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

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

Lin Xiang  1 , Jun-Hui Zhao, Yao Tang, Jun-Wu Tan, Liang-Bo Li, Cheng Gong
Affiliations

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

Lin Xiang et al. Medicine (Baltimore). .

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.

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Conflict of interest statement

The authors have no conflicts of interest to disclose.

Figures

Figure 1.
Figure 1.
Construction of the prognostic model based on survival-related mRNAs. (A) Volcano plot of differentially expressed mRNAs. (B) Forest plot of the univariable Cox regression analysis results of mRNA signature. (D and G) Kaplan–Meier curves of OS of the cPTC patients in the training set (D) and testing set (G), using the 4-mRNA prognostic model. (E and H) The area under the time-dependent ROC curve (AUC) in the training set (E) and testing set (H). (C, F, and I) PCA plot of the cPTC patients in the entire set (C), training set (F), and testing set (I), according to analysis using the prognostic model. cPTC = classical papillary thyroid carcinoma, PCA = principal component analysis.
Figure 2.
Figure 2.
Assessment of prognostic signatures in training sets. (A and B) Distribution of the patients’ survival status and risk score. (C and D) Heatmap and box diagram of the 4 prognostic mRNAs.
Figure 3.
Figure 3.
Independent validation of the 4-mRNA risk score model. Forest plot for 4-mRNA signature in the training set (A and B) and testing set (C and D), based on Cox regression analysis.
Figure 4.
Figure 4.
Stratification analyses of the patients’ OS which was adjusted to the age and stage using the 4-mRNA signature in the entire set. (A–C) Kaplan–Meier curves of the age cohort based on the risk model. (D–F) Kaplan–Meier curves of the stage cohort based on the risk model. OS = overall survival.
Figure 5.
Figure 5.
Functional enrichment of the co-expressed protein-coding genes of 4-mRNAs. (A and B) Distribution of enrichment map of the terms of GO (A) and KEGG (B). (C) Distribution of top 10 hub genes network. (D and E) Distribution of Hallmark genes-related pathways, according to GSEA analysis. GO = gene ontology, GSEA = Gene Set Enrichment Analysis, KEGG = Kyoto Encyclopedia of Genes and Genomes.
Figure 6.
Figure 6.
The expression profiles of top 10 hub genes.
Figure 7.
Figure 7.
Relationships between clinical parameters and PPBP mRNA. (A) Kaplan–Meier curves for the entire cohort based on PPBP expression. (B–C) The expression level of PPBP in different T and AJCC stages. PPBP = pro-platelet basic protein.
See this image and copyright information in PMC

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