目的 低级别胶质瘤（low-grade glioma,LGG）是一种具有显著分子异质性的恶性肿瘤。乳酸化作为由乳酸驱动的翻译后修饰,可通过重塑肿瘤微环境来调控肿瘤进展。本研究旨在基于乳酸化相关基因（lactylation-related genes,LRGs）构建LGG预后预测模型,探索相关分子靶标。方法 从分子签名数据库（molecular signatures database,MSigDB）获取LRGs,结合癌症基因组图谱（the cancer genome atlas,TCGA）和中国脑胶质瘤基因组图谱（Chinese glioma genome atlas,CGGA）数据,筛选LGG中差异表达且与预后相关的LRGs。采用非负矩阵分解（non-negative matrix factorization,NMF）聚类将患者进行分型,通过差异表达基因和多种机器学习算法,构建乳酸化相关风险评分（lactylation-related risk score,LRRS）,评估LRRS与LGG预后的关系。采用实时荧光定量PCR及蛋白质印迹方法检测人胶质瘤细胞以及正常人星形胶质细胞中与LGG预后相关基因PDPN的表达差异情况。利用小干扰RNA沉默人胶质瘤细胞HS683中PDPN基因表达,分别利用CCK-8、划痕及Transwell试验验证沉默PDPN表达对HS683细胞增殖、侵袭及迁移能力的影响。结果 NMF聚类将LGG分为两个具有显著不同通路富集的分子亚型。所构建的LRRS模型包含2个与LGG预后相关的关键基因,高风险组患者总生存期显著缩短。实验验证表明,建模关键基因PDPN在胶质瘤细胞中异常高表达,沉默PDPN可显著抑制HS683细胞的增殖、迁移和侵袭能力。结论 构建的乳酸化相关预后模型能够有效预测LGG患者的生存结局,建模关键基因PDPN可作为潜在治疗靶点进行深入研究。
Abstract
Objective Low-grade glioma (LGG) is a malignant tumor with significant molecular heterogeneity. Lactylation, a lactate-driven post-translational modification, regulates tumor progression by remodeling the tumor microenvironment. This study aims to establish a prognostic prediction model for LGG based on lactylation-related genes (LRGs) and explore their relevant molecular targets. Methods LRGs were retrieved from the MSigDB database. By integrating data from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA), LRGs that were differentially expressed and prognosis-related in LGG were screened. Patients were stratified into subtypes using non-negative matrix factorization (NMF) clustering. A Lactylation-Related Risk Score (LRRS) was constructed using differentially expressed genes and multiple machine learning algorithms, and the relationship between LRRS and LGG prognosis was evaluated. Quantitative real-time polymerase chain reaction and Western blotting were used to detect the expression differences of PDPN (a prognosis-related gene in LGG) in human glioma cells and normal human astrocytes. Small interfering RNA was employed to knock down PDPN expression in the human glioma cell line HS683. Cell Counting Kit-8 assay, wound healing assay, and Transwell assay were respectively used to verify the effects of PDPN knockdown on the proliferation, invasion, and migration abilities of HS683 cells. Results NMF clustering classified LGG into two molecular subtypes with significantly different pathway enrichments. The constructed LRRS model contained 2 key genes associated with LGG prognosis, and patients in the high-risk group exhibited significantly shortened overall survival. Experimental validation demonstrated that PDPN was abnormally highly expressed in glioma cells, and knockdown of PDPN significantly inhibited the proliferation, migration, and invasion abilities of the HS683 cells. Conclusion The constructed lactylation-related prognostic model can effectively predict the survival outcomes of LGG patients. PDPN, a modeling-related gene for LGG, may serve as a candidate worth further in vivo validation.
关键词
低级别胶质瘤 /
乳酸化 /
预后模型 /
足突蛋白
Key words
low-grade glioma /
lactylation /
prognostic model /
podoplanin
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基金
长沙市自然科学基金“基于群体智能优化算法识别脑胶质瘤预后关键基因及其作用机制的研究”（KQ2208172）
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