肿瘤细胞治疗研究
英文原题：Development of prognostic indicator based on NAD+ metabolism related genes in glioma.
PARP9、BST1、NMNAT2和CD38这四个基因可能是胶质瘤患者重要的分子生物标志物和治疗靶点。
研究表明，烟酰胺腺嘌呤二核苷酸（NAD+）代谢可促进胶质瘤的发生和发展。然而，NAD+代谢在胶质瘤中的具体作用和机制尚不清楚，且尚无关于NAD+代谢相关基因预测胶质瘤患者生存的系统研究。
本研究基于癌症基因组图谱（TCGA）和中国脑胶质瘤基因组图谱（CGGA）数据库中脑胶质瘤病例的表达数据进行。首先，通过共识聚类将TCGA脑胶质瘤病例基于49个NAD+代谢相关基因（NMRGs）分为不同亚型。通过将49个NMRGs与正常样本和脑胶质瘤样本之间的差异表达基因（DEGs）取交集，获得NAD+代谢相关差异表达基因（NMR-DEGs）。然后通过Cox分析和最小收缩和选择算子（LASSO）回归分析构建风险模型。通过生存曲线和受试者工作特征（ROC）曲线验证模型的有效性。此外，通过Cox分析对风险模型进行独立预后分析。然后，我们还鉴定了高 risk 组和低 risk 组之间不同的免疫细胞、HLA家族基因和免疫检查点。最后，还在单细胞水平上探索了模型基因的功能。
共识聚类将胶质瘤患者分为两个亚型，两个亚型的总体生存期（OS）存在差异。通过重叠5,995个差异表达基因（DEGs）和49个NAD+代谢相关基因（NMRGs），共筛选出11个NAD+代谢相关差异表达基因（NMR-DEGs）。随后，通过Cox回归和最小绝对收缩和选择算子（Lasso）回归分析获得四个模型基因PARP9、BST1、NMNAT2和CD38，并构建风险模型。高风险组的OS较低。受试者工作特征（ROC）曲线在1年、3年和5年的曲线下面积（AUCs）均>0.7。Cox分析显示，年龄、G3级、G4级、IDH状态、ATRX状态、BCR状态和风险评分是可靠的独立预后因素。此外，在高风险组和低风险组之间发现了三种不同的免疫细胞——活化的肥大细胞、活化的NK细胞和初始B细胞，24个不同的HLA家族基因如HLA-DPA1和HLA-H，以及8个不同的免疫检查点如ICOS、LAG3和CD274。模型基因与增殖、细胞分化和凋亡显著相关。
BACKGROUND: Studies have shown that Nicotinamide adenine dinucleotide (NAD+) metabolism can promote the occurrence and development of glioma. However, the specific effects and mechanisms of NAD+ metabolism in glioma are unclear and there were no systematic researches about NAD+ metabolism related genes to predict the survival of patients with glioma. METHODS: The research was performed based on expression data of glioma cases in the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases. Firstly, TCGA-glioma cases were classified into different subtypes based on 49 NAD+ metabolism-related genes (NMRGs) by consensus clustering. NAD+ metabolism-related differentially expressed genes (NMR-DEGs) were gotten by intersecting the 49 NMRGs and differentially expressed genes (DEGs) between normal and glioma samples. Then a risk model was built by Cox analysis and the least shrinkage and selection operator (LASSO) regression analysis. The validity of the model was verified by survival curves and receiver operating characteristic (ROC) curves. In addition, independent prognostic analysis of the risk model was performed by Cox analysis. Then, we also identified different immune cells, HLA family genes and immune checkpoints between high and low risk groups. Finally, the functions of model genes at single-cell level were also explored. RESULTS: Consensus clustering classified glioma patients into two subtypes, and the overall survival (OS) of the two subtypes differed. A total of 11 NAD+ metabolism-related differentially expressed genes (NMR-DEGs) were screened by overlapping 5,995 differentially expressed genes (DEGs) and 49 NAD+ metabolism-related genes (NMRGs). Next, four model genes, PARP9, BST1, NMNAT2, and CD38, were obtained by Cox regression and least absolute shrinkage and selection operator (Lasso) regression analyses and to construct a risk model. The OS of high-risk group was lower. And the area under curves (AUCs) of Receiver operating characteristic (ROC) curves were >0.7 at 1, 3, and 5 years. Cox analysis showed that age, grade G3, grade G4, IDH status, ATRX status, BCR status, and risk Scores were reliable independent prognostic factors. In addition, three different immune cells, Mast cells activated, NK cells activated and B cells naive, 24 different HLA family genes, such as HLA-DPA1 and HLA-H, and 8 different immune checkpoints, such as ICOS, LAG3, and CD274, were found between the high and low risk groups. The model genes were significantly relevant with proliferation, cell differentiation, and apoptosis. CONCLUSION: The four genes, PARP9, BST1, NMNAT2, and CD38, might be important molecular biomarkers and therapeutic targets for glioma patients.
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