Head and neck cancer (HNC) remains one of the most malignant tumors with a significantly high mortality. DNA methylation exerts a vital role in the prognosis of HNC. In this study, we try to screen abnormal differential methylation genes (DMGs) and pathways in Head–Neck Squamous Cell Carcinoma via integral bioinformatics analysis. Data of gene expression microarrays and gene methylation microarrays were obtained from the Cancer Genome Atlas database. Aberrant DMGs were identified by the R Limma package. We conducted the Cox regression analysis to select the prognostic aberrant DMGs and site‐specific methylation. Five aberrant DMGs were recognized that significantly correlated with overall survival. The prognostic model was constructed based on five DMGs (PAX9, STK33, GPR150, INSM1, and EPHX3). The five DMG models acted as prognostic biomarkers for HNC. The area under the curve based on the five DMGs predicting 5‐year survival is 0.665. Moreover, the correlation between the DMGs/site‐specific methylation and gene expression was also explored. The findings demonstrated that the five DMGs can be used as independent prognostic biomarkers for predicting the prognosis of patients with HNC. Our study might lay the groundwork for further mechanism exploration in HNC and may help identify diagnostic biomarkers for early stage HNC.

Immune infiltration in Prostate Cancer (PCa) was reported to be strongly associated with clinical outcomes. However, previous research could not elucidate the diversity of different immune cell types that contribute to the functioning of the immune response system. In the present study, the CIBERSORT method was employed to evaluate the relative proportions of immune cell profiling in PCa samples, adjacent tumor samples and normal samples. Three types of molecular classification were identified in tumor samples using the ‘CancerSubtypes’ package of the R software. Each subtype had specific molecular and clinical characteristics. In addition, functional enrichment was analyzed in each subtype. The submap and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms were also used to predict clinical response to the immune checkpoint blockade. Moreover, the Genomics of Drug Sensitivity in Cancer (GDSC) database was employed to screen for potential chemotherapeutic targets for the treatment of PCa. The results showed that Cluster I was associated with advanced PCa and was more likely to respond to immunotherapy. The findings demonstrated that differences in immune responses may be important drivers of PCa progression and response to treatment. Therefore, this comprehensive assessment of the 22 immune cell types in the PCa Tumor Environment (TEM) provides insights on the mechanisms of tumor response to immunotherapy and may help clinicians explore the development of new drugs.

Aberrant lipid metabolism is an early event in tumorigenesis and has been found in a variety of tumor types, especially prostate cancer (PCa). Therefore, We hypothesize that PCa can be stratified into metabolic subgroups based on glycolytic and cholesterogenic related genes, and the different subgroups are closely related to the immune microenvironment. Bioinformatics analysis of genomic, transcriptomic, and clinical data from a comprehensive cohort of PCa patients was performed. Datasets included the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) dataset, GSE70768, our previously published PCa cohort. The unsupervised cluster analysis was employed to stratify PCa samples based on the expression of metabolic-related genes. Four molecular subtypes were identified, named Glycolytic, Cholesterogenic, Mixed, and Quiescent. Each metabolic subtype has specific features. Among the 4 subtypes, the cholesterogenic subtype exhibited better median survival, whereas patients with high expression of glycolytic genes showed the shortest survival. The mitochondrial pyruvate carriers (MPC) 1 exhibited expression difference between PCa metabolic subgroups, but not for MPCs 2. Glycolytic subtypes had lower immune cell scores, while Cholesterogenic subgroups had higher immune cell scores. Our results demonstrated that metabolic classifications based on specific glycolytic and cholesterol-producing pathways provide new biological insights into previously established subtypes and may guide develop personalized therapies for unique tumor metabolism characteristics.Abbreviations: ICGC = international cancer genome consortium, MPC = mitochondrial pyruvate carrier, PCa = prostate cancer, PFI = progression-free interval, TCGA = the cancer genome atlas, TME = tumor microenvironment.

Inflammasomes are closely associated with the progression of multiple cancers. We established an inflammasome-related gene (IRG)-based model to predict the survival of patients with hepatocellular carcinoma (HCC). The RNA-sequencing data and clinical information of HCC patients were downloaded from the cancer genome atlas-liver hepatocellular carcinoma database, and the differentially expressed inflammasome-related gene were screened. Seven prognostic differentially expressed inflammasome-related genes were identified by univariate Cox analysis and incorporated into the risk model using least absolute shrinkage and selection operator-Cox algorithm. The predictive accuracy of the risk model was evaluated through the Kaplan–Meier, receiver operating characteristic and Cox regression analyses. The performance of the model was verified in the International Cancer Genome Consortium-Liver Cancer - RIKEN, JP cohort. A nomogram was constructed to predict the 1-, 2-, 3- ,and 5-year survival of HCC patients, and its performance was evaluated using calibration curves. The significantly enriched gene ontology terms, Kyoto encyclopedia of genes and genomes pathways and infiltrating immune cell populations associated with the IRG model were also analyzed to explore of the potential molecular mechanisms and immunotherapeutic targets. An independent and highly accurate prognostic model consisting of 7 IRGs was established and verified in 2 independent HCC cohorts. The IRG model was significantly associated with cell division and cell cycle. In addition, the high-risk group was more likely to have greater infiltration of immune cells and higher expression of immune checkpoint-related genes compared to the low-risk group. An IRG-based model was established to predict 1-, 2-, 3-, and 5-year survival rate in individual HCC patients, which provides new insights into the role of inflammasomes in HCC.