Fu Cong, Sun Lin, Zhang Lili, Zhou Tong, Bi Yanzhi
Department of Oncology, Changzhou Cancer (Fourth People's) Hospital, Changzhou, 213000, China.
Department of Oncology, Affiliated Hospital of Soochow University, Changzhou, 213000, China.
Discov Oncol. 2025 Jul 1;16(1):1189. doi: 10.1007/s12672-025-03047-4.
Nasopharyngeal carcinoma (NPC) is the most prevalent type of head- and -neck cancer, and its diagnosis and treatment are currently facing significant challenges. This study aimed to identify biomarkers associated with NPC by performing bioinformatic analysis on the GSE12452, GSE53819, and GSE64634 datasets from the GEO database. First, differentially expressed genes (DEGs) between NPC and normal nasopharyngeal tissues were screened. Then, these DEGs were subjected to RobustRank Aggregation analysis. Through Receiver Operating Characteristic (ROC) analysis and three machine-learning models, biomarkers such as DNAH5, ZMYND10, LRRC6, ARMC4, DNAI2, and DNALI1 were identified. Enrichment analysis was performed to uncover the common pathways of these biomarkers. Using the Comparative Toxicogenomics Database (CTD), target drugs for NPC were predicted based on these biomarkers. Additionally, immune infiltration analysis was carried out to study the relationship between these biomarkers and immune cells. A regulatory network was also constructed. It was found that these biomarkers are mainly involved in cytokine-cytokine receptor interaction, and some are part of common cancer-related signaling pathways. In addition, quantitative real time polymerase chain reaction (qRT-PCR) results showed that the expression levels of all biomarkers were significantly elevated in normal cell samples. DNAH5 and ZMYND10 were significantly higher in normal surrounding tissues. These findings provided potential support for the early clinical diagnosis and treatment of nasopharyngeal carcinoma patients.
鼻咽癌(NPC)是头颈部最常见的癌症类型,其诊断和治疗目前面临重大挑战。本研究旨在通过对来自基因表达综合数据库(GEO)的GSE12452、GSE53819和GSE64634数据集进行生物信息学分析,来识别与鼻咽癌相关的生物标志物。首先,筛选出鼻咽癌组织与正常鼻咽组织之间的差异表达基因(DEG)。然后,对这些DEG进行稳健排序聚合分析。通过受试者工作特征(ROC)分析和三种机器学习模型,确定了DNAH5、ZMYND10、LRRC6、ARMC4、DNAI2和DNALI1等生物标志物。进行富集分析以揭示这些生物标志物的共同途径。利用比较毒理基因组学数据库(CTD),基于这些生物标志物预测鼻咽癌的靶向药物。此外,还进行了免疫浸润分析,以研究这些生物标志物与免疫细胞之间的关系。还构建了一个调控网络。发现这些生物标志物主要参与细胞因子-细胞因子受体相互作用,有些是常见癌症相关信号通路的一部分。此外,定量实时聚合酶链反应(qRT-PCR)结果显示,所有生物标志物在正常细胞样本中的表达水平均显著升高。DNAH5和ZMYND10在正常周围组织中显著更高。这些发现为鼻咽癌患者的早期临床诊断和治疗提供了潜在支持。
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