Integrating multi-omics and machine learning to unravel mechanisms of lymph node metastasis in papillary carcinoma with and without thyroiditis - PubMed
5 days ago
- #papillary thyroid carcinoma
- #lymph node metastasis
- #machine learning
- Study focuses on lymph node metastasis (LNM) mechanisms in papillary thyroid carcinoma (PTC) with and without thyroiditis.
- PTC cases categorized as PTC-thyroiditis and PTC-blank, each showing distinct clinical and genomic profiles.
- PTC-blank exhibits higher tumor stages and mutations in BRAF and MUC16 compared to PTC-thyroiditis.
- LNM in PTC-blank linked to ECM remodeling and collagen fiber accumulation, involving PI16+ fibroblast subclusters.
- LNM in PTC-thyroiditis involves immune-related pathways without significant fibroblast infiltration or ECM changes.
- A 17-gene predictive model for LNM developed, with KNN classifier showing high accuracy.
- Mendelian randomization identifies SHISA5 as a causal risk gene for thyroid cancer.
- Molecular docking reveals strong binding affinity between SHISA5 and acetaminophen, suggesting therapeutic potential.
- Findings highlight distinct LNM mechanisms and offer insights into subtype-specific management strategies for PTC patients.