Prognostic prediction of patients having classical papillary thyroid carcinoma with a 4 mRNA-based risk model

Medicine (Baltimore). 2024 Jun 7;103(23):e38472. doi: 10.1097/MD.0000000000038472.

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

  • Biomarkers, Tumor* / genetics
  • Female
  • Gene Expression Profiling / methods
  • Gene Expression Regulation, Neoplastic
  • Humans
  • Kaplan-Meier Estimate
  • Male
  • Middle Aged
  • Myosin Heavy Chains / genetics
  • Prognosis
  • Proportional Hazards Models
  • RNA, Messenger / genetics
  • RNA, Messenger / metabolism
  • Risk Assessment / methods
  • Thyroid Cancer, Papillary* / genetics
  • Thyroid Cancer, Papillary* / mortality
  • Thyroid Cancer, Papillary* / pathology
  • Thyroid Neoplasms* / genetics
  • Thyroid Neoplasms* / mortality
  • Thyroid Neoplasms* / pathology
  • Transcription Factors / genetics

Substances

  • Biomarkers, Tumor
  • RNA, Messenger
  • Myosin Heavy Chains
  • Transcription Factors