Construction and validation of a metabolic gene-associated prognostic model for cervical carcinoma and the role on tumor microenvironment and immunity

Granulocytes and mast cells in AllergoOncology-Bridging allergy to cancer: An EAACI position paper

Derived from the myeloid lineage, granulocytes, including basophils, eosinophils, and neutrophils, along with mast cells, play important, often disparate, roles across the allergic disease spectrum. While these cells and their mediators are commonly associated with allergic inflammation, they also exhibit several functions either promoting or restricting tumor growth. In this Position Paper we discuss common granulocyte and mast cell features relating to immunomodulatory functions in allergy and in cancer. We highlight key mechanisms which may inform cancer treatment and propose pertinent areas for future research. We suggest areas where understanding the communication between granulocytes, mast cells, and the tumor microenvironment, will be crucial for identifying immune mechanisms that may be harnessed to counteract tumor development. For example, a comprehensive understanding of allergic and immune factors driving distinct neutrophil states and those mechanisms that link mast cells with immunotherapy resistance, might enable targeted manipulation of specific subpopulations, leading to precision immunotherapy in cancer. We recommend specific areas of investigation in AllergoOncology and knowledge exchange across disease contexts to uncover pertinent reciprocal functions in allergy and cancer and allow therapeutic manipulation of these powerful cell populations. These will help address the unmet needs in stratifying and managing patients with allergic diseases and cancer.

Construction and validation of a metabolic-related genes prognostic model for oral squamous cell carcinoma based on bioinformatics

BACKGROUND

Oral squamous cell carcinoma (OSCC) accounts for a frequently-occurring head and neck cancer, which is characterized by high rates of morbidity and mortality. Metabolism-related genes (MRGs) show close association with OSCC development, metastasis and progression, so we constructed an MRGs-based OSCC prognosis model for evaluating OSCC prognostic outcome.

METHODS

This work obtained gene expression profile as well as the relevant clinical information from the The Cancer Genome Atlas (TCGA) database, determined the MRGs related to OSCC by difference analysis, screened the prognosis-related MRGs by performing univariate Cox analysis, and used such identified MRGs for constructing the OSCC prognosis prediction model through Lasso-Cox regression. Besides, we validated the model with the GSE41613 dataset based on Gene Expression Omnibus (GEO) database.

RESULTS

The present work screened 317 differentially expressed MRGs from the database, identified 12 OSCC prognostic MRGs through univariate Cox regression, and then established a clinical prognostic model composed of 11 MRGs by Lasso-Cox analysis. Based on the optimal risk score threshold, cases were classified as low- or high-risk group. As suggested by Kaplan-Meier (KM) analysis, survival rate was obviously different between the two groups in the TCGA training set (P < 0.001). According to subsequent univariate and multivariate Cox regression, risk score served as the factor to predict prognosis relative to additional clinical features (P < 0.001). Besides, area under ROC curve (AUC) values for patient survival at 1, 3 and 5 years were determined as 0.63, 0.70, and 0.76, separately, indicating that the prognostic model has good predictive accuracy. Then, we validated this clinical prognostic model using GSE41613. To enhance our model prediction accuracy, age, gender, risk score together with TNM stage were incorporated in a nomogram. As indicated by results of ROC curve and calibration curve analyses, the as-constructed nomogram had enhanced prediction accuracy compared with clinicopathological features alone, besides, combining clinicopathological characteristics with risk score contributed to predicting patient prognosis and guiding clinical decision-making.

CONCLUSION

In this study, 11 MRGs prognostic models based on TCGA database showed superior predictive performance and had a certain clinical application prospect in guiding individualized.

Regulatory pattern of abnormal promoter CpG island methylation in the glioblastoma multiforme classification

Glioblastoma (GBM) is characterized by extensive genetic and phenotypic heterogeneity. However, it remains unexplored primarily how CpG island methylation abnormalities in promoter mediate glioblastoma typing. First, we presented a multi-omics scale map between glioblastoma sample clusters constructed based on promoter CpG island (PCGI) methylation-driven genes, using datasets including methylation profiles, expression profiles, and single-cell sequencing data from multiple highly annotated public clinical cohorts. Second, we identified differences in the tumor microenvironment between the two glioblastoma sample clusters and resolved key signaling pathways between cell clusters at the single-cell level based on comprehensive comparative analyses to investigate the reasons for survival differences between two of these clusters. Finally, we developed a diagnostic map and a prediction model for glioblastoma, and compared theoretical differences of drug sensitivity between two glioblastoma sample clusters. In summary, this study established a classification system for dissecting promoter CpG island methylation heterogeneity in glioblastoma and provides a new perspective for the diagnosis and treatment of glioblastoma.