An oxidative stress-related lncRNA prognostic risk model for thyroid cancer

Abstract

Purpose: Oxidative stress-elated genes (OSRGs) and long non-coding RNA (lncRNA) have been reported to be associated with cancer prognosis, but the prognostic role of oxidative stress-related lncRNAs (OSRlncRNAs) in thyroid cancer (THCA) is unclear. Methods:RNA-sequencing data and OSRGs were downloaded from public databases. Differentially expressed OSRGs (DE-OSRGs) were identified by limma or DESeq2 packages. Pearson correlation analysis was performed to screen OSRlncRNAs. Furthermore, prognostic risk model was constructed by Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses. A nomogram was further developed. Moreover, functional enrichment analyses were performed on differentially expressed genes (DEGs) between high- and low-risk groups. Finally, a lncRNA-mRNA co-expression network was constructed to analyze the regulatory relationship of model lncRNA. Results: The prognostic risk model based on two OSRlncRNA (DPP4-DT, SAP30-DT) was constructed. The predictive power of the nomogram was accurate and reliable (c-index = 0.942). The neuroactive ligand-receptor interactions, thyroid hormone synthesis, and cytokine-cytokine receptor interactions pathways are important in THCA. The co-expression network results showed that 88 DEGs were regulated by DPP4-DT. Conclusion:The prognostic risk model constructed based on two OSRlncRNA (DPP4-DT, SAP30-DT) could effectively predict the prognosis of THCA patients and provided insights for new personalized prediction and treatment for THCA patients.