Computational identification of lncRNAs associated with graphene therapy in glioblastoma multiforme

Abstract

Abstract Glioblastoma multiforme represents the most prevalent primary malignant brain tumor, while long non-coding RNA assumes a pivotal role in the pathogenesis and progression of glioblastoma multiforme. Nonetheless, the successful delivery of long non-coding RNA-based therapeutics to the tumor site has encountered significant obstacles attributable to inadequate biocompatibility and inefficient drug delivery systems. In this context, using a biofunctional surface modification of graphene oxide has emerged as a promising strategy to surmount these challenges. Through the change of the graphene oxide surface, enhanced biocompatibility can be achieved, facilitating efficient transport of long non-coding RNA-based therapeutics specifically to the tumor site. This innovative approach presents the opportunity to exploit the therapeutic potential inherent in long non-coding RNA biology for treating glioblastoma multiforme patients. This study aimed to extract relevant genes from The Cancer Genome Atlas database and associate them with long non-coding RNAs to identify Graphene Therapy-related long non-coding RNA. We conducted a series of analyses to achieve this goal, including univariate Cox regression, Least Absolute Shrinkage and Selection Operator regression, and multivariate Cox regression. The resulting Graphene Therapy-related long non-coding RNAs were utilized to develop a risk score model. Subsequently, we conducted Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses on the identified Graphene Therapy-related long non-coding RNAs. Additionally, we employed the risk model to construct the Tumor Microenvironment model and analyze drug sensitivity. To validate our findings, we referenced the IMvigor 210 immunotherapy model. Finally, we investigated differences in the tumor stemness index. Through our investigation, we identified four promising Graphene Therapy-related long non-coding RNAs (AC011405.1, HOXC13-AS, LINC01127, and LINC01574) that could be utilized for the treatment of glioblastoma multiforme patients. Furthermore, we identified 16 compounds that could be utilized in graphene therapy. Our study offers novel insights into treating glioblastoma multiforme, and the identified Graphene Therapy-related long non-coding RNAs and compounds hold promise for further research in this field. Furthermore, conducting additional biological experiments will be essential to validate the clinical significance of our model. These experiments can help confirm the potential therapeutic value and efficacy of the identified Graphene Therapy-related long non-coding RNAs and compounds in treating glioblastoma multiforme.