Identification of Metabolism-Related Proteins as Biomarkers of Insulin Resistance and Potential Mechanisms of m<sup>6</sup>A Modification
Background: Insulin resistance (IR) is a major contributing factor to the pathogenesis of metabolic syndrome and type 2 diabetes mellitus (T2D). Adipocyte metabolism is known to play a crucial role in IR. Therefore, the aims of this study were to identify metabolism-related proteins that could be us...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Nutrients |
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| Online Access: | https://www.mdpi.com/2072-6643/15/8/1839 |
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| author | Yan-Ling Li Long Li Yu-Hong Liu Li-Kun Hu Yu-Xiang Yan |
| author_facet | Yan-Ling Li Long Li Yu-Hong Liu Li-Kun Hu Yu-Xiang Yan |
| author_sort | Yan-Ling Li |
| collection | DOAJ |
| description | Background: Insulin resistance (IR) is a major contributing factor to the pathogenesis of metabolic syndrome and type 2 diabetes mellitus (T2D). Adipocyte metabolism is known to play a crucial role in IR. Therefore, the aims of this study were to identify metabolism-related proteins that could be used as potential biomarkers of IR and to investigate the role of N<sup>6</sup>-methyladenosine (m<sup>6</sup>A) modification in the pathogenesis of this condition. Methods: RNA-seq data on human adipose tissue were retrieved from the Gene Expression Omnibus database. The differentially expressed genes of metabolism-related proteins (MP-DEGs) were screened using protein annotation databases. Biological function and pathway annotations of the MP-DEGs were performed through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses. Key MP-DEGs were screened, and a protein–protein interaction (PPI) network was constructed using STRING, Cytoscape, MCODE, and CytoHubba. LASSO regression analysis was used to select primary hub genes, and their clinical performance was assessed using receiver operating characteristic (ROC) curves. The expression of key MP-DEGs and their relationship with m<sup>6</sup>A modification were further verified in adipose tissue samples collected from healthy individuals and patients with IR. Results: In total, 69 MP-DEGs were screened and annotated to be enriched in pathways related to hormone metabolism, low-density lipoprotein particle and carboxylic acid transmembrane transporter activity, insulin signaling, and AMPK signaling. The MP-DEG PPI network comprised 69 nodes and 72 edges, from which 10 hub genes (<i>FASN</i>, <i>GCK</i>, <i>FGR</i>, <i>FBP1</i>, <i>GYS2</i>, <i>PNPLA3</i>, <i>MOGAT1</i>, <i>SLC27A2</i>, <i>PNPLA3</i>, and <i>ELOVL6</i>) were identified. <i>FASN</i> was chosen as the key gene because it had the highest maximal clique centrality (MCC) score. <i>GCK</i>, <i>FBP1</i>, and <i>FGR</i> were selected as primary genes by LASSO analysis. According to the ROC curves, <i>GCK</i>, <i>FBP1</i>, <i>FGR</i>, and <i>FASN</i> could be used as potential biomarkers to detect IR with good sensitivity and accuracy (AUC = 0.80, 95% CI: 0.67–0.94; AUC = 0.86, 95% CI: 0.74–0.94; AUC = 0.83, 95% CI: 0.64–0.92; AUC = 0.78, 95% CI: 0.64–0.92). The expression of <i>FASN</i>, <i>GCK</i>, <i>FBP1</i>, and <i>FGR</i> was significantly correlated with that of <i>IGF2BP3</i>, <i>FTO</i>, <i>EIF3A</i>, <i>WTAP</i>, <i>METTL16</i>, and <i>LRPPRC</i> (<i>p</i> < 0.05). In validation clinical samples, the <i>FASN</i> was moderately effective for detecting IR (AUC = 0.78, 95% CI: 0.69–0.80), and its expression was positively correlated with the methylation levels of <i>FASN</i> (r = 0.359, <i>p</i> = 0.001). Conclusion: Metabolism-related proteins play critical roles in IR. Moreover, <i>FASN</i> and <i>GCK</i> are potential biomarkers of IR and may be involved in the development of T2D via their m<sup>6</sup>A modification. These findings offer reliable biomarkers for the early detection of T2D and promising therapeutic targets. |
| first_indexed | 2024-03-11T04:40:49Z |
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| id | doaj.art-e34a74e4c7a9442f93183f4232cb47aa |
| institution | Directory Open Access Journal |
| issn | 2072-6643 |
| language | English |
| last_indexed | 2024-03-11T04:40:49Z |
| publishDate | 2023-04-01 |
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| series | Nutrients |
| spelling | doaj.art-e34a74e4c7a9442f93183f4232cb47aa2023-11-17T20:45:11ZengMDPI AGNutrients2072-66432023-04-01158183910.3390/nu15081839Identification of Metabolism-Related Proteins as Biomarkers of Insulin Resistance and Potential Mechanisms of m<sup>6</sup>A ModificationYan-Ling Li0Long Li1Yu-Hong Liu2Li-Kun Hu3Yu-Xiang Yan4Department of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing 100069, ChinaDepartment of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing 100053, ChinaDepartment of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing 100069, ChinaDepartment of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing 100069, ChinaDepartment of Epidemiology and Biostatistics, School of Public Health, Capital Medical University, Beijing 100069, ChinaBackground: Insulin resistance (IR) is a major contributing factor to the pathogenesis of metabolic syndrome and type 2 diabetes mellitus (T2D). Adipocyte metabolism is known to play a crucial role in IR. Therefore, the aims of this study were to identify metabolism-related proteins that could be used as potential biomarkers of IR and to investigate the role of N<sup>6</sup>-methyladenosine (m<sup>6</sup>A) modification in the pathogenesis of this condition. Methods: RNA-seq data on human adipose tissue were retrieved from the Gene Expression Omnibus database. The differentially expressed genes of metabolism-related proteins (MP-DEGs) were screened using protein annotation databases. Biological function and pathway annotations of the MP-DEGs were performed through Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses. Key MP-DEGs were screened, and a protein–protein interaction (PPI) network was constructed using STRING, Cytoscape, MCODE, and CytoHubba. LASSO regression analysis was used to select primary hub genes, and their clinical performance was assessed using receiver operating characteristic (ROC) curves. The expression of key MP-DEGs and their relationship with m<sup>6</sup>A modification were further verified in adipose tissue samples collected from healthy individuals and patients with IR. Results: In total, 69 MP-DEGs were screened and annotated to be enriched in pathways related to hormone metabolism, low-density lipoprotein particle and carboxylic acid transmembrane transporter activity, insulin signaling, and AMPK signaling. The MP-DEG PPI network comprised 69 nodes and 72 edges, from which 10 hub genes (<i>FASN</i>, <i>GCK</i>, <i>FGR</i>, <i>FBP1</i>, <i>GYS2</i>, <i>PNPLA3</i>, <i>MOGAT1</i>, <i>SLC27A2</i>, <i>PNPLA3</i>, and <i>ELOVL6</i>) were identified. <i>FASN</i> was chosen as the key gene because it had the highest maximal clique centrality (MCC) score. <i>GCK</i>, <i>FBP1</i>, and <i>FGR</i> were selected as primary genes by LASSO analysis. According to the ROC curves, <i>GCK</i>, <i>FBP1</i>, <i>FGR</i>, and <i>FASN</i> could be used as potential biomarkers to detect IR with good sensitivity and accuracy (AUC = 0.80, 95% CI: 0.67–0.94; AUC = 0.86, 95% CI: 0.74–0.94; AUC = 0.83, 95% CI: 0.64–0.92; AUC = 0.78, 95% CI: 0.64–0.92). The expression of <i>FASN</i>, <i>GCK</i>, <i>FBP1</i>, and <i>FGR</i> was significantly correlated with that of <i>IGF2BP3</i>, <i>FTO</i>, <i>EIF3A</i>, <i>WTAP</i>, <i>METTL16</i>, and <i>LRPPRC</i> (<i>p</i> < 0.05). In validation clinical samples, the <i>FASN</i> was moderately effective for detecting IR (AUC = 0.78, 95% CI: 0.69–0.80), and its expression was positively correlated with the methylation levels of <i>FASN</i> (r = 0.359, <i>p</i> = 0.001). Conclusion: Metabolism-related proteins play critical roles in IR. Moreover, <i>FASN</i> and <i>GCK</i> are potential biomarkers of IR and may be involved in the development of T2D via their m<sup>6</sup>A modification. These findings offer reliable biomarkers for the early detection of T2D and promising therapeutic targets.https://www.mdpi.com/2072-6643/15/8/1839type 2 diabetesinsulin resistancem<sup>6</sup>A modificationbioinformatics analysis |
| spellingShingle | Yan-Ling Li Long Li Yu-Hong Liu Li-Kun Hu Yu-Xiang Yan Identification of Metabolism-Related Proteins as Biomarkers of Insulin Resistance and Potential Mechanisms of m<sup>6</sup>A Modification Nutrients type 2 diabetes insulin resistance m<sup>6</sup>A modification bioinformatics analysis |
| title | Identification of Metabolism-Related Proteins as Biomarkers of Insulin Resistance and Potential Mechanisms of m<sup>6</sup>A Modification |
| title_full | Identification of Metabolism-Related Proteins as Biomarkers of Insulin Resistance and Potential Mechanisms of m<sup>6</sup>A Modification |
| title_fullStr | Identification of Metabolism-Related Proteins as Biomarkers of Insulin Resistance and Potential Mechanisms of m<sup>6</sup>A Modification |
| title_full_unstemmed | Identification of Metabolism-Related Proteins as Biomarkers of Insulin Resistance and Potential Mechanisms of m<sup>6</sup>A Modification |
| title_short | Identification of Metabolism-Related Proteins as Biomarkers of Insulin Resistance and Potential Mechanisms of m<sup>6</sup>A Modification |
| title_sort | identification of metabolism related proteins as biomarkers of insulin resistance and potential mechanisms of m sup 6 sup a modification |
| topic | type 2 diabetes insulin resistance m<sup>6</sup>A modification bioinformatics analysis |
| url | https://www.mdpi.com/2072-6643/15/8/1839 |
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