Identification of potential biomarkers and pathways associated with carotid atherosclerotic plaques in type 2 diabetes mellitus: A transcriptomics study
Type 2 diabetes mellitus (T2DM) affects the formation of carotid atherosclerotic plaques (CAPs) and patients are prone to plaque instability. It is crucial to clarify transcriptomics profiles and identify biomarkers related to the progression of T2DM complicated by CAPs. Ten human CAP samples were o...
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2022-09-01
|
| Series: | Frontiers in Endocrinology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fendo.2022.981100/full |
| _version_ | 1798033180768337920 |
|---|---|
| author | Tian Yu Tian Yu Baofeng Xu Baofeng Xu Meihua Bao Yuanyuan Gao Yuanyuan Gao Qiujuan Zhang Xuejiao Zhang Rui Liu Rui Liu |
| author_facet | Tian Yu Tian Yu Baofeng Xu Baofeng Xu Meihua Bao Yuanyuan Gao Yuanyuan Gao Qiujuan Zhang Xuejiao Zhang Rui Liu Rui Liu |
| author_sort | Tian Yu |
| collection | DOAJ |
| description | Type 2 diabetes mellitus (T2DM) affects the formation of carotid atherosclerotic plaques (CAPs) and patients are prone to plaque instability. It is crucial to clarify transcriptomics profiles and identify biomarkers related to the progression of T2DM complicated by CAPs. Ten human CAP samples were obtained, and whole transcriptome sequencing (RNA-seq) was performed. Samples were divided into two groups: diabetes mellitus (DM) versus non-DM groups and unstable versus stable groups. The Limma package in R was used to identify lncRNAs, circRNAs, and mRNAs. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, protein-protein interaction (PPI) network creation, and module generation were performed for differentially expressed mRNAs. Cytoscape was used to create a transcription factor (TF)-mRNA regulatory network, lncRNA/circRNA-mRNA co-expression network, and a competitive endogenous RNA (ceRNA) network. The GSE118481 dataset and RT-qPCR were used to verify potential mRNAs.The regulatory network was constructed based on the verified core genes and the relationships were extracted from the above network. In total, 180 differentially expressed lncRNAs, 343 circRNAs, and 1092 mRNAs were identified in the DM versus non-DM group; 240 differentially expressed lncRNAs, 390 circRNAs, and 677 mRNAs were identified in the unstable versus stable group. Five circRNAs, 14 lncRNAs, and 171 mRNAs that were common among all four groups changed in the same direction. GO/KEGG functional enrichment analysis showed that 171 mRNAs were mainly related to biological processes, such as immune responses, inflammatory responses, and cell adhesion. Five circRNAs, 14 lncRNAs, 46 miRNAs, and 54 mRNAs in the ceRNA network formed a regulatory relationship. C22orf34—hsa-miR-6785-5p—RAB37, hsacirc_013887—hsa-miR-6785-5p/hsa-miR-4763-5p/hsa-miR-30b-3p—RAB37, MIR4435-1HG—hsa-miR-30b-3p—RAB37, and GAS5—hsa-miR-30b-3p—RAB37 may be potential RNA regulatory pathways. Seven upregulated mRNAs were verified using the GSE118481 dataset and RT-qPCR. The regulatory network included seven mRNAs, five circRNAs, six lncRNAs, and 14 TFs. We propose five circRNAs (hsacirc_028744, hsacirc_037219, hsacirc_006308, hsacirc_013887, and hsacirc_045622), six lncRNAs (EPB41L4A-AS1, LINC00969, GAS5, MIR4435-1HG, MIR503HG, and SNHG16), and seven mRNAs (RAB37, CCR7, CD3D, TRAT1, VWF, ICAM2, and TMEM244) as potential biomarkers related to the progression of T2DM complicated with CAP. The constructed ceRNA network has important implications for potential RNA regulatory pathways. |
| first_indexed | 2024-04-11T20:26:11Z |
| format | Article |
| id | doaj.art-90c655013c7c4fe7b18349766751b9ae |
| institution | Directory Open Access Journal |
| issn | 1664-2392 |
| language | English |
| last_indexed | 2024-04-11T20:26:11Z |
| publishDate | 2022-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Endocrinology |
| spelling | doaj.art-90c655013c7c4fe7b18349766751b9ae2022-12-22T04:04:40ZengFrontiers Media S.A.Frontiers in Endocrinology1664-23922022-09-011310.3389/fendo.2022.981100981100Identification of potential biomarkers and pathways associated with carotid atherosclerotic plaques in type 2 diabetes mellitus: A transcriptomics studyTian Yu0Tian Yu1Baofeng Xu2Baofeng Xu3Meihua Bao4Yuanyuan Gao5Yuanyuan Gao6Qiujuan Zhang7Xuejiao Zhang8Rui Liu9Rui Liu10Department of Very Important People (VIP) Unit, China-Japan Union Hospital of Jilin University, Changchun, ChinaDepartment of Endocrinology, China-Japan Union Hospital of Jilin University, Changchun, ChinaDepartment of Stroke Center, First Hospital of Jilin University, Changchun, ChinaSchool of Stomatology, Changsha Medical University, Changsha, ChinaSchool of Stomatology, Changsha Medical University, Changsha, ChinaDepartment of Very Important People (VIP) Unit, China-Japan Union Hospital of Jilin University, Changchun, ChinaDepartment of Endocrinology, China-Japan Union Hospital of Jilin University, Changchun, ChinaDepartment of Very Important People (VIP) Unit, China-Japan Union Hospital of Jilin University, Changchun, ChinaDepartment of Endocrinology, China-Japan Union Hospital of Jilin University, Changchun, ChinaDepartment of Very Important People (VIP) Unit, China-Japan Union Hospital of Jilin University, Changchun, ChinaSchool of Stomatology, Changsha Medical University, Changsha, ChinaType 2 diabetes mellitus (T2DM) affects the formation of carotid atherosclerotic plaques (CAPs) and patients are prone to plaque instability. It is crucial to clarify transcriptomics profiles and identify biomarkers related to the progression of T2DM complicated by CAPs. Ten human CAP samples were obtained, and whole transcriptome sequencing (RNA-seq) was performed. Samples were divided into two groups: diabetes mellitus (DM) versus non-DM groups and unstable versus stable groups. The Limma package in R was used to identify lncRNAs, circRNAs, and mRNAs. Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses, protein-protein interaction (PPI) network creation, and module generation were performed for differentially expressed mRNAs. Cytoscape was used to create a transcription factor (TF)-mRNA regulatory network, lncRNA/circRNA-mRNA co-expression network, and a competitive endogenous RNA (ceRNA) network. The GSE118481 dataset and RT-qPCR were used to verify potential mRNAs.The regulatory network was constructed based on the verified core genes and the relationships were extracted from the above network. In total, 180 differentially expressed lncRNAs, 343 circRNAs, and 1092 mRNAs were identified in the DM versus non-DM group; 240 differentially expressed lncRNAs, 390 circRNAs, and 677 mRNAs were identified in the unstable versus stable group. Five circRNAs, 14 lncRNAs, and 171 mRNAs that were common among all four groups changed in the same direction. GO/KEGG functional enrichment analysis showed that 171 mRNAs were mainly related to biological processes, such as immune responses, inflammatory responses, and cell adhesion. Five circRNAs, 14 lncRNAs, 46 miRNAs, and 54 mRNAs in the ceRNA network formed a regulatory relationship. C22orf34—hsa-miR-6785-5p—RAB37, hsacirc_013887—hsa-miR-6785-5p/hsa-miR-4763-5p/hsa-miR-30b-3p—RAB37, MIR4435-1HG—hsa-miR-30b-3p—RAB37, and GAS5—hsa-miR-30b-3p—RAB37 may be potential RNA regulatory pathways. Seven upregulated mRNAs were verified using the GSE118481 dataset and RT-qPCR. The regulatory network included seven mRNAs, five circRNAs, six lncRNAs, and 14 TFs. We propose five circRNAs (hsacirc_028744, hsacirc_037219, hsacirc_006308, hsacirc_013887, and hsacirc_045622), six lncRNAs (EPB41L4A-AS1, LINC00969, GAS5, MIR4435-1HG, MIR503HG, and SNHG16), and seven mRNAs (RAB37, CCR7, CD3D, TRAT1, VWF, ICAM2, and TMEM244) as potential biomarkers related to the progression of T2DM complicated with CAP. The constructed ceRNA network has important implications for potential RNA regulatory pathways.https://www.frontiersin.org/articles/10.3389/fendo.2022.981100/fulltype 2 diabetes mellituscarotid atherosclerosistranscriptomebiomarkerpathwaysstable plaque |
| spellingShingle | Tian Yu Tian Yu Baofeng Xu Baofeng Xu Meihua Bao Yuanyuan Gao Yuanyuan Gao Qiujuan Zhang Xuejiao Zhang Rui Liu Rui Liu Identification of potential biomarkers and pathways associated with carotid atherosclerotic plaques in type 2 diabetes mellitus: A transcriptomics study Frontiers in Endocrinology type 2 diabetes mellitus carotid atherosclerosis transcriptome biomarker pathways stable plaque |
| title | Identification of potential biomarkers and pathways associated with carotid atherosclerotic plaques in type 2 diabetes mellitus: A transcriptomics study |
| title_full | Identification of potential biomarkers and pathways associated with carotid atherosclerotic plaques in type 2 diabetes mellitus: A transcriptomics study |
| title_fullStr | Identification of potential biomarkers and pathways associated with carotid atherosclerotic plaques in type 2 diabetes mellitus: A transcriptomics study |
| title_full_unstemmed | Identification of potential biomarkers and pathways associated with carotid atherosclerotic plaques in type 2 diabetes mellitus: A transcriptomics study |
| title_short | Identification of potential biomarkers and pathways associated with carotid atherosclerotic plaques in type 2 diabetes mellitus: A transcriptomics study |
| title_sort | identification of potential biomarkers and pathways associated with carotid atherosclerotic plaques in type 2 diabetes mellitus a transcriptomics study |
| topic | type 2 diabetes mellitus carotid atherosclerosis transcriptome biomarker pathways stable plaque |
| url | https://www.frontiersin.org/articles/10.3389/fendo.2022.981100/full |
| work_keys_str_mv | AT tianyu identificationofpotentialbiomarkersandpathwaysassociatedwithcarotidatheroscleroticplaquesintype2diabetesmellitusatranscriptomicsstudy AT tianyu identificationofpotentialbiomarkersandpathwaysassociatedwithcarotidatheroscleroticplaquesintype2diabetesmellitusatranscriptomicsstudy AT baofengxu identificationofpotentialbiomarkersandpathwaysassociatedwithcarotidatheroscleroticplaquesintype2diabetesmellitusatranscriptomicsstudy AT baofengxu identificationofpotentialbiomarkersandpathwaysassociatedwithcarotidatheroscleroticplaquesintype2diabetesmellitusatranscriptomicsstudy AT meihuabao identificationofpotentialbiomarkersandpathwaysassociatedwithcarotidatheroscleroticplaquesintype2diabetesmellitusatranscriptomicsstudy AT yuanyuangao identificationofpotentialbiomarkersandpathwaysassociatedwithcarotidatheroscleroticplaquesintype2diabetesmellitusatranscriptomicsstudy AT yuanyuangao identificationofpotentialbiomarkersandpathwaysassociatedwithcarotidatheroscleroticplaquesintype2diabetesmellitusatranscriptomicsstudy AT qiujuanzhang identificationofpotentialbiomarkersandpathwaysassociatedwithcarotidatheroscleroticplaquesintype2diabetesmellitusatranscriptomicsstudy AT xuejiaozhang identificationofpotentialbiomarkersandpathwaysassociatedwithcarotidatheroscleroticplaquesintype2diabetesmellitusatranscriptomicsstudy AT ruiliu identificationofpotentialbiomarkersandpathwaysassociatedwithcarotidatheroscleroticplaquesintype2diabetesmellitusatranscriptomicsstudy AT ruiliu identificationofpotentialbiomarkersandpathwaysassociatedwithcarotidatheroscleroticplaquesintype2diabetesmellitusatranscriptomicsstudy |