Plasma Lipidomics Profiling Reveals Biomarkers for Papillary Thyroid Cancer Diagnosis

Nan Jiang; Zhenya Zhang; Xianyang Chen; Guofen Zhang; Ying Wang; Lijie Pan; Chengping Yan; Guoshan Yang; Li Zhao; Jiarui Han; Teng Xue

doi:10.3389/fcell.2021.682269

Plasma Lipidomics Profiling Reveals Biomarkers for Papillary Thyroid Cancer Diagnosis

Front Cell Dev Biol. 2021 Jun 21:9:682269. doi: 10.3389/fcell.2021.682269. eCollection 2021.

Authors

Nan Jiang¹, Zhenya Zhang¹, Xianyang Chen², Guofen Zhang¹, Ying Wang³, Lijie Pan¹, Chengping Yan¹, Guoshan Yang¹, Li Zhao¹, Jiarui Han², Teng Xue⁴

Affiliations

¹ Department of General Surgery, First Hospital of Tsinghua University, Beijing, China.
² BaoFeng Key Laboratory of Genetics and Metabolism, Beijing, China.
³ Department of Oncology, Tai'an City Central Hospital, Tai'an, China.
⁴ Zhongguancun Biological and Medical Big Data Center, Beijing, China.

Abstract

The objective of this study was to identify potential biomarkers and possible metabolic pathways of malignant and benign thyroid nodules through lipidomics study. A total of 47 papillary thyroid carcinomas (PTC) and 33 control check (CK) were enrolled. Plasma samples were collected for UPLC-Q-TOF MS system detection, and then OPLS-DA model was used to identify differential metabolites. Based on classical statistical methods and machine learning, potential biomarkers were characterized and related metabolic pathways were identified. According to the metabolic spectrum, 13 metabolites were identified between PTC group and CK group, and a total of five metabolites were obtained after further screening. Its metabolic pathways were involved in glycerophospholipid metabolism, linoleic acid metabolism, alpha-linolenic acid metabolism, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, Phosphatidylinositol signaling system and the metabolism of arachidonic acid metabolism. The metabolomics method based on PROTON nuclear magnetic resonance (NMR) had great potential for distinguishing normal subjects from PTC. GlcCer(d14:1/24:1), PE-NME (18:1/18:1), SM(d16:1/24:1), SM(d18:1/15:0), and SM(d18:1/16:1) can be used as potential serum markers for the diagnosis of PTC.

Keywords: lipidomics; orthogonal partial least square discriminant analysis; papillary thyroid carcinoma; pathway; plasma samples.