Abstract
Lipid metabolism plays important roles not only in the structural basis and energy supply of healthy cells but also in the oncogenesis and progression of cancers. In this study, we investigated the prognostic value of lipid metabolism-related genes in papillary thyroid cancer (PTC). The recurrence predictive gene signature was developed and internally and externally validated based on PTC datasets including The Cancer Genome Atlas (TCGA) and GSE33630 datasets. Univariate, LASSO, and multivariate Cox regression analysis were applied to assess prognostic genes and build the prognostic gene signature. The expression profiles of prognostic genes were further determined by immunohistochemistry of tissue microarray using in-house cohorts, which enrolled 97 patients. Kaplan-Meier curve, time-dependent receiver operating characteristic curve, nomogram, and decision curve analyses were used to assess the performance of the gene signature. We identified four recurrence-related genes, PDZK1IP1, TMC3, LRP2 and KCNJ13, and established a four-gene signature recurrence risk model. The expression profiles of the four genes in the TCGA and in-house cohort indicated that stage T1/T2 PTC and locally advanced PTC exhibit notable associations not only with clinicopathological parameters but also with recurrence. Calibration analysis plots indicate the excellent predictive performance of the prognostic nomogram constructed based on the gene signature. Single-sample gene set enrichment analysis showed that high-risk cases exhibit changes in several important tumorigenesis-related pathways, such as the intestinal immune network and the p53 and Hedgehog signaling pathways. Our results indicate that lipid metabolism-related gene profiling represents a potential marker for prognosis and treatment decisions for PTC patients.