Characterizing hub biomarkers for metabolic-induced endothelial dysfunction and unveiling their regulatory roles in EndMT through RNA sequencing and machine learning approaches
BackgroundMetabolic disorder and endothelial dysfunction (ED) are key events in the development and pathophysiology of atherosclerosis and are associated with an elevated risk of Cardiovascular disease (CVD). The pathophysiology remains incompletely understood.MethodsLeftover serum samples were coll...
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Frontiers Media S.A.
2025-05-01
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| Series: | Frontiers in Cardiovascular Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fcvm.2025.1585030/full |
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| author | Qi Sun Longchuan Xie He An Wei Chen Qirong Yang Peng Wang Yijun Tang Chunyan Peng Chunyan Peng |
| author_facet | Qi Sun Longchuan Xie He An Wei Chen Qirong Yang Peng Wang Yijun Tang Chunyan Peng Chunyan Peng |
| author_sort | Qi Sun |
| collection | DOAJ |
| description | BackgroundMetabolic disorder and endothelial dysfunction (ED) are key events in the development and pathophysiology of atherosclerosis and are associated with an elevated risk of Cardiovascular disease (CVD). The pathophysiology remains incompletely understood.MethodsLeftover serum samples were collected and stored at −20 °C until study. Serum specimens were mixed to obtain pooled high glucose serum (GLU group) (11.97 ± 2.09 mmol/L); pooled elevated low-density lipoprotein serum (LDL group) [3.465 (3.3275, 3.6425 mmol/L)]; pooled high triglycerides serum (1.15 ± 0.35 mmol/L) (TG group); Subsequently, Human umbilical vein endothelial cells (HUVECs) were exposed to culture media supplemented with these pooled serum or control serum for 72 h. Whole transcriptome sequencing was performed to characterize gene expression profiles and data were analyzed using GSEA, GO, KEGG. qPCR was used to validate the gene expression.ResultsA total of 306 mRNAs and 523 lncRNAs were identified as differentially expressed in the GLU group, 335 mRNAs and 471 lncRNAs in the LDL group, and 364 mRNAs and 562 lncRNAs in the TG group, compared to the control group. These genes are primarily involved in inflammation, lipid metabolism, and EndMT pathways. By integrating differentially expressed mRNA and curated EndMT-related gene sets from the KEGG, GO, and dbEMT2.0 databases, we identified 52 differentially expressed genes associated with EndMT under metabolic stress conditions. Utilizing machine learning techniques, we established an EndMT-associated gene diagnostic signature comprising CD36, ISG15, HSPB2, and IRS2 for the diagnosis of AS, which achieved an AUC of 0.997. The model was subsequently validated across three independent external cohorts (GSE43292, GSE28829, GSE163154), in which it consistently demonstrated strong diagnostic performance, with AUC values of 0.958, 0.808, and 0.884, respectively. The ceRNA networks associated with EndMT are constructed and related lncRNAs including LINC002381, VIM-AS1, and ELF-AS1 were significantly upregulated in peripheral blood samples.ConclusionsThis study identified novel biomarkers for ED. These findings may provide both a potential biomarker and therapeutic target for the prevention and treatment of atherosclerosis and CAD. |
| format | Article |
| id | doaj-art-942804ec4f944e9e83b02f8034ca59ed |
| institution | OA Journals |
| issn | 2297-055X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Cardiovascular Medicine |
| spelling | doaj-art-942804ec4f944e9e83b02f8034ca59ed2025-08-20T01:50:00ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2025-05-011210.3389/fcvm.2025.15850301585030Characterizing hub biomarkers for metabolic-induced endothelial dysfunction and unveiling their regulatory roles in EndMT through RNA sequencing and machine learning approachesQi Sun0Longchuan Xie1He An2Wei Chen3Qirong Yang4Peng Wang5Yijun Tang6Chunyan Peng7Chunyan Peng8Clinical Molecular Diagnostic Center, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, ChinaClinical Molecular Diagnostic Center, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, ChinaClinical Molecular Diagnostic Center, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, ChinaClinical Molecular Diagnostic Center, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, ChinaClinical Molecular Diagnostic Center, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, ChinaClinical Molecular Diagnostic Center, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, ChinaClinical Molecular Diagnostic Center, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, ChinaClinical Molecular Diagnostic Center, Taihe Hospital, Hubei University of Medicine, Shiyan, Hubei, ChinaHubei Key Laboratory of Embryonic Stem Cell Research, Hubei University of Medicine, Shiyan, Hubei, ChinaBackgroundMetabolic disorder and endothelial dysfunction (ED) are key events in the development and pathophysiology of atherosclerosis and are associated with an elevated risk of Cardiovascular disease (CVD). The pathophysiology remains incompletely understood.MethodsLeftover serum samples were collected and stored at −20 °C until study. Serum specimens were mixed to obtain pooled high glucose serum (GLU group) (11.97 ± 2.09 mmol/L); pooled elevated low-density lipoprotein serum (LDL group) [3.465 (3.3275, 3.6425 mmol/L)]; pooled high triglycerides serum (1.15 ± 0.35 mmol/L) (TG group); Subsequently, Human umbilical vein endothelial cells (HUVECs) were exposed to culture media supplemented with these pooled serum or control serum for 72 h. Whole transcriptome sequencing was performed to characterize gene expression profiles and data were analyzed using GSEA, GO, KEGG. qPCR was used to validate the gene expression.ResultsA total of 306 mRNAs and 523 lncRNAs were identified as differentially expressed in the GLU group, 335 mRNAs and 471 lncRNAs in the LDL group, and 364 mRNAs and 562 lncRNAs in the TG group, compared to the control group. These genes are primarily involved in inflammation, lipid metabolism, and EndMT pathways. By integrating differentially expressed mRNA and curated EndMT-related gene sets from the KEGG, GO, and dbEMT2.0 databases, we identified 52 differentially expressed genes associated with EndMT under metabolic stress conditions. Utilizing machine learning techniques, we established an EndMT-associated gene diagnostic signature comprising CD36, ISG15, HSPB2, and IRS2 for the diagnosis of AS, which achieved an AUC of 0.997. The model was subsequently validated across three independent external cohorts (GSE43292, GSE28829, GSE163154), in which it consistently demonstrated strong diagnostic performance, with AUC values of 0.958, 0.808, and 0.884, respectively. The ceRNA networks associated with EndMT are constructed and related lncRNAs including LINC002381, VIM-AS1, and ELF-AS1 were significantly upregulated in peripheral blood samples.ConclusionsThis study identified novel biomarkers for ED. These findings may provide both a potential biomarker and therapeutic target for the prevention and treatment of atherosclerosis and CAD.https://www.frontiersin.org/articles/10.3389/fcvm.2025.1585030/fullcardiovascular diseaseendothelial dysfunctionlipid metabolismEndMThub genesbioinformatics analysis |
| spellingShingle | Qi Sun Longchuan Xie He An Wei Chen Qirong Yang Peng Wang Yijun Tang Chunyan Peng Chunyan Peng Characterizing hub biomarkers for metabolic-induced endothelial dysfunction and unveiling their regulatory roles in EndMT through RNA sequencing and machine learning approaches Frontiers in Cardiovascular Medicine cardiovascular disease endothelial dysfunction lipid metabolism EndMT hub genes bioinformatics analysis |
| title | Characterizing hub biomarkers for metabolic-induced endothelial dysfunction and unveiling their regulatory roles in EndMT through RNA sequencing and machine learning approaches |
| title_full | Characterizing hub biomarkers for metabolic-induced endothelial dysfunction and unveiling their regulatory roles in EndMT through RNA sequencing and machine learning approaches |
| title_fullStr | Characterizing hub biomarkers for metabolic-induced endothelial dysfunction and unveiling their regulatory roles in EndMT through RNA sequencing and machine learning approaches |
| title_full_unstemmed | Characterizing hub biomarkers for metabolic-induced endothelial dysfunction and unveiling their regulatory roles in EndMT through RNA sequencing and machine learning approaches |
| title_short | Characterizing hub biomarkers for metabolic-induced endothelial dysfunction and unveiling their regulatory roles in EndMT through RNA sequencing and machine learning approaches |
| title_sort | characterizing hub biomarkers for metabolic induced endothelial dysfunction and unveiling their regulatory roles in endmt through rna sequencing and machine learning approaches |
| topic | cardiovascular disease endothelial dysfunction lipid metabolism EndMT hub genes bioinformatics analysis |
| url | https://www.frontiersin.org/articles/10.3389/fcvm.2025.1585030/full |
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