A new ferroptosis-related gene model for prognostic prediction of papillary thyroid carcinoma

ABSTRACT

Papillary thyroid carcinoma (PTC) is a highly heterogeneous malignancy with diverse prognoses. Ferroptosis is a new type of cell death dependent on iron. Nevertheless, the predictive ability of ferroptosis-related genes for PTC is unclear. Based on the mRNA expression information from The Cancer Genome Atlas, we compared tumor and normal tissues in terms of the gene expression, for identifying differentially expressed genes (DEGs). Then, the risk score of a 5-gene signature was calculated and a prognostic model was established to test the predictive value of this gene signature by virtue of the LASSO Cox regression. The 5 genes were validated in PTC tissues by RT-qPCR.At last, functional analysis was implemented to investigate the underlying mechanisms. We found a total of 45 ferroptosis-related genes expressed differentially between tumor and normal tissues. 6 DEGs exhibited a significant relevance to the overall survival (OS) (P< 0.05). We classified patients into group with high risk and group with low risk based on the median risk score of a 5-gene signature. Patients in the group with low risk presented a remarkably higher OS relative to the group with high risk (P< 0.01). The Cox regression analysis displayed the independent predictive ability of the risk score. The receiver operating characteristic analysis helped to validate the predictive power owned by the gene signature. After validation, the 5 genes were abnormally expressed between PTC and normal tissues. Functional analysis showed two groups had different immune status. A new ferroptosis-related gene signature can predict the outcomes of PTC patients.

ABSTRACT

KEYWORDS:

• Ferroptosis
• prognosis
• papillary thyroid carcinoma
• overall survival
• gene model

Acknowledgements

All authors acknowledge the contributions from TCGA project.

Disclosure of potential conflicts of interest

The authors declare that they do not have any conflicts of interest regarding the paper.

Author contributions

Yongquan Chu designed this study; Xiaoyu Qian took charge of data analysis, prepared the figures, and wrote the manuscript; Jian Tang, Lin Li, Ziqiang Chen, Liang Chen took charge of data collection as well as the critical reading regarding the manuscript. The final manuscript has been read and approved by all authors.

Data Availability Statement

The data analyzed in the study can be found on the TCGA database (https://portal.gdc.cancer.gov/).

Additional information

Funding

This study was funded by the Science and Technology Project of Jiaxing City (2019AD32259) .