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多形性胶质母细胞瘤中与石墨烯疗法相关的长链非编码RNA的计算鉴定

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

作者信息

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

机构信息

Computational Systems Biology Lab (CSBL), The Marine Biomedical Research Institute, Guangdong Medical University, Zhanjiang 524023, China.

Department of Physical Medicine and Rehabilitation, Zibo Central Hospital, Zibo 255000, China.

出版信息

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

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.

摘要

多形性胶质母细胞瘤是最常见的原发性恶性脑肿瘤,而长链非编码RNA在多形性胶质母细胞瘤的发病机制和进展中起关键作用。然而,基于长链非编码RNA的治疗药物成功递送至肿瘤部位却遇到了重大障碍,这归因于生物相容性不足和药物递送系统效率低下。在此背景下,对氧化石墨烯进行生物功能表面修饰已成为克服这些挑战的一种有前景的策略。通过改变氧化石墨烯的表面,可以实现增强的生物相容性,从而促进基于长链非编码RNA的治疗药物特异性地转运至肿瘤部位。这种创新方法为利用长链非编码RNA生物学固有的治疗潜力治疗多形性胶质母细胞瘤患者提供了机会。本研究旨在从癌症基因组图谱数据库中提取相关基因,并将它们与长链非编码RNA相关联,以鉴定与石墨烯治疗相关的长链非编码RNA。为实现这一目标,我们进行了一系列分析,包括单变量Cox回归、最小绝对收缩和选择算子回归以及多变量Cox回归。将所得的与石墨烯治疗相关的长链非编码RNA用于建立风险评分模型。随后,我们对鉴定出的与石墨烯治疗相关的长链非编码RNA进行了基因本体论和京都基因与基因组百科全书通路分析。此外,我们使用风险模型构建肿瘤微环境模型并分析药物敏感性。为了验证我们的发现,我们参考了IMvigor210免疫治疗模型。最后,我们研究了肿瘤干性指数的差异。通过我们的研究,我们鉴定出四种有前景的与石墨烯治疗相关的长链非编码RNA(AC011405.1、HOXC13-AS、LINC01127和LINC01574),可用于治疗多形性胶质母细胞瘤患者。此外,我们还鉴定出16种可用于石墨烯治疗的化合物。我们的研究为多形性胶质母细胞瘤的治疗提供了新的见解,并且鉴定出的与石墨烯治疗相关的长链非编码RNA和化合物在该领域的进一步研究中具有前景。此外,额外的生物学实验对于验证我们模型的临床意义至关重要。这些实验有助于确认鉴定出的与石墨烯治疗相关的长链非编码RNA和化合物在治疗多形性胶质母细胞瘤中的潜在治疗价值和疗效。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/33f6/10754320/6dfa33f48bc8/fcad293_ga1.jpg

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