To investigate the expression of mRNA in esophageal cancer (ESCA) tissues and its potential and diagnostic and prognostic value by high-throughput sequencing data. Using the Cancer Genome Atlas Program (TCGA) database in USA by integrative bioinformatics analysis methods, the gene expression profiles and clinical data of 173 patients with ECSA were collected. The mRNA expression levels in ESCA tissue and para-cancerous tissue samples were analyzed using DESeq2, edgeR and limma to screen the differentially expressed genes (DEGs). DEGs-related protein network diagrams were drawn. GO and KEGG function enrichment analysis were performed and the hub genes were screened and the survival analysis of hub genes was analyzed. Genes related to the prognosis of ESCA were selected and their prognostic value in ESCA was analyzed. Finally, the receiver operating characteristic curve was drawn to evaluate its diagnostic value. The results showed that using TCGA cancer data, a total of 620 up-regulated DEGs and 668 down-regulated DEGs with significant differential expression between ESCA and para-cancerous tissues were screened. DEGs were mainly involved in receptor complexes, ubiquitin ligase complexes, etc., playing GTPase activity, phospholipid binding, and other molecular functions, and participating in the regulation of intracellular substance transport, small molecule metabolism, and other biological processes. Protein functional enrichment analysis showed that these proteins were mainly enriched in the IL-17 signaling pathway, TNF signaling pathway, Toll-like receptor signaling pathway, Epstein-Barr virus infection, neutrophil extracellular trap formation, and other pathways involved in the formation and development process of ESCA. Survival analysis showed that the overall survival rate of ESCA patients with high expression of KIF4A, RAD51AP1, and CDKN3 was significantly shortened, and the difference was statistically significant (P<0.05). Furthermore, the areas under the curve (AUC) of KIF4A, RAD51AP1, and CDKN3 for diagnosing esophageal cancer were 0.956, 0.951 and 0.979, respectively, with sensitivities and specificities both exceeding 80%. Additionally, ROC results of the combined diagnostic model of these three genes showed an AUC of 0.979, with sensitivities and specificities of 0.914 and 1, respectively. This indicates that KIF4A, RAD51AP1 and CDKN3 have individual or combined auxiliary diagnostic value for ESCA. In conclusion, KIF4A, RAD51AP1 and CDKN3 have high diagnostic efficiency for ESCA, and their increased expression is closely related to the prognosis, suggesting that these three genes could be used as auxiliary diagnostic and prognostic factors for ESCA.
利用高通量测序数据探讨食道癌（ESCA）组织中的mRNA的表达及其潜在辅助诊断及预后评估价值。通过整合的生物信息学分析方法，收集美国癌症基因组图谱（TCGA）数据库中173例ESCA患者的基因表达谱及临床数据，利用DESeq2、edgeR和limma分析ESCA组织与癌旁组织样本中的mRNA表达水平的差异，筛选差异表达基因（DEGs）；绘制DEGs相关蛋白网络图，对其进行GO和KEGG功能富集分析并筛选枢纽基因，随后对枢纽基因行生存分析，选出与ESCA预后相关的基因并分析其在ESCA中的预后价值；最后绘制受试者工作特征曲线以评价其对ESCA的诊断价值。结果显示，共筛选出在ESCA与癌旁组织之间有显著差异表达的上调DEGs 620个，下调DEGs 668个。DEGs主要参与受体、泛素连接酶等复合物的形成，发挥GTP酶活性、磷脂结合等分子功能，参与调节细胞内物质转运、小分子代谢等生物过程。蛋白功能富集分析显示，这些蛋白主要富集在IL-17信号通路、TNF信号通路、Toll样受体信号通路、EB病毒感染、以及中性粒细胞胞外陷阱形成等通路上，参与ESCA的形成和发展过程。生存分析显示，KIF4A、RAD51AP1和CDKN3高表达的ESCA患者总生存率明显缩短（P均<0.05）；同时，KIF4A、RAD51AP1和CDKN3诊断ESCA的曲线下面积分别为 0.956、0.951和 0.979，灵敏度和特异度均在80%以上；这三者的联合诊断模型ROC结果显示AUC达到0.979，灵敏度和特异度分别为0.914和1；表明KIF4A、RAD51AP1及CDKN3对ESCA具有单独或联合的辅助诊断价值。综上，KIF4A、RAD51AP1及CDKN3对ESCA具有较高的诊断效能，它们的表达升高与ESCA患者的预后可能密切相关，提示这3个基因有可能作为ESCA的辅助诊断和预后评估因子。.