Developing a Prognostic Stratification Model Based on Glutathione Metabolism in Thyroid Cancer and Validating RRM2's Tumor-Promoting Role

Glutathione (GSH), the most abundant antioxidant in cells, acts as free radical scavenger and detoxifying agent. Elevation of GSH metabolism protects tumor from damage of oxidant and even promotes tumor progression. However, the clinical value of GSH metabolism in thyroid cancer (THCA) remained largely unknown. Collecting large THCA cohort from The Cancer Genome Atlas (TCGA) database, we systematically analyzed molecular characteristics of 24 GSH metabolism-related enzymes. GSH metabolism related enzymes including RRM2 and IDH2 were upregulated in tumor samples and negatively correlated with disease free survival (DFS). To expand the application of the GSH metabolism related enzymes in prognostic prediction, we for the first time built a risk stratification model based on the GSH metabolism related enzymes via LASSO Cox regression algorithm and validated its prediction performance. Patients were categorized into high- and low-risk groups according to the median of risk score. As supposed, high-risk patients suffered from dismal DFS. The key molecule in this process, RRM2, was screened by correlation analysis,and it was experimentally confirmed that the abnormally high expression of RRM2, which acts as a pro-carcinogenic molecule, enhances the proliferation, invasion, and migration of THCA cells in vitro and in vivo. Overall design: To investigate the effect of RRM2 overexpression on the transcriptional level of papillary thyroid cancer cells, mRNA sequencing analysis was performed on wild strain TPC-1 cells and RRM2 overexpression strain (RRM2-OE-TPC-1 cells) in this study. Each cell line was assayed in biological triplicate.Differential expression between two conditions was determined using DESeq2 (v1.20.0). P values were adjusted for multiple testing using the Benjamini–Hochberg procedure to control the false discovery rate (FDR). Genes with an adjusted P = 0.05 were considered significantly differentially expressed.