インテグレイティブマルチオミクスによる甲状腺癌の分子サブタイプの精密化と癌進行予測の向上：後ろ向きコホート研究

Current molecular classification model for thyroid cancer (TC), which relies on BRAF-RAS score genes has limited efficacy in differentiating follicular-patterned tumors from normal thyroid (NT) tissues and lack predictive power for disease progression. This study aimed to refine TC classification and develop predictive systems for tumor progression.

A multi-omics analysis integrating transcriptomics (SNUH-mRNASeq) and proteomics (SNUH-TMT-Pro) from retrospectively collected human tissues representing NT and tumors with mutation profiles was conducted in a single center. A novel gene set, termed NPF genes, was identified and used to construct a decision-tree based classification system and a risk stratification model for tumor progression. The discriminatory potential of protein markers was validated through immunohistochemistry (SNUH-IHC) using tissue microarrays. External datasets (TCGA-THCA, SNUH-DIA-Pro, and CellDis-Pro) were employed to validate both the classification and risk stratification systems.

Clustering based on NPF genes separated papillary thyroid cancer (PTC) with BRAFV600E mutation (PTC-B), follicular thyroid cancer (FTC) regardless of mutation status and NT, classifying them into BRAFV600E-like, RAS-like, and NT-like subtypes. The decision-tree based classification model with NPF genes demonstrated high accuracy (0.92, 95% CI 0.88-0.95) and Kappa statistics (0.88). IHC of three protein biomarkers (MATN2, FN1, and PLSCR4) confirmed the molecular subtypes in SNUH-IHC, with findings consistent across external datasets. Additionally, higher NP-score and NF-score predicted poorer prognosis in BRAFV600E-like and RAS-like TCs.

The NPF gene set and classification model refine TC classification, improve diagnostic accuracy, and enable better risk stratification. These advancements offer a foundation for personalized therapeutic strategies in TC management.