Table_1_Identifying 8-mRNAsi Based Signature for Predicting Survival in Patients With Head and Neck Squamous Cell Carcinoma via Machine Learning.DOCX (13.19 kB)
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Table_1_Identifying 8-mRNAsi Based Signature for Predicting Survival in Patients With Head and Neck Squamous Cell Carcinoma via Machine Learning.DOCX

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posted on 23.11.2020, 23:45 by Yuxi Tian, Juncheng Wang, Chao Qin, Gangcai Zhu, Xuan Chen, Zhixiang Chen, Yuexiang Qin, Ming Wei, Zhexuan Li, Xin Zhang, Yunxia Lv, Gengming Cai

Cancer stem cells (CSCs) have been characterized by several exclusive features that include differentiation, self-renew, and homeostatic control, which allows tumor maintenance and spread. Recurrence and therapeutic resistance of head and neck squamous cell carcinomas (HNSCC) have been identified to be attributed to CSCs. However, the biomarkers led to the development of HNSCC stem cells remain less defined. In this study, we quantified cancer stemness by mRNA expression-based stemness index (mRNAsi), and found that mRNAsi indices were higher in HNSCC tissues than that in normal tissue. A significantly higher mRNAsi was observed in HPV positive patients than HPV negative patients, as well as in male patients than in female patients. The 8-mRNAsi signature was identified from the genes in two modules which were mostly related to mRNAsi screened by weighted gene co-expression network analysis. In this prognostic signatures, high expression of RGS16, LYVE1, hnRNPC, ANP32A, and AIMP1 focus in promoting cell proliferation and tumor progression. While ZNF66, PIK3R3, and MAP2K7 are associated with a low risk of death. The riskscore of eight signatures have a powerful capacity for 1-, 3-, 5-year of overall survival prediction (5-year AUC 0.77, 95% CI 0.69–0.85). These findings based on stemness indices may provide a novel understanding of target therapy for suppressing HNSCC stem cells.

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