Fang Han1, Wenfei Li2, Tao Chen3, Yutong Yao4, Jinglong Li2, Di Wang5, Zhanqiu Wang2. 1. Department of Radiology, Affiliated Zhongshan Hospital of DaLian University, Dalian, Liaoning, China. dlhanfang@163.com. 2. Department of Radiology, The First Hospital of Qinhuangdao, Qinhuangdao, Hebei, China. 3. Department of PET-CT, Xiangyang Central Hospital, Xiangyang, Hubei, China. 4. Department of Radiology, Affiliated Zhongshan Hospital of DaLian University, Dalian, Liaoning, China. 5. Department of Endocrine, The First Hospital of Qinhuangdao, Qinhuangdao, Hebei, China.
Abstract
BACKGROUND: Previous studies reported that ferroptosis-related genes can regulate the process of tumor cell changes by regulating iron metabolism. However, the prognostic value of ferroptosis-related genes in LC remains to be further elucidated. METHODS: Ferroptosis-related gene expression profiles of coexisting ferroptosis-related genes were extracted from both cohorts (TCGA and GSE27020) for eligible analysis. LASSO Cox regression was utilized to build an optimum ferroptosis-related prognostic model. Kaplan-Meier curve was performed by log-rank test, and time-dependent ROC curve was constructed to evaluate the predictive power of this signature in both cohorts. GO and KEGG enrichment analysis was used to investigate the potential mechanism of differential enrichment signal pathways. RESULTS: 112 LC patients from the TCGA cohort and 108 LC patients with clinical information from the GEO cohorts were eventually included in the study. Three ferroptosis-related genes were identified as an independent risk factor to establish the prognostic risk score. Kaplan-Meier curve represented that patients with high-risk group favors with worse OS than their low-risk group (P = 0.04). The good performance of the gene signature for predicting OS was evaluated by area under the curve (AUC) of time-dependent ROC curves achieved 0.74 at 3 years, and 0.70 at 5 years. Similar performance has been proved in the external validation cohort. GO and KEGG enrichment analysis have been performed to explore the signaling pathways and underlying mechanisms were significantly active in LC patients. CONCLUSION: In summary, our study developed a ferroptosis-related model that could be an effective biomarker to predict the prognosis of laryngeal cancer.
BACKGROUND: Previous studies reported that ferroptosis-related genes can regulate the process of tumor cell changes by regulating iron metabolism. However, the prognostic value of ferroptosis-related genes in LC remains to be further elucidated. METHODS: Ferroptosis-related gene expression profiles of coexisting ferroptosis-related genes were extracted from both cohorts (TCGA and GSE27020) for eligible analysis. LASSO Cox regression was utilized to build an optimum ferroptosis-related prognostic model. Kaplan-Meier curve was performed by log-rank test, and time-dependent ROC curve was constructed to evaluate the predictive power of this signature in both cohorts. GO and KEGG enrichment analysis was used to investigate the potential mechanism of differential enrichment signal pathways. RESULTS: 112 LC patients from the TCGA cohort and 108 LC patients with clinical information from the GEO cohorts were eventually included in the study. Three ferroptosis-related genes were identified as an independent risk factor to establish the prognostic risk score. Kaplan-Meier curve represented that patients with high-risk group favors with worse OS than their low-risk group (P = 0.04). The good performance of the gene signature for predicting OS was evaluated by area under the curve (AUC) of time-dependent ROC curves achieved 0.74 at 3 years, and 0.70 at 5 years. Similar performance has been proved in the external validation cohort. GO and KEGG enrichment analysis have been performed to explore the signaling pathways and underlying mechanisms were significantly active in LC patients. CONCLUSION: In summary, our study developed a ferroptosis-related model that could be an effective biomarker to predict the prognosis of laryngeal cancer.
Authors: Patricia P Yee; Yiju Wei; Soo-Yeon Kim; Tong Lu; Stephen Y Chih; Cynthia Lawson; Miaolu Tang; Zhijun Liu; Benjamin Anderson; Krishnamoorthy Thamburaj; Megan M Young; Dawit G Aregawi; Michael J Glantz; Brad E Zacharia; Charles S Specht; Hong-Gang Wang; Wei Li Journal: Nat Commun Date: 2020-10-27 Impact factor: 14.919