RNF43 p.G659fs 通过 PI3K/AKT/mTOR 信号通路和 HLA-E 上调导致 MSI-high 结直肠癌中 NK 细胞功能障碍
RNF43 p.G659fs leads to natural killer cell dysfunction in MSI-high colorectal cancer through PI3K/AKT/mTOR signaling and HLA-E up-regulation.
CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
英文原题:Identifying Differential Expression Genes and Prognostic Signature Based on Subventricular Zone Involved Glioblastoma.
Identifying Differential Expression Genes and Prognostic Signature Based on Subventricular Zone Involved Glioblastoma.
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研究表明,胶质母细胞瘤(GBM)细胞起源于脑室下区(SVZ),且GBM与SVZ的接触与更差的预后和更高的复发率相关。然而,关于GBM与SVZ之间差异表达基因(DEGs)的研究尚缺乏。
我们对7例累及SVZ的GBM及配对的非肿瘤SVZ组织进行了深度RNA测序。评估了DEGs及富集情况。我们从中国脑胶质瘤基因组图谱(CGGA)和癌症基因组图谱(TCGA)数据库中获取了GBM患者的表达谱和临床数据。利用最小绝对收缩和选择算子Cox回归模型在CGGA队列中构建多基因特征。TCGA队列的GBM患者数据用于验证。
我们鉴定出GBM与健康SVZ样本之间的137个DEGs(97个上调,40个下调)。富集分析显示,DEGs主要富集于免疫相关条目,包括体液免疫反应调节、T细胞分化和对肿瘤坏死因子的反应,以及MAPK、cAMP、PPAR、PI3K-Akt和NF-κb信号通路。构建了一个八基因(BCAT1、HPX、NNMT、TBX5、RAB42、TNFRSF19、C16orf86和TRPC5)特征。GBM患者被分为两个风险组。高风险患者的总生存期较低风险患者显著缩短。单因素和多因素回归分析表明,风险评分水平是一个独立的预后因素。GBM患者的高风险评分与1p19q共缺失和IDH1突变呈负相关。免疫浸润分析进一步显示,高风险评分与活化的NK细胞和单核细胞计数呈负相关,但与巨噬细胞和活化树突状细胞计数以及更高的PD-L1 mRNA表达呈正相关。
本研究基于GBM与健康SVZ之间的DEGs开发了一种新的基因特征,用于判断GBM患者的预后。靶向这些基因可能成为GBM的一种治疗策略。
Background: Studies have suggested that glioblastoma (GBM) cells originate from the subventricular zone (SVZ) and that GBM contact with the SVZ correlated with worse prognosis and higher recurrence.
However, research on differentially expressed genes (DEGs) between GBM and the SVZ is lacking. Methods: We performed deep RNA sequencing on seven SVZ-involved GBMs and paired tumor-free SVZ tissues. DEGs and enrichment were assessed.
We obtained GBM patient expression profiles and clinical data from the Chinese Glioma Genome Atlas (CGGA) and The Cancer Genome Atlas (TCGA) databases. The least absolute shrinkage and selection operator Cox regression model was utilized to construct a multigene signature in the CGGA cohort. GBM patient data from TCGA cohort were used for validation. Results: We identified 137 (97 up- and 40 down-regulated) DEGs between GBM and healthy SVZ samples. Enrichment analysis revealed that DEGs were mainly enriched in immune-related terms, including humoral immune response regulation, T cell differentiation, and response to tumor necrosis factor, and the MAPK, cAMP, PPAR, PI3K-Akt, and NF-κb signaling pathways. An eight-gene ( BCAT1 , HPX , NNMT , TBX5 , RAB42 , TNFRSF19 , C16orf86 , and TRPC5 ) signature was constructed.
GBM patients were stratified into two risk groups. High-risk patients showed significantly reduced overall survival compared with low-risk patients. Univariate and multivariate regression analyses indicated that the risk score level represented an independent prognostic factor. High risk score of GBM patients negatively correlated with 1p19q codeletion and IDH1 mutation.
Immune infiltration analysis further showed that the high risk score was negatively correlated with activated NK cell and monocyte counts, but positively correlated with macrophage and activated dendritic cell counts and higher PD-L1 mRNA expression. Conclusion: Here, a novel gene signature based on DEGs between GBM and healthy SVZ was developed for determining GBM patient prognosis. Targeting these genes may be a therapeutic strategy for GBM.
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