Identification of hub genes and regulatory networks in histologically unstable carotid atherosclerotic plaque by bioinformatics analysis
Abstract Objective This study identified underlying genetic molecules associated with histologically unstable carotid atherosclerotic plaques through bioinformatics analysis that may be potential biomarkers and therapeutic targets. Methods Three transcriptome datasets (GSE41571, GSE120521 and E-MTAB...
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| Format: | Article |
| Language: | English |
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BMC
2022-06-01
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| Series: | BMC Medical Genomics |
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| Online Access: | https://doi.org/10.1186/s12920-022-01257-1 |
| _version_ | 1828335342349975552 |
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| author | Julong Guo Yachan Ning Zhixiang Su Lianrui Guo Yongquan Gu |
| author_facet | Julong Guo Yachan Ning Zhixiang Su Lianrui Guo Yongquan Gu |
| author_sort | Julong Guo |
| collection | DOAJ |
| description | Abstract Objective This study identified underlying genetic molecules associated with histologically unstable carotid atherosclerotic plaques through bioinformatics analysis that may be potential biomarkers and therapeutic targets. Methods Three transcriptome datasets (GSE41571, GSE120521 and E-MTAB-2055) and one non-coding RNA dataset (GSE111794) that met histological grouping criteria of unstable plaque were downloaded. The common differentially expressed genes (co-DEGs) of unstable plaques identified from three mRNA datasets were annotated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomics (KEGG). A protein–protein interaction (PPI) network was constructed to present the interaction between co-DEGs and screen out hub genes. MiRNet database and GSE111794 dataset were used to identify the miRNAs targeting hub genes. Associated transcription factors (TFs) and drugs were also predicted. These predicted results were used to construct miRNA/TFs-hub gene and drug-hub gene regulatory networks. Results A total of 105 co-DEGs were identified, including 42 up-regulated genes and 63 down-regulated genes, which were mainly enriched in collagen-containing extracellular matrix, focal adhesion, actin filament bundle, chemokine signaling pathway and regulates of actin cytoskeleton. Ten hub genes (up-regulated: HCK, C1QC, CD14, FCER1G, LCP1 and RAC2; down-regulated: TPM1, MYH10, PLS3 and FMOD) were screened. HCK and RAC2 were involved in chemokine signaling pathway, MYH10 and RAC2 were involved in regulation of actin cytoskeleton. We also predicted 12 miRNAs, top5 TFs and 25 drugs targeting hub genes. In the miRNA/TF-hub gene regulatory network, PLS3 was the most connected hub genes and was targeted by six miRNAs and all five screened TFs. In the drug-hub gene regulatory network, HCK was targeted by 20 drugs including 10 inhibitors. Conclusions We screened 10 hub genes and predicted miRNAs and TFs targeting them. These molecules may play a crucial role in the progression of histologically unstable carotid plaques and serve as potential biomarkers and therapeutic targets. |
| first_indexed | 2024-04-13T21:45:08Z |
| format | Article |
| id | doaj.art-271fc2d111d84d5fb754b37f586052ef |
| institution | Directory Open Access Journal |
| issn | 1755-8794 |
| language | English |
| last_indexed | 2024-04-13T21:45:08Z |
| publishDate | 2022-06-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Genomics |
| spelling | doaj.art-271fc2d111d84d5fb754b37f586052ef2022-12-22T02:28:37ZengBMCBMC Medical Genomics1755-87942022-06-0115111110.1186/s12920-022-01257-1Identification of hub genes and regulatory networks in histologically unstable carotid atherosclerotic plaque by bioinformatics analysisJulong Guo0Yachan Ning1Zhixiang Su2Lianrui Guo3Yongquan Gu4Department of Vascular Surgery, Xuanwu Hospital, Capital Medical UniversityDepartment of Intensive Care Medicine, Xuanwu Hospital, Capital Medical UniversityDepartment of Vascular Surgery, Xuanwu Hospital, Capital Medical UniversityDepartment of Vascular Surgery, Xuanwu Hospital, Capital Medical UniversityDepartment of Vascular Surgery, Xuanwu Hospital, Capital Medical UniversityAbstract Objective This study identified underlying genetic molecules associated with histologically unstable carotid atherosclerotic plaques through bioinformatics analysis that may be potential biomarkers and therapeutic targets. Methods Three transcriptome datasets (GSE41571, GSE120521 and E-MTAB-2055) and one non-coding RNA dataset (GSE111794) that met histological grouping criteria of unstable plaque were downloaded. The common differentially expressed genes (co-DEGs) of unstable plaques identified from three mRNA datasets were annotated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomics (KEGG). A protein–protein interaction (PPI) network was constructed to present the interaction between co-DEGs and screen out hub genes. MiRNet database and GSE111794 dataset were used to identify the miRNAs targeting hub genes. Associated transcription factors (TFs) and drugs were also predicted. These predicted results were used to construct miRNA/TFs-hub gene and drug-hub gene regulatory networks. Results A total of 105 co-DEGs were identified, including 42 up-regulated genes and 63 down-regulated genes, which were mainly enriched in collagen-containing extracellular matrix, focal adhesion, actin filament bundle, chemokine signaling pathway and regulates of actin cytoskeleton. Ten hub genes (up-regulated: HCK, C1QC, CD14, FCER1G, LCP1 and RAC2; down-regulated: TPM1, MYH10, PLS3 and FMOD) were screened. HCK and RAC2 were involved in chemokine signaling pathway, MYH10 and RAC2 were involved in regulation of actin cytoskeleton. We also predicted 12 miRNAs, top5 TFs and 25 drugs targeting hub genes. In the miRNA/TF-hub gene regulatory network, PLS3 was the most connected hub genes and was targeted by six miRNAs and all five screened TFs. In the drug-hub gene regulatory network, HCK was targeted by 20 drugs including 10 inhibitors. Conclusions We screened 10 hub genes and predicted miRNAs and TFs targeting them. These molecules may play a crucial role in the progression of histologically unstable carotid plaques and serve as potential biomarkers and therapeutic targets.https://doi.org/10.1186/s12920-022-01257-1AtherosclerosisUnstable carotid artery plaqueBioinformaticsPotential biomarkerTherapeutic target |
| spellingShingle | Julong Guo Yachan Ning Zhixiang Su Lianrui Guo Yongquan Gu Identification of hub genes and regulatory networks in histologically unstable carotid atherosclerotic plaque by bioinformatics analysis BMC Medical Genomics Atherosclerosis Unstable carotid artery plaque Bioinformatics Potential biomarker Therapeutic target |
| title | Identification of hub genes and regulatory networks in histologically unstable carotid atherosclerotic plaque by bioinformatics analysis |
| title_full | Identification of hub genes and regulatory networks in histologically unstable carotid atherosclerotic plaque by bioinformatics analysis |
| title_fullStr | Identification of hub genes and regulatory networks in histologically unstable carotid atherosclerotic plaque by bioinformatics analysis |
| title_full_unstemmed | Identification of hub genes and regulatory networks in histologically unstable carotid atherosclerotic plaque by bioinformatics analysis |
| title_short | Identification of hub genes and regulatory networks in histologically unstable carotid atherosclerotic plaque by bioinformatics analysis |
| title_sort | identification of hub genes and regulatory networks in histologically unstable carotid atherosclerotic plaque by bioinformatics analysis |
| topic | Atherosclerosis Unstable carotid artery plaque Bioinformatics Potential biomarker Therapeutic target |
| url | https://doi.org/10.1186/s12920-022-01257-1 |
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