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Background <p>Thyroid cancer (THCA) exhibits high molecular heterogeneity, posing challenges for precise prognosis and personalized therapy. Most existing models rely on single-omics data and limited algorithms, reducing robustness and clinical value.</p> Methods <p>We integrated five omics layers from THCA patients using eleven clustering algorithms to identify molecular subtypes. Based on stable prognosis-related genes (SPRGs), we applied 99 combinations of ten machine learning methods to construct a robust prognostic model—Consensus Machine Learning-Driven Signature (CMLS). The model was validated across multiple internal and external cohorts. Immunogenomic characteristics and drug sensitivity were also evaluated.</p> Results <p>Three molecular subtypes (CS1–CS3) with distinct clinical outcomes and molecular features were identified; CS2 showed the worst prognosis. A nine-gene CMLS was established, demonstrating strong prognostic performance across cohorts. Patients in the low-CMLS group had better outcomes, stronger immune infiltration, higher TMB/TNB, and greater predicted responsiveness to immunotherapy. Conversely, the high-CMLS group exhibited poor prognosis and lower immunotherapy sensitivity. Drug screening identified six candidate agents for high-CMLS patients.</p> Conclusion <p>Our study provides a robust multiomics-based classification of THCA and develops a clinically relevant CMLS model for prognostic prediction and therapy guidance. These findings may facilitate risk stratification and inform personalized treatment strategies in clinical practice.</p>

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Integrated multiomics analysis and machine learning refine molecular subtypes and prognosis for thyroid cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年6月23日

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  • Peng Zhang,
  • Meizhong Qin,
  • Fen Li,
  • Kunpeng Hu,
  • He Huang,
  • Cuicui Li

摘要

Background

Thyroid cancer (THCA) exhibits high molecular heterogeneity, posing challenges for precise prognosis and personalized therapy. Most existing models rely on single-omics data and limited algorithms, reducing robustness and clinical value.

Methods

We integrated five omics layers from THCA patients using eleven clustering algorithms to identify molecular subtypes. Based on stable prognosis-related genes (SPRGs), we applied 99 combinations of ten machine learning methods to construct a robust prognostic model—Consensus Machine Learning-Driven Signature (CMLS). The model was validated across multiple internal and external cohorts. Immunogenomic characteristics and drug sensitivity were also evaluated.

Results

Three molecular subtypes (CS1–CS3) with distinct clinical outcomes and molecular features were identified; CS2 showed the worst prognosis. A nine-gene CMLS was established, demonstrating strong prognostic performance across cohorts. Patients in the low-CMLS group had better outcomes, stronger immune infiltration, higher TMB/TNB, and greater predicted responsiveness to immunotherapy. Conversely, the high-CMLS group exhibited poor prognosis and lower immunotherapy sensitivity. Drug screening identified six candidate agents for high-CMLS patients.

Conclusion

Our study provides a robust multiomics-based classification of THCA and develops a clinically relevant CMLS model for prognostic prediction and therapy guidance. These findings may facilitate risk stratification and inform personalized treatment strategies in clinical practice.