AI-Powered Biomarker System Redefines Thyroid Cancer Progression and Subtypes
Posted on 21 Jul 2025
Differentiated thyroid carcinoma (DTC) is typically indolent, and some patients can be managed without immediate surgery. However, it remains a significant clinical challenge to determine which patients are suitable for active surveillance and to identify when disease progression is likely to occur. Traditional staging methods often fail to detect the transition from a stable disease state to one of rapid progression, making early intervention difficult. Now, an innovative dynamic biomarker system can identify critical transitions where the disease begins to progress rapidly, offering a potential window for early detection and intervention.
This dynamic biomarker system, developed by researchers at the First Affiliated Hospital of Zhengzhou University (Henan, China), used an optimized dynamic network biomarker (DNB) algorithm for the identification of a "tipping point" in Stage II DTC. The team introduced a scoring system called TCPSLevel to quantify individual patient risk by capturing early-warning molecular signals, showing superior performance compared to traditional staging methods. They applied AI-powered consensus clustering to over 1,100 thyroid cancer samples and identified three reproducible molecular subtypes, each with distinct immune profiles and progression risks. The most aggressive subtype was linked to the gene ASPH, which was also experimentally validated. To enable broader clinical use, the researchers developed a simplified 12-gene classifier called miniPC, which allows for accurate subtype prediction across different datasets.
The study demonstrated that patients with high TCPSLevel scores had more advanced disease and worse outcomes, highlighting the scoring system’s value in early risk stratification. The AI framework successfully redefined thyroid cancer progression and molecular subtypes, offering a more practical path toward personalized treatment. By integrating multi-omics data, machine learning, and single-cell analysis, the tool provides powerful insights into the molecular underpinnings of thyroid cancer. The researchers plan to further validate the tool in clinical settings and explore its application in guiding treatment decisions.
“This score outperforms traditional staging in identifying high-risk individuals,” said Dr. Ge Zhang, co-author and collaborator on the thyroid cancer biomarker study.
