Profiling pro-neural to mesenchymal transition identifies a lncRNA signature in glioma.
Profiling pro-neural to mesenchymal transition identifies a lncRNA signature in glioma.
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分析前神经向间质转化，识别神经胶质瘤中的 lncRNA 特征
DOI：
10.1186/s12967-020-02552-0
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发表时间：
2020-10-07
影响因子：
7.4
通讯作者：
Cheng W
中科院分区：
文献类型：
--
作者：
Liang Q;Guan G;Li X;Wei C;Wu J;Cheng P;Wu A;Cheng W
背景胶质瘤的分子分类为研究胶质瘤的生物学和治疗策略奠定了基础。胶质瘤的前神经间质转化（PMT）与侵袭性表型、不良预后和治疗抵抗有关。最近的研究表明，长链非编码RNA（lncRNA）是肿瘤间质转化的关键介质。然而，lncRNA和PMT在胶质瘤中的关系还没有系统的investigated.MethodsGene表达谱从癌症基因组图谱（TCGA），中国胶质瘤基因组图谱（CGGA），GSE 16011，和伦勃朗与可用的临床和基因组信息进行分析。生物信息学方法，如加权基因共表达网络分析（WGCNA），基因集富集分析（GSEA），考克斯分析，和最小绝对收缩和选择算子（LASSO）分析performed. ResultsAccordingto PMT分数，我们证实，PMT状态与胶质瘤的危险行为和预后不良正相关。WGCNA分析共鉴定出149条PMT相关lncRNA，其中10条与PMT相关，4、LINC 01503、CRNDE、OSMR-AS 1、SNHG 18、AC145343.2、RP 11 -25K21.6、RP 11 -38L15.2）进一步筛选，以构建PMT相关的lncRNA风险特征，这可以将病例分为两组，具有不同的症状。多因素考克斯回归分析表明，签名是高级别胶质瘤的独立预后因素。高风险病例更可能被归类为间充质亚型，其通过招募巨噬细胞、中性粒细胞和调节性T细胞来增强免疫抑制状态。此外，6 lncRNA的签名可以作为竞争的内源性RNA，以促进PMT在glioblastoma.ConclusionsWe分析PMT状态在胶质瘤，并建立了一个PMT相关的10-lncRNA签名胶质瘤，可以独立预测胶质瘤的生存和触发PMT，增强免疫抑制。
BackgroundMolecular classification has laid the framework for exploring glioma biology and treatment strategies. Pro-neural to mesenchymal transition (PMT) of glioma is known to be associated with aggressive phenotypes, unfavorable prognosis, and treatment resistance. Recent studies have highlighted that long non-coding RNAs (lncRNAs) are key mediators in cancer mesenchymal transition. However, the relationship between lncRNAs and PMT in glioma has not been systematically investigated.MethodsGene expression profiles from The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), GSE16011, and Rembrandt with available clinical and genomic information were used for analyses. Bioinformatics methods such as weighted gene co-expression network analysis (WGCNA), gene set enrichment analysis (GSEA), Cox analysis, and least absolute shrinkage and selection operator (LASSO) analysis were performed.ResultsAccording to PMT scores, we confirmed that PMT status was positively associated with risky behaviors and poor prognosis in glioma. The 149 PMT-related lncRNAs were identified by WGCNA analysis, among which 10 (LINC01057, TP73-AS1, AP000695.4, LINC01503, CRNDE, OSMR-AS1, SNHG18, AC145343.2, RP11-25K21.6, RP11-38L15.2) with significant prognostic value were further screened to construct a PMT-related lncRNA risk signature, which could divide cases into two groups with distinct prognoses. Multivariate Cox regression analyses indicated that the signature was an independent prognostic factor for high-grade glioma. High-risk cases were more likely to be classified as the mesenchymal subtype, which confers enhanced immunosuppressive status by recruiting macrophages, neutrophils, and regulatory T cells. Moreover, six lncRNAs of the signature could act as competing endogenous RNAs to promote PMT in glioblastoma.ConclusionsWe profiled PMT status in glioma and established a PMT-related 10-lncRNA signature for glioma that could independently predict glioma survival and trigger PMT, which enhanced immunosuppression.
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影响因子：
10.5
作者：
Amankulor NM;Kim Y;Arora S;Kargl J;Szulzewsky F;Hanke M;Margineantu DH;Rao A;Bolouri H;Delrow J;Hockenbery D;Houghton AM;Holland EC
通讯作者：
Holland EC
WGCNA：用于加权相关网络分析的 R 包。
影响因子：
3
作者：
Langfelder P;Horvath S
通讯作者：
Horvath S
影响因子：
11.2
作者：
Lau J;Ilkhanizadeh S;Wang S;Miroshnikova YA;Salvatierra NA;Wong RA;Schmidt C;Weaver VM;Weiss WA;Persson AI
通讯作者：
Persson AI
影响因子：
15.9
作者：
Ostrom, Quinn T.;Bauchet, Luc;Barnholtz-Sloan, Jill S.
通讯作者：
Barnholtz-Sloan, Jill S.
影响因子：
9.7
作者：
Jia, Peng;Cai, Heng;Xue, Yixue
通讯作者：
Xue, Yixue