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.
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.
Neural decoding is useful for understanding brain functions and developing neural interface applications. However, neural interfaces based on rigid electronics often suffer from recording instability due to the foreign body responses caused by their mechanical mismatch with soft tissues, limiting the longitudinal accuracy of neural decoding methods. Herein, it is reported that flexible electronics can be integrated with machine learning algorithms to achieve long-term reliable neural decoding. Wet-spun conductive polymer microfibers showed mechanical robustness and flexibility, low impedance, and chronic biocompatibility, enabling intracerebral neural recordings in epileptic mice at a high signal-to-noise ratio eight weeks after implantation. When the signals recorded by the flexible electrodes were used in machine learning analyses with diverse complex algorithms, they consistently showed higher prediction accuracy for epileptic seizures than stiff metal electrode signals, particularly in the case of using long-term recordings for testing or small-sample datasets for training. A real-time warning system based on the flexible neural electrodes was built that predicted seizures eight minutes in advance with a low false alarm rate. Our work bridges flexible electronics and artificial intelligence for neural decoding applications such as long-term treatment of chronic neurological disorders.
Neural decoding is useful for understanding brain functions and developing neural interface applications. However, neural interfaces based on rigid electronics often suffer from recording instability due to the foreign body responses caused by their mechanical mismatch with soft tissues, limiting the longitudinal accuracy of neural decoding methods. Herein, it is reported that flexible electronics can be integrated with machine learning algorithms to achieve long-term reliable neural decoding. Wet-spun conductive polymer microfibers showed mechanical robustness and flexibility, low impedance, and chronic biocompatibility, enabling intracerebral neural recordings in epileptic mice at a high signal-to-noise ratio eight weeks after implantation. When the signals recorded by the flexible electrodes were used in machine learning analyses with diverse complex algorithms, they consistently showed higher prediction accuracy for epileptic seizures than stiff metal electrode signals, particularly in the case of using long-term recordings for testing or small-sample datasets for training. A real-time warning system based on the flexible neural electrodes was built that predicted seizures eight minutes in advance with a low false alarm rate. Our work bridges flexible electronics and artificial intelligence for neural decoding applications such as long-term treatment of chronic neurological disorders.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.