Pengping Li, Kexin Yin, Yuwei Xie, Ke Sun, Rongguo Li, Zhenyu Wang, Haigang Jin, Shaowen Wang, Yuqing Huang
Department of Thyroid & Breast Surgery, The First People's Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, Hangzhou, Zhejiang, China.
The First Affiliated Hospital of Anhui Medical University, Hefei, Anhui, China.
Front Immunol. 2025 Jan 13;15:1478904. doi: 10.3389/fimmu.2024.1478904. eCollection 2024.
While most thyroid cancer patients have a favorable prognosis, anaplastic thyroid carcinoma (ATC) remains a particularly aggressive form with a median survival time of just five months. Conventional therapies offer limited benefits for this type of thyroid cancer. Our study aims to identify ATC patients who might bene t from immunotherapy.
Our study uses multiple algorithms by R4.2.0, and gene expression and clinical data are collected from TCGA, GEO and local cohort. In vitro experiments, such as western blot and immunofluorescence staining, are performed.
Using a set of five genes uniquely expressed across various types of thyroid cancer, we developed a machine-learning model to distinguish each type within the GEO dataset of thyroid cancer patients (GSE60542, GSE76039, GSE33630, GSE53157, GSE65144, GSE29265, GSE82208, GSE27155, GSE58545, GSE54958, and GSE32662). These genes allowed us to stratify ATC into three distinct groups, each exhibiting significantly different responses to anti-PD1 therapy as determined by consensus clustering. Through weighted gene co-expression network analysis (WGCNA), we identified 12 differentially expressed genes closely associated with immunotherapy outcomes. This led to the creation of a refined signature for predicting ATC's immune responsiveness to anti-PD1 therapy, which was further validated using thyroid cancer cohorts from TCGA and nine melanoma cohorts from clinical trials. Among the 12 genes, HLF stood out due to its strong association with various cancer hallmarks.
Our study revealed that HLF impedes ATC progression by down-regulating the epithelial-to-mesenchymal transition (EMT) pathway, reducing T cell exhaustion, and increasing sensitivity to sorafenib, as demonstrated through our experiments.
虽然大多数甲状腺癌患者预后良好，但未分化甲状腺癌（ATC）仍然是一种特别侵袭性的类型，中位生存时间仅为五个月。传统疗法对这种类型的甲状腺癌益处有限。我们的研究旨在确定可能从免疫疗法中获益的ATC患者。
我们的研究使用了R4.2.0的多种算法，并从TCGA、GEO和本地队列中收集基因表达和临床数据。进行了体外实验，如蛋白质免疫印迹和免疫荧光染色。
利用一组在各种类型甲状腺癌中独特表达的五个基因，我们开发了一个机器学习模型，以区分甲状腺癌患者GEO数据集（GSE60542、GSE76039、GSE33630、GSE53157、GSE65144、GSE29265、GSE82208、GSE27155、GSE58545、GSE54958和GSE32662）中的每种类型。这些基因使我们能够将ATC分为三个不同的组，通过共识聚类确定，每组对抗PD1治疗表现出显著不同的反应。通过加权基因共表达网络分析（WGCNA），我们确定了12个与免疫治疗结果密切相关的差异表达基因。这导致创建了一个用于预测ATC对抗PD1治疗免疫反应性的精细特征，并使用来自TCGA的甲状腺癌队列和来自临床试验的九个黑色素瘤队列进行了进一步验证。在这12个基因中，HLF因其与各种癌症特征的强烈关联而脱颖而出。
我们的研究表明，如我们的实验所示，HLF通过下调上皮-间质转化（EMT）途径、减少T细胞耗竭和增加对索拉非尼的敏感性来阻碍ATC进展。
