Identification of Potential Biomarkers and Drugs for Papillary Thyroid Carcinoma Using Computational Analysis and Molecular Docking
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Le résumé fourni par la source
BACKGROUND: Papillary thyroid carcinoma (PTC), the most common thyroid malignancy, presents with multiple variants. This study aimed to identify potential biomarkers and therapeutic candidates for PTC through computational analyses and molecular docking. METHODS: Gene expression data related to PTC were obtained from the TCGA-THCA and GEO datasets (GSE35570 and GSE33630) to identify differentially expressed genes (DEGs). Functional enrichment analysis was performed on the DEGs, followed by construction of a protein-protein interaction (PPI) network. Hub genes were identified using recursive feature elimination (RFE) and LASSO regression analyses. A nomogram incorporating these hub genes was developed, and its diagnostic performance was evaluated using receiver operating characteristic (ROC) curves. Furthermore, the relationship between hub genes and immune cell infiltration was investigated. Potential drug candidates targeting the hub genes were predicted and validated through molecular docking. RESULTS: Common DEGs across the three datasets were enriched in pathways such as ECM-receptor interaction, proteoglycans in cancer, and cell adhesion molecules. Significantly enriched GO terms included 'binding,' 'receptor activity,' 'integral component of membrane,' 'cytoplasm,' 'cell adhesion,' and 'immune response.' A PPI network was constructed by intersecting the common DEGs with PTC-related targets. Machine learning algorithms identified three hub genes: SRY-box transcription factor 4 (SOX4), cyclin D1 (CCND1), and lymphatic vessel endothelial hyaluronan receptor 1 (LYVE1). These hub genes exhibited differential expression in PTC and were used to construct a reliable diagnostic model. Furthermore, molecular docking revealed stable binding between CCND1 and Tipifarnib, suggesting potential therapeutic relevance. DISCUSSION: While previous studies have applied bioinformatics and molecular docking in PTC research, this study uniquely integrates both approaches to identify the hub gene CCND1 and its potential targeting drug, Tipifarnib, as promising molecular markers and therapeutic candidates for PTC. CONCLUSION: The hub gene CCND1 and its targeting drug candidate Tipifarnib may contribute to PTC treatment.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Identification of Potential Biomarkers and Drugs for Papillary Thyroid Carcinoma Using Computational Analysis and Molecular Docking
- Date Crossref
- 01/04/2026
- Éditeur
- Bentham Science Publishers Ltd.
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Bengbu Medical College pays non établi dans la noticeUniversité ou école supérieure
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Anhui University of Traditional Chinese Medicine pays non établi dans la noticeUniversité ou école supérieure
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Anhui Medical University pays non établi dans la noticeUniversité ou école supérieure
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First Affiliated Hospital of Anhui Medical University pays non établi dans la noticeÉtablissement de santé
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Heilongjiang University of Chinese Medicine Integrative Medicine Department pays non établi dans la noticeUniversité ou école supérieure
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Bengbu Third People's Hospital Affiliated to Bengbu Medical University Department of Dermatology pays non établi dans la noticeUniversité ou école supérieure
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Anhui University of Chinese Medicine Graduate School pays non établi dans la noticeUniversité ou école supérieure
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The First Affiliated Hospital of Anhui University ofChinese Medicine Department of Endocrinology pays non établi dans la noticeUniversité ou école supérieure
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Heilongjiang University ofChinese Medicine Integrative Medicine Department pays non établi dans la noticeUniversité ou école supérieure
Bengbu Medical College, Anhui University of Traditional Chinese Medicine et Anhui Medical University, avec 6 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.