Aging-associated genes TNFRSF12A and CHI3L1 contribute to thyroid cancer: An evidence for the involvement of hypoxia as a driver

Abstract

The prevalence of thyroid cancer (TC) is high in the elderly. The present study was based on the hypothesis that genes, which have increased activity with aging, may play a role in the development of TC. A large-scale literature-based data analysis was conducted to explore the genes that are implicated in both TC and aging. Subsequently, a mega-analysis of 16 RNA expression datasets (1,222 samples: 439 healthy controls, and 783 patients with TC) was conducted to test a set of genes associated with aging but not TC. To uncover a possible link between these genes and TC, a functional pathway analysis was conducted, and the results were validated by analysis of gene co-expression. A multiple linear regression (MLR) model was employed to study the possible influence of sample size, population region and study age on the gene expression levels in TC. A total of 262 and 816 genes were identified to have increased activity with aging and TC, respectively; with a significant overlap of 63 genes (P<3.82×10^-35). The mega-analysis revealed two aging-associated genes (CHI3L1 and TNFRSF12A) to be significantly associated with TC (P<2.05×10^-8), and identified the association with multiple hypoxia-driven pathways through functional pathway analysis, also confirmed by the co-expression analysis. The MLR analysis identified population region as a significant factor contributing to the expression levels of CHI3L1 and TNFRSF12A in TC samples (P<3.24×10^-4). The determination of genes that promote aging was warranted due to their possible involvement in TC. The present study suggests CHI3L1 and TNFRSF12A as novel common risk genes associated with both aging and TC.

Keywords: aging; mega-analysis; multiple linear regression model; pathway analysis; thyroid cancer.

Figure 1.

Effect size, 95% CI and weights of the expression levels of TNFRSF12A and CHI3L1. (A) Mega-analysis results for CHI3L1; (B) Mega-analysis results for TNFRSF12A. Fixed-effect model and random-effects model were employed for CHI3L1 and TNFRSF12A, respectively. CI, confidence interval.

Figure 2.

Hypoxia driven interaction network that links CHI3L1 and TNFRSF12A to thyroid cancer. (A) The network generated in Pathway Studio environment (www.pathwaystudio.com). Each relation (edge) in the figure has one or more supporting references. (B) Network containing only protein-protein interactions validated by 14 thyroid cancer expression datasets.

Figure 3.

Protein-protein interaction network generated in Cytoscape for CHI3L1 and TNFRSF12A and the ‘bridge’ genes. Each edge represents a significant association (P<1×10⁻⁴) between the mRNA expression levels encoded by CHI3L1 and TNFRSF12A. Positive associations are highlighted in red; negative associations are highlighted in blue. A red node represents an overall positive association within the network, whereas a blue node represents an overall negative association. The size of the node represents the centrality of the respective protein. The larger the size, the higher the centrality of the protein. The labels on the edge represent the correction coefficients in terms of Fisher's Z-value as derived from the mega-analysis.