Cuprotosis is a novel and different cell death mechanism from the existing known ones that can be used to explore new approaches to treating cancer. Just like ferroptosis and pyroptosis, cuprotosis-related genes regulate various types of tumorigenesis, invasion and metastasis. However, the relationship between cuprotosis related long non-coding RNA (cuprotosis-related lncRNA) in glioma development and prognosis has not been investigated. We obtained relevant data from the Genotype-Tissue Expression (GTEx), Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA) and published articles.First, we identi ed 365 cuprotosis-related lncRNAs based on 10 cuprotosis-related differential genes (|R 2 |>0.4, p < 0.001). Then using lasso and Cox regression analysis methods, 12 prognostic cuprotosisrelated lncRNAs were obtained and constructed the CuLncSigi risk score formula. Our next step was to divide the tumor gliomas into two groups (high-risk and low-risk) based on the median risk score, and we found that patients in the high-risk group had a signi cantly worse prognosis. We used internal and external validation methods to simultaneously analyze and validate that the risk score model has good predictive power for patients with glioma. Next, we also performed enrichment analyses such as GSEA and aaGSEA and evaluated the relationship between immune-related drugs and tumor treatment. In conclusion, we successfully constructed a formula of cuprotosis-related lncRNAs with a powerful predictive function. More importantly, our study paves the way for exploring cuprotosis mechanisms in glioma occurrence and development, and helps to nd new relevant biomarkers for glioma early identi cation and diagnosis and to investigate new therapeutic approaches.

Hospital Acquired Pneumonia (HAP) is one of the most common complications and late causes of death in TBI patients. Targeted prevention and treatment of HAP are of great significance for improving the prognosis of TBI patients. In the previous clinical observation, we found that folic acid treatment for TBI patients has a good effect on preventing and treating HAP. We conducted this retrospective cohort study to demonstrate what we observed by selecting 293 TBI patients from two medical centers and analyzing their hospitalization data. The result showed that the incidence of HAP was significantly lower in TBI patients who received folic acid treatment (44.1% vs. 63.0%, p = 0.012). Multivariate logistic regression analysis showed that folic acid treatment was an independent protective factor for the occurrence of HAP in TBI patients (OR = 0.418, p = 0.031), especially in high-risk groups of HAP, such as the old (OR: 1.356 vs. 2.889), ICU (OR: 1.775 vs. 5.996) and severe TBI (OR: 0.975 vs. 5.424) patients. At the same time, cohort studies of HAP patients showed that folic acid also had a good effect on delaying the progression of HAP, such as reducing the chance of tracheotomy (26.1% vs. 50.8%, p = 0.041), and reduced the length of hospital stay (15 d vs. 19 d, p = 0.029) and ICU stay (5 d vs. 8 d, p = 0.046). Therefore, we believe that folic acid treatment in TBI patients has the potential for preventing and treating HAP, and it is worthy of further clinical research.

Today, numerous international researchers have demonstrated that N7-methylguanosine (m7G) related long non-coding RNAs (m7G-related lncRNAs) are closely linked to the happenings and developments of various human beings’ cancers. However, the connection between m7G-related lncRNAs and glioma prognosis has not been investigated. We did this study to look for new potential biomarkers and construct an m7G-related lncRNA prognostic signature for glioma. We identified those lncRNAs associated with DEGs from glioma tissue sequences as m7G-related lncRNAs. First, we used Pearson’s correlation analysis to identify 28 DEGs by glioma and normal brain tissue gene sequences and predicated 657 m7G-related lncRNAs. Then, eight lncRNAs associated with prognosis were obtained and used to construct the m7G risk score model by lasso and Cox regression analysis methods. Furthermore, we used Kaplan-Meier analysis, time-dependent ROC, principal component analysis, clinical variables, independent prognostic analysis, nomograms, calibration curves, and expression levels of lncRNAs to determine the model’s accuracy. Importantly, we validated the model with external and internal validation methods and found it has strong predictive power. Finally, we performed functional enrichment analysis (GSEA, aaGSEA enrichment analyses) and analyzed immune checkpoints, associated pathways, and drug sensitivity based on predictors. In conclusion, we successfully constructed the formula of m7G-related lncRNAs with powerful predictive functions. Our study provides instructional value for analyzing glioma pathogenesis and offers potential research targets for glioma treatment and scientific research.

Cuprotosis is a novel and different cell death mechanism from the existing known ones that can be used to explore new approaches to treating cancer. Just like ferroptosis and pyroptosis, cuprotosis-related genes regulate various types of tumorigenesis, invasion and metastasis. However, the relationship between cuprotosis related long non-coding RNA (cuprotosis-related lncRNA) in glioma development and prognosis has not been investigated. We obtained relevant data from the Genotype-Tissue Expression (GTEx), Cancer Genome Atlas (TCGA), Chinese Glioma Genome Atlas (CGGA) and published articles. First, we identified 365 cuprotosis-related lncRNAs based on 10 cuprotosis-related differential genes (|R2|>0.4, p < 0.001). Then using lasso and Cox regression analysis methods, 12 prognostic cuprotosis-related lncRNAs were obtained and constructed the CuLncSigi risk score formula. Our next step was to divide the tumor gliomas into two groups (high-risk and low-risk) based on the median risk score, and we found that patients in the high-risk group had a significantly worse prognosis. We used internal and external validation methods to simultaneously analyze and validate that the risk score model has good predictive power for patients with glioma. Next, we also performed enrichment analyses such as GSEA and aaGSEA and evaluated the relationship between immune-related drugs and tumor treatment. In conclusion, we successfully constructed a formula of cuprotosis-related lncRNAs with a powerful predictive function. More importantly, our study paves the way for exploring cuprotosis mechanisms in glioma occurrence and development, and helps to find new relevant biomarkers for glioma early identification and diagnosis and to investigate new therapeutic approaches.

No abstract