目的:本研究的目的是检测有关遗传性牙龈纤维瘤病的未被发现的生物信息学信息,并从已发表的数据集中找到焦点。
方法:从GEO数据库收集包含HGF和健康组牙龈组织表达谱的两个已发表数据集。GSE4250用于基数分析,包括差异表达的基因分析,富集分析,层次聚类分析,和蛋白质-蛋白质相互作用网络。从蛋白质相互作用网络图获得关键基因。使用GSE58482进行验证。
结果:通过阵列分析表达谱,有785个基因(380个上调的基因,405个下调基因)在HGF牙龈组织和健康牙龈组织之间差异表达。KEGG和GO富集分析获得了候选途径。差异表达的基因与激活的途径如皮肤屏障途径和角质化包膜途径相关。抑制途径包括离子稳态途径,受体配体活性途径,和细胞群增殖途径。关键基因如F2R,通过外部验证证实TGM7和MMP13具有差异表达。
结论:通过生物信息学方法,我们发现了新的发现,包括几个途径和关键基因。这些发现值得今后的关注和研究。
OBJECTIVE: The objective of this study was to detect the undiscovered bioinformatics information about hereditary gingival fibromatosis and find focuses from published datasets.
METHODS: Two published datasets containing gingival tissue expression profiles of HGF and healthy groups were collected from GEO database. GSE4250 was utilized for cardinality analysis, including the differentially expressed gene analysis, enrichment analyses, hierarchical clustering analysis, and protein-protein interaction network. Key genes were obtained from the protein interaction network plot. GSE58482 was utilized for validation.
RESULTS: Analysis of the expression profiling by array, there were 785 genes (380 upregulated genes, 405 downregulated genes) expressed differentially between HGF gingival tissue and healthy gingival tissue. KEGG and GO enrichment analyses obtained candidate pathways. Differentially expressed genes were associated with activated pathways like skin barrier pathway and cornified envelope pathway. Repressed pathways included ion homeostasis pathway, receptor ligand activity pathway, and cell population proliferation pathway. Key genes such as F2R, TGM7, and MMP13 were confirmed with differential expression by external validation.
CONCLUSIONS: By bioinformatics approaches, we found new discoveries including several pathways and key genes. These discoveries deserve attention and research in the future.