Identification of lipid metabolism-related genes as prognostic indicators in papillary thyroid cancer

列线图 癌变 基因表达谱 比例危险模型 基因签名 生物 肿瘤科 基因 甲状腺乳突癌 微阵列分析技术 乳腺癌 微阵列 接收机工作特性 单变量 生存分析 基因表达 甲状腺癌 内科学 癌症研究 医学 癌症 多元统计 遗传学 统计 数学
作者
Shishuai Wen,Yi Luo,Weili Wu,Tingting Zhang,Yichen Yang,Qinghai Ji,Yijun Wu,Ruihan Shi,Ben Ma,Ming Xu,Ning Qu
出处
期刊:Acta Biochimica et Biophysica Sinica [Oxford University Press]
卷期号:53 (12): 1579-1589 被引量:22
标识
DOI:10.1093/abbs/gmab145
摘要

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.
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