ArticleNature communications2024
An individualized protein-based prognostic model to stratify pediatric patients with papillary thyroid carcinoma.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Progress and trends on machine learning in proteomics during 1997-2024: a bibliometric analysis.Frontiers in medicine · 2025Pooled it
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- Risk factors for postoperative recurrence of thyroid cancer patients in children, adolescents, and young adults.Translational pediatrics · 2026Article
- Proteogenomic characterization delineates clinically relevant subtypes of advanced differentiated thyroid cancer.Cell reports. Medicine · 2026Article
- A machine learning-based framework for prognostic prediction and tumor microenvironment characterization of locally advanced cervical cancer with concurrent chemoradiotherapy.NPJ precision oncology · 2025Article
- Single-Cell Transcriptomics Reveals ITGA2-Mediated Metabolic Reprogramming and Immune Crosstalk in Pediatric Thyroid Carcinogenesis.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Age-specific tumor-associated macrophage biomarkers underlie metastasis and immune dysregulation in thyroid cancer.npj aging · 2025Article
- Identification of prognostic genes related to T cell proliferation in papillary thyroid cancer based on single-cell RNA sequencing and bulk RNA sequencing data.Clinical and experimental medicine · 2025Article
- Development of a carbon nanoparticle-guided nomogram for predicting lateral cervical lymph node metastasis in clinically node-negative papillary thyroid carcinoma.American journal of translational research · 2025Article
- Integrative Multi-Omics Analysis Reveals Key Metabolic Regulators and Prognostic Biomarkers in Pediatric and Adult Thyroid Cancer.Journal of Cancer · 2025Article
- Comprehensive analysis of the papillary thyroid carcinoma identifies CSGALNACT1 as a proliferation driver and prognostic biomarker.Frontiers in cell and developmental biology · 2025Article
- Comprehensive Mass Spectral Libraries of Human Thyroid Tissues and Cells.Scientific data · 2024Article
- The 2024 Report on the Human Proteome from the HUPO Human Proteome Project.Journal of proteome research · 2024Review
- Individualizing the lifesaving journey for calciphylaxis: addressing rapidly progressive attacks with multidimensional and AI research for regenerative medicine.Renal failure · 2024Article
- Application of machine learning for mass spectrometry-based multi-omics in thyroid diseases.Frontiers in molecular biosciences · 2024Review
Corrections and comments
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Authors and funding
13 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Pediatric papillary thyroid carcinomas (PPTCs) exhibit high inter-tumor heterogeneity and currently lack widely adopted recurrence risk stratification criteria. Hence, we propose a machine learning-based objective method to individually predict their recurrence risk. We retrospectively collect and evaluate the clinical factors and proteomes of 83 pediatric benign (PB), 85 pediatric malignant (PM) and 66 adult malignant (AM) nodules, and quantify 10,426 proteins by mass spectrometry. We find 243 and 121 significantly dysregulated proteins from PM vs. PB and PM vs. AM, respectively. Function and pathway analyses show the enhanced activation of the inflammatory and immune system in PM patients compared with the others. Nineteen proteins are selected to predict recurrence using a machine learning model with an accuracy of 88.24%. Our study generates a protein-based personalized prognostic prediction model that can stratify PPTC patients into high- or low-recurrence risk groups, providing a reference for clinical decision-making and individualized treatment.
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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.