Multi-omics integration for the identification of key genes associated with pancreatic islet function in type 2 diabetes

IF 0.3 Q4 NUTRITION & DIETETICS

CAHIERS DE NUTRITION ET DE DIETETIQUE Pub Date : 2026-08-01 Epub Date: 2026-09-24 DOI:10.1016/j.cnd.2026.08.006

Viridiana Basaldúa-Maciel, Fernando Martínez-Esquivias, Juan Manuel Guzmán-Flores

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Multi-omics datasets were integrated using OmicsNet 2.0 to construct a protein-protein interaction network. Functional enrichment analyses were performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Hub genes were identified based on their connectivity within the interaction network. This study was conducted exclusively using publicly available, de-identified datasets. Therefore, approval from a Research Ethics Committee was not required according to institutional and applicable ethical guidelines.</div></div><div>Genes associated with T2D were retrieved from DisGeNET, MalaCards, and the Comparative Toxicogenomics Database. Transcriptomic data from human pancreatic islets were obtained from the Gene Expression Omnibus (GEO), while proteomic data were collected from the Omics Discovery Index. Multi-omics datasets were integrated using OmicsNet 2.0 to construct a protein-protein interaction network. Functional enrichment analyses were performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Hub genes were identified based on their connectivity within the interaction network. This study was conducted exclusively using publicly available, de-identified datasets. Therefore, approval from a Research Ethics Committee was not required according to institutional and applicable ethical guidelines.</div><div><h3>Results</h3><div>The integration of 3,260 genes, 6,918 mRNA, and 92 proteins revealed biological processes associated with metabolic regulation, cellular metabolism, and enzyme binding. Functional enrichment analyses highlighted mechanisms involved in mitochondrial metabolism, intracellular signaling, transcriptional regulation, and insulin secretion. Enriched pathways included Ras/MAPK signaling, basal transcription factors, gap junctions, and oxidative phosphorylation. Network analysis identified ten highly connected hub genes: <em>SRC</em>, <em>TP53</em>, <em>AKT1</em>, <em>CTNNB1</em>, <em>EP300</em>, <em>MAPK1</em>, <em>MAPK3</em>, <em>CREBBP</em>, <em>EGFR</em>, and <em>HSP90AA1</em>. These genes are associated with key biological functions involved in glucose uptake, insulin receptor signaling, energy metabolism, and pancreatic B-cell survival.</div></div><div>The integration of 3,260 genes, 6,918 mRNA, and 92 proteins revealed biological processes associated with metabolic regulation, cellular metabolism, and enzyme binding. Functional enrichment analyses highlighted mechanisms involved in mitochondrial metabolism, intracellular signaling, transcriptional regulation, and insulin secretion. Enriched pathways included Ras/MAPK signaling, basal transcription factors, gap junctions, and oxidative phosphorylation. Network analysis identified ten highly connected hub genes: <em>SRC</em>, <em>TP53</em>, <em>AKT1</em>, <em>CTNNB1</em>, <em>EP300</em>, <em>MAPK1</em>, <em>MAPK3</em>, <em>CREBBP</em>, <em>EGFR</em>, and <em>HSP90AA1</em>. These genes are associated with key biological functions involved in glucose uptake, insulin receptor signaling, energy metabolism, and pancreatic B-cell survival.</div><div><h3>Conclusion</h3><div>Multi-omics integration enabled the identification of hub genes and biological pathways potentially involved in the pathophysiology of T2D in pancreatic islets. These findings provide insights into molecular mechanisms underlying B-cell dysfunction and may contribute to the development of future precision medicine strategies aimed at preserving pancreatic islet function.</div></div><div>Multi-omics integration enabled the identification of hub genes and biological pathways potentially involved in the pathophysiology of T2D in pancreatic islets. 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引用次数: 0

Abstract

Objective

To identify key molecular alterations and hub genes associated with pancreatic islet function in type 2 diabetes (T2D) through a multi-omics integration approach.

Methods

Genes associated with T2D were retrieved from DisGeNET, MalaCards, and the Comparative Toxicogenomics Database. Transcriptomic data from human pancreatic islets were obtained from the Gene Expression Omnibus (GEO), while proteomic data were collected from the Omics Discovery Index. Multi-omics datasets were integrated using OmicsNet 2.0 to construct a protein-protein interaction network. Functional enrichment analyses were performed using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. Hub genes were identified based on their connectivity within the interaction network. This study was conducted exclusively using publicly available, de-identified datasets. Therefore, approval from a Research Ethics Committee was not required according to institutional and applicable ethical guidelines.

Results

The integration of 3,260 genes, 6,918 mRNA, and 92 proteins revealed biological processes associated with metabolic regulation, cellular metabolism, and enzyme binding. Functional enrichment analyses highlighted mechanisms involved in mitochondrial metabolism, intracellular signaling, transcriptional regulation, and insulin secretion. Enriched pathways included Ras/MAPK signaling, basal transcription factors, gap junctions, and oxidative phosphorylation. Network analysis identified ten highly connected hub genes: SRC, TP53, AKT1, CTNNB1, EP300, MAPK1, MAPK3, CREBBP, EGFR, and HSP90AA1. These genes are associated with key biological functions involved in glucose uptake, insulin receptor signaling, energy metabolism, and pancreatic B-cell survival.

Conclusion

Multi-omics integration enabled the identification of hub genes and biological pathways potentially involved in the pathophysiology of T2D in pancreatic islets. These findings provide insights into molecular mechanisms underlying B-cell dysfunction and may contribute to the development of future precision medicine strategies aimed at preserving pancreatic islet function.

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多组学整合鉴定与2型糖尿病胰岛功能相关的关键基因

目的通过多组学整合方法鉴定与2型糖尿病（T2D）患者胰岛功能相关的关键分子改变和枢纽基因。通过多组学整合方法鉴定与2型糖尿病（T2D）胰岛功能相关的关键分子改变和枢纽基因。方法从DisGeNET、MalaCards和比较毒物基因组学数据库中检索与T2D相关的基因。人胰岛的转录组学数据来自基因表达Omnibus (GEO)，蛋白质组学数据来自组学发现指数（Omics Discovery Index）。利用OmicsNet 2.0对多组学数据集进行整合，构建蛋白-蛋白相互作用网络。使用基因本体（GO）和京都基因与基因组百科全书（KEGG）途径进行功能富集分析。枢纽基因是根据它们在互作网络中的连通性来鉴定的。本研究仅使用公开可用的、去识别的数据集进行。因此，根据机构和适用的伦理准则，不需要研究伦理委员会的批准。与T2D相关的基因从DisGeNET、MalaCards和比较毒物基因组学数据库中检索。人胰岛的转录组学数据来自基因表达Omnibus (GEO)，蛋白质组学数据来自组学发现指数（Omics Discovery Index）。利用OmicsNet 2.0对多组学数据集进行整合，构建蛋白-蛋白相互作用网络。使用基因本体（GO）和京都基因与基因组百科全书（KEGG）途径进行功能富集分析。枢纽基因是根据它们在互作网络中的连通性来鉴定的。本研究仅使用公开可用的、去识别的数据集进行。因此，根据机构和适用的伦理准则，不需要研究伦理委员会的批准。结果3260个基因、6918个mRNA和92个蛋白的整合揭示了与代谢调节、细胞代谢和酶结合相关的生物学过程。功能富集分析强调了涉及线粒体代谢、细胞内信号、转录调节和胰岛素分泌的机制。富集的途径包括Ras/MAPK信号、基础转录因子、间隙连接和氧化磷酸化。网络分析鉴定出10个高度连接的枢纽基因：SRC、TP53、AKT1、CTNNB1、EP300、MAPK1、MAPK3、CREBBP、EGFR和HSP90AA1。这些基因与葡萄糖摄取、胰岛素受体信号传导、能量代谢和胰腺b细胞存活等关键生物学功能相关。3260个基因、6918个mRNA和92个蛋白的整合揭示了与代谢调节、细胞代谢和酶结合相关的生物过程。功能富集分析强调了涉及线粒体代谢、细胞内信号、转录调节和胰岛素分泌的机制。富集的途径包括Ras/MAPK信号、基础转录因子、间隙连接和氧化磷酸化。网络分析鉴定出10个高度连接的枢纽基因：SRC、TP53、AKT1、CTNNB1、EP300、MAPK1、MAPK3、CREBBP、EGFR和HSP90AA1。这些基因与葡萄糖摄取、胰岛素受体信号传导、能量代谢和胰腺b细胞存活等关键生物学功能相关。结论多组学整合可识别胰岛T2D病理生理中可能参与的枢纽基因和生物学途径。这些发现提供了对b细胞功能障碍的分子机制的见解，并可能有助于未来旨在保护胰岛功能的精准医学策略的发展。多组学整合能够识别可能参与胰岛T2D病理生理的枢纽基因和生物学途径。这些发现提供了对b细胞功能障碍的分子机制的见解，并可能有助于未来旨在保护胰岛功能的精准医学策略的发展。

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来源期刊

CAHIERS DE NUTRITION ET DE DIETETIQUE NUTRITION & DIETETICS-

CiteScore

0.60

自引率

33.30%

发文量

审稿时长

65 days

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