Lu Xing1, Mingzhu Guo1, Xiaoqi Zhang2, Xiaoqian Zhang3, Feng Liu4,5. 1. School of Stomatology, Shandong University & Shandong Key Laboratory of Oral Tissue Regeneration & Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration, Shandong University, Wenhua West Road No. 44, Jinan, 250014, Shandong, China. 2. Department of Orthodontics, State Key Laboratory Oral Disease, Sichuan University, West China Hospital Stomatology, Chengdu, 610041, Sichuan, China. 3. Department of Stomatology, Haiyuan College of Kunming Medical University, Kunming, 650000, Yunnan, China. 4. School of Stomatology, Shandong University & Shandong Key Laboratory of Oral Tissue Regeneration & Shandong Engineering Laboratory for Dental Materials and Oral Tissue Regeneration, Shandong University, Wenhua West Road No. 44, Jinan, 250014, Shandong, China. kqjzk@sdu.edu.cn. 5. Emergency Department, Stomatology Hospital of Shandong University, Jinan, 250014, Shandong, China. kqjzk@sdu.edu.cn.
Abstract
PURPOSE: Head and neck squamous cell carcinoma (HNSCC) is a common cancer with high mortality and poor prognosis partially owing to lack of application of predictive markers. Increasing evidence has suggested that metabolic dysregulation plays an important part in tumorigenesis. We aim to identify a prognostic metabolic pathway (MP) signature in HNSCC. METHODS: Single sample gene-set enrichment analysis (ssGSEA) was used in metabolic gene sets to develop a metabolism-based prognostic risk score (MPRS) for HNSCC using Cox regression analysis (univariate, LASSO, and stepwise multiple cox analysis), which was then validated in different subgroups, and association with clinical and mutational features was analyzed. RESULTS: Seventy-two dysregulated metabolic pathways were identified, and a six-MP signature (6MPS) was constructed which can effectively distinguish between the high- and low-risk patients in both training and testing sets, accompanied with high sensitivity and specificity (AUC = 0.7) in prognosis prediction. 6MPS was also applicable to patients of different subgroups. Furthermore, 6MPS is not only an independent prognostic predictor but also associated with clinicopathological and mutational features. Higher tumor stage and tumor mutation burden (TMB) have a higher MPRS. CONCLUSION: 6MPS functions not only as a promising predictor of prognosis and survival but also as potential marker for therapeutic schedule monitoring.
PURPOSE: Head and neck squamous cell carcinoma (HNSCC) is a common cancer with high mortality and poor prognosis partially owing to lack of application of predictive markers. Increasing evidence has suggested that metabolic dysregulation plays an important part in tumorigenesis. We aim to identify a prognostic metabolic pathway (MP) signature in HNSCC. METHODS: Single sample gene-set enrichment analysis (ssGSEA) was used in metabolic gene sets to develop a metabolism-based prognostic risk score (MPRS) for HNSCC using Cox regression analysis (univariate, LASSO, and stepwise multiple cox analysis), which was then validated in different subgroups, and association with clinical and mutational features was analyzed. RESULTS: Seventy-two dysregulated metabolic pathways were identified, and a six-MP signature (6MPS) was constructed which can effectively distinguish between the high- and low-risk patients in both training and testing sets, accompanied with high sensitivity and specificity (AUC = 0.7) in prognosis prediction. 6MPS was also applicable to patients of different subgroups. Furthermore, 6MPS is not only an independent prognostic predictor but also associated with clinicopathological and mutational features. Higher tumor stage and tumor mutation burden (TMB) have a higher MPRS. CONCLUSION: 6MPS functions not only as a promising predictor of prognosis and survival but also as potential marker for therapeutic schedule monitoring.
Entities:
Keywords:
Biomarker; Head and neck squamous cell carcinoma; Metabolic pathway; Prognosis; Survival
Authors: Kacper Guglas; Marta Bogaczyńska; Tomasz Kolenda; Marcel Ryś; Anna Teresiak; Renata Bliźniak; Izabela Łasińska; Jacek Mackiewicz; Katarzyna Lamperska Journal: Contemp Oncol (Pozn) Date: 2017-12-30
Authors: Andrea Carenzo; Mara S Serafini; Elisa Roca; Alberto Paderno; Davide Mattavelli; Chiara Romani; Pierre Saintigny; Senada Koljenović; Lisa Licitra; Loris De Cecco; Paolo Bossi Journal: Cells Date: 2020-08-03 Impact factor: 6.600