Yang Tao, Zhang Ruiguang, Cui Zhenfen, Zheng Bowen, Zhu Xiaowei, Yang Xinyu, Huang Qiang
Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin 300000, P.R. China.
Department of Neurosurgery, Heji Hospital Affiliated to Changzhi Medical College, Changzhi, Shanxi 046000, P.R. China.
Oncol Lett. 2024 Mar 28;27(5):238. doi: 10.3892/ol.2024.14371. eCollection 2024 May.
Glucose metabolism, as a novel theory to explain tumor cell behavior, has been intensively studied in various tumors. The present study explored the long non-coding RNAs (lncRNAs) related to glycolysis in grade II-III glioma, aiming to provide a promising target for further research. Pearson correlation analysis was used to identify glycolysis-related lncRNAs. Univariate/multivariate Cox regression analysis and the Least Absolute Shrinkage and Selection Operator algorithm were applied to identify glycolysis-related lncRNAs to construct a prognosis prediction model. Subsequently, multi-dimensional evaluations were used to verify whether the risk model could predict the prognosis and survival rate of patients with grade II-III glioma. Finally, it was verified by functional experiments. The present study finally identified seven glycolysis-related lncRNAs (CRNDE, AC022034.1, RHOQ-AS1, AL159169.2, AL133215.2, AC007098.1 and LINC02587) to construct a prognosis prediction model. The present study further investigated the underlying immune microenvironment, somatic landscape and functional enrichment pathways. Additionally, individualized immunotherapeutic strategies and candidate compounds were identified to guide clinical treatment. The experimental results demonstrated that CRNDE could increase the proliferation of SHG-44 cells. In conclusion, a large sample of human grade II-III glioma in The Cancer Genome Atlas database was used to construct a risk model using glycolysis-related lncRNAs to predict the prognosis of patients with grade II-III glioma.
葡萄糖代谢作为一种解释肿瘤细胞行为的新理论，已在各种肿瘤中得到深入研究。本研究探讨了与II-III级胶质瘤糖酵解相关的长链非编码RNA（lncRNA），旨在为进一步研究提供有前景的靶点。采用Pearson相关分析来识别与糖酵解相关的lncRNA。应用单因素/多因素Cox回归分析和最小绝对收缩和选择算子算法来识别与糖酵解相关的lncRNA，以构建预后预测模型。随后，采用多维度评估来验证风险模型是否能够预测II-III级胶质瘤患者的预后和生存率。最后，通过功能实验进行验证。本研究最终确定了7种与糖酵解相关的lncRNA（CRNDE、AC022034.1、RHOQ-AS1、AL159169.2、AL133215.2、AC007098.1和LINC02587），构建了预后预测模型。本研究进一步探究了潜在的免疫微环境、体细胞图谱和功能富集途径。此外，还确定了个体化的免疫治疗策略和候选化合物，以指导临床治疗。实验结果表明，CRNDE可促进SHG-44细胞的增殖。总之，利用癌症基因组图谱数据库中的大量人类II-III级胶质瘤样本，使用与糖酵解相关的lncRNA构建风险模型，以预测II-III级胶质瘤患者的预后。
