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ORIGINAL RESEARCH article

Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 15 - 2024 | doi: 10.3389/fimmu.2024.1478904
This article is part of the Research Topic Community Series in Novel Biomarkers in Tumor Immunity and Immunotherapy: Volume II View all 13 articles

Construction of an Anaplastic Thyroid Cancer Stratification Signature to Guide Immune Therapy Selection and Validation of the Pivotal Gene HLF through In Vitro Experiments

Provisionally accepted
Pengping Li Pengping Li 1Kexin Yin Kexin Yin 1Yuwei Xie Yuwei Xie 2Ke Sun Ke Sun 1Rongguo Li Rongguo Li 1Zhenyu Wang Zhenyu Wang 1Haigang Jin Haigang Jin 1*Shaowen Wang Shaowen Wang 3*Yuqing Huang Yuqing Huang 1*
  • 1 Department of Thyroid&Breast Surgery, The First People’s Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, Hangzhou, Zhejiang, China., Hangzhou, China
  • 2 First Affiliated Hospital of Anhui Medical University, Hefei, Anhui Province, China
  • 3 The university of hong kong-Shenzhen hospital, shenzhen, China

The final, formatted version of the article will be published soon.

    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 benefit from immunotherapy. 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-tomesenchymal transition (EMT) pathway, reducing T cell exhaustion, and increasing sensitivity to sorafenib, as demonstrated through our in-vitro experiments.

    Keywords: Anaplastic thyroid cancer (ATC), T Cell Immunity, machine learning, prediction, Model

    Received: 11 Aug 2024; Accepted: 24 Dec 2024.

    Copyright: © 2024 Li, Yin, Xie, Sun, Li, Wang, Jin, Wang and Huang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence:
    Haigang Jin, Department of Thyroid&Breast Surgery, The First People’s Hospital of Xiaoshan District, Xiaoshan Affiliated Hospital of Wenzhou Medical University, Hangzhou, Zhejiang, China., Hangzhou, China
    Shaowen Wang, The university of hong kong-Shenzhen hospital, shenzhen, China
    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., Hangzhou, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.