Identification of crucial genes related to heart failure based on GEO database
Abstract Background The molecular biological mechanisms underlying heart failure (HF) remain poorly understood. Therefore, it is imperative to use innovative approaches, such as high-throughput sequencing and artificial intelligence, to investigate the pathogenesis, diagnosis, and potential treatmen...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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BMC
2023-07-01
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| Series: | BMC Cardiovascular Disorders |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12872-023-03400-x |
| _version_ | 1797769661279895552 |
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| author | Yongliang Chen Jing Xue Xiaoli Yan Da-guang Fang Fangliang Li Xuefei Tian Peng Yan Zengbin Feng |
| author_facet | Yongliang Chen Jing Xue Xiaoli Yan Da-guang Fang Fangliang Li Xuefei Tian Peng Yan Zengbin Feng |
| author_sort | Yongliang Chen |
| collection | DOAJ |
| description | Abstract Background The molecular biological mechanisms underlying heart failure (HF) remain poorly understood. Therefore, it is imperative to use innovative approaches, such as high-throughput sequencing and artificial intelligence, to investigate the pathogenesis, diagnosis, and potential treatment of HF. Methods First, we initially screened Two data sets (GSE3586 and GSE5406) from the GEO database containing HF and control samples from the GEO database to establish the Train group, and selected another dataset (GSE57345) to construct the Test group for verification. Next, we identified the genes with significantly different expression levels in patients with or without HF and performed functional and pathway enrichment analyses. HF-specific genes were identified, and an artificial neural network was constructed by Random Forest. The ROC curve was used to evaluate the accuracy and reliability of the constructed model in the Train and Test groups. Finally, immune cell infiltration was analyzed to determine the role of the inflammatory response and the immunological microenvironment in the pathogenesis of HF. Results In the Train group, 153 significant differentially expressed genes (DEGs) associated with HF were found to be abnormal, including 81 down-regulated genes and 72 up-regulated genes. GO and KEGG enrichment analyses revealed that the down-regulated genes were primarily enriched in organic anion transport, neutrophil activation, and the PI3K-Akt signaling pathway. The upregulated genes were mainly enriched in neutrophil activation and the calcium signaling. DEGs were identified using Random Forest, and finally, 16 HF-specific genes were obtained. In the ROC validation and evaluation, the area under the curve (AUC) of the Train and Test groups were 0.996 and 0.863, respectively. Conclusions Our research revealed the potential functions and pathways implicated in the progression of HF, and designed an RNA diagnostic model for HF tissues using machine learning and artificial neural networks. Sensitivity, specificity, and stability were confirmed by ROC curves in the two different cohorts. |
| first_indexed | 2024-03-12T21:12:14Z |
| format | Article |
| id | doaj.art-b2fd1c9728a24e94983e661a1cc45a1b |
| institution | Directory Open Access Journal |
| issn | 1471-2261 |
| language | English |
| last_indexed | 2024-03-12T21:12:14Z |
| publishDate | 2023-07-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Cardiovascular Disorders |
| spelling | doaj.art-b2fd1c9728a24e94983e661a1cc45a1b2023-07-30T11:06:53ZengBMCBMC Cardiovascular Disorders1471-22612023-07-0123111210.1186/s12872-023-03400-xIdentification of crucial genes related to heart failure based on GEO databaseYongliang Chen0Jing Xue1Xiaoli Yan2Da-guang Fang3Fangliang Li4Xuefei Tian5Peng Yan6Zengbin Feng7Department of Cardiac Surgery, Affiliated Hospital of Chengde Medical UniversityExperimental Center of Morphology, College of Basic Medicine, Chengde Medical UniversityExperimental Center of Morphology, College of Basic Medicine, Chengde Medical UniversityDepartment of Cardiac Surgery, Affiliated Hospital of Chengde Medical UniversityExperimental Center of Morphology, College of Basic Medicine, Chengde Medical UniversityDepartment of Cardiac Surgery, Affiliated Hospital of Chengde Medical UniversityExperimental Center of Morphology, College of Basic Medicine, Chengde Medical UniversityDepartment of Cardiac Surgery, Affiliated Hospital of Chengde Medical UniversityAbstract Background The molecular biological mechanisms underlying heart failure (HF) remain poorly understood. Therefore, it is imperative to use innovative approaches, such as high-throughput sequencing and artificial intelligence, to investigate the pathogenesis, diagnosis, and potential treatment of HF. Methods First, we initially screened Two data sets (GSE3586 and GSE5406) from the GEO database containing HF and control samples from the GEO database to establish the Train group, and selected another dataset (GSE57345) to construct the Test group for verification. Next, we identified the genes with significantly different expression levels in patients with or without HF and performed functional and pathway enrichment analyses. HF-specific genes were identified, and an artificial neural network was constructed by Random Forest. The ROC curve was used to evaluate the accuracy and reliability of the constructed model in the Train and Test groups. Finally, immune cell infiltration was analyzed to determine the role of the inflammatory response and the immunological microenvironment in the pathogenesis of HF. Results In the Train group, 153 significant differentially expressed genes (DEGs) associated with HF were found to be abnormal, including 81 down-regulated genes and 72 up-regulated genes. GO and KEGG enrichment analyses revealed that the down-regulated genes were primarily enriched in organic anion transport, neutrophil activation, and the PI3K-Akt signaling pathway. The upregulated genes were mainly enriched in neutrophil activation and the calcium signaling. DEGs were identified using Random Forest, and finally, 16 HF-specific genes were obtained. In the ROC validation and evaluation, the area under the curve (AUC) of the Train and Test groups were 0.996 and 0.863, respectively. Conclusions Our research revealed the potential functions and pathways implicated in the progression of HF, and designed an RNA diagnostic model for HF tissues using machine learning and artificial neural networks. Sensitivity, specificity, and stability were confirmed by ROC curves in the two different cohorts.https://doi.org/10.1186/s12872-023-03400-xHeart failureDiagnosisSupport Vector MachineRandom ForestArtificial neural network |
| spellingShingle | Yongliang Chen Jing Xue Xiaoli Yan Da-guang Fang Fangliang Li Xuefei Tian Peng Yan Zengbin Feng Identification of crucial genes related to heart failure based on GEO database BMC Cardiovascular Disorders Heart failure Diagnosis Support Vector Machine Random Forest Artificial neural network |
| title | Identification of crucial genes related to heart failure based on GEO database |
| title_full | Identification of crucial genes related to heart failure based on GEO database |
| title_fullStr | Identification of crucial genes related to heart failure based on GEO database |
| title_full_unstemmed | Identification of crucial genes related to heart failure based on GEO database |
| title_short | Identification of crucial genes related to heart failure based on GEO database |
| title_sort | identification of crucial genes related to heart failure based on geo database |
| topic | Heart failure Diagnosis Support Vector Machine Random Forest Artificial neural network |
| url | https://doi.org/10.1186/s12872-023-03400-x |
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