcriptome high-throughput sequencing was performed on the cells in the control group and the model group to analyze the difference in gene expression and obtain the lists of differentially expressed mRNA and differentially expressed lncRNA respectively;

[0009] (3) Based on the differentially expressed mRNA list obtained in step (2), a protein-protein interaction network, i.e., a PPI network, is constructed; the gene set in the PPI network is analyzed to obtain the hub gene;

[0010] (4) Based on the differential lncRNA list obtained in step (2), predict the miRNAs with binding sites, and extract the top 300 miRNA-lncRNA interaction pairs;

[0011] (5) Based on the miRNA list obtained in step (4), predict the mRNAs with which they have binding sites, and take the intersection with the differential mRNA list obtained in step (2) to obtain mRNA-miRNA interaction pairs;

[0012] (6) Draw the ceRNA regulatory network based on the results of step (4) and step (5);

[0013] (7) Obtain human autophagy-related genes, take the intersection with the differential mRNA list obtained in step (2) to obtain differentially expressed autophagy-related mRNAs, and perform co-expression analysis with the differential lncRNA list obtained in step (2) to obtain autophagy-related lncRNAs; (8) Extract the most significant lncRNA in the co-expression analysis in the co-expression network of step (7), extract the ceRNA regulatory network corresponding to the lncRNA in step (6), and finally obtain the ceRNA regulatory network related to coronary heart disease autophagy.

[0014] Furthermore, in step (1), an in vitro human coronary endothelial cell oxidative stress model is constructed by H2O2 stimulation, and is experimentally evaluated by CCK-8 assay, MDA, LDH, and ROS.

[0015] Furthermore, step (2) specifically adopts the following steps:

[0016] 2.1: CuffCompare spliced ​​transcript alignment, excluding transcripts with single exons or length ≤200nt, excluding lncRNAs predicted to have coding potential, distinguishing between known lncRNAs and novel lncRNAs;

[0017] 2.2: Bowtie2 and eXpress software were used to obtain the FPKM values ​​and counts of mRNA and lncRNA;

[0018] 2.3: The DESeq2 R package was used to screen differentially expressed mRNA and lncRNA. The default screening conditions were p-value < 0.05 and |log2 FC| > 1. Genes that met the threshold conditions were defined as differentially expressed genes.

[0019] Furthermore, in step (3), the hub gene is the intersection of seven algorithms: MNC, MCC, EPC, DMNC, proximity, Radiality, and betweenness.

[0020] Furthermore, in step (7), the differentially expressed autophagy-related mRNA is obtained by using the Venn diagram to obtain the intersection of the differential mRNA list obtained in step (2) and the human autophagy-related gene list.

[0021] Furthermore, in step (7), the co-expression analysis was performed using the rcorr() function in the Hmisc R package to perform Pearson correlation analysis on the differentially expressed lncRNAs and differentially expressed autophagy-related mRNAs obtained in step (2); the Pearson correlation coefficient |r|>0.95 and p value <0.01 were used as the significant co-expression criteria, and the lncRNAs that met this threshold standard were defined as autophagy-related lncRNAs; and Cytoscape was used for visualization.

[0022] A coronary heart disease autophagy-related ceRNA regulatory network obtained by the above-mentioned construction method.

[0023] Furthermore, the ceRNA regulatory network related to autophagy in CHD includes:

[0024] The lncRNAs related to autophagy in CHD include: lncRNA LUCAT1;

[0025] The miRNAs related to autophagy in CHD include: hsa-miR-619-5p, hsa-miR-1273h-5p, and hsa-miR-6776-5p;

[0026] The autophagy-related mRNAs in CHD include: ARHGEF39, MPL, PRRG4, SLC5A5, GPRC5A, NKX3-1, LRRC25, CLDN16, PRELP, STMN3, and PILRA.

[0027] Application of the above-mentioned coronary heart disease autophagy-related ceRNA regulatory network in the preparation of reagents or chips for diagnosing coronary heart disease.

[0028] Beneficial effects: The present invention provides a method for constructing and applying a ceRNA regulatory network related to autophagy in coronary heart disease, simulates the pathogenesis of coronary heart disease with an in vitro human coronary endothelial cell oxidative stress model, and explores potential autophagy-related lncRNA-miRNA-mRNA regulatory mechanisms based on high-throughput sequencing data combined with integrated bioinformatics analysis, providing new ideas and strong support for the study of the pathogenesis and diagnostic and therapeutic targets of coronary heart disease. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 This is an example of an oxidative stress model evaluation.

[0030] Figure 2 This is the result of high-throughput sequencing differential expression analysis in the embodiment.

[0031] Figure 3 This is an embodiment of PPI network construction and hub gene identification.

[0032] Figure 4 This is an example of ceRNA regulatory network construction.

[0033] Figure 5 This is an example of co-expression analysis of autophagy-related mRNA and lncRNA.

[0034] Figure 6 This is an example of an autophagy-related ceRNA regulatory network.

[0035] Figure 7 This is an in vitro validation of the autophagy-related ceRNA regulatory network. DETAILED DESCRIPTION

[0036] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0037] It should be noted that the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or apparatus comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or apparatuses.

[0038] Example 1

[0039] A method for constructing a ceRNA regulatory network related to coronary heart disease autophagy comprises the following steps:

[0040] 1. Establishment of human coronary artery endothelial cell oxidative stress model

[0041] Human coronary artery endothelial cells (HCAEC) were purchased from ScienCell Research Laboratory. Hydrogen peroxide (H2O2) was purchased from Merck. HCAEC were treated with 0.8 mM H2O2 for 12 h to construct an in vitro oxidative stress model, and a control group (control) and an oxidative stress injury group (H2O2) were set up.

[0042] CCK-8 assay (cell activity detection): HCAEC was cultured at 5x10 4 The cells were plated at a density of cells / ml in a 96-well plate, with 100ul per well. PBS was pre-painted around the well plate to reduce volatility and the cells were stabilized in an incubator for 12h. A cell oxidative stress model was constructed (0.8mM, 12hH2O2), and a blank group and a control group were set up. The cells were rinsed once with PBS, and a mixed solution was prepared at a ratio of 90μl DMEM and 10μl CCK-8 reagent per well. 100μl was added to each well and incubated in the dark for 1-2h. The absorbance was measured at 450nm on an enzyme reader.

[0043] LDH detection (lactate dehydrogenase cytotoxicity detection): HCAEC was plated and stabilized for 12 hours until the density reached about 80%; a blank control group, a normal cell group, a model group and a sample maximum enzyme activity control group were set up, with 8 replicate wells in each group and 200 μl in each well; the 96-well plate was taken out 1 hour before the expected detection point, 20 μl of LDH release reagent was added to the replicate wells in the sample maximum enzyme activity control group, the mixture was mixed by pipetting, and the plate was returned to the culture plate; the 96-well plate was taken out, centrifuged at room temperature and 400 g for 5 minutes, 120 μl of the supernatant from each replicate well of each group was respectively aspirated and transferred to a new 96-well plate; 60 μl of LDH detection working solution (prepared and used immediately) was added to each well, and incubated on a shaker at room temperature in the dark for 30 minutes; the absorbance was measured at 490 nm by an enzyme reader.

[0044] MDA detection (lipid oxidation detection): collect cells from the control group and model group, add 0.1ml cell lysis buffer to fully lyse, centrifuge at 12000g, 4℃ for 10min, collect supernatant for later use; determine the cell protein concentration by BCA method; prepare MDA working solution and standard; use 0.1ml lysis buffer as blank control, add 0.1ml of the above standard respectively, add 0.1ml sample for determination; add 0.2ml MDA detection working solution to each tube; boil in water bath for 15min; centrifuge at 1000g for 10min; take 200μl of supernatant from each tube and add it to 96-well plate, and measure the absorbance value at 532nm with enzyme reader.

[0045] ROS detection (DCFH-DA reactive oxygen species detection): HCAEC were plated and stabilized for 12 hours until the density reached about 80%; protected from light, DCFH-DA probe was diluted with serum-free DMEM at a ratio of 1:1000; cell culture medium was removed, washed with PBS, 50 μl was added to each well, and incubated in an incubator for 15 min in the dark; washed with PBS; protected from light, Hoechst was diluted with DMEM at a ratio of 1:200; 50 μl was added to each well, and incubated in an incubator for 10 min in the dark; washed with PBS; DCFH-DA and Hoechst fluorescence signals were observed under an inverted fluorescence microscope and images were acquired.

[0046] Analysis results: The results suggest that the cell activity is significantly reduced in the HCAEC oxidative stress model ( Figure 1 A), LDH release level increased significantly ( Figure 1 B), H2O2 treatment of HCAEC significantly increased the occurrence of intracellular lipid peroxidation ( Figure 1 C), the ROS content in HCAEC group treated with H2O2 was significantly increased, while that in control group was maintained at a low level ( Figure 1 DE). The above evaluation index results showed that the in vitro oxidative stress model of human coronary artery endothelial cells was successfully established by treating HCAEC with 0.8mM H2O2 for 12h, and H2O2 treatment significantly reduced cell activity, increased cell membrane damage and cytotoxicity, and promoted intracellular lipid peroxidation.

[0047] 2. Transcriptome high-throughput sequencing and differential expression analysis

[0048] Identification of lncRNA: CuffCompare spliced ​​transcript alignment; elimination of single exon or transcripts ≤200 nt in length; elimination of lncRNAs predicted to have coding potential; differentiation of known lncRNAs and novel lncRNAs; quantitative and differential analysis: Bowtie2 and eXpress software were used to obtain the FPKM values ​​and counts of mRNA and lncRNA; DESeq2 R package was used to screen differentially expressed mRNA and lncRNA, and the default differential screening conditions were p-value < 0.05 and |log2 FC| > 1.

[0049] Analysis results: A total of 795 differentially expressed mRNAs were obtained, of which 470 mRNAs were upregulated and 325 mRNAs were downregulated ( Figure 2 AB), among the 27657 candidate lncRNA molecules, with the same screening threshold, a total of 208 differentially expressed lncRNAs were obtained, of which 83 were upregulated and 125 were downregulated ( Figure 2 CD).

[0050] 3.PPI network construction and hub gene identification

[0051] The differential mRNA list was sorted and entered into the online STRING database (https: / / cn.string-db.org / ) to establish a protein interaction network; the gene set in the PPI network was sorted and the hub gene was screened using the cytoHubba plug-in in Cytoscape; the intersection of seven algorithms, including MNC, MCC, EPC, DMNC, proximity, Radiality, and betweenness, was defined as the hub gene and visualized.

[0052] Analysis results: PPI interaction analysis was performed on the differentially expressed mRNA molecule list using the online database STRING. With a credibility score of >0.7 as the cutoff, a protein interaction network consisting of 123 nodes and 509 interaction edges was screened. Using the MCODE module in Cytoscape, a PPI network containing 28 node genes and 360 interaction edges was extracted, with the highest interaction score of 26.667 ( Figure 3 A). Sort by interaction score from high to low, showing the 2nd to 4th group of PPI networks ( Figure 3 B). Using seven algorithms, including MNC, MCC, EPC, DMNC, close, Radiality and betweenness, the hub genes are predicted, and the predicted intersection of the seven sets is displayed in the form of an upset graph ( Figure 3 C). Finally, 13 hub genes were identified, including CDC20, TOP2A, KIF20A, DLGAP5, PLK1, AURKA, CEP55, CENPE, CCNB2, CDCA8, NDC80, KIF23, and CCNA2. At the same time, the top 15 node interactions in the MCC algorithm were extracted and visualized as a hub gene interaction network diagram ( Figure 3 D).

[0053] 4. Construction of ceRNA regulatory network

[0054] Miranda software was used to predict 404,162 pairs of lncRNA-miRNA binding relationships between 208 differentially expressed lncRNAs. After sorting by predicted P value from high to low, the top 300 pairs of lncRNA-miRNA interaction networks were visualized using the R network package ( Figure 4A), which includes 186 differentially expressed lncRNAs and 11 miRNA targets. Based on the above 6 most enriched miRNA molecules, the corresponding target mRNA molecules were predicted using miRDB, TargetScan and miRWalk databases, respectively, and the intersection with the differentially expressed mRNA was taken to obtain the final mRNA target, and the miRNA-mRNA network was established.

[0055] Analysis results: Finally, 165 differentially expressed lncRNAs (74 downregulated, 91 upregulated), 6 miRNAs, and 17 differentially expressed mRNAs (8 downregulated, 9 upregulated) were included in the ceRNA network construction, forming a total of 221 pairs of lncRNA-miRNA interactions and 19 pairs of miRNA-mRNA interactions ( Figure 4 B).

[0056] 5. Co-expression analysis of autophagy-related mRNA and lncRNA

[0057] The online human autophagy database HADb obtained 222 human autophagy-related genes, took the intersection with the differentially expressed mRNA, identified the differentially expressed autophagy-related mRNA, and described its expression in detail in the volcano plot. The rcorr() function in the Hmisc R package was used to perform Pearson correlation analysis on the differentially expressed lncRNA and the differentially expressed autophagy-related mRNA. The co-expression relationship between autophagy-related differential mRNA and differential lncRNA was analyzed by the Pearson correlation analysis algorithm, and the co-expression heat map was drawn according to the correlation coefficient score ( Figure 5 C). The lncRNA molecules with co-expression correlation coefficient |r|>0.95 and P value <0.01 were defined as autophagy-related lncRNAs.

[0058] Analysis results: Finally, 11 up-regulated and 1 down-regulated autophagy-related mRNAs were identified ( Figure 5 AB), 76 differentially expressed autophagy-related lncRNA molecules that met the criteria were identified, including 43 down-regulated lncRNAs and 33 up-regulated lncRNAs. The autophagy-related mRNA-lncRNA co-expression network contained 211 pairs of mRNA-lncRNA co-expression relationships ( Figure 5 D).

[0059] 6. Construction of autophagy-related ceRNA regulatory network

[0060] The Ensembl database (https: / / asia.ensembl.org / index.html) was queried and it was found that the transcripts ENST00000648015, ENST00000650047 and ENST00000649467, which were enriched with the most autophagy-related genes in the constructed co-expression network, were all transcripts of lncRNA LUCAT1. At the same time, GO enrichment analysis of lncRNA LUCAT1 showed that its biological function was mainly enriched in the lysosomal entry of the CC module.

[0061] The lncRNA-miRNA-mRNA involved in the three transcripts corresponding to lncRNA LUCAT1 were extracted from the above four ceRNA networks. The Sankey diagram shows that lncRNA LUCAT1 regulates the expression of 11 downstream mRNAs by targeting hsa-miR-619-5p, hsa-miR-1273h-5p and hsa-miR-6776-5p ( Figure 6 ).

[0062] Analysis results: The ceRNA regulatory network related to autophagy in CHD includes: the screened lncRNAs related to autophagy in CHD include: lncRNA LUCAT1; the screened miRNAs related to autophagy in CHD include: hsa-miR-619-5p, hsa-miR-1273h-5p, hsa-miR-6776-5p; the screened mRNAs related to autophagy in CHD include: ARHGEF39, MPL, PRRG4, SLC5A5, GPRC5A, NKX3-1, LRRC25, CLDN16, PRELP, STMN3, and PILRA.

[0063] 7. In vitro validation of the autophagy-related ceRNA regulatory network

[0064] First, qPCR verification was performed between the control group cells and the oxidative stress model group cells. The specific primers were as follows: lncRNA LUCAT1: F: 5'-GCTCGGATTGCCTTAGACAG-3', R: 5'-GGGTGAGCTTCTTGTGAGGA-3'; hsa-miR-6776-5p: F: 5'-CGCGTCTGGGTGCAGTG-3', R: 5'-AGTGCAGGGTCCGAGGTATT-3'; LRRC25: F: 5'-TGCTACGCAACCCCTTGTC-3', R: 5'-GGCCAGAGCAGTTGTCTCG-3'. It was found that in human coronary artery endothelial cells with oxidative stress, the expression levels of lncRNA LUCAT1 and LRRC25 were significantly increased, which was consistent with the sequencing results, while the expression level of hsa-miR-6776-5p was significantly decreased ( Figure 7 AC).

[0065] Furthermore, the lncRNA LUCAT1 small interfering knockout system was constructed, and the siRNA sequences were as follows. si-LUCAT1-3, F: CGAUCUCGGUUCACUGCAATT, R: UUGCAGUGAACCGAGAUCGTT; si-LUCAT1-2, F: CCUGUACAGUUGUGUCCAATT, R: UUGGACACAACUGUACAGGTT; si-LUCAT1-1, F: GCUGUCCCUCAGUGUUCUATT, R: UAGAACACUGAGGGACAGCTT. In the cells knocked out by lncRNA LUCAT1, hsa-miR-6776-5p was detected to be upregulated and LRRC25 was downregulated ( Figure 7 DF). Therefore, the results suggest that lncRNA LUCAT1 may regulate the expression of LRRC25 by sponging hsa-miR-6776-5p.

[0066] It should be understood that the present invention is described by way of example only and can be modified within the scope and spirit of the present invention. The preferred specific embodiments of the present invention are described in detail above. It should be understood that ordinary skills in the art can make many modifications and changes based on the concept of the present invention without creative work. Therefore, all technical solutions that can be obtained by technicians in the technical field through logical analysis, reasoning or limited experiments based on the concept of the present invention on the basis of the prior art should be within the scope of protection determined by the claims.

Claims

1. A method for constructing a ceRNA regulatory network related to coronary heart disease autophagy as claimed in claim 1, characterized in that The following steps are involved: (1) Establish an in vitro human coronary artery endothelial cell oxidative stress model and set up a control group and a model group; (2) Transcriptome high-throughput sequencing was performed on the cells in the control group and the model group to analyze the difference in gene expression and obtain the lists of differentially expressed mRNA and differentially expressed lncRNA; (3) constructing a protein-protein interaction network, i.e., a PPI network, based on the differentially expressed mRNA list obtained in step (2); analyzing the gene set in the PPI network to obtain the hub gene; (4) Based on the differential lncRNA list obtained in step (2), predict the miRNAs with binding sites, and extract the top 300 miRNA-lncRNA interaction pairs; (5) Based on the miRNA list obtained in step (4), predict the mRNAs with which they have binding sites, and take the intersection with the differential mRNA list obtained in step (2) to obtain mRNA-miRNA interaction pairs; (6) Draw the ceRNA regulatory network based on the results of step (4) and step (5); (7) Obtain human autophagy-related genes, take the intersection with the differential mRNA list obtained in step (2) to obtain differentially expressed autophagy-related mRNAs, and perform co-expression analysis with the differential lncRNA list obtained in step (2) to obtain autophagy-related lncRNAs; (8) Extract the most significant lncRNA in the co-expression analysis in the co-expression network of step (7), extract the ceRNA regulatory network corresponding to the lncRNA in step (6), and finally obtain the ceRNA regulatory network related to CHD autophagy.

2. The method for constructing the coronary heart disease autophagy-related ceRNA regulatory network according to claim 1, characterized in that: In the step (1), an in vitro human coronary endothelial cell oxidative stress model is constructed by H2O2 stimulation, and is experimentally evaluated by CCK-8 experiment, MDA, LDH, and ROS.

3. The method for constructing the coronary heart disease autophagy-related ceRNA regulatory network according to claim 1, characterized in that: The step (2) specifically adopts the following steps: 2.1: CuffCompare spliced ​​transcript alignment, excluding transcripts with single exons or length ≤200nt, excluding lncRNAs predicted to have coding potential, distinguishing between known lncRNAs and novel lncRNAs; 2.2: Bowtie2 and eXpress software were used to obtain the FPKM values ​​and counts of mRNA and lncRNA; 2.3: The DESeq2 R package was used to screen differentially expressed mRNA and lncRNA. The default screening conditions were p-value < 0.05 and |log2 FC| > 1. Genes that met the threshold conditions were defined as differentially expressed genes.

4. The method for constructing the coronary heart disease autophagy-related ceRNA regulatory network according to claim 1, characterized in that: In the step (3), the hub gene is the intersection of seven algorithms: MNC, MCC, EPC, DMNC, proximity, Radiality, and betweenness.

5. The method for constructing the coronary heart disease autophagy-related ceRNA regulatory network according to claim 1, characterized in that: The differentially expressed autophagy-related mRNA in step (7) is obtained by using the Venn diagram to obtain the intersection of the differential mRNA list obtained in step (2) and the human autophagy-related gene list.

6. The method for constructing the coronary heart disease autophagy-related ceRNA regulatory network according to claim 1, characterized in that: In the co-expression analysis in step (7), the rcorr() function in the Hmisc R package is used to perform Pearson correlation analysis on the differential lncRNAs and differentially expressed autophagy-related mRNAs obtained in step (2); the Pearson correlation coefficient |r|>0.95 and p value <0.01 are used as significant co-expression standards, and the lncRNAs that meet this threshold standard are defined as autophagy-related lncRNAs; and Cytoscape is used for visualization.

7. A coronary heart disease autophagy-related ceRNA regulatory network obtained according to the construction method described in any one of claims 1 to 6.

8. The coronary heart disease autophagy-related ceRNA regulatory network according to claim 7, characterized in that: The coronary heart disease autophagy-related ceRNA regulatory network includes: The lncRNAs related to autophagy in CHD include: lncRNA LUCAT1; CHD autophagy-related miRNAs include: hsa-miR-619-5p, hsa-miR-1273h-5p, hsa-miR-6776-5p; The autophagy-related mRNAs in CHD include: ARHGEF39, MPL, PRRG4, SLC5A5, GPRC5A, NKX3-1, LRRC25, CLDN16, PRELP, STMN3, and PILRA.

9. Use of the coronary heart disease autophagy-related ceRNA regulatory network according to any of claims 7 or 8 in the preparation of a reagent or chip for diagnosing coronary heart disease.

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