Xu Wei, Xu Jian, Wang Zhiqiang, Jiang Yuequan
Chongqing Key Laboratory of Translational Research for Cancer Metastasis and Individualized Treatment, Chongqing University Cancer Hospital, Chongqing 400030, China.
J Oncol. 2021 Dec 27;2021:8223263. doi: 10.1155/2021/8223263. eCollection 2021.
Esophageal cancer (ESCA) is one of the most aggressive malignancies globally with an undesirable five-year survival rate. Here, this study was conducted for determining specific functional genes linked with ESCA initiation and progression.
Gene expression profiling of ESCA was curated from TCGA (containing 160 ESCA and 11 nontumor specimens) and GSE38129 (30 paired ESCA and nontumor tissues) datasets. Differential expression analysis was conducted between ESCA and nontumor tissues with adjusted value <0.05 and |log2fold-change|>1. Weighted gene coexpression network analysis (WGCNA) was conducted for determining the ESCA-specific coexpression modules and genes. Thereafter, ESCA-specific differentially expressed genes (DEGs) were intersected. Functional enrichment analysis was then presented with clusterProfiler package. Protein-protein interaction was conducted, and hub genes were determined. Association of hub genes with pathological staging was evaluated, and survival analysis was presented among ESCA patients.
This study determined 91 ESCA-specific DEGs following intersection of DEGs and ESCA-specific genes in TCGA and GSE38129 datasets. They were remarkably linked to cell cycle progression and carcinogenic pathways like the p53 signaling pathway, cellular senescence, and apoptosis. Ten ESCA-specific hub genes were determined, containing , 1, 2, 20, 1, 5, 11, 20, 2, and 2. They were prominently associated with pathological staging. Among them, KIF11 upregulation was in relation to undesirable prognosis of ESCA patients.
Collectively, we determined ESCA-specific coexpression modules and hub genes, which offered the foundation for future research concerning the mechanistic basis of ESCA.
食管癌（ESCA）是全球最具侵袭性的恶性肿瘤之一，五年生存率不理想。在此，进行本研究以确定与ESCA发生和进展相关的特定功能基因。
从TCGA（包含160个ESCA和11个非肿瘤标本）和GSE38129（30对ESCA和非肿瘤组织）数据集中整理ESCA的基因表达谱。在ESCA和非肿瘤组织之间进行差异表达分析，调整后p值<0.05且|log2倍数变化|>1。进行加权基因共表达网络分析（WGCNA）以确定ESCA特异性共表达模块和基因。此后，对ESCA特异性差异表达基因（DEG）进行交集分析。然后用clusterProfiler软件包进行功能富集分析。进行蛋白质-蛋白质相互作用分析，并确定枢纽基因。评估枢纽基因与病理分期的相关性，并对ESCA患者进行生存分析。
本研究在TCGA和GSE38129数据集中对DEG和ESCA特异性基因进行交集分析后确定了91个ESCA特异性DEG。它们与细胞周期进程以及致癌途径如p53信号通路、细胞衰老和凋亡显著相关。确定了10个ESCA特异性枢纽基因，包括KIF11、CCNB1、CCNB2、CDC20、CDK1、CKS1B、CKS2、NEK2、TPX2和AURKA。它们与病理分期显著相关。其中，KIF11上调与ESCA患者的不良预后相关。
总体而言，我们确定了ESCA特异性共表达模块和枢纽基因，为未来关于ESCA机制基础的研究提供了基础。
