Prognostic prediction of patients having classical papillary thyroid carcinoma with a 4 mRNA-based risk model.
The dysregulation of protein-coding genes involved in various biological functions is closely associated with the progression of thyroid cancer.
APA
Xiang L, Zhao JH, et al. (2024). Prognostic prediction of patients having classical papillary thyroid carcinoma with a 4 mRNA-based risk model.. Medicine, 103(23), e38472. https://doi.org/10.1097/MD.0000000000038472
MLA
Xiang L, et al.. "Prognostic prediction of patients having classical papillary thyroid carcinoma with a 4 mRNA-based risk model.." Medicine, vol. 103, no. 23, 2024, pp. e38472.
PMID
38847736
Abstract
The dysregulation of protein-coding genes involved in various biological functions is closely associated with the progression of thyroid cancer. This study aimed to investigate the effects of dysregulated gene expressions on the prognosis of classical papillary thyroid carcinoma (cPTC). Using expression profiling datasets from the Cancer Genome Atlas (TCGA) database, we performed differential expression analysis to identify differentially expressed genes (DEGs). Cox regression and Kaplan-Meier analysis were used to identify DEGs, which were used to construct a risk model to predict the prognosis of cPTC patients. Functional enrichment analysis unveiled the potential significance of co-expressed protein-encoding genes in tumors. We identified 4 DEGs (SALL3, PPBP, MYH1, and SYNDIG1), which were used to construct a risk model to predict the prognosis of cPTC patients. These 4 genes were independent of clinical parameters and could be functional in cPTC carcinogenesis. Furthermore, PPBP exhibited a strong correlation with poorer overall survival (OS) in the advanced stage of the disease. This study suggests that the 4-gene signature could be an independent prognostic biomarker to improve prognosis prediction in cPTC patients older than 46.
MeSH Terms
Humans; Thyroid Cancer, Papillary; Thyroid Neoplasms; Prognosis; Female; Male; Middle Aged; Biomarkers, Tumor; RNA, Messenger; Kaplan-Meier Estimate; Gene Expression Profiling; Risk Assessment; Gene Expression Regulation, Neoplastic; Myosin Heavy Chains; Transcription Factors; Proportional Hazards Models
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