Glioma is a highly invasive primary brain tumour, making it challenging to accurately predict prognosis for glioma patients. Cuproptosis is a recently discovered cell death attracting significant attention in the tumour field. Whether cuproptosis‐related genes have prognostic predictive value has not been clarified. In this study, uni‐/multi‐variate Cox and Lasso regression analyses were applied to construct a risk model based on cuproptosis‐related lncRNAs using TCGA and CGGA cohorts. A nomogram was constructed to quantify individual risk, including clinical and genic characteristics and risk. GO and KEGG analyses were used to define functional enrichment of DEGs. Tumour mutation burden (TMB) and immune checkpoint analyses were performed to evaluate potential responses to ICI therapy. Ten prognostic lncRNAs were obtained from Cox regression. Based on the median risk score, patients were divided into high‐ and low‐risk groups. Either for grade 2–3 or for grade 4, glioma patients with high‐risk exhibited significant poorer prognoses. The risk was an independent risk factor associated with overall survival. The high‐risk group was functionally associated with immune responses and cancer‐related pathways. The high‐risk group was associated with higher TMB scores. The expression levels of many immune checkpoints in the high‐risk group were significantly higher than those in the low‐risk group. Differentiated immune pathways were primarily enriched in the IFN response, immune checkpoint and T‐cell co‐stimulation pathways. In conclusion, we established a risk model based on cuproptosis‐related lncRNAs showing excellent prognostic prediction ability but also indicating the immuno‐microenvironment status of glioma.

Recent studies have identified glycosylated RNAs (glycoRNAs) as a novel class of biomolecules potentially involved in cancers and immunological diseases. However, their presence and functional roles in glioma remain unexplored. GlycoRNAs were extracted from glioma cells and detected using Ac4ManNAz labeling and Northern blot. Small RNA deep sequencing and qRT-PCR were employed to determine RNA types and content. A sequence-specific RNA-capture magnetic bead system was developed to enrich specific glycoRNAs, such as U2 and U4. Glycan components were analyzed using liquid chromatography-mass spectrometry. CCK-8, adhesion, ki67, TUNEL staining assays were used to evaluate cell viability, adhesion, proliferation and apoptosis. Glioma cells were found to be rich in glycoRNAs, predominantly small RNAs, with U2 and U4 being particularly abundant. These glycoRNAs primarily contained fucosylated and sialylated complex glycans. The depletion of cell-surface glycoRNAs at the observed time point significantly inhibited glioma cell viability and proliferation, without altering cell adhesion or apoptosis levels. This study underscored the significant role of glycoRNAs in glioma proliferation and provided a foundation for further research into their potential as novel biomarkers and therapeutic targets for glioma. Glioma cells U87 and LN229 showed significant enrichment in glycoRNAs, predominantly small RNAs, with U2 and U4 being particularly abundant and specific. Furthermore, these glycoRNAs were found to be modified by multiple glycans, primarily complex, fucosylated, and sialylated structures.

Parkinson’s disease (PD) is the second most common worldwide age-related neurodegenerative disorder without effective treatments. Cuproptosis is a newly proposed conception of cell death extensively studied in oncological diseases. Currently, whether cuproptosis contributes to PD remains largely unclear. The dataset GSE22491 was studied as the training dataset, and GSE100054 was the validation dataset. According to the expression levels of cuproptosis-related genes (CRGs) and differentially expressed genes (DEGs) between PD patients and normal samples, we obtained the differentially expressed CRGs. The protein-protein interaction (PPI) network was achieved through the Search Tool for the Retrieval of Interacting Genes. Meanwhile, the disease-associated module genes were screened from the weighted gene co-expression network analysis (WGCNA). Afterward, the intersection genes of WGCNA and PPI were obtained and enriched using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Subsequently, the key genes were identified from the datasets. The receiver operating characteristic curves were plotted and a PPI network was constructed, and the PD-related miRNAs and key genes-related miRNAs were intersected and enriched. Finally, the 2 hub genes were verified via qRT-PCR in the cell model of the PD and the control group. 525 DEGs in the dataset GSE22491 were identified, including 128 upregulated genes and 397 downregulated genes. Based on the PPI network, 41 genes were obtained. Additionally, the dataset was integrated into 34 modules by WGCNA. 36 intersection genes found from WGCNA and PPI were significantly abundant in 7 pathways. The expression levels of the genes were validated, and 2 key genes were obtained, namely peptidase inhibitor 3 (PI3) and neuroserpin family I member 1 (SERPINI1). PD-related miRNAs and key genes-related miRNAs were intersected into 29 miRNAs including hsa-miR-30c-2-3p. At last, the qRT-PCR results of 2 hub genes showed that the expressions of mRNA were up-regulated in PD. Taken together, this study demonstrates the coordination of cuproptosis in PD. The key genes and miRNAs offer novel perspectives in the pathogenesis and molecular targeting treatment for PD.

Sleep disorders are a common health problem affecting a significant proportion of the adult population. Emerging evidence suggests that dietary factors, particularly polyunsaturated fatty acid (PUFA) intake, may play a role in modulating sleep quality. This study aims to investigate the association between omega‐3 (ω‐3) and omega‐6 (ω‐6) PUFA and sleep disorders using cross‐sectional survey data and data from genome‐wide association studies (GWAS). Using data from the National Health and Nutrition Examination Survey (NHANES, 2005–2018), we analyzed a cohort of 31,920 participants, with the primary independent variables being intake of ω‐3 and ω‐6 PUFAs. Multivariate regression was used to assess associations, and restricted cubic spline analysis was used to explore potential non‐linear dose–response relationships. Two‐sample Mendelian randomization (MR) analyses were performed to evaluate the causal effects of levels of multiple fatty acids on the risk of sleep disorders. For analysis on NHANES data, the participants with sleep disorders had significantly lower ω‐3 PUFA intake (1.71 ± 1.11 g) compared to those without sleep disorders (1.78 ± 1.14 g, p < 0.001). Regression analysis revealed that higher ω‐3 PUFA intake was associated with a reduced risk of sleep disorders, while the MR analyses showed that a higher ratio of ω‐6 to total fatty acid levels was causally associated with a lower risk of sleep disorders (IVW OR = 0.930, 95% CI: 0.880–0.983, p = 0.011). Our findings suggest that increased ω‐3 FA intake and increased ratio of ω‐6 to total fatty acid level may be associated with a lower risk of sleep disorders, highlighting the potential benefits of dietary modification for sleep health. Future research should further explore these associations and consider intervention studies to establish causality and optimal dietary recommendations to prevent sleep disorders.