Wu Hao, Yang Liang, Weng Xiaokun
First School of Clinical Medicine, Gansu University of Chinese Medicine, Lanzhou, Gansu, China.
Department of Neurosurgery, Shanghai Jiao Tong University Affiliated Sixth People's Hospital South Campus, Shanghai, China.
Front Genet. 2025 May 20;16:1583202. doi: 10.3389/fgene.2025.1583202. eCollection 2025.
Esophageal squamous cell carcinoma (ESCC) does not have distinct and highly sensitive biomarkers, making its diagnosis difficult. Consequently, identifying dependable biomarkers is critical, as these indicators can facilitate accurate ESCC diagnosis and enable effective prognostic evaluation.
ESCC datasets (GSE29001, GSE20347, GSE45670, and GSE161533) were sourced from the GEO, and the Limma package identified differentially expressed genes (DEGs). To characterize co-expression network, weighted gene co-expression network analysis (WGCNA) was performed, allowing for the identification of relevant co-expression modules. To assess the biological pathways of intersecting genes, we performed pathway enrichment analysis using Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO). The Support Vector Machine Recursive Feature Elimination (SVM), along with Least Absolute Shrinkage and Selection Operator (LASSO) regression, was applied to identify clinical biomarkers. Finally, the differences of immune cell infiltration were also detected.
1,019 genes were derived by integrating DEGs with co-expressed module genes. KEGG and GO revealed a strong association between these genes and processes such as chemotaxis and IL-17 signaling pathways. Two hub genes ( and ) were selected through LASSO regression and SVM. Additionally, ROC curve analysis confirmed their potential for reliable diagnostic performance. Furthermore, differences in immune cell infiltration were observed.
Collectively, and emerged as promising candidate biomarkers, offering novel insights to enhance early detection and guide targeted treatment strategies for ESCC.
食管鳞状细胞癌（ESCC）没有独特且高度敏感的生物标志物，这使得其诊断困难。因此，识别可靠的生物标志物至关重要，因为这些指标有助于准确诊断ESCC并进行有效的预后评估。
ESCC数据集（GSE29001、GSE20347、GSE45670和GSE161533）来自基因表达综合数据库（GEO），Limma软件包用于识别差异表达基因（DEGs）。为了表征共表达网络，进行了加权基因共表达网络分析（WGCNA），以识别相关的共表达模块。为了评估交集基因的生物学途径，我们使用京都基因与基因组百科全书（KEGG）和基因本体论（GO）进行了途径富集分析。支持向量机递归特征消除（SVM）以及最小绝对收缩和选择算子（LASSO）回归被用于识别临床生物标志物。最后，还检测了免疫细胞浸润的差异。
通过将DEGs与共表达模块基因整合，得到了1019个基因。KEGG和GO分析表明这些基因与趋化性和IL-17信号通路等过程密切相关。通过LASSO回归和SVM选择了两个关键基因（和）。此外，受试者工作特征曲线（ROC）分析证实了它们具有可靠的诊断性能。此外，还观察到了免疫细胞浸润的差异。
总体而言，和成为了有前景的候选生物标志物，为加强ESCC的早期检测和指导靶向治疗策略提供了新的见解。
