Qin Meiling, Li Xinxin, Gong Xun, Hu Yuan, Tang Min
School of Life Sciences, Jiangsu University, Zhenjiang, 212013, Jiangsu, China.
Department of Otolaryngology Head and Neck Surgery, Affiliated People's Hospital of Jiangsu University, Zhenjiang, 212001, Jiangsu, China.
Sci Rep. 2025 May 6;15(1):15745. doi: 10.1038/s41598-025-99017-5.
Head and neck squamous cell carcinoma (HNSCC) is a highly aggressive malignancy with complex molecular underpinnings. Hodgkin lymphoma (HL), another distinct cancer type, shares several biological characteristics with HNSCC, particularly regarding immune system involvement. However, the molecular crosstalk between HNSCC and HL remains largely unexplored. This study aims to elucidate shared molecular mechanisms, identify potential diagnostic biomarkers, and uncover therapeutic targets through an integrative approach combining bioinformatics and machine learning techniques. Publicly available RNA sequencing datasets were utilized to identify differentially expressed genes (DEGs) in HNSCC, while weighted gene co-expression network analysis (WGCNA) was applied to uncover HL-associated gene modules. The intersection of HNSCC DEGs and HL-related modules was evaluated using protein-protein interaction (PPI) network analysis. Candidate hub genes were selected via machine learning algorithms, including LASSO regression, random forest, and support vector machine-recursive feature elimination (SVM-RFE). Prognostic and diagnostic values were assessed using survival analysis and ROC curves. Furthermore, scRNA-seq data were analyzed to assess gene expression in the tumor microenvironment, and drug sensitivity was evaluated to identify potential therapeutic agents. A total of 150 shared genes were identified at the intersection of HNSCC DEGs and HL-associated gene modules. PPI network analysis highlighted 16 candidate hub genes, among which IL6, CXCL13, and PLAU were prioritized through machine learning methods. Survival analysis revealed that high expression of CXCL13 and PLAU, and low expression of IL6, were significantly associated with poor prognosis in HNSCC patients. ROC curve analysis validated their diagnostic performance. Single-cell RNA-seq data confirmed the expression of these biomarkers in macrophages, epithelial cells, and fibroblasts within the tumor microenvironment. Drug sensitivity analysis identified Andrographolide, Rituximab, and Amiloride as potential therapeutic agents. This study identified IL6, CXCL13, and PLAU as critical biomarkers involved in immune regulation and tumor progression in both HNSCC and HL. These findings provide valuable insights into the shared molecular mechanisms and suggest novel therapeutic strategies for patients affected by these diseases.
头颈部鳞状细胞癌（HNSCC）是一种具有复杂分子基础的高度侵袭性恶性肿瘤。霍奇金淋巴瘤（HL）是另一种独特的癌症类型，与HNSCC具有若干生物学特征，尤其是在免疫系统参与方面。然而，HNSCC与HL之间的分子相互作用在很大程度上仍未得到探索。本研究旨在通过结合生物信息学和机器学习技术的综合方法，阐明共同的分子机制，识别潜在的诊断生物标志物，并揭示治疗靶点。利用公开可用的RNA测序数据集来识别HNSCC中差异表达的基因（DEG），同时应用加权基因共表达网络分析（WGCNA）来揭示与HL相关的基因模块。使用蛋白质-蛋白质相互作用（PPI）网络分析评估HNSCC DEG与HL相关模块的交集。通过机器学习算法，包括套索回归、随机森林和支持向量机递归特征消除（SVM-RFE），选择候选枢纽基因。使用生存分析和ROC曲线评估预后和诊断价值。此外，分析单细胞RNA测序数据以评估肿瘤微环境中的基因表达，并评估药物敏感性以识别潜在的治疗药物。在HNSCC DEG与HL相关基因模块的交集中共鉴定出150个共享基因。PPI网络分析突出了16个候选枢纽基因，其中白细胞介素6（IL6）、CXC趋化因子配体13（CXCL13）和纤溶酶原激活物（PLAU）通过机器学习方法被优先考虑。生存分析显示，CXCL13和PLAU的高表达以及IL6的低表达与HNSCC患者的不良预后显著相关。ROC曲线分析验证了它们的诊断性能。单细胞RNA测序数据证实了这些生物标志物在肿瘤微环境中的巨噬细胞、上皮细胞和成纤维细胞中的表达。药物敏感性分析确定穿心莲内酯、利妥昔单抗和阿米洛利为潜在的治疗药物。本研究确定IL6、CXCL13和PLAU是参与HNSCC和HL免疫调节和肿瘤进展的关键生物标志物。这些发现为共同的分子机制提供了有价值的见解，并为受这些疾病影响的患者提出了新的治疗策略。
