Background. Lysine crotonylation (Kcr) is a newly identified posttranslational modification type regulated by various enzymes and coenzymes, including lysine crotonyltransferase, lysine decrotonylase, and binding proteins. However, the role of Kcr regulators in head and neck squamous cell carcinoma (HNSCC) remains unknown. The aim of this study was to establish and validate a Kcr-related prognostic signature of HNSCC and to assess the clinical predictive value of this signature. Methods. The mRNA expression profiles and clinicopathological data from The Cancer Genome Atlas (TCGA) database were downloaded to explore the clinical significance and prognostic value of these regulators in HNSCC. The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to generate the Kcr-related prognostic signature for HNSCC. Subsequently, the GSE65858 dataset from the Gene Expression Omnibus (GEO) database was used to validate the signature. The prognostic value of the signature was evaluated using the Kaplan-Meier survival, receiver operating characteristic (ROC) curve, and univariate and multivariate Cox regression analyses. Results. We established a nine-gene risk signature associated with the prognosis of HNSCC based on Kcr regulators. High-risk patients demonstrated significantly poorer overall survival (OS) than low-risk patients in the training (TCGA) and validation (GEO) datasets. Then, the time-dependent receiver operating characteristic (ROC) curve analysis showed that the nine-gene risk signature was more accurate for predicting the 5-year OS than other clinical parameters, including age, gender, T stage, N stage, and histologic grade in the TCGA and GEO datasets. Moreover, the Cox regression analysis showed that the constructed risk signature was an independent risk factor for HNSCC. Conclusion. Our study identified and validated a nine-gene signature for HNSCC based on Kcr regulators. These results might contribute to prognosis stratification and treatment escalation for HNSCC patients.

Background: The immune system plays a crucial role in rectal adenocarcinoma (READ). Immune-related genes may help predict READ prognoses.Methods: The Cancer Genome Atlas dataset and GSE56699 were used as the training and validation datasets, respectively, and differentially expressed genes (DEGs) were identified. The optimal DEG combination was determined, and the prognostic risk model was constructed. The correlation between optimal DEGs and immune infiltrating cells was evaluated.Results: Nine DEGs were selected for analysis. Moreover, ADAMDEC1 showed a positive correlation with six immune infiltrates, most notably with B cells and dendritic cells. F13A1 was also positively correlated with six immune infiltrates, particularly macrophage and dendritic cells, whereas LGALS9C was negatively correlated with all immune infiltrates except B cells. Additionally, the prognostic risk model was strongly correlated with the actual situation. We retained only three prognosis risk factors: age, pathologic stage, and prognostic risk model. The stratified analysis revealed that lower ages and pathologic stages have a better prognosis with READ. Age and mRNA prognostic factors were the most important factors in determining the possibility of 3- and 5-year survival.Conclusion: In summary, we identified a nine-gene prognosis risk model that is applicable to the treatment of READ. Altogether, characteristics such as the gene signature and age have a strong predictive value for prognosis risk.