Development and validation of risk prediction and neural network models for dilated cardiomyopathy based on WGCNA
BackgroundDilated cardiomyopathy (DCM) is a progressive heart condition characterized by ventricular dilatation and impaired myocardial contractility with a high mortality rate. The molecular characterization of DCM has not been determined yet. Therefore, it is crucial to discover potential biomarke...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2023-10-01
|
| Series: | Frontiers in Medicine |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2023.1239056/full |
| _version_ | 1827799278453522432 |
|---|---|
| author | Wei Yu Lingjiao Li Xingling Tan Xiaozhu Liu Chengliang Yin Junyi Cao |
| author_facet | Wei Yu Lingjiao Li Xingling Tan Xiaozhu Liu Chengliang Yin Junyi Cao |
| author_sort | Wei Yu |
| collection | DOAJ |
| description | BackgroundDilated cardiomyopathy (DCM) is a progressive heart condition characterized by ventricular dilatation and impaired myocardial contractility with a high mortality rate. The molecular characterization of DCM has not been determined yet. Therefore, it is crucial to discover potential biomarkers and therapeutic options for DCM.MethodsThe hub genes for the DCM were screened using Weighted Gene Co-expression Network Analysis (WGCNA) and three different algorithms in Cytoscape. These genes were then validated in a mouse model of doxorubicin (DOX)-induced DCM. Based on the validated hub genes, a prediction model and a neural network model were constructed and validated in a separate dataset. Finally, we assessed the diagnostic efficiency of hub genes and their relationship with immune cells.ResultsA total of eight hub genes were identified. Using RT-qPCR, we validated that the expression levels of five key genes (ASPN, MFAP4, PODN, HTRA1, and FAP) were considerably higher in DCM mice compared to normal mice, and this was consistent with the microarray results. Additionally, the risk prediction and neural network models constructed from these genes showed good accuracy and sensitivity in both the combined and validation datasets. These genes also demonstrated better diagnostic power, with AUC greater than 0.7 in both the combined and validation datasets. Immune cell infiltration analysis revealed differences in the abundance of most immune cells between DCM and normal samples.ConclusionThe current findings indicate an underlying association between DCM and these key genes, which could serve as potential biomarkers for diagnosing and treating DCM. |
| first_indexed | 2024-03-11T19:49:04Z |
| format | Article |
| id | doaj.art-111fb92199bc4c978ea2838ba0581e98 |
| institution | Directory Open Access Journal |
| issn | 2296-858X |
| language | English |
| last_indexed | 2024-03-11T19:49:04Z |
| publishDate | 2023-10-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj.art-111fb92199bc4c978ea2838ba0581e982023-10-05T11:57:01ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2023-10-011010.3389/fmed.2023.12390561239056Development and validation of risk prediction and neural network models for dilated cardiomyopathy based on WGCNAWei Yu0Lingjiao Li1Xingling Tan2Xiaozhu Liu3Chengliang Yin4Junyi Cao5Chongqing Medical University, Chongqing, ChinaChongqing Medical University, Chongqing, ChinaChongqing Medical University, Chongqing, ChinaChongqing Medical University, Chongqing, ChinaFaculty of Medicine, Macau University of Science and Technology, Macau, ChinaDepartment of Medical Quality Control, The First People’s Hospital of Zigong City, Zigong, ChinaBackgroundDilated cardiomyopathy (DCM) is a progressive heart condition characterized by ventricular dilatation and impaired myocardial contractility with a high mortality rate. The molecular characterization of DCM has not been determined yet. Therefore, it is crucial to discover potential biomarkers and therapeutic options for DCM.MethodsThe hub genes for the DCM were screened using Weighted Gene Co-expression Network Analysis (WGCNA) and three different algorithms in Cytoscape. These genes were then validated in a mouse model of doxorubicin (DOX)-induced DCM. Based on the validated hub genes, a prediction model and a neural network model were constructed and validated in a separate dataset. Finally, we assessed the diagnostic efficiency of hub genes and their relationship with immune cells.ResultsA total of eight hub genes were identified. Using RT-qPCR, we validated that the expression levels of five key genes (ASPN, MFAP4, PODN, HTRA1, and FAP) were considerably higher in DCM mice compared to normal mice, and this was consistent with the microarray results. Additionally, the risk prediction and neural network models constructed from these genes showed good accuracy and sensitivity in both the combined and validation datasets. These genes also demonstrated better diagnostic power, with AUC greater than 0.7 in both the combined and validation datasets. Immune cell infiltration analysis revealed differences in the abundance of most immune cells between DCM and normal samples.ConclusionThe current findings indicate an underlying association between DCM and these key genes, which could serve as potential biomarkers for diagnosing and treating DCM.https://www.frontiersin.org/articles/10.3389/fmed.2023.1239056/fulldilated cardiomyopathyweighted gene co-expression network analysisrisk prediction modelneural network modelbioinformatics |
| spellingShingle | Wei Yu Lingjiao Li Xingling Tan Xiaozhu Liu Chengliang Yin Junyi Cao Development and validation of risk prediction and neural network models for dilated cardiomyopathy based on WGCNA Frontiers in Medicine dilated cardiomyopathy weighted gene co-expression network analysis risk prediction model neural network model bioinformatics |
| title | Development and validation of risk prediction and neural network models for dilated cardiomyopathy based on WGCNA |
| title_full | Development and validation of risk prediction and neural network models for dilated cardiomyopathy based on WGCNA |
| title_fullStr | Development and validation of risk prediction and neural network models for dilated cardiomyopathy based on WGCNA |
| title_full_unstemmed | Development and validation of risk prediction and neural network models for dilated cardiomyopathy based on WGCNA |
| title_short | Development and validation of risk prediction and neural network models for dilated cardiomyopathy based on WGCNA |
| title_sort | development and validation of risk prediction and neural network models for dilated cardiomyopathy based on wgcna |
| topic | dilated cardiomyopathy weighted gene co-expression network analysis risk prediction model neural network model bioinformatics |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2023.1239056/full |
| work_keys_str_mv | AT weiyu developmentandvalidationofriskpredictionandneuralnetworkmodelsfordilatedcardiomyopathybasedonwgcna AT lingjiaoli developmentandvalidationofriskpredictionandneuralnetworkmodelsfordilatedcardiomyopathybasedonwgcna AT xinglingtan developmentandvalidationofriskpredictionandneuralnetworkmodelsfordilatedcardiomyopathybasedonwgcna AT xiaozhuliu developmentandvalidationofriskpredictionandneuralnetworkmodelsfordilatedcardiomyopathybasedonwgcna AT chengliangyin developmentandvalidationofriskpredictionandneuralnetworkmodelsfordilatedcardiomyopathybasedonwgcna AT junyicao developmentandvalidationofriskpredictionandneuralnetworkmodelsfordilatedcardiomyopathybasedonwgcna |