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基于生物信息学方法探讨基质金属蛋白酶-13通过肿瘤坏死因子信号通路对舌鳞状细胞癌恶性生物学行为的影响。

Exploring the effects of matrix metalloproteinase-13 on the malignant biological behavior of tongue squamous cell carcinoma via the TNF signaling pathway based on bioinformatics methods.

作者信息

Lu Junqin, Zhu Yeqian, Zhang Jie, Cao Ningning

机构信息

Department of Stomatology, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, China.

出版信息

Transl Cancer Res. 2024 Jul 31;13(7):3814-3825. doi: 10.21037/tcr-24-1016. Epub 2024 Jul 26.

DOI:10.21037/tcr-24-1016
PMID:39145072
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11319986/
Abstract

BACKGROUND

Identification of the etiology, molecular mechanisms, and carcinogenic pathways of tongue squamous cell carcinoma (TSCC) is crucial for developing new diagnostic and therapeutic strategies. This study used bioinformatics methods to identify key genes in TSCC and explored the potential functions and pathway mechanisms related to the malignant biological behavior of TSCC.

METHODS

Gene chip data sets (i.e., GSE13601 and GSE34106) containing the data of both TSCC patients and normal control subjects were selected from the Gene Expression Omnibus (GEO) database. Using a gene expression analysis tool (GEO2R) of the GEO database, the differentially expressed genes (DEGs) were identified using the following criteria: |log fold change| >1, and P<0.05. The GEO2R tool was also used to select the upregulated DEGs in the chip candidates based on a P value <0.05. A Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, Gene Ontology (GO) function analysis, and a protein-protein interaction (PPI) network analysis were then conducted. The results were displayed using R language packages, including volcano plots, Venn diagrams, heatmaps, and enriched pathway bubble charts. Genes from the MalaCards database were compared with the candidate genes, and a thorough review of the literature was conducted to determine the clinical significance of these genes. Finally, feature gene-directed chemical drugs or targeted drugs were predicted using the Comparative Toxicogenomics Database (CTD).

RESULTS

In total, 767 upregulated DEGs were identified from GSE13601 and 695 from GSE34106. By intersecting the upregulated DEGs from both data sets using a Venn diagram, 100 DEGs related to TSCC were identified. The enrichment analysis of the KEGG signaling pathways identified the majority of the pathways associated with the upregulated DEGs, including the Toll-like receptor signaling pathway, the extracellular matrix-receptor interaction, the tumor necrosis factor (TNF) signaling pathway, cytokine-cytokine receptor interaction, the chemokine signaling pathway, the interlukin-17 signaling pathway, and natural killer cell-mediated cytotoxicity. The PPI network and module analyses of the shared DEGs ultimately resulted in five clusters and 55 candidate genes. A further intersection analysis of the TSCC-related genes in the MalaCards database via a Venn diagram identified three important shared DEGs; that is, matrix metalloproteinase-1 (), , and . In the CTD, seven drugs related to were identified for treating tongue tumors.

CONCLUSIONS

This study identified key genes and signaling pathways involved in TSCC and thus extended understandings of the molecular mechanisms that underlie the development and progression of TSCC. Additionally, this study showed that may influence the malignant biological behavior of TSCC through the TNF signaling pathway. This finding could provide a theoretical basis for research into early differential diagnosis and targeted treatment.

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ff4/11319986/c3addda4d697/tcr-13-07-3814-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ff4/11319986/508e98a8f89c/tcr-13-07-3814-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ff4/11319986/2de64f70fee9/tcr-13-07-3814-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ff4/11319986/55b12b8a8994/tcr-13-07-3814-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ff4/11319986/16d88d4047fe/tcr-13-07-3814-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ff4/11319986/c3addda4d697/tcr-13-07-3814-f5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ff4/11319986/508e98a8f89c/tcr-13-07-3814-f1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ff4/11319986/2de64f70fee9/tcr-13-07-3814-f2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ff4/11319986/55b12b8a8994/tcr-13-07-3814-f3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ff4/11319986/16d88d4047fe/tcr-13-07-3814-f4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/4ff4/11319986/c3addda4d697/tcr-13-07-3814-f5.jpg
摘要

背景

确定舌鳞状细胞癌(TSCC)的病因、分子机制和致癌途径对于开发新的诊断和治疗策略至关重要。本研究采用生物信息学方法鉴定TSCC中的关键基因,并探讨与TSCC恶性生物学行为相关的潜在功能和通路机制。

方法

从基因表达综合数据库(GEO)中选择包含TSCC患者和正常对照受试者数据的基因芯片数据集(即GSE13601和GSE34106)。使用GEO数据库的基因表达分析工具(GEO2R),按照以下标准鉴定差异表达基因(DEG):|log倍数变化|>1,且P<0.05。还使用GEO2R工具基于P值<0.05在芯片候选基因中选择上调的DEG。然后进行京都基因与基因组百科全书(KEGG)通路分析、基因本体(GO)功能分析和蛋白质-蛋白质相互作用(PPI)网络分析。结果使用R语言包展示,包括火山图、维恩图、热图和富集通路气泡图。将来自MalaCards数据库的基因与候选基因进行比较,并对文献进行全面综述以确定这些基因的临床意义。最后,使用比较毒理基因组学数据库(CTD)预测特征基因导向的化学药物或靶向药物。

结果

从GSE13601中总共鉴定出767个上调的DEG,从GSE34106中鉴定出695个。通过使用维恩图对两个数据集中上调的DEG进行交叉分析,鉴定出100个与TSCC相关的DEG。KEGG信号通路的富集分析确定了与上调的DEG相关的大多数通路,包括Toll样受体信号通路、细胞外基质-受体相互作用、肿瘤坏死因子(TNF)信号通路、细胞因子-细胞因子受体相互作用、趋化因子信号通路、白细胞介素-17信号通路和自然杀伤细胞介导的细胞毒性。对共享DEG的PPI网络和模块分析最终产生了五个簇和55个候选基因。通过维恩图对MalaCards数据库中与TSCC相关的基因进行进一步交叉分析,确定了三个重要的共享DEG;即基质金属蛋白酶-1()、和。在CTD中,鉴定出七种与相关的用于治疗舌肿瘤的药物。

结论

本研究鉴定了参与TSCC的关键基因和信号通路,从而扩展了对TSCC发生和发展基础分子机制的理解。此外,本研究表明可能通过TNF信号通路影响TSCC的恶性生物学行为。这一发现可为早期鉴别诊断和靶向治疗的研究提供理论依据。

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