Diagnostic signature composed of seven genes in HIF-1 signaling pathway for preeclampsia
Abstract Purpose In this study, we explored the relationship of genes in HIF-1 signaling pathway with preeclampsia and establish a logistic regression model for diagnose preeclampsia using bioinformatics analysis. Method Two microarray datasets GSE75010 and GSE35574 were downloaded from the Gene Exp...
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| Format: | Article |
| Language: | English |
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BMC
2023-04-01
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| Series: | BMC Pregnancy and Childbirth |
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| Online Access: | https://doi.org/10.1186/s12884-023-05559-9 |
| _version_ | 1797849855116181504 |
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| author | Xun Yang Ling Yu Yiling Ding Mengyuan Yang |
| author_facet | Xun Yang Ling Yu Yiling Ding Mengyuan Yang |
| author_sort | Xun Yang |
| collection | DOAJ |
| description | Abstract Purpose In this study, we explored the relationship of genes in HIF-1 signaling pathway with preeclampsia and establish a logistic regression model for diagnose preeclampsia using bioinformatics analysis. Method Two microarray datasets GSE75010 and GSE35574 were downloaded from the Gene Expression Omnibus database, which was using for differential expression analysis. DEGs were performed the Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and Gene set enrichment analysis (GSEA). Then we performed unsupervised consensus clustering analysis using genes in HIF-1 signaling pathway, and clinical features and immune cell infiltration were compared between these clusters, as well as the least absolute shrinkage and selection operator (LASSO) method to screened out key genes to constructed logistic regression model, and receiver operating characteristic (ROC) curve was plotted to evaluate the accuracy of the model. Results 57 DEGs were identified, of which GO, KEGG and analysis GSEA showed DEGs were mostly involved in HIF-1 signaling pathway. Two subtypes were identified of preeclampsia and 7 genes in HIF1-signaling pathway were screened out to establish the logistic regression model for discrimination preeclampsia from controls, of which the AUC are 0.923 and 0.845 in training and validation datasets respectively. Conclusion Seven genes (including MKNK1, ARNT, FLT1, SERPINE1, ENO3, LDHA, BCL2) were screen out to build potential diagnostic model of preeclampsia. |
| first_indexed | 2024-04-09T18:50:56Z |
| format | Article |
| id | doaj.art-b76b2fc609ae4054a18d6ae2fd158f1e |
| institution | Directory Open Access Journal |
| issn | 1471-2393 |
| language | English |
| last_indexed | 2024-04-09T18:50:56Z |
| publishDate | 2023-04-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Pregnancy and Childbirth |
| spelling | doaj.art-b76b2fc609ae4054a18d6ae2fd158f1e2023-04-09T11:29:19ZengBMCBMC Pregnancy and Childbirth1471-23932023-04-012311810.1186/s12884-023-05559-9Diagnostic signature composed of seven genes in HIF-1 signaling pathway for preeclampsiaXun Yang0Ling Yu1Yiling Ding2Mengyuan Yang3Department of Obstetrics and Gynecology, The Second Xiangya Hospital, Central South UniversityDepartment of Obstetrics and Gynecology, The Second Xiangya Hospital, Central South UniversityDepartment of Obstetrics and Gynecology, The Second Xiangya Hospital, Central South UniversityDepartment of Obstetrics and Gynecology, The Second Xiangya Hospital, Central South UniversityAbstract Purpose In this study, we explored the relationship of genes in HIF-1 signaling pathway with preeclampsia and establish a logistic regression model for diagnose preeclampsia using bioinformatics analysis. Method Two microarray datasets GSE75010 and GSE35574 were downloaded from the Gene Expression Omnibus database, which was using for differential expression analysis. DEGs were performed the Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and Gene set enrichment analysis (GSEA). Then we performed unsupervised consensus clustering analysis using genes in HIF-1 signaling pathway, and clinical features and immune cell infiltration were compared between these clusters, as well as the least absolute shrinkage and selection operator (LASSO) method to screened out key genes to constructed logistic regression model, and receiver operating characteristic (ROC) curve was plotted to evaluate the accuracy of the model. Results 57 DEGs were identified, of which GO, KEGG and analysis GSEA showed DEGs were mostly involved in HIF-1 signaling pathway. Two subtypes were identified of preeclampsia and 7 genes in HIF1-signaling pathway were screened out to establish the logistic regression model for discrimination preeclampsia from controls, of which the AUC are 0.923 and 0.845 in training and validation datasets respectively. Conclusion Seven genes (including MKNK1, ARNT, FLT1, SERPINE1, ENO3, LDHA, BCL2) were screen out to build potential diagnostic model of preeclampsia.https://doi.org/10.1186/s12884-023-05559-9PlacentaMicroarrayIntegrated analysisConsensus clusteringLogistic regression modelImmune cell infiltration |
| spellingShingle | Xun Yang Ling Yu Yiling Ding Mengyuan Yang Diagnostic signature composed of seven genes in HIF-1 signaling pathway for preeclampsia BMC Pregnancy and Childbirth Placenta Microarray Integrated analysis Consensus clustering Logistic regression model Immune cell infiltration |
| title | Diagnostic signature composed of seven genes in HIF-1 signaling pathway for preeclampsia |
| title_full | Diagnostic signature composed of seven genes in HIF-1 signaling pathway for preeclampsia |
| title_fullStr | Diagnostic signature composed of seven genes in HIF-1 signaling pathway for preeclampsia |
| title_full_unstemmed | Diagnostic signature composed of seven genes in HIF-1 signaling pathway for preeclampsia |
| title_short | Diagnostic signature composed of seven genes in HIF-1 signaling pathway for preeclampsia |
| title_sort | diagnostic signature composed of seven genes in hif 1 signaling pathway for preeclampsia |
| topic | Placenta Microarray Integrated analysis Consensus clustering Logistic regression model Immune cell infiltration |
| url | https://doi.org/10.1186/s12884-023-05559-9 |
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