Related Experiment Video
Updated: Sep 16, 2025
In Vivo Inhibition of MicroRNA to Decrease Tumor Growth in Mice
Published on: August 23, 2019
Single-cell RNA sequencing and multi-omics analysis of prognosis-related staging in papillary thyroid cancer
Guo Ji1, Hanlin Sun2, Simo Chen3
1Department of Pathology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, Middle 301 Yanchang Road, Shanghai, 200072, China.
Background:
Papillary thyroid cancer (PTC) is the most common thyroid cancer, but current molecular features inadequately stratify its risk. Whether distinct underlying mechanisms can further classify PTC and improve prognostic precision remains unclear.
Methods:
We integrated single-cell RNA sequencing data (158,577 cells from 11 PTC patients; GEO: GSE184362) with bulk-RNA sequencing data from The Cancer Genome Atlas Thyroid Carcinoma (TCGA-THCA) cohort (501 patients). Multi-omics analyses were employed to elucidate PTC heterogeneity, identify malignant cell differentiation and prognosis-related genes (MCD&PRGs), and construct a novel molecular classification, the Oncogenic Signature Of Papillary Thyroid Carcinoma Classification (OSPTCC). A prognostic risk score was developed, and the classification's prognostic relevance was further explored in an independent institutional cohort using qRT-PCR.
Results:
Single-cell analysis revealed three malignant cell differentiation states (PTC1-3) and a 34-gene signature (MCD&PRGs). This formed the basis of our Oncogenic Signature Of Papillary Thyroid Carcinoma Classification (OSPTCC), defining three subtypes: Inflammation-associated (IPTCC), BRAF/autophagy-related (BAPTCC), and lipid metabolism-related (LPTCC). These subtypes showed distinct molecular profiles and significantly different progression-free survival (IPTCC poorest, P = 0.044). A 7-gene risk score derived from MCD&PRGs independently predicted prognosis (multivariate HR = 21.511, P < 0.001). qRT-PCR validation in an independent cohort (n = 48) using key markers (DEPTOR, APOE, APOC1) confirmed that OSPTCC-based risk stratification correlated with adverse clinical features, including higher recurrence rates in the high-risk group (P = 0.007).
Conclusions:
This study introduces OSPTCC, a prognostically significant molecular classification for PTC based on tumor cell differentiation states. The identified subtypes, characterized by distinct biological mechanisms, provide deeper insights into PTC's molecular pathology and offer a framework for improved risk stratification and potential precision therapies.
