Department of General Surgery, The Third Affiliated Hospital of Anhui Medical University (The First People's Hospital of Hefei), Hefei, Anhui, China.
Digestive Endoscopy Department, Jiangsu Province Hospital, The First Affiliated Hospital with Nanjing Medical University, Nanjing, Jiangsu, China.
Front Endocrinol (Lausanne). 2023 Dec 8;14:1247709. doi: 10.3389/fendo.2023.1247709. eCollection 2023.
Thyroid carcinoma (THCA), the most common endocrine neoplasm, typically exhibits an indolent behavior. However, in some instances, lymph node metastasis (LNM) may occur in the early stages, with the underlying mechanisms not yet fully understood.
LNM potential was defined as the tumor's capability to metastasize to lymph nodes at an early stage, even when the tumor volume is small. We performed differential expression analysis using the 'Limma' R package and conducted enrichment analyses using the Metascape tool. Co-expression networks were established using the 'WGCNA' R package, with the soft threshold power determined by the 'pickSoftThreshold' algorithm. For unsupervised clustering, we utilized the 'ConsensusCluster Plus' R package. To determine the topological features and degree centralities of each node (protein) within the Protein-Protein Interaction (PPI) network, we used the CytoNCA plugin integrated with the Cytoscape tool. Immune cell infiltration was assessed using the Immune Cell Abundance Identifier (ImmuCellAI) database. We applied the Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine (SVM), and Random Forest (RF) algorithms individually, with the 'glmnet,' 'e1071,' and 'randomForest' R packages, respectively. Ridge regression was performed using the 'oncoPredict' algorithm, and all the predictions were based on data from the Genomics of Drug Sensitivity in Cancer (GDSC) database. To ascertain the protein expression levels and subcellular localization of genes, we consulted the Human Protein Atlas (HPA) database. Molecular docking was carried out using the mcule 1-click Docking server online. Experimental validation of gene and protein expression levels was conducted through Real-Time Quantitative PCR (RT-qPCR) and immunohistochemistry (IHC) assays.
Through WGCNA and PPI network analysis, we identified twelve hub genes as the most relevant to LNM potential from these two modules. These 12 hub genes displayed differential expression in THCA and exhibited significant correlations with the downregulation of neutrophil infiltration, as well as the upregulation of dendritic cell and macrophage infiltration, along with activation of the EMT pathway in THCA. We propose a novel molecular classification approach and provide an online web-based nomogram for evaluating the LNM potential of THCA (http://www.empowerstats.net/pmodel/?m=17617_LNM). Machine learning algorithms have identified ERBB3 as the most critical gene associated with LNM potential in THCA. ERBB3 exhibits high expression in patients with THCA who have experienced LNM or have advanced-stage disease. The differential methylation levels partially explain this differential expression of ERBB3. ROC analysis has identified ERBB3 as a diagnostic marker for THCA (AUC=0.89), THCA with high LNM potential (AUC=0.75), and lymph nodes with tumor metastasis (AUC=0.86). We have presented a comprehensive review of endocrine disruptor chemical (EDC) exposures, environmental toxins, and pharmacological agents that may potentially impact LNM potential. Molecular docking revealed a docking score of -10.1 kcal/mol for Lapatinib and ERBB3, indicating a strong binding affinity.
In conclusion, our study, utilizing bioinformatics analysis techniques, identified gene modules and hub genes influencing LNM potential in THCA patients. ERBB3 was identified as a key gene with therapeutic implications. We have also developed a novel molecular classification approach and a user-friendly web-based nomogram tool for assessing LNM potential. These findings pave the way for investigations into the mechanisms underlying differences in LNM potential and provide guidance for personalized clinical treatment plans.
甲状腺癌（THCA）是最常见的内分泌肿瘤，通常表现出惰性行为。然而，在某些情况下，淋巴结转移（LNM）可能在早期发生，其潜在机制尚不完全清楚。
LNM 潜能被定义为肿瘤在早期向淋巴结转移的能力，即使肿瘤体积较小。我们使用“Limma”R 包进行差异表达分析，并使用 Metascape 工具进行富集分析。使用“WGCNA”R 包建立共表达网络，通过“pickSoftThreshold”算法确定软阈值功率。对于无监督聚类，我们使用“ConsensusCluster Plus”R 包。为了确定蛋白质 - 蛋白质相互作用（PPI）网络中每个节点（蛋白质）的拓扑特征和度中心性，我们使用集成了 Cytoscape 工具的 CytoNCA 插件。使用 Immune Cell Abundance Identifier（ImmuCellAI）数据库评估免疫细胞浸润。我们分别应用最小绝对收缩和选择算子（LASSO）、支持向量机（SVM）和随机森林（RF）算法，分别使用“glmnet”、“e1071”和“randomForest”R 包。岭回归使用“oncoPredict”算法进行，所有预测均基于癌症药物敏感性基因组学（GDSC）数据库中的数据。为了确定基因的蛋白表达水平和亚细胞定位，我们参考了人类蛋白图谱（HPA）数据库。使用 mcule 1-click Docking 服务器在线进行分子对接。通过实时定量 PCR（RT-qPCR）和免疫组织化学（IHC）检测实验验证基因和蛋白表达水平。
通过 WGCNA 和 PPI 网络分析，我们从这两个模块中确定了 12 个与 LNM 潜能最相关的枢纽基因。这些 12 个枢纽基因在 THCA 中表现出差异表达，并与中性粒细胞浸润的下调、树突状细胞和巨噬细胞浸润的上调以及 EMT 途径的激活显著相关。我们提出了一种新的分子分类方法，并提供了一个用于评估 THCA 的 LNM 潜能的在线网络基础诺模图（http://www.empowerstats.net/pmodel/?m=17617_LNM）。机器学习算法已经确定 ERBB3 是与 THCA 的 LNM 潜能最相关的关键基因。在经历 LNM 或患有晚期疾病的 THCA 患者中，ERBB3 表达较高。差异甲基化水平部分解释了 ERBB3 的这种差异表达。ROC 分析已将 ERBB3 确定为 THCA（AUC=0.89）、具有高 LNM 潜能的 THCA（AUC=0.75）和具有肿瘤转移的淋巴结（AUC=0.86）的诊断标志物。我们对内分泌干扰化学物质（EDC）暴露、环境毒素和可能影响 LNM 潜能的药理学制剂进行了全面综述。分子对接显示 Lapatinib 和 ERBB3 的对接得分为-10.1 kcal/mol，表明具有很强的结合亲和力。
总之，我们的研究利用生物信息学分析技术，确定了影响 THCA 患者 LNM 潜能的基因模块和枢纽基因。确定 ERBB3 是具有治疗意义的关键基因。我们还开发了一种新的分子分类方法和用户友好的基于网络的诺模图工具，用于评估 LNM 潜能。这些发现为研究 LNM 潜能差异的潜在机制铺平了道路，并为个性化临床治疗方案提供了指导。
