Ferroptosis-related gene signature predicts the prognosis of papillary thyroid carcinoma

被引:30
|
作者
Shi, Jinyuan [1 ]
Wu, Pu [1 ]
Sheng, Lei [2 ]
Sun, Wei [1 ]
Zhang, Hao [1 ]
机构
[1] China Med Univ, Dept Thyroid Surg, Hosp 1, Shenyang, Peoples R China
[2] Shandong Univ, Dept Thyroid Surg, Gen Surg, Qilu Hosp, Jinan, Shandong, Peoples R China
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
Papillary thyroid carcinoma; Ferroptosis genes; Gene signature; Prognosis; TCGA; ANGIOPOIETIN-LIKE; 7; MAST-CELLS; CANCER CELLS; TRANSFERRIN; RECEPTORS; SURVIVAL; HORMONE; PROLIFERATION; IRON;
D O I
10.1186/s12935-021-02389-7
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Background Papillary thyroid carcinoma (PTC) is the most common type of thyroid cancer (TC), accounting for more than 80% of all cases. Ferroptosis is a novel iron-dependent and Reactive oxygen species (ROS) reliant type of cell death which is distinct from the apoptosis, necroptosis and pyroptosis. Considerable studies have demonstrated that ferroptosis is involved in the biological process of various cancers. However, the role of ferroptosis in PTC remains unclear. This study aims at exploring the expression of ferroptosis-related genes (FRG) and their prognostic values in PTC. Methods A ferroptosis-related gene signature was constructed using lasso regression analysis through the PTC datasets of the Cancer Genome Atlas (TCGA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed to investigate the bioinformatics functions of significantly different genes (SDG) of ferroptosis. Additionally, the correlations of ferroptosis and immune cells were assessed through the single-sample gene set enrichment analysis (ssGSEA) and CIBERSORT database. Finally, SDG were test in clinical PTC specimens and normal thyroid tissues. Results LASSO regression model was utilized to establish a novel FRG signature with 10 genes (ANGPTL7, CDKN2A, DPP4, DRD4, ISCU, PGD, SRXN1, TF, TFRC, TXNRD1) to predicts the prognosis of PTC, and the patients were separated into high-risk and low-risk groups by the risk score. The high-risk group had poorer survival than the low-risk group (p < 0.001). Receiver operating characteristic (ROC) curve analysis confirmed the signature's predictive capacity. Multivariate regression analysis identified the prognostic signature-based risk score was an independent prognostic indicator for PTC. The functional roles of the DEGs in the TGCA PTC cohort were explored using GO enrichment and KEGG pathway analyses. Immune related analysis demonstrated that the most types of immune cells and immunological function in the high-risk group were significant different with those in the low-risk group. Quantitative Real-Time Polymerase Chain Reaction (qRT-PCR) verified the SDG have differences in expression between tumor tissue and normal thyroid tissue. In addition, cell experiments were conducted to observe the changes in cell morphology and expression of signature's genes with the influence of ferroptosis induced by sorafenib. Conclusions We identified differently expressed FRG that may involve in PTC. A ferroptosis-related gene signature has significant values in predicting the patients' prognoses and targeting ferroptosis may be an alternative for PTC's therapy.
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页数:14
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