Identification of anoikis-related genes in heart failure: bioinformatics and experimental validation
Abstract Background Heart failure (HF) is a common clinical syndrome caused by ventricular dysfunction and one of the leading causes of mortality worldwide. Previous studies have suggested that anoikis is relevant to HF. This study aimed to identify hub genes associated with anoikis that may offer t...
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BMC
2025-08-01
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| Series: | Hereditas |
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| Online Access: | https://doi.org/10.1186/s41065-025-00532-2 |
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| author | Lina Zhang Jianjun Gu Yan Jiang Juan Xue Ye Zhu |
| author_facet | Lina Zhang Jianjun Gu Yan Jiang Juan Xue Ye Zhu |
| author_sort | Lina Zhang |
| collection | DOAJ |
| description | Abstract Background Heart failure (HF) is a common clinical syndrome caused by ventricular dysfunction and one of the leading causes of mortality worldwide. Previous studies have suggested that anoikis is relevant to HF. This study aimed to identify hub genes associated with anoikis that may offer therapeutic targets for HF. Materials and methods Gene expression data for GSE36074 were obtained from the Gene Expression Omnibus (GEO) and anoikis-related genes (ARGs) were extracted from GeneCards. GEO2R was used to screen for differentially expressed genes (DEGs), then by overlapping DEGs with ARGs, differentially expressed ARGs (DEARGs) were screened. The biological functions of the DEARGs were determined using DAVID. Subsequently, two machine learning (ML) algorithms were employed to identify hub DEARGs: least absolute shrinkage and selection operator (LASSO) and random forest (RF). In addition, miRNA-hub DEARGs and drug-hub DEARGs networks were constructed. Lastly, the hub DEARGs were validated by quantitative reverse transcription PCR (RT-qPCR) and Immunofluorescence (IF). Results A total of 138 DEARGs were identified in GSE36074. Functional analysis of DEARGs revealed that they were primarily enriched in the positive regulation of the apoptotic process, PI3K-Akt, and FoxO signaling pathways. Subsequently, two hub DEARGs (Tln1 and TGFβ2) were screened using LASSO and RF algorithms. According to the miRNA–hub DEARGs networks, Tln1 and TGFβ2 were regulated by 34 and 68 miRNAs, respectively. Moreover, drug-hub DEARGs networks showed that Gemogenovatucel-t, Lerdelimumab, Belagenpumatucel-l, Fresolimumab, Bintrafusp alfa, Trabedersen and Luspatercept-aamt are potential drugs that could target TGFβ2. Finally, RT-qPCR and IF validation of two key DEARGs (Tln1 and TGFβ2) supported our bioinformatics analysis. Conclusions These findings suggest that Tln1 and TGFβ2 may play important roles in HF development through the regulation of anoikis and may serve as therapeutic targets for HF. Clinical trial number Not applicable. |
| format | Article |
| id | doaj-art-cef0caeae3194645b8cd1409635bce21 |
| institution | Kabale University |
| issn | 1601-5223 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | BMC |
| record_format | Article |
| series | Hereditas |
| spelling | doaj-art-cef0caeae3194645b8cd1409635bce212025-08-20T04:03:17ZengBMCHereditas1601-52232025-08-01162111310.1186/s41065-025-00532-2Identification of anoikis-related genes in heart failure: bioinformatics and experimental validationLina Zhang0Jianjun Gu1Yan Jiang2Juan Xue3Ye Zhu4Department of Cardiology, Affiliated Hospital of Nantong UniversityDepartment of Respiratory and Critical Care Medicine, Affiliated Hospital and Medical School of Nantong UniversityDepartment of Cardiology, Affiliated Hospital of Nantong UniversityDepartment of Obstetrics, Xuzhou Cancer HospitalDepartment of Cardiology, Northern Jiangsu People’s HospitalAbstract Background Heart failure (HF) is a common clinical syndrome caused by ventricular dysfunction and one of the leading causes of mortality worldwide. Previous studies have suggested that anoikis is relevant to HF. This study aimed to identify hub genes associated with anoikis that may offer therapeutic targets for HF. Materials and methods Gene expression data for GSE36074 were obtained from the Gene Expression Omnibus (GEO) and anoikis-related genes (ARGs) were extracted from GeneCards. GEO2R was used to screen for differentially expressed genes (DEGs), then by overlapping DEGs with ARGs, differentially expressed ARGs (DEARGs) were screened. The biological functions of the DEARGs were determined using DAVID. Subsequently, two machine learning (ML) algorithms were employed to identify hub DEARGs: least absolute shrinkage and selection operator (LASSO) and random forest (RF). In addition, miRNA-hub DEARGs and drug-hub DEARGs networks were constructed. Lastly, the hub DEARGs were validated by quantitative reverse transcription PCR (RT-qPCR) and Immunofluorescence (IF). Results A total of 138 DEARGs were identified in GSE36074. Functional analysis of DEARGs revealed that they were primarily enriched in the positive regulation of the apoptotic process, PI3K-Akt, and FoxO signaling pathways. Subsequently, two hub DEARGs (Tln1 and TGFβ2) were screened using LASSO and RF algorithms. According to the miRNA–hub DEARGs networks, Tln1 and TGFβ2 were regulated by 34 and 68 miRNAs, respectively. Moreover, drug-hub DEARGs networks showed that Gemogenovatucel-t, Lerdelimumab, Belagenpumatucel-l, Fresolimumab, Bintrafusp alfa, Trabedersen and Luspatercept-aamt are potential drugs that could target TGFβ2. Finally, RT-qPCR and IF validation of two key DEARGs (Tln1 and TGFβ2) supported our bioinformatics analysis. Conclusions These findings suggest that Tln1 and TGFβ2 may play important roles in HF development through the regulation of anoikis and may serve as therapeutic targets for HF. Clinical trial number Not applicable.https://doi.org/10.1186/s41065-025-00532-2AnoikisMachine learningBioinformaticsHeart failureHub genes |
| spellingShingle | Lina Zhang Jianjun Gu Yan Jiang Juan Xue Ye Zhu Identification of anoikis-related genes in heart failure: bioinformatics and experimental validation Hereditas Anoikis Machine learning Bioinformatics Heart failure Hub genes |
| title | Identification of anoikis-related genes in heart failure: bioinformatics and experimental validation |
| title_full | Identification of anoikis-related genes in heart failure: bioinformatics and experimental validation |
| title_fullStr | Identification of anoikis-related genes in heart failure: bioinformatics and experimental validation |
| title_full_unstemmed | Identification of anoikis-related genes in heart failure: bioinformatics and experimental validation |
| title_short | Identification of anoikis-related genes in heart failure: bioinformatics and experimental validation |
| title_sort | identification of anoikis related genes in heart failure bioinformatics and experimental validation |
| topic | Anoikis Machine learning Bioinformatics Heart failure Hub genes |
| url | https://doi.org/10.1186/s41065-025-00532-2 |
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