Nine-gene signature and nomogram for predicting survival in patients with head and neck squamous cell carcinoma - Yang, Fan; Zhou, Liu-qing; Yang, Hui-wen; Wang, Yan-jun (Frontiers in Genetics, 2022)
Head and neck squamous cell carcinomas (HNSCC) is derived from the mucosal linings of the upper aerodigestive tract, salivary glands, thyroid, oropharynx, larynx, and hypopharynx [1]. Current efforts aim to identify novel genes and pathways underlying HNSCC. Despite advances in HNSCC research, diagnosis, and treatment, its incidence continues to rise and the mortality of advanced HNSCC is expected to rise by 50%[2]. Thus, effective biomarkers are urgently required to predict patients’ prognosis and guide personalized treatment.Clinical coupled with gene expression data were abstracted from The Cancer Genome Atlas (TCGA) and dataset GSM16076 from gene expression omnibus (GEO). We then carried out cluster analysis and principal component analysis (PCA) in order to identify tumor microenvironment (TME)-linked genes. Differential gene expression analysis between HNSCC and non-malignant tissues was done using R. Next, crucial TME genes were analyzed using univariate and LASSO Cox regression assessments and selected to create a risk model and a prognostic gene signature. Multivariate Cox regression assessment, Kaplan–Meier curve, Time‐dependent receiver operating characteristic (ROC), nomogram, decision curve analysis (DCA) and concordance index (C-index) were adopted to assess the accuracy of the prognostic risk model. Potential molecular mechanisms were assessed using gene set enrichment analyses (GSEA).A novel HNSCC prognostic model was created on the basis of the genes GTSE1, LRRN4CL, CRYAB, SHOX2, ASNS, KRT23, ANGPT2, HOXA9, and CARD11. Area under the ROC curve (AUC) value (0.769) revealed that the model effectively predicted overall survival (OS) in the TCGA dataset. Cox regression assessment highlighted the nine‐gene signature as a reliable independent prognostic and therapeutic factor in HNSCC. Moreover, the prognostic nomogram developed using Cox multivariate regression analysis exhibited a superior C-index over other clinical signatures and its calibration curve had a high concordance level between estimated OS and the observed OS. This means its clinical net might precisely estimate the one-, three-, along with five-year survival in HNSCC. GSEA to some extent revealed the immune and tumor-linked cascades. Moreover, TCGA exhibited a remarkable difference between the HNSCC low- risk and high-risk groups (p<0.01).Overall, the nine TME-linked gene signature, as well as nomogram may effectively improve the estimation of HNSCC prognosis.