Landscape and the immune patterns of cuproptosis in oral squamous cell carcinoma.

BACKGROUND: Oral squamous cell carcinoma is an increasingly prevalent cancer type characterized by high incidence and mortality rates. Its early detection is challenging, primarily because of the absence of early molecular markers. Cuproptosis is a novel regulatory mechanism of cell death with implications in various cancers. In this study, we aimed to study cuproptosis-related genes in oral squamous cell carcinoma to identify their prognostic value. METHODS: By analyzing genomic, bulk RNA-seq, and single-cell RNA-seq data, we investigated 13 cuproptosis-related genes in The Cancer Genome Atlas-Oral Squamous Cell Carcinoma dataset and Gene Expression Omnibus repository (GSE172577). RESULTS: ATP7A, ATP7B, and DLST were the most frequently mutated genes, with nine of our studied genes associated with overall survival. Single-cell analysis was conducted to identify cuproptosis-related tumor cells in oral squamous cell carcinoma, which revealed two distinct patterns based on the expression of cuproptosis-related genes. These patterns exhibit differences in genetic alterations and tumor immune microenvironment. Finally, we developed a cuproptosis index using a random forest algorithm based on cuproptosis pattern-related genes in which higher levels were linked to poorer prognosis. CONCLUSION: Our findings provide valuable insights into the mechanisms underlying oral squamous cell carcinoma-associated cuproptosis.

Development and Validation of a Nomogram Model Based on Hematological Indicators for Predicting the Prognosis of Diffused Gliomas.

Background: Diffused gliomas are aggressive malignant brain tumors. Various hematological factors have been proven to predict the prognosis of patients with gliomas. The aim of this study is to integrate these hematological markers and develop a comprehensive system for predicting the prognosis of patients with gliomas. Method: This retrospective study included 723 patients pathologically diagnosed with diffused gliomas. Hematological indicators were collected preoperatively, including neutrophil-to-lymphocyte ratio (NLR), lymphocyte-monocyte ratio (LMR), platelet-to-lymphocyte ratio (PLR), albumin globulin ratio (AGR), platelet distribution width (PDW), red blood cell distribution width (RDW), fibrinogen (FIB), and prognostic nutritional index (PNI). Least absolute shrinkage and selection operator (LASSO) Cox was applied to screen the hematological indicators for a better prediction of patients' prognosis and to build an inflammation-nutrition score. A nomogram model was developed to predict the overall survival (OS), which included age, tumor grade, IDH-1 mutations, and inflammation-nutrition score. Result: Patients were randomly divided into a primary cohort (n = 509) and a validation cohort (n = 214). There was no difference in age and IDH-1 mutation frequency between the cohorts. In the primary cohort, NLR, LMR, AGR, FIB, and PNI were selected to build an inflammation nutrition score. Patients with a high-risk inflammation-nutrition score had a short median OS of 17.40 months compared with 27.43 months in the low-risk group [HR 2.54; 95% CI (1.91-3.37); p < 0.001]. Moreover, age, tumor grade, IDH-1 mutations, and inflammation-nutrition score were independent prognostic factors in the multivariate analysis and thus were included in the nomogram model. The nomogram model showed a high prediction value with a Harrell's concordance index (C-index) of 0.75 [95% CI (0.72-0.77)]. The validation cohort supported these results. Conclusion: The prognostic nomogram model provided a high prognostic predictive power for patients with gliomas.