Department of Otorhinolaryngology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China.
Department of Gynecology, The First Affiliated Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China.
Biomed Res Int. 2019 Aug 7;2019:7376034. doi: 10.1155/2019/7376034. eCollection 2019.
The mechanism and gene markers of head and neck squamous cell carcinoma (HNSCC), a common malignant tumor, have not yet been identified. The aim of this study was to identify the key genes and pathways associated with HNSCC and to further analyze its molecular mechanism and prognostic significance. In this study, the expression profile chip data GSE6631 from Gene Expression Omnibus (GEO) included paired HNSCC tumor and normal samples from 22 patients; the RNAseq tertiary dataset of HNSCC and corresponding clinical information from The Cancer Genome Atlas (TCGA) included biological information of 12 normal head and neck tissues and 111 HNSCC sample tissues. Differentially expressed genes (DEGs) were screened by R software, and the pathway enrichment analysis of DEGs was performed by DAVID, String, and Sytoscape software programs. Combining the GEO and the TCGA databases, we used bioinformatics technology to screen out 50 DEGs in HNSCC and enrich the biological functions and key pathways of HNSCC. Then we performed Gene Ontology (GO) enrichment analysis, the Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway analysis, protein-protein interaction (PPI) analysis, and survival analysis on these DEGs. Using CMap, we identified candidate small molecules that might reverse HNSCC gene expression. Finally, four most important small molecules that could provide more reliable biomarkers for early diagnosis and individualized control of HNSCC were identified.
头颈部鳞状细胞癌(HNSCC)是一种常见的恶性肿瘤,其发生机制和基因标志物尚未确定。本研究旨在鉴定与 HNSCC 相关的关键基因和通路,并进一步分析其分子机制和预后意义。本研究中,从基因表达综合数据库(GEO)中筛选出了包含 22 例配对 HNSCC 肿瘤和正常样本的表达谱芯片数据 GSE6631;从癌症基因组图谱(TCGA)中筛选出了包含 12 例正常头颈部组织和 111 例 HNSCC 样本组织的 HNSCC 的 RNAseq 三级数据集和相应的临床信息。通过 R 软件筛选差异表达基因(DEGs),并通过 DAVID、String 和 Sytoscape 软件程序对 DEGs 进行通路富集分析。结合 GEO 和 TCGA 数据库,我们运用生物信息学技术筛选出 HNSCC 中的 50 个 DEG,并对 HNSCC 的生物学功能和关键通路进行富集。然后对这些 DEGs 进行基因本体(GO)富集分析、京都基因与基因组百科全书(KEGG)信号通路分析、蛋白质-蛋白质相互作用(PPI)分析和生存分析。利用 CMap,我们鉴定出可能逆转 HNSCC 基因表达的候选小分子。最后,确定了 4 种最重要的小分子,它们可能为 HNSCC 的早期诊断和个体化治疗提供更可靠的生物标志物。