ArticleScientific reports2024
Identification and validation of potential common biomarkers for papillary thyroid carcinoma and Hashimoto's thyroiditis through bioinformatics analysis and machine learning.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Biomarkers associated with Hashimoto's thyroiditis induced by viral infection: An integrative bioinformatics and machine learning.Journal of translational autoimmunity · 2026Article
- Review
- Precision Thyroid Oncology: A Review of Multi-Omics Biomarkers and Spatiotemporal Technologies.International journal of general medicine · 2026Review
- Identification of Potential Biomarkers and Drugs for Papillary Thyroid Carcinoma Using Computational Analysis and Molecular Docking.Current medicinal chemistry · 2026Article
- Advances in genetic and molecular insights into thyroid disorders: Implications for diagnosis, prognosis, and personalized treatment.Saudi medical journal · 2025Review
- Exploring the molecular mechanisms of comorbidity between thyroid cancer and breast cancer through multi-omics data.Scientific reports · 2025Article
- Association of thyroglobulin antibody and thyroid peroxidase antibody status with aggressive features in papillary thyroid carcinoma with Hashimoto's thyroiditis.Gland surgery · 2025Article
- Hashimoto's thyroiditis is associated with reduced invasiveness in papillary thyroid carcinoma: a propensity score-matched retrospective cohort study.Frontiers in immunology · 2025Article
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Authors and funding
4 authors.
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Abstract
There is a growing body of evidence suggesting that Hashimoto's thyroiditis (HT) may contribute to an increased risk of papillary thyroid carcinoma (PTC). However, the exact relationship between HT and PTC is still not fully understood. The objective of this study was to identify potential common biomarkers that may be associated with both PTC and HT. Three microarray datasets from the GEO database and RNA-seq dataset from TCGA database were collected to identify shared differentially expressed genes (DEGs) between HT and PTC. A total of 101 genes was identified as common DEGs, primarily enriched inflammation- and immune-related pathways through GO and KEGG analysis. We performed protein-protein interaction analysis and identified six significant modules comprising a total of 29 genes. Subsequently, tree hub genes (CD53, FCER1G, TYROBP) were selected using random forest (RF) algorithms for the development of three diagnostic models. The artificial neural network (ANN) model demonstrates superior performance. Notably, CD53 exerted the greatest influence on the ANN model output. We analyzed the protein expressions of the three genes using the Human Protein Atlas database. Moreover, we observed various dysregulated immune cells that were significantly associated with the hub genes through immune infiltration analysis. Immunofluorescence staining confirmed the differential expression of CD53, FCER1G, and TYROBP, as well as the results of immune infiltration analysis. Lastly, we hypothesise that benzylpenicilloyl polylysine and aspirinmay be effective in the treatment of HT and PTC and may prevent HT carcinogenesis. This study indicates that CD53, FCER1G, and TYROBP play a role in the development of HT and PTC, and may contribute to the progression of HT to PTC. These hub genes could potentially serve as diagnostic markers and therapeutic targets for PTC and HT.
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