Wenfei Li1, Min Fu1, Kun Zhao2, Fang Han3, Ning Bu4, Zhanqiu Wang5. 1. Department of Radiology, First Hospital of Qinhuangdao, Wenhua Road 258, Qinhuangdao, 066000, Hebei, China. 2. Department of Otolaryngology, The First Hospital of Qinhuangdao, Qinhuangdao, Hebei, China. 3. Department of Radiology, Affiliated Zhongshan Hospital of DaLian University, Dalian, Liaoning, China. 4. Department of Anesthesiology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China. 5. Department of Radiology, First Hospital of Qinhuangdao, Wenhua Road 258, Qinhuangdao, 066000, Hebei, China. wangzhanqiu2007@sina.com.
Abstract
PURPOSE: Despite advances in the development of treatments for laryngeal cancer (LC), including surgical treatments and radio-chemotherapy, the survival rate of LC remains low. Therefore, novel metabolic signatures are urgently needed to evaluate the prognosis of LC patients. METHODS: Differentially expressed metabolic genes were extracted via bioinformatics analysis from the raw data of The Cancer Genome Atlas and Gene Expression Omnibus (GEO) databases. Univariate Cox regression and LASSO analyses were performed to identify metabolic genes that were significantly correlated with overall survival (OS). Using the Kaplan-Meier analysis and receiver operating characteristics, the prognostic power of candidate signatures was evaluated in the two databases. Gene Set Enrichment Analysis (GSEA) was performed to explore significant signaling pathways and underlying mechanisms in the high- and low-risk groups. RESULTS: Thirteen metabolism genes showed superior ability to predict OS for LC when compared to clinical variables, and patients in the high-risk group showed significantly poorer OS than those in the low-risk group. The area under the curve of receiver operating curves for 5- and 3-year OS was 0.929 and 0.899, respectively, which were better than the OS obtained with clinicopathological variables. Similar results obtained in the GEO cohort indicated that this gene signature could differentiate between LC patients with and without recurrence. CONCLUSION: To our knowledge, this study is the first to report that the 13 metabolic genes could serve as an independent biomarker for LC, which could provide vital prognostic information and prediction for personalized treatment of LC.
PURPOSE: Despite advances in the development of treatments for laryngeal cancer (LC), including surgical treatments and radio-chemotherapy, the survival rate of LC remains low. Therefore, novel metabolic signatures are urgently needed to evaluate the prognosis of LC patients. METHODS: Differentially expressed metabolic genes were extracted via bioinformatics analysis from the raw data of The Cancer Genome Atlas and Gene Expression Omnibus (GEO) databases. Univariate Cox regression and LASSO analyses were performed to identify metabolic genes that were significantly correlated with overall survival (OS). Using the Kaplan-Meier analysis and receiver operating characteristics, the prognostic power of candidate signatures was evaluated in the two databases. Gene Set Enrichment Analysis (GSEA) was performed to explore significant signaling pathways and underlying mechanisms in the high- and low-risk groups. RESULTS: Thirteen metabolism genes showed superior ability to predict OS for LC when compared to clinical variables, and patients in the high-risk group showed significantly poorer OS than those in the low-risk group. The area under the curve of receiver operating curves for 5- and 3-year OS was 0.929 and 0.899, respectively, which were better than the OS obtained with clinicopathological variables. Similar results obtained in the GEO cohort indicated that this gene signature could differentiate between LC patients with and without recurrence. CONCLUSION: To our knowledge, this study is the first to report that the 13 metabolic genes could serve as an independent biomarker for LC, which could provide vital prognostic information and prediction for personalized treatment of LC.
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