Bioinformatics analysis of the prognostic and clinical value of senescence-related gene signature in papillary thyroid cancer

Abstract

Cellular senescence can both inhibit and promote the occurrence of tumors, so how to apply cellular senescence therapy is of great importance. However, it is worth to be analyzed from multiple perspectives by researchers, especially for tumors with a high incidence like papillary thyroid cancer (PTC). We obtained senescence-related differentially expressed genes (SRGs) from The Cancer Genome Atlas (TCGA) and gene expression omnibus database. Enrichment analysis of SRGs was performed via gene ontology and Kyoto Encyclopedia of Genes and Genomes. Prognostic model was constructed by univariate and multivariate Cox regression analysis. Evaluation of clinical value was analyzed via Receiver operating characteristic curve, Kaplan-Meier curve and Cox regression. Immune infiltrates were investigated through ESTIMATE and single-sample gene set enrichment analysis. Immunohistochemical images were obtained from The Human Protein Atlas. Twenty-seven SRGs from TCGA cohort and gene expression omnibus datasets were found. These genes are mainly concentrated in senescence-related terms and pathways, including "DNA damage response, signal transduction by p53 class mediator," "signal transduction in response to DNA damage," "p53 signaling pathway" and "Endocrine resistance." Based on SRGs, prognostic model was constructed by E2F transcription factor 1, snail family transcriptional repressor 1 and phospholipase A2 receptor 1. PTC patients were divided into a low-risk group and a high-risk group according to the median value (cutoff point = 0.969) of risk score in TCGA cohort. The diagnostic efficiency of this model is good (area under curve = 0.803, 0.809, and 0.877 at 1, 2, and 3 years in TCGA; area under curve = 0.964, 0.813 in GPL570 and GPL96), particularly advanced grade, state and tumor mutation burden, such as Stage III - IV, T3 - 4, H-tumor mutation burden. Furthermore, High-risk group was significantly associated with poor prognosis and more immune infiltration. Our prognostic model has a good diagnostic and prognostic efficacy, and there is a certain clinical application value. In addition, we provide the first new insight into the genesis, diagnosis, prognosis and treatment of PTC based on senescence-related genes.

Figure 1.

Identification of senescence-related differentially expressed genes (SRGs).

Figure 2.

Gene functional enrichment analysis of SRGs. SRGs = senescence-related differentially expressed genes.

Figure 3.

(A-B) The assessment of prognostic model in efficiency and clinical value, (C-D) The results of ROC curve (C) and K-M curve (D) in TCGA cohort, (E-F) The results of ROC curve in GPL570 (E) and GPL96 (F). K-M = Kaplan–Meier, ROC = receiver operating characteristic, TCGA = the cancer genome atlas.

Figure 4.

Independent and stratified prognostic analysis for clinical parameters.

Figure 5.

Immune infiltration analysis between low-risk and high-risk groups.

Figure 6.

The expression levels of E2F1, SNAI1 and PLA2R1. E2F1 = E2F transcription factor 1, PLA2R1 = phospholipase A2 receptor 1, SNAI1 = snail family transcriptional repressor 1.