Myocardial infarction (MI) is a type of coronary heart disease, which refers to the ischemic necrosis of the heart muscle. A large number of studies have discussed the mechanism of MI from the perspective of competing endogenous RNA (ceRNA) network. However, the mechanisms underlying the function of lncRNAs in MI have still not been explained in an explicit manner. Therefore, we constructed a scale-free lncRNA-associated ceRNA network to identify some crucial lncRNAs in MI. Results showed that the given disease genes for MI were involved in the network, the degrees of which were significantly larger than the other nodes of the network. For measuring the network centrality, we then constructed a hub subnetwork. The miRNAs and mRNAs in the hub subnetwork have been validated to function in MI-related biological function. In addition, we identified 2 MI-related functional modules from the lncRNA-associated ceRNA network, which suggested that lncRNA exerted function in local network. Enrichment analysis showed that these functional modules corresponded to some similar and different pathways related to cardiovascular disease. More importantly, 3 MI-related crucial lncRNAs, CTD-3092A11.2, RP5-821D11.7 and CTC-523E23.1 were detected as potential biomarkers, which may be involved in MI-related biological progresses. Our study identified 20 functional lncRNAs based on ceRNA network analysis, which may provide novel diagnosis and therapeutic targets for MI from the ceRNA network perspective.
Citation: Beibei Zhu, Yue Mao, Mei Li. Identification of functional lncRNAs through constructing a lncRNA-associated ceRNA network in myocardial infarction[J]. Mathematical Biosciences and Engineering, 2021, 18(4): 4293-4310. doi: 10.3934/mbe.2021215
Myocardial infarction (MI) is a type of coronary heart disease, which refers to the ischemic necrosis of the heart muscle. A large number of studies have discussed the mechanism of MI from the perspective of competing endogenous RNA (ceRNA) network. However, the mechanisms underlying the function of lncRNAs in MI have still not been explained in an explicit manner. Therefore, we constructed a scale-free lncRNA-associated ceRNA network to identify some crucial lncRNAs in MI. Results showed that the given disease genes for MI were involved in the network, the degrees of which were significantly larger than the other nodes of the network. For measuring the network centrality, we then constructed a hub subnetwork. The miRNAs and mRNAs in the hub subnetwork have been validated to function in MI-related biological function. In addition, we identified 2 MI-related functional modules from the lncRNA-associated ceRNA network, which suggested that lncRNA exerted function in local network. Enrichment analysis showed that these functional modules corresponded to some similar and different pathways related to cardiovascular disease. More importantly, 3 MI-related crucial lncRNAs, CTD-3092A11.2, RP5-821D11.7 and CTC-523E23.1 were detected as potential biomarkers, which may be involved in MI-related biological progresses. Our study identified 20 functional lncRNAs based on ceRNA network analysis, which may provide novel diagnosis and therapeutic targets for MI from the ceRNA network perspective.
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