Exploring the genetic profiles linked to senescence in thyroid tumors: insights on predicting disease progression and immune responses
IntroductionThyroid cancer (THCA) is the most common endocrine tumor. Research on Cell Senescence Associated Genes (CSAGs), which impact many cancers, remains limited in the THCA field.MethodsIn this study, we downloaded THCA sample data from several public databases and selected a set of CSAGs for...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-02-01
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| Series: | Frontiers in Oncology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2025.1545656/full |
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| Summary: | IntroductionThyroid cancer (THCA) is the most common endocrine tumor. Research on Cell Senescence Associated Genes (CSAGs), which impact many cancers, remains limited in the THCA field.MethodsIn this study, we downloaded THCA sample data from several public databases and selected a set of CSAGs for subsequent analysis. Differential expression genes (DEGs) obtained through differential analysis were intersected with prognostic genes identified by Cox regression analysis to explore the correlation among these crossed genes. We constructed a prognostic model using the Least Absolute Shrinkage and Selection Operator (LASSO) algorithm and verified its efficacy. Kaplan-Meier survival curves were plotted, and Receiver Operating Characteristic (ROC) curves rigorously confirmed the accuracy of model predictions.ResultsTo evaluate the predictive power of prognostic models across different phenotypic traits, we performed survival analysis, Gene Set Enrichment Analysis (GSEA), and immune-related differential analysis. Differences in tumor mutation burden (TMB) and treatment response between high-risk and low-risk patient groups were also analyzed. Finally, the predictive effect of our model on immunotherapy response was validated, showing promising results for THCA patients.DiscussionOur study enhances the understanding of THCA cell senescence and provides new therapeutic insights. The proposed model not only accurately predicts patient survival but also reveals factors related to immunotherapy response, offering new perspectives for personalized medicine. |
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| ISSN: | 2234-943X |