Objectives: Laryngeal cancer (LC) is a globally prevalent and highly lethal tumor. Despite extensive efforts, the underlying mechanisms of LC remain inadequately understood. This study aims to conduct an innovative bioinformatic analysis to identify hub genes that could potentially serve as biomarkers or therapeutic targets in LC.
Methods: We acquired a dataset consisting of 117 LC patient samples, 16 746 LC gene RNA sequencing data points, and 9 clinical features from the Cancer Genome Atlas (TCGA) database in the United States. We employed weighted gene co-expression network analysis (WGCNA) to construct multiple co-expression gene modules. Subsequently, we assessed the correlations between these co-expression modules and clinical features to validate their associations. We also explored the interplay between modules to identify pivotal genes within disease pathways. Finally, we used the Kaplan-Meier plotter to validate the correlation between enriched genes and LC prognosis.
Results: WGCNA analysis led to the creation of a total of 16 co-expression gene modules related to LC. Four of these modules (designated as the yellow, magenta, black, and brown modules) exhibited significant correlations with 3 clinical features: The age of initial pathological diagnosis, cancer status, and pathological N stage. Specifically, the yellow and magenta gene modules displayed negative correlations with the age of pathological diagnosis (r=-0.23, P<0.05; r=-0.33, P<0.05), while the black and brown gene modules demonstrated negative associations with cancer status (r=-0.39, P<0.05; r=-0.50, P<0.05). The brown gene module displayed a positive correlation with pathological N stage. Gene Ontology (GO) enrichment analysis identified 77 items, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis identified 30 related signaling pathways, including the calcium signaling pathway, cytokine-cytokine receptor interaction, neuro active ligand-receptor interaction, and regulation of lipolysis in adipocytes, etc. Consequently, central genes within these modules that were significantly linked to the overall survival rate of LC patients were identified. Central genes included CHRNB4, FOXL2, KCNG1, LOC440173, ADAMTS15, BMP2, FAP, and KIAA1644.
Conclusions: This study, utilizing WGCNA and subsequent validation, pinpointed 8 genes with potential as gene biomarkers for LC. These findings offer valuable references for the clinical diagnosis, prognosis, and treatment of LC.
目的: 喉癌(laryngeal cancer，LC)是全球最常见的头颈部肿瘤之一，世界范围内的死亡率和发病率都很高。尽管付出了巨大的努力，但人们对喉癌发生恶性进展的机制仍然认识不足。本研究旨在通过深度生物信息学分析，寻找可作为喉癌生物标志物或治疗靶点的关键基因标志物。方法: 从美国肿瘤基因组图谱(the Cancer Genome Atlas，TCGA)数据库中收集117个喉癌患者样本，16 746个喉癌基因RNA测序数据和9个临床特征，采用加权基因共表达网络分析(weighted gene coexpression network analysis，WGCNA)构建多个共表达基因模块。对相关的共表达模块和临床特征进行研究，验证它们之间的相关性，并利用模块之间的关联来探索疾病发生通路中的关键基因，最后运用Kaplan-Meier plotter验证富集后的基因与喉癌预后的相关性。结果: 通过WGCNA，共构建出16个喉癌共表达基因模块，并发现其中的4个共表达模块(黄色、洋红、黑色、褐色共表达模块)与3个临床特征(初始病理诊断年龄、癌症状态和病理N分期)有相关性。其中，黄色和洋红基因共表达模块与病理诊断的年龄呈负相关(分别为r=-0.23，P<0.05；r=-0.33，P<0.05)；黑色和褐色基因共表达模块与癌症状态呈负相关(分别为r=-0.39，P<0.05；r=-0.50，P<0.05)。另外，褐色基因共表达模块与病理N分期呈现显著正相关。基因本体(gene ontology，GO)富集分析鉴定出77条相关通路，京都基因与基因组百科全书(Kyoto Encyclopedia of Genes and Genomes，KEGG)通路富集分析鉴定出30条相关信号通路，包括钙信号通路、细胞因子-细胞因子受体相互作用、神经活性配体-受体相互作用和脂肪细胞脂肪分解调控等。随后，我们确定了这些模块中的中枢基因。中枢基因与喉癌患者的总生存率显著相关，包括CHRNB4，FOXL2，KCNG1，LOC440173，ADAMTS15，BMP2，FAP和KIAA1644。结论: 通过WGCNA和验证，筛选出8个可作为喉癌潜在基因生物标志物的基因，为喉癌的临床诊断、预后和治疗提供参考。.
目的: 喉癌(laryngeal cancer，LC)是全球最常见的头颈部肿瘤之一，世界范围内的死亡率和发病率都很高。尽管付出了巨大的努力，但人们对喉癌发生恶性进展的机制仍然认识不足。本研究旨在通过深度生物信息学分析，寻找可作为喉癌生物标志物或治疗靶点的关键基因标志物。
方法: 从美国肿瘤基因组图谱(the Cancer Genome Atlas，TCGA)数据库中收集117个喉癌患者样本，16 746个喉癌基因RNA测序数据和9个临床特征，采用加权基因共表达网络分析(weighted gene coexpression network analysis，WGCNA)构建多个共表达基因模块。对相关的共表达模块和临床特征进行研究，验证它们之间的相关性，并利用模块之间的关联来探索疾病发生通路中的关键基因，最后运用Kaplan-Meier plotter验证富集后的基因与喉癌预后的相关性。
结果: 通过WGCNA，共构建出16个喉癌共表达基因模块，并发现其中的4个共表达模块(黄色、洋红、黑色、褐色共表达模块)与3个临床特征(初始病理诊断年龄、癌症状态和病理N分期)有相关性。其中，黄色和洋红基因共表达模块与病理诊断的年龄呈负相关(分别为r=-0.23， P<0.05；r=-0.33，P<0.05)；黑色和褐色基因共表达模块与癌症状态呈负相关(分别为r=-0.39，P<0.05；r=-0.50，P<0.05)。另外，褐色基因共表达模块与病理N分期呈现显著正相关。基因本体(gene ontology，GO)富集分析鉴定出77条相关通路，京都基因与基因组百科全书(Kyoto Encyclopedia of Genes and Genomes，KEGG)通路富集分析鉴定出30条相关信号通路，包括钙信号通路、细胞因子-细胞因子受体相互作用、神经活性配体-受体相互作用和脂肪细胞脂肪分解调控等。随后，我们确定了这些模块中的中枢基因。中枢基因与喉癌患者的总生存率显著相关，包括CHRNB4，FOXL2，KCNG1，LOC440173，ADAMTS15，BMP2，FAP和KIAA1644。
结论: 通过WGCNA和验证，筛选出8个可作为喉癌潜在基因生物标志物的基因，为喉癌的临床诊断、预后和治疗提供参考。
Keywords: co-expression gene modules; gene biomarker; laryngeal cancer; weighted gene co-expression network analysis.