ArticleDose-response : a publication of International Hormesis Society
Identification of an Autophagy-Related Signature Predicting Overall Survival for Papillary Thyroid Carcinoma.
Article in Dose-response : a publication of International Hormesis Society. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Comprehensive analyses of biological function and tumor microenvironment with cuproptosis regulators and construction of a cuproptosis-related scoring system in thyroid cancer based on bioinformatics and experimental validation.Frontiers in genetics · 2026Article
- Identification of oxidative stress-related genes in papillary thyroid carcinoma and their common targets with resveratrol based on bioinformatics, network pharmacology, and molecular docking.Discover oncology · 2025Article
- Identification of potential targetable genes in papillary, follicular, and anaplastic thyroid carcinoma using bioinformatics analysis.Endocrine · 2024Article
- A novel prognostic model for papillary thyroid cancer based on epithelial-mesenchymal transition-related genes.Cancer medicine · 2022Article
- Article
- Overexpression of lncRNA SLC26A4-AS1 inhibits papillary thyroid carcinoma progression through recruiting ETS1 to promote ITPR1-mediated autophagy.Journal of cellular and molecular medicine · 2021Article
- Identification of an autophagy-related gene signature for survival prediction in patients with cervical cancer.Journal of ovarian research · 2020Article
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Authors and funding
3 authors.
Funding
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Abstract
backgroundPapillary thyroid carcinoma usually shows an excellent prognosis. However, its recurrence or persistence rate is high. In this study, we used bioinformatics to identify autophagy-related genes (ARGs) and establish a novel scoring system for papillary thyroid carcinoma.
methodsWe collected ARGs sequencing data of patients with papillary thyroid carcinoma from The Cancer Genome Atlas database. Differentially expressed ARGs were identified by the "Limma" package in R language. After univariate and multivariate Cox regression analysis, an ARG signature was developed. The established prognostic signature was evaluated by Kaplan-Meier curve and time-dependent receiver operating characteristic.
resultsA sum of 26 differentially expressed ARGs were identified. Gene set enrichment analysis revealed that several significantly oncological signatures were enriched, such as autophagy, p53 signaling pathway, apoptosis, human cytomegalovirus infection, and platinum drug resistance. After univariate and multivariate analysis, 3 ARGs (
conclusionsWe established a novel scoring system based on 3 ARGs, which provides a promising tool for the development of personalized therapy.
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