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基于生物信息学探讨肥胖基因与疾病发生的关联性
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Abstract:
方法：利用网络药理学和蛋白质互作网络可视化与富集分析技术，寻找肥胖基因及其互作小RNA与疾病的关联性。运用Disgenet、GeneCards、TTD、OMIM数据库寻找肥胖与糖尿病、乳腺癌的相关基因靶点，再结合Uniprot数据库进行基因去重、映射后得到肥胖和各疾病的关键基因数据。利用Venny在线数据处理平台获取肥胖与各疾病之间的关联基因。将关联基因利用STRING在线分析平台构建靶蛋白相互作用网络图并进行拓扑学分析，并将结果运用Cytoscape软件进一步筛选获得各疾病与肥胖之间具有强关联性的核心靶点基因。将所得核心靶点基因利用DAVID在线数据库和微生信在线分析平台进行GO和KEGG富集分析。结果：肥胖–乳腺癌交集基因460个，肥胖–糖尿病交集基因607个，肥胖–乳腺癌核心基因23个，肥胖–糖尿病核心基因24个。在此基础上预测与肥胖疾病基因相互作用的miRNA，共获得了肥胖与乳腺癌相关核心基因或作miRNA138个；肥胖与糖尿病相关核心基因或作miRNA144个。本研究运用生物信息学方法，对肥胖与糖尿病及乳腺癌之间的关系和潜在作用机制进行研究，再通过对miRNA的研究，为我们了解与肥胖相关的并发症的机制提供了新的视角，也为我们探索和改进治疗方法提供了新思路。
Methods: Network pharmacology and protein-protein interaction network visualization and enrichment analysis techniques were used to find the association between obesity genes and their interaction small RNAs and diseases. Disgenet, GeneCards, TTD, and OMIM databases were used to search for gene targets related to obesity, diabetes and breast cancer, and then combined with Uniprot databases for gene deduplication and mapping, the key gene data of obesity and various diseases were obtained. The Venny online data processing platform was used to obtain the associated genes between obesity and various diseases. The associated genes were constructed by using the STRING online analysis platform to construct the target protein interaction network diagram and topoological analysis, and the results were further screened by Cytoscape software to obtain the core target genes with strong associations between various diseases and obesity. The obtained core target genes were enriched by GO and KEGG using the DAVID online database and the online analysis platform of microbiosis. Results: There were 460 obesity-breast cancer intersection genes, 607 obesity-diabetes intersection genes, 23 obesity-breast cancer core genes, and 24 obesity-diabetes core genes. On this basis, a total of 138 core genes or miRNAs related to obesity and breast cancer were obtained. There are 144 core genes or miRNAs related to obesity and diabetes. This study uses bioinformatics methods to study the relationship and potential mechanism of obesity with diabetes and breast cancer, and then provides a new perspective for us to understand the mechanism of obesity-related complications through the study of miRNAs, and also provides new ideas for us to explore and improve treatment methods.
| [1] | Rosen, E.D. and Spiegelman, B.M. (2000) Molecular Regulation of Adipogenesis. Annual Review of Cell and Developmental Biology, 16, 145-171. https://doi.org/10.1146/annurev.cellbio.16.1.145 |
| [2] | 拓嘉怡, 项永兵. 癌症流行现况病因以及营养流行病学的研究进展[J]. 肿瘤, 2023, 43(4): 359-366. |
| [3] | 燕声. 年轻时肥胖, 癌症风险高[N]. 保健时报, 2023-11-16(004). |
| [4] | K？nner, A.C. and Brüning, J.C. (2012) Selective Insulin and Leptin Resistance in Metabolic Disorders. Cell Metabolism, 16, 144-152. https://doi.org/10.1016/j.cmet.2012.07.004 |
| [5] | Schwartz, B. and Yehuda-Shnaidman, E. (2014) Putative Role of Adipose Tissue in Growth and Metabolism of Colon Cancer Cells. Frontiers in Oncology, 4, Article 164. https://doi.org/10.3389/fonc.2014.00164 |
| [6] | 李丹丹, 王东梅, 曹卫平, 翟凤婷. 基于生物信息学探讨肥胖与不孕症的关系[J]. 中国生育健康杂志, 2022, 33(5): 415-419. |
| [7] | 宁美微, 刘湘. FTO和ABCG2基因多态性与超重/肥胖和代谢参数的相关性研究[D]: [硕士学位论文]. 武汉: 湖北中医药大学, 2022. |
| [8] | Morton, L.M., Slager, S.L., Cerhan, J.R., Wang, S.S., Vajdic, C.M., Skibola, C.F., et al. (2014) Etiologic Heterogeneity among Non-Hodgkin Lymphoma Subtypes: The Interlymph Non-Hodgkin Lymphoma Subtypes Project. JNCI Monographs, 2014, 130-144. https://doi.org/10.1093/jncimonographs/lgu013 |
| [9] | Gargalionis, A. and Basdra, E. (2013) Insights in Micrornas Biology. Current Topics in Medicinal Chemistry, 13, 1493-1502. https://doi.org/10.2174/15680266113139990098 |
| [10] | Pi？ero, J., Ramírez-Anguita, J.M., Saüch-Pitarch, J., Ronzano, F., Centeno, E., Sanz, F., et al. (2019) The Disgenet Knowledge Platform for Disease Genomics: 2019 Update. Nucleic Acids Research, 48, D845-D855. https://doi.org/10.1093/nar/gkz1021 |
| [11] | Amberger, J.S. and Hamosh, A. (2017) Searching Online Mendelian Inheritance in Man (OMIM): A Knowledgebase of Human Genes and Genetic Phenotypes. Current Protocols in Bioinformatics, 58, 1.2.1-1.2.12. https://doi.org/10.1002/cpbi.27 |
| [12] | Bateman, A., Martin, M., Orchard, S., Magrane, M., Agivetova, R., Ahmad, S., et al. (2020) Uniprot: The Universal Protein Knowledgebase in 2021. Nucleic Acids Research, 49, D480-D489. https://doi.org/10.1093/nar/gkaa1100 |
| [13] | Zhou, Y., Zhang, Y., Zhao, D., Yu, X., Shen, X., Zhou, Y., et al. (2023) TTD: Therapeutic Target Database Describing Target Druggability Information. Nucleic Acids Research, 52, D1465-D1477. https://doi.org/10.1093/nar/gkad751 |
| [14] | Stelzer, G., Rosen, N., Plaschkes, I., Zimmerman, S., Twik, M., Fishilevich, S., et al. (2016) The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses. Current Protocols in Bioinformatics, 54, 1.30.1-1.30.33. https://doi.org/10.1002/cpbi.5 |
| [15] | Szklarczyk, D., Kirsch, R., Koutrouli, M., Nastou, K., Mehryary, F., Hachilif, R., et al. (2022) The STRING Database in 2023: Protein-Protein Association Networks and Functional Enrichment Analyses for Any Sequenced Genome of Interest. Nucleic Acids Research, 51, D638-D646. https://doi.org/10.1093/nar/gkac1000 |
| [16] | 党春晓, 刘鹏飞, 王鼎, 等. 基于生物信息学分析库欣综合征核心基因与互作miRNA[J]. 中南医学科学杂志, 2023, 51(1): 15-18. |
| [17] | Huang, H., Lin, Y., Cui, S., Huang, Y., Tang, Y., Xu, J., et al. (2021) Mirtarbase Update 2022: An Informative Resource for Experimentally Validated miRNA-Target Interactions. Nucleic Acids Research, 50, D222-D230. https://doi.org/10.1093/nar/gkab1079 |
| [18] | Tang, D., Chen, M., Huang, X., Zhang, G., Zeng, L., Zhang, G., et al. (2023) SRplot: A Free Online Platform for Data Visualization and Graphing. PLOS ONE, 18, e0294236. https://doi.org/10.1371/journal.pone.0294236 |
| [19] | 彭浩原, 郑伟, 许建, 等. 基于网络药理学和分子对接探讨桫椤叶治疗非小细胞肺癌的作用机制[J/OL]. 合成化学: 1-14. https://doi.org/10.15952/j.cnki.cjsc.1005-1511.23168, 2024-06-06. |
| [20] | 卿菁, 周衡, 陈莉, 赵伟, 吴青, 李珊珊. 香芹酚对2型糖尿病肝损伤的影响及机制[J]. 中华中医药学刊, 2024, 42(2): 131-135. |
| [21] | Jiao, W., Mi, S., Sang, Y., Jin, Q., Chitrakar, B., Wang, X., et al. (2022) Integrated Network Pharmacology and Cellular Assay for the Investigation of an Anti-Obesity Effect of 6-shogaol. Food Chemistry, 374, Article ID: 131755. https://doi.org/10.1016/j.foodchem.2021.131755 |
| [22] | Ghosh, S. (2013) An IL-6 Link between Obesity and Cancer. Frontiers in Bioscience, 5, 461-478. https://doi.org/10.2741/e628 |
| [23] | Tzanavari, T., Giannogonas, P. and Karalis, K.P. (2010) TNF-α and Obesity. In: Kollias, G. and Sfikakis, P.P., Eds., Current Directions in Autoimmunity, Karger, 145-156. https://doi.org/10.1159/000289203 |
| [24] | Ji, C. and Guo, X. (2019) The Clinical Potential of Circulating Micrornas in Obesity. Nature Reviews Endocrinology, 15, 731-743. https://doi.org/10.1038/s41574-019-0260-0 |
| [25] | Chen, R., Xin, G. and Zhang, X. (2019) Long Non-Coding RNA HCP5 Serves as a ceRNA Sponging miR-17-5p and miR-27a/b to Regulate the Pathogenesis of Childhood Obesity via the MAPK Signaling Pathway. Journal of Pediatric Endocrinology and Metabolism, 32, 1327-1339. https://doi.org/10.1515/jpem-2018-0432 |
