Gene promoter methylation signature predicts survival of head and neck squamous cell carcinoma patients

Abstract

Infection with high-risk types of human papilloma virus (HPV) is currently the best-established prognostic marker for head and neck squamous cell carcinoma (HNSCC), one of the most common and lethal human malignancies worldwide. Clinical trials have been launched to address the concept of treatment de-escalation for HPV-positive HNSCC with the final aim to reduce treatment related toxicity and debilitating long-term impacts on the quality of life. However, HPV-related tumors are mainly restricted to oropharyngeal SCC (OPSCC) and there is an urgent need to establish reliable biomarkers for all patients at high risk for treatment failure. A patient cohort (n D 295) with mainly non-OPSCC (72.9%) and a low prevalence of HPV16-related tumors (8.8%) was analyzed by MassARRAY to determine a previously established prognostic methylation score (MS). Kaplan-Meier revealed a highly significant correlation between a high MS and a favorable survival for OPSCC (P D 0.0004) and for non-OPSCC (P<0.0001), which was confirmed for all HNSCC by multivariate Cox regression models (HR: 9.67, 95% ], P<0.0001). Next, we established a minimal methylation signature score (MMSS), which consists of ten most informative of the originally 62 CpG units used for the MS. The prognostic value of the MMSS was confirmed by Kaplan-Meier analysis for all HNSCC (P<0.0001) and non-OPSCC (P D 0.0002), and was supported by multivariate Cox regression models for all HNSCC (HR: 2.15, 95% CI [1.36-3.41], P D 0.001). In summary, the MS and the MMSS exhibit an excellent performance as prognosticators for survival, which is not limited by the anatomical site, and both could be implemented in future clinical trials.

Identification of an energy metabolism‑related gene signature in ovarian cancer prognosis

“…Screening of robust energy metabolism-related prognostic feature genes. LASSO is a regression modeling with a large number of potential prognostic features; it can perform automatic feature selection that results in signatures with generally effective performance for predicting prognosis (42). The LASSO method has been used in combination with the Cox model for survival analysis, and has been successfully applied for building sparse signatures for survival prognosis in a range of application areas, including oncology (43)(44)(45).…”

Human Papillomavirus-Related Head and Neck Cancer

“…[28,49,50]. Interestingly, the clinical outcome of HNSCC patients, including HPV-related OPSCC, could be predicted depending on differentially methylated promoters of 5 host genes (ALDH1A2, OSR2, IRX4, GRIA4, and GATA4) [51,53].…”

Development and validation of 10-gene signature for predicting survival in patients with prostate cancer

“…Least absolute shrinkage and selection operator (LASSO) is a popular method for regression modeling with a large number of potential prognostic features, because it can perform automatic feature selection in a manner that results in signatures with generally good prognostic performance [10]. The LASSO method has been extended to the Cox model for survival analysis and has been successfully applied for the purpose of building sparse signatures for survival prognosis in many application areas including oncology [11][12][13], We first use the training set samples to perform univariate Cox proportional hazards regression analysis on each gene, with log rank p < 0.05 as a threshold to identify genes with significant prognosis, and Then, the R software package glmnet [14] was used to screen the genes with robust prognostic characteristics.…”