Identification of lipid metabolism-related genes as prognostic indicators in papillary thyroid cancer
유두갑상선암의 예후지표로서의 지질대사 관련 유전자의 동정
Identificación de genes relacionados con el metabolismo de los lípidos como indicadores de pronóstico en el cáncer papilar de tiroides
Identification de gènes liés au métabolisme des lipides comme indicateurs pronostiques dans le cancer papillaire de la thyroïde
Идентификация генов, связанных с метаболизмом липидов, как прогностических индикаторов папиллярного рака щитовидной железы
Shishuai Wen ¹ ², Yi Luo ¹ ², Weili Wu 吴伟力 ³, Tingting Zhang 张婷婷 ¹ ², Yichen Yang ¹ ², Qinghai Ji 嵇庆海 ¹ ², Yijun Wu 邬一军 ⁴, Rongliang Shi 史荣亮 ¹ ², Ben Ma 马奔 ¹ ², Midie Xu 许蜜蝶 ⁵, Ning Qu 渠宁 ¹ ²
¹ Department of Head and Neck Surgery, Fudan University Shanghai Cancer Center, Shanghai 200032, China
中国 上海 复旦大学附属肿瘤医院 头颈外科
² Department of Oncology, Shanghai Medical College, Fudan University, Shanghai 200032, China
中国 上海 复旦大学上海医学院 肿瘤学系
³ Department of Surgical Oncology, Third Affiliated Hospital of Wenzhou Medical University, Wenzhou 325200, China
中国 温州 温州医科大学附属第三医院 肿瘤外科
⁴ Department of Thyroid Surgery, Zhejiang University, School of Medicine, The First Affiliated Hospital, Hangzhou 310003, China
中国 杭州 浙江大学医学院附属第一医院 甲状腺外科
⁵ Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai 200032, China
中国 上海 复旦大学附属肿瘤医院 病理科
Acta Biochimica et Biophysica Sinica, 23 October 2021
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.