Abstract
Introduction:
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.
Methods:
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.
Results:
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.
Discussion:
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 in-vitro experiments.