Evidence map›Paper›PMID 41483448›Full record

ArticleDiscover oncology2026

Exploring molecular mechanisms of radioactive iodine therapy in thyroid cancer using single-cell RNA sequencing data.

Lin Guo, Yiren Feng, Senhe Jiang, Xuehan Wang, Gang Jin

Abstract read
In one paragraph

Article in Discover oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Lin Guo *Department of Nuclear Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150000, China.
Yiren Feng *Department of Nuclear Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150000, China.
Senhe JiangDepartment of Otolaryngology, The First Affiliated Hospital of Harbin Medical University, Harbin, 150000, China.
Xuehan WangDepartment of Nuclear Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150000, China.
Gang JinDepartment of Nuclear Medicine, The Second Affiliated Hospital of Harbin Medical University, Harbin, 150000, China. jin@hrbmu.edu.cn.

Funding

Harbin Medical University Horizontal Project 070500020293
6 · The paper itself

Abstract

objectiveTo investigate the role of ubiquitination-related differentially expressed genes (URDEGs) in thyroid carcinoma (THCA) and their implications for predicting responses to radioactive iodine (RAI) therapy.

methodsWe analyzed data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases using TCGA biolinks and GEO query tools for data acquisition and normalization. Differential expression analysis was performed with DESeq2 to identify URDEGs between RAI-sensitive and RAI- refractory groups. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted using ClusterProfiler. A prognostic risk model was built using Cox regression and Least Absolute Shrinkage and Selection Operator (LASSO) regression. Single-cell RNA sequencing data were analyzed with Seurat for clustering and annotation. Gene Set Variation Analysis (GSVA) was used to assess pathway enrichment across various cell subpopulations.

resultsA total of 25 URDEGs were identified, which were enriched in processes related to cytoskeleton organization and pathways including the peroxisome proliferator-activated receptor (PPAR) signaling pathway. A prognostic model based on four URDEGs—INSM2, TAGLN3, MDGA2, and SIK1—demonstrated strong predictive capability. Immune infiltration analysis revealed significant differences in immune cell composition between high- and low-risk groups. Single-cell analysis identified 74,497 cells across 22 clusters and nine cell types, revealing gene expression patterns that highlight cellular heterogeneity.

conclusionURDEGs show potential as biomarkers for predicting RAI therapy response and prognosis in THCA. These findings provide insights for personalized therapeutic strategies targeting specific molecular pathways in THCA patients.

Indexed as

PathwaysPrognostic risk modelTHCAURDEGs

Identifiers

PMID41483448
PMCPMC12864557

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.