Computational identification of long non-coding RNAs associated with graphene therapy in glioblastoma multiforme

Zhuoheng Zou; Ming Zhang; Shang Xu; Youzhong Zhang; Junzheng Zhang; Zesong Li; Xiao Zhu

doi:10.1093/braincomms/fcad293

Computational identification of long non-coding RNAs associated with graphene therapy in glioblastoma multiforme

Brain Commun. 2023 Oct 25;6(1):fcad293. doi: 10.1093/braincomms/fcad293. eCollection 2024.

Authors

Zhuoheng Zou¹, Ming Zhang², Shang Xu¹, Youzhong Zhang¹, Junzheng Zhang¹, Zesong Li³, Xiao Zhu¹

Affiliations

¹ Computational Systems Biology Lab (CSBL), The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang 524023, China.
² Department of Physical Medicine and Rehabilitation, Zibo Central Hospital, Zibo 255000, China.
³ Guangdong Provincial Key Laboratory of Systems Biology and Synthetic Biology for Urogenital Tumors, Shenzhen Key Laboratory of Genitourinary Tumor, Department of Urology, The First Affiliated Hospital of Shenzhen University, Shenzhen Second People's Hospital (Shenzhen Institute of Translational Medicine), Shenzhen 518035, China.

Abstract

Glioblastoma multiforme represents the most prevalent primary malignant brain tumour, 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 tumour site has encountered significant obstacles attributable to inadequate biocompatibility and inefficient drug delivery systems. In this context, the use of a biofunctional surface modification of graphene oxide has emerged as a promising strategy to surmount these challenges. By changing the surface of graphene oxide, enhanced biocompatibility can be achieved, facilitating efficient transport of long non-coding RNA-based therapeutics specifically to the tumour 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 tumour microenvironment model and analyse drug sensitivity. To validate our findings, we referenced the IMvigor210 immunotherapy model. Finally, we investigated differences in the tumour 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 treating glioblastoma multiforme patients. Furthermore, we identified 16 compounds that could be utilized in graphene therapy. Our study offers novel insights into the treatment of glioblastoma multiforme, and the identified graphene therapy-related long non-coding RNAs and compounds hold promise for further research in this field. Furthermore, 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.

Keywords: drug screening; glioblastoma multiforme; graphene oxide therapy; independent prognostic model; lncRNAs.