Objective: This study set out to institute an effective nomogram to predict the prognosis of nasopharyngeal carcinoma (NPC) using magnetic resonance imaging (MRI)-detected residual tumor at the end of intensity-modulated radiotherapy (IMRT). Background: This study retrospectively analyzed the prognostic factors of NPC using MRIdetected residual tumor at the end of IMRT, in order to individualize the treatment of patients with poor prognosis as early as possible. Methods: Overall, 162 NPC patients with local or regional residual tumor at the end of IMRT were retrospectively analyzed. Based on multivariate Cox regression analysis using the backward stepwise method, a nomogram was generated to predict the prognosis of these patients. Identification, calibration, clinical applicability and reproducibility were evaluated by C-index, time-dependent AUC, calibration curve and bootstrap verification. According to the best cut-off value of total score of prognoses calculated by X-tile software, all patients were separated into either low-risk or high-risk group. Results: The nomogram identified age, chemotherapy, N stage, lymph nodes necrosis are significant predictors of prognosis. The AUC of the prediction model is 0.754, and the consistency index is 0.724 (95% confidence interval is 0.659-0.788). The model has good discrimination ability. Through bootstrapping test, the consistency index, corrected slope was 0.723, 0.861, respectively. The calibration slope of predicting 3-year and 5-year overall survival was 1.006 and 1.071, respectively. The calibration curve showed satisfactory calibration effect and good net benefit. The best cut-off value of total score of prognoses calculated by X-tile software was 149.1. Kaplan-Meier survival curve showed that OS and DMFS in the high-risk group were substantially reduced compared to those in the low-risk group. Conclusion:We constructed and validated a new nomogram to help clinicians understand the prognosis of NPC patients with residue at the end of IMRT. With an estimate of the individual risk, clinicians can start treatment decisions as early as possible for high-risk patients with poor prognosis.

Background: This study aimed to use a bioinformatics pipeline to explore the underlying mechanisms and identify genetic mutations that can be utilized to prognosticate individuals with head and neck squamous cell carcinoma (HNSCC). Methods: SNP-related data was accessed using the TCGA database. Mutation and expression analyses were performed between the mutant samples and wild-type samples. Kaplan‐Meier analysis was conducted to select the candidate mutant genes that affect overall survival. Correlation analysis, GSEA analysis and drug sensitivity analysis of the candidate genes were performed. Results: Down-regulation of FAT1, KMT2B, XIRP2 and ZNF347 expression were observed in the tumors with mutations. Kaplan‐Meier analysis indicated that reduced levels of FAT1, XIRP2 was significantly associated with better overall survival, while reduced levels of KMT2B, and ZNF347 were significantly correlated to worse overall survival. Additionally, SNPs of the four genes were found to participate in several pathways associated with HNSCC development. Furthermore, FAT1 mutation was sensitive to several anti-tumor drugs, such as PI-103, Belinostat and Ruxolitinib. Conclusion: SNPs in FAT1, KMT2B, XIRP2 and ZNF347 may be used as prognostic biomarkers in the treatment of HNSCC.