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Abstract Background Ferroptosis is a novel type of programmed cell death in various tumors; however, underlying mechanisms remain unclear. We aimed to develop ferroptosis-related long non-coding RNA (FRlncRNA) risk scores to predict lower-grade glioma (LGG) prognosis and to conduct functional analyses to explore potential mechanisms. Methods LGG-related RNA sequencing data were extracted from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. Pearson correlation analysis was used to identify the FRlncRNAs, univariate Cox regression analysis was for identify the prognostic FRlncRNAs, and then intersection FRlncRNAs were screened between TCGA and CGGA. Least absolute shrinkage and selection operator (LASSO) Cox regression was used to develop a risk score to predict LGG prognosis. Results A total of nine FRlncRNAs were screened to construct the novel prognostic risk score of LGG, and high-risk score patients had a worse overall survival than low-risk score patients both in TCGA and CGGA datasets. The risk score was quite correlated with clinicopathological characteristics (age, WHO grade, status of MGMT Methtlation, IDH mutation, 1p/19q codeletion, and TMB), and could promote current molecular subtyping systems. Comprehensive analyses revealed that signaling pathways of B-cell receptor and T-cell receptor, immune cells of macrophage cell and CD4+ T cell, tumor microenvironment of stroma score and immune score, and immune checkpoints of PD-1, PD-L1, and CTLA4 were all enriched in the high-risk score group. Conclusion The nine FRlncRNAs risk scores was a promising biomarker to predict the LGG’s prognosis and distinguish the characteristics of molecular and immune.

Abstract Parotid mucoepidermoid carcinoma (P-MEC) is a significant histopathological subtype of salivary gland cancer with inherent heterogeneity and complexity. Existing clinical models inadequately offer personalized treatment options for patients. In response, we assessed the efficacy of four machine learning algorithms vis-à-vis traditional analysis in forecasting the overall survival (OS) of P-MEC patients. Using the SEER database, we analyzed data from 882 postoperative P-MEC patients (stages I–IVA). Single-factor Cox regression and four machine learning techniques (random forest, LASSO, XGBoost, best subset regression) were employed for variable selection. The optimal model was derived via stepwise backward regression, Akaike Information Criterion (AIC), and Area Under the Curve (AUC). Bootstrap resampling facilitated internal validation, while prediction accuracy was gauged through C-index, time-dependent ROC curve, and calibration curve. The model’s clinical relevance was ascertained using decision curve analysis (DCA). The study found 3-, 5-, and 10-year OS rates of 0.887, 0.841, and 0.753, respectively. XGBoost, BSR, and LASSO stood out in predictive efficacy, identifying seven key prognostic factors including age, pathological grade, T stage, N stage, radiation therapy, chemotherapy, and marital status. A subsequent nomogram revealed a C-index of 0.8499 (3-year), 0.8557 (5-year), and 0.8375 (10-year) and AUC values of 0.8670, 0.8879, and 0.8767, respectively. The model also highlighted the clinical significance of postoperative radiotherapy across varying risk levels. Our prognostic model, grounded in machine learning, surpasses traditional models in prediction and offer superior visualization of variable importance.

This research dives into the intricate immune landscape of head and neck cancer (HNC), with a keen focus on the roles of specific immune cell subpopulations and their linked genes. We used tumour RNA-seq (in-house cohort: n = 192, TCGA-HNSC: n = 546) and Mendelian randomization to pinpoint key SNPs in immune cells that have a causal connection to HNC. Our discoveries unveil a spectrum of tumour immune phenotypes that either offer protection against or increase the risk of HNC. We underscore the therapeutic promise of Complement C3d Receptor 2 (CR2), a gene closely tied to immune cells, with its increased expression in tumour tissues linked to a more favourable prognosis. This is correlated with heightened immune pathway activity, stronger resistance to radiochemotherapy, and improved immunotherapy responses. Our research emphasises the pivotal role of CR2 in immune regulation and the significance of immune cells in tumour progression, highlighting the potential of CR2-targeted therapeutic interventions.

As educational sectors and stakeholders are increasingly engaged in the practice of international education, there have been many discussions regarding two persisting issues with the practice, which can be summarised as Western-centrism and elitism. Applying concepts from the study of political economy, this paper analyses major political constraints underlying these two challenges. The paper suggests that the origin of these political constraints is a Western-dominated neoliberal world order, with powerful effects on the educational choices made by advantaged groups in non-Western societies. The analysis highlights a political-economic cycle based on the West's export of educational products in exchange for human and economic capitals, which sustains the current ecology of international education. The paper also discusses possible ways to redress the political constraints for international education to be more inclusive and equitable. It concludes with notes on new challenges underway and their effects on the future directions of international education. [ABSTRACT FROM AUTHOR]