ArticleEuropean journal of medical research2025
Identification of thyroid cancer biomarkers using WGCNA and machine learning.
Article in European journal of medical research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Exploring the mechanism of Dieda Qili Tablet on fracture healing based on network pharmacology combined with machine learning models.Scientific reports · 2026Article
- Understanding Immune Cell Adaptation to Tumor Hypoxia for Maximized Therapeutic Efficacy of Immunotherapy: Biology and Non-invasive Imaging Application.Cancer research and treatment · 2026Review
- Development and validation of a machine learning model for predicting high-risk distant metastatic recurrence in differentiated thyroid cancer.Frontiers in medicine · 2026Article
- Precision Thyroid Oncology: A Review of Multi-Omics Biomarkers and Spatiotemporal Technologies.International journal of general medicine · 2026Review
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Authors and funding
10 authors.
Funding
Abstract
objectiveThe incidence of thyroid cancer (TC) is increasing in China, largely due to overdiagnosis from widespread screening and improved ultrasound technology. Identifying precise TC biomarkers is crucial for accurate diagnosis and effective treatment.
methodsTC patient data were obtained from TCGA. DEGs were analyzed using DESeq2, and WGCNA identified gene modules associated with TC. Machine learning algorithms (XGBoost, LASSO, RF) identified key biomarkers, with ROC and AUC > 0.95 indicating strong diagnostic performance. Immune cell infiltration and biomarker correlation were analyzed using CIBERSORT.
resultsFour key genes (P4HA2, TFF3, RPS6KA5, EYA1) were found as potential biomarkers. High P4HA2 expression was associated with suppressed anti-tumor immune responses and promoted disease progression. In vitro studies showed that P4HA2 upregulation increased TC cell growth and migration, while its suppression reduced these activities.
conclusionThrough bioinformatics and experimental validation, we identified P4HA2 as a key potential thyroid cancer biomarker. This finding provides new molecular targets for diagnosis and treatment. P4HA2 has the potential to be a diagnostic or therapeutic target, which could have significant implications for improving clinical outcomes in thyroid cancer patients.
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