Identification of key signaling pathways and novel computational drug target for oral cancer, metabolic disorders and periodontal disease

IF 3.5 Q3 Biochemistry, Genetics and Molecular Biology Journal of Genetic Engineering and Biotechnology Pub Date : 2024-10-22 DOI:10.1016/j.jgeb.2024.100431
Mohammad Khursheed Alam , Md. Faruk Hosen , Kiran Kumar Ganji , Kawsar Ahmed , Francis M. Bui
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Abstract

Aim

Due to conventional endocrinological methods, there is presently no shared work available, and no therapeutic options have been demonstrated in oral cancer (OC) and periodontal disease (PD), type 2 diabetes (T2D), and obese patients. The aim of this study is to determine the similar molecular pathways and potential therapeutic targets in PD, OC, T2D, and obesity that may be used to anticipate the progression of the disease.

Methods

Four Gene Expression Omnibus (GEO) microarray datasets (GSE29221, GSE15773, GSE16134, and GSE13601) are used for finding differentially expressed genes (DEGs) for T2D, obese, and PD patients with OC in order to explore comparable pathways and therapeutic medications. Gene ontology (GO) and pathway analysis were used to investigate the functional annotations of the genes. The hub genes were then identified using protein-protein interaction (PPI) networks, and the most significant PPI components were evaluated using a clustering approach.

Results

These three gene expression-based datasets yielded a total of seven common DEGs. According to the GO annotation, the majority of the DEGs were connected with the microtubule cytoskeleton structure involved in mitosis. The KEGG pathways revealed that the concordant DEGs are connected to the cell cycle and progesterone-mediated oocyte maturation. Based on topological analysis of the PPI network, major hub genes (CCNB1, BUB1, TTK, PLAT, and AHNAK) and notable modules were revealed. This work additionally identified the connection of TF genes and miRNAs with common DEGs, as well as TF activity.

Conclusion

Predictive drug analysis yielded concordant drug compounds involved with T2D, OC, PD, and obesity disorder, which might be beneficial for examining the diagnosis, treatment, and prognosis of metabolic disorders and Oral cancer.
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确定口腔癌、代谢紊乱和牙周病的关键信号通路和新型计算药物靶点
目的由于采用传统的内分泌学方法,目前还没有共同的研究成果,也没有针对口腔癌(OC)、牙周病(PD)、2 型糖尿病(T2D)和肥胖症患者的治疗方案。本研究的目的是确定 PD、OC、T2D 和肥胖症中相似的分子通路和潜在的治疗靶点,以用于预测疾病的进展。方法使用四个基因表达总库(GEO)微阵列数据集(GSE29221、GSE15773、GSE16134 和 GSE13601)寻找 T2D、肥胖症和 PD 患者与 OC 的差异表达基因(DEGs),以探索相似的通路和治疗药物。基因本体(GO)和通路分析用于研究基因的功能注释。然后利用蛋白质-蛋白质相互作用(PPI)网络确定了枢纽基因,并利用聚类方法评估了最重要的 PPI 成分。根据 GO 注释,大多数 DEGs 与参与有丝分裂的微管细胞骨架结构有关。KEGG 通路显示,一致的 DEGs 与细胞周期和孕酮介导的卵母细胞成熟有关。基于 PPI 网络的拓扑分析,发现了主要的枢纽基因(CCNB1、BUB1、TTK、PLAT 和 AHNAK)和显著的模块。结论预测性药物分析得出了与 T2D、OC、PD 和肥胖症相关的药物化合物,这可能有利于研究代谢紊乱和口腔癌的诊断、治疗和预后。
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来源期刊
Journal of Genetic Engineering and Biotechnology
Journal of Genetic Engineering and Biotechnology Biochemistry, Genetics and Molecular Biology-Biotechnology
CiteScore
5.70
自引率
5.70%
发文量
159
审稿时长
16 weeks
期刊介绍: Journal of genetic engineering and biotechnology is devoted to rapid publication of full-length research papers that leads to significant contribution in advancing knowledge in genetic engineering and biotechnology and provide novel perspectives in this research area. JGEB includes all major themes related to genetic engineering and recombinant DNA. The area of interest of JGEB includes but not restricted to: •Plant genetics •Animal genetics •Bacterial enzymes •Agricultural Biotechnology, •Biochemistry, •Biophysics, •Bioinformatics, •Environmental Biotechnology, •Industrial Biotechnology, •Microbial biotechnology, •Medical Biotechnology, •Bioenergy, Biosafety, •Biosecurity, •Bioethics, •GMOS, •Genomic, •Proteomic JGEB accepts
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