Identification of an Immune Score-Based Gene Panel with Prognostic Power for Oral Squamous Cell Carcinoma

• Published in: Medical science monitor Vol. 26; p. e922854
• Main Authors: Huang, Su-Ning, Li, Guo-Sheng, Zhou, Xian-Guo, Chen, Xiao-Yi, Yao, Yu-Xuan, Zhang, Xiao-Guohui, Liang, Yao, Li, Ming-Xuan, Chen, Gang, Huang, Zhi-Guang, Dang, Yi-Wu, Li, Jing, Li, Ping, Tang, Xiao-Zhun, Rong, Min-Hua
• Format: Journal Article
• Language: English
• Published: United States International Scientific Literature, Inc 12.06.2020
• Subjects: Cytokines - genetics; Cytokines - immunology; Database Analysis; Databases, Genetic; Female; Gene Expression Profiling; Humans; Immune System - cytology; Immune System - immunology; Lymphocytes, Tumor-Infiltrating; Male; Mouth Neoplasms - genetics; Mouth Neoplasms - immunology; Multivariate Analysis; Prognosis; Proportional Hazards Models; Protein Interaction Maps; Receptors, Cytokine - genetics; Receptors, Cytokine - immunology; Risk Assessment; RNA, Messenger; RNA-Seq; ROC Curve; Squamous Cell Carcinoma of Head and Neck - genetics; Squamous Cell Carcinoma of Head and Neck - immunology; Support Vector Machine; Transcriptome; Tumor Microenvironment - genetics; Tumor Microenvironment - immunology
• ISSN: 1643-3750; 1234-1010; 1643-3750
• DOI: 10.12659/MSM.922854

BACKGROUND Oral squamous cell carcinoma (OSCC) is the sixth most prevalent cancer worldwide, with low 5-year survival rate. To identify novel prognostic markers for OSCC and determine the immune and stromal landscape of OSCC, a risk signature for OSCC patients was constructed in this study. MATERIAL AND METHODS Immune and stromal scores for OSCC samples from the Genomic Data Commons Data Portal were computed to delineate the tumor microenvironment landscape of oral cancer based on the Estimation of STromal and Immune cells in MAlignant Tumours using Expression data algorithm. An immune score-based risk signature was constructed by combining random forest and support vector machine methods. Correlation analysis of risk signature gene expression and immune cell infiltration was conducted, and the distinguishing power of individual signature genes was evaluated by analyzing receiver operating characteristics (ROC) curves. Differentially enriched pathways between high and low risk groups were investigated via gene set variation analysis. ROC curves were plotted for signature genes to examine their ability to distinguish the recurrence and survival status of OSCC patients from GSE84846. RESULTS An immune score-related risk signature composed of ARMH1, F2RL2, AC004687.1, COL6A5, AC008750.1, RAB19, CRLF2, GRIP2, and FAM162B performed well in the prognostic stratification of OSCC patients and could effectively distinguish their survival status. Lists of pathways, including cytokine-cytokine receptor interaction and cell adhesion molecules displayed remarkable differential enrichment between high and low risk OSCC patients. CONCLUSIONS An immune score-based risk signature constructed presently may be useful to decide appropriate treatment options for individual OSCC patients.