Integrative discovery and targeted proteomics elucidate the plasma exosomal landscape in thyroid disorders, with emphasis on papillary thyroid carcinoma complicating Hashimoto’s thyroiditis - Summary - MDSpire

Targeted Proteomics and Integrative Approaches Reveal the Plasma Exosomal Profile in Thyroid Disorders, Focusing on Papillary Thyroid Carcinoma Associated with Hashimoto’s Thyroiditis

  • By

  • Yawei Zhang

  • Xiaolong Ma

  • Wenjing Shi

  • Tianyi Dong

  • Zhen Wang

  • Mao Ding

  • Xingchen Shang

  • Xingsong Tian

  • May 25, 2026

Share

Objective:

To identify plasma-derived exosomal biomarkers associated with papillary thyroid carcinoma (PTC) in the context of Hashimoto’s thyroiditis (HT).

Key Findings:
  • A total of 45 differentially expressed proteins (DEPs) were identified between PTC-W and HT.
  • DEPs were predominantly involved in extracellular matrix–receptor interactions and immune response pathways.
  • SVEP1 and IGKV3-7 were validated as having consistent and statistically significant expression levels.
Interpretation:

SVEP1 and IGKV3-7 may serve as promising plasma exosomal biomarkers for PTC in patients with HT.

Limitations:
  • The study included a small sample size of only three patients per group.
  • Further validation in larger cohorts is necessary to confirm findings.
Conclusion:

The study provides insights into the progression from HT to PTC and highlights potential non-invasive diagnostic biomarkers.

Original Source(s)

Integrative discovery and targeted proteomics elucidate the plasma exosomal landscape in thyroid disorders, with emphasis on papillary thyroid carcinoma complicating Hashimoto’s thyroiditis

  • by Yawei Zhang, Xiaolong Ma, Wenjing Shi, Tianyi Dong, Zhen Wang, Mao Ding, Xingchen Shang, Xingsong Tian

  • May 25, 2026

Related Content

Inflammatory myofibroblastic tumour: unravelling the interplay of inflammation, kinase signalling, and therapeutic resistance

From patient-derived tumor organoids to personalized cancer therapy: advancing treatment for advanced solid tumors

Multitask learning for early treatment response and survival prediction in lung cancer radiotherapy using sequential CBCT imaging