Department of Thyroid and Breast Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China.
Department of Gastrointestinal Surgery, Zhongnan Hospital of Wuhan University, Wuhan, China.
J Endocrinol Invest. 2023 Aug;46(8):1633-1650. doi: 10.1007/s40618-023-02022-6. Epub 2023 Feb 1.
Anaplastic thyroid cancer (ATC) is a rare and lethal malignant cancer. In recent years, the application of molecular-driven targeted therapy and immunotherapy has markedly improved the prognosis of ATC. This study aimed to identify characteristic genes for ATC diagnosis and revealed the role of ATC characteristic genes in drug sensitivity and immune cell infiltration.
We downloaded ATC RNA-sequencing data from the GEO database. Following the combination and normalization of the dataset, we first divided the combined datasets into the training cohort and the validation cohort. We identified differentially expressed genes (DEGs) in ATC by differential expression analysis in the training cohort. We used two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) to identify ATC characteristic genes. The CIBERSORT algorithm was performed to calculate the abundance of various immune cells in ATC. Finally, we validated the expression of ATC characteristic genes by quantitative RT-PCR (RT-qPCR) in ATC cell lines and immunohistochemistry (IHC).
A total of 425 DEGs were identified in the training cohort, including 240 upregulated genes and 185 downregulated genes. Four ATC characteristic genes (ADM, PXDN, MMP1, and TFF3) were identified, and their diagnostic value was validated in the validation cohort (AUC in ROC analysis > 0.75). We established a practical gene expression-based nomogram to accurately predict the probability of ATC. We also found that ATC characteristic biomarkers are associated with the tumor immune microenvironment and drug sensitivity.
ADM, PXDN, MMP1, and TFF3 might serve as potential ATC diagnostic biomarkers and may be helpful for ATC molecular targeted therapy and immunotherapy.
间变性甲状腺癌（ATC）是一种罕见且致命的恶性肿瘤。近年来，分子驱动的靶向治疗和免疫治疗的应用显著改善了 ATC 的预后。本研究旨在鉴定用于 ATC 诊断的特征基因，并揭示 ATC 特征基因在药物敏感性和免疫细胞浸润中的作用。
我们从 GEO 数据库下载了 ATC 的 RNA-seq 数据。在对数据集进行组合和归一化后，我们首先将合并的数据集分为训练队列和验证队列。我们通过在训练队列中的差异表达分析鉴定 ATC 中的差异表达基因（DEGs）。我们使用两种机器学习算法，最小绝对收缩和选择算子（LASSO）和支持向量机递归特征消除（SVM-RFE）来识别 ATC 特征基因。使用 CIBERSORT 算法计算 ATC 中各种免疫细胞的丰度。最后，我们通过 ATC 细胞系的定量 RT-PCR（RT-qPCR）和免疫组织化学（IHC）验证 ATC 特征基因的表达。
在训练队列中鉴定出 425 个 DEGs，包括 240 个上调基因和 185 个下调基因。鉴定出 4 个 ATC 特征基因（ADM、PXDN、MMP1 和 TFF3），并在验证队列中验证了其诊断价值（ROC 分析中的 AUC>0.75）。我们建立了一个实用的基于基因表达的列线图，以准确预测 ATC 的概率。我们还发现 ATC 特征生物标志物与肿瘤免疫微环境和药物敏感性相关。
ADM、PXDN、MMP1 和 TFF3 可能作为潜在的 ATC 诊断生物标志物，有助于 ATC 的分子靶向治疗和免疫治疗。
