The Potential Mechanism of Curcumin in Treating Oral Squamous Cell Carcinoma Based on Integrated Bioinformatic Analysis.

IF 2.6 4区 生物学 Q3 BIOCHEMISTRY & MOLECULAR BIOLOGY
International Journal of Genomics Pub Date : 2023-10-14 eCollection Date: 2023-01-01 DOI:10.1155/2023/8860321
Wu Siyuan, Lv Xiaozhi, Wu Jialin, Haigang Wei, Shiwei Liu, Chen Zou, Jing Song, Li Xia, Ai Yilong
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引用次数: 0

Abstract

Aims: This study explores the effects of curcumin as a therapeutic agent against oral squamous cell carcinoma (OSCC).

Methods: We acquired the targets of curcumin from three digital databases, including the Comparative Toxicogenomics Database, Search Tool for Interactions of Chemicals, and SwissTargetPrediction. Then, we identified the differentially expressed genes (DEGs) and the weighted gene coexpression network analysis-based key modules using the expression profiles of GSE23558 to acquire the OSCC-related genes. Additionally, the GeneCards and Online Mendelian Inheritance in Man databases were also used to identify the OSCC-related genes. Finally, curcumin-OSCC interaction genes were obtained by overlapping curcumin targets and OSCC-related genes. The enrichment analysis was performed by the ClusterProfiler algorithm and Metascape, respectively. Then, a protein-protein interaction network was created, and the maximal clique centrality algorithm was used to identify the top 10 hub genes. Besides, we examined the expression levels of hub genes in OSCC using The Cancer Genome Atlas database.

Results: 927 DEGs were identified, including 308 upregulated ones and 619 downregulated ones. The cluster one-step network construction function of the WGCNA algorithm recognized a soft-thresholding power of 6, and 9083 genes were acquired. 2591 OSCC-related genes were obtained by overlapping the GSE23558-identified genes and the OSCC-related genes from disease target bases. Finally, we identified 70 candidate drug-disease interaction genes by overlapping the disease-related genes with the curcumin target. The enrichment analysis suggested that response to oxidative stress, epithelial cell proliferation, and AGE/RAGE pathway might involve in the effect of curcumin on OSCC. The topologic study identified the ten hub genes, including VEGFA, AKT1, TNF, HIF1A, EGFR, JUN, STAT3, MMP9, EGF, and MAPK3. A significant difference was observed in VEGFA, AKT1, TNF, HIF1A, EGFR, MMP9, EGF, and MAPK3 expression levels between head and neck squamous cell carcinoma and the normal controls. However, no significant difference was observed in JUN (P = 0.14) and STAT3 (P = 0.054).

Conclusion: This study provided an overview and basis for the potential mechanism of curcumin against OSCC. The following experiments should be performed to further understand the effectiveness and safety of curcumin in treating OSCC.

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基于生物信息学综合分析的姜黄素治疗口腔鳞状细胞癌的潜在机制。
目的:本研究探讨姜黄素作为口腔鳞状细胞癌(OSCC)治疗剂的作用。方法:我们从三个数字数据库中获得姜黄素的靶标,包括比较毒理学数据库、化学物质相互作用搜索工具和SwissTargetPrediction。然后,我们使用GSE23558的表达谱鉴定了差异表达基因(DEGs)和基于加权基因共表达网络分析的关键模块,以获得OSCC相关基因。此外,基因卡和人类在线孟德尔遗传数据库也被用于识别OSCC相关基因。最后,通过重叠姜黄素靶点和OSCC相关基因获得了姜黄素OSCC相互作用基因。富集分析分别通过ClusterProfiler算法和Metascape进行。然后,建立了蛋白质-蛋白质相互作用网络,并使用最大团中心性算法来识别前10个枢纽基因。此外,我们使用癌症基因组图谱数据库检测了OSCC中枢纽基因的表达水平。结果:共鉴定出927个DEG,其中上调的308个,下调的619个。WGCNA算法的聚类一步网络构建函数识别出6的软阈值幂,并获得9083个基因。通过将GSE23558鉴定的基因与来自疾病靶碱基的OSCC相关基因重叠,获得2591个OSCC相关的基因。最后,我们通过将疾病相关基因与姜黄素靶点重叠,确定了70个候选药物-疾病相互作用基因。富集分析表明,姜黄素对氧化应激、上皮细胞增殖和AGE/RAGE通路的反应可能与OSCC的作用有关。拓扑研究确定了10个枢纽基因,包括VEGFA、AKT1、TNF、HIF1A、EGFR、JUN、STAT3、MMP9、EGF和MAPK3。头颈部鳞状细胞癌和正常对照组之间的VEGFA、AKT1、TNF、HIF1A、EGFR、MMP9、EGF和MAPK3表达水平存在显著差异。结论:本研究为姜黄素对抗OSCC的潜在机制提供了综述和依据。应进行以下实验,以进一步了解姜黄素治疗OSCC的有效性和安全性。
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来源期刊
International Journal of Genomics
International Journal of Genomics BIOCHEMISTRY & MOLECULAR BIOLOGY-BIOTECHNOLOGY & APPLIED MICROBIOLOGY
CiteScore
5.40
自引率
0.00%
发文量
33
审稿时长
17 weeks
期刊介绍: International Journal of Genomics is a peer-reviewed, Open Access journal that publishes research articles as well as review articles in all areas of genome-scale analysis. Topics covered by the journal include, but are not limited to: bioinformatics, clinical genomics, disease genomics, epigenomics, evolutionary genomics, functional genomics, genome engineering, and synthetic genomics.
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