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Background <p>Thyroid carcinoma (THCA) is a malignant tumor. The role of lipotoxicity-related genes (L-RGs) in THCA pathogenesis remains unclear.</p> Methods <p>This study integrated transcriptomic and clinical data from The Cancer Genome Atlas to identify and validate lipotoxicity-related prognostic genes in THCA. A prognostic model was developed using Cox regression and machine learning techniques, with performance assessment based on the immune microenvironment and drug sensitivity. Prognostic gene expression was further validated through Reverse Transcription Quantitative PCR (RT-qPCR) in clinical samples.</p> Results <p>IL11 and SNAI1 were identified as prognostic markers. Patients in the high-risk group exhibited elevated mortality. The constructed nomogram demonstrated strong predictive accuracy. Differential immune cell analysis revealed 26 immune cell types, including plasmacytoid dendritic cells (pDCs). The high-risk group showed heightened sensitivity to cisplatin and gefitinib, suggesting potential drug resistance. RT-qPCR confirmed significantly higher IL11 expression and lower SNAI1 expression in THCA tissues compared to normal controls.</p> Conclusion <p>IL11 and SNAI1, identified as lipotoxicity-related prognostic genes, provide valuable insights into THCA pathophysiology and offer potential therapeutic targets.</p>

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Transcriptome and experimental validation identify prognostic genes associated with lipotoxicity in thyroid carcinoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月9日

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  • Bin Lin,
  • Xianqiang Yu,
  • Min Li,
  • Weizheng Mao,
  • Xiaodong Ding

摘要

Background

Thyroid carcinoma (THCA) is a malignant tumor. The role of lipotoxicity-related genes (L-RGs) in THCA pathogenesis remains unclear.

Methods

This study integrated transcriptomic and clinical data from The Cancer Genome Atlas to identify and validate lipotoxicity-related prognostic genes in THCA. A prognostic model was developed using Cox regression and machine learning techniques, with performance assessment based on the immune microenvironment and drug sensitivity. Prognostic gene expression was further validated through Reverse Transcription Quantitative PCR (RT-qPCR) in clinical samples.

Results

IL11 and SNAI1 were identified as prognostic markers. Patients in the high-risk group exhibited elevated mortality. The constructed nomogram demonstrated strong predictive accuracy. Differential immune cell analysis revealed 26 immune cell types, including plasmacytoid dendritic cells (pDCs). The high-risk group showed heightened sensitivity to cisplatin and gefitinib, suggesting potential drug resistance. RT-qPCR confirmed significantly higher IL11 expression and lower SNAI1 expression in THCA tissues compared to normal controls.

Conclusion

IL11 and SNAI1, identified as lipotoxicity-related prognostic genes, provide valuable insights into THCA pathophysiology and offer potential therapeutic targets.