Comprehensive analyses of glycolysis-related lncRNAs for ovarian cancer patients.
Comprehensive analyses of glycolysis-related lncRNAs for ovarian cancer patients.
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卵巢癌患者糖酵解相关 lncRNA 的综合分析
DOI：
10.1186/s13048-021-00881-2
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发表时间：
2021-09-24
影响因子：
4
通讯作者：
Tong J
中科院分区：
文献类型：
--
作者：
Zheng J;Guo J;Zhu L;Zhou Y;Tong J
研究背景糖酵解和lncRNA在卵巢癌的生长、增殖、侵袭和转移中起重要作用。然而，关于糖酵解相关lncRNAs（GRL）在OC中的研究尚不清楚.在此，我们首先构建了一个基于GRL的风险模型与OC的患者。Methodsprocessed RNA测序（RNA-seq）配置文件与临床病理数据下载TCGA和糖酵解相关基因（GRG）从MSigDB获得。糖酵解相关基因（GRG）和注释的lncRNA之间的皮尔逊相关系数（|R|> 0.4和p < 0.05）来鉴定GRL。在筛选预后GRL后，使用单变量和考克斯回归构建基于5个GRL的风险模型。通过两个验证集对已识别的风险模型进行了验证。此外，通过丰富的算法探索了风险组之间的临床病理学，生物学功能，缺氧评分，免疫微环境，免疫检查点，免疫检查点阻断，化疗药物敏感性，N6-甲基腺苷（m6 A）调节剂和铁蛋白沉积相关基因的差异。结果共获得535个GRL，其中35个GRL具有显著的预后价值。构建并验证了包含5个GRL的预后标志，可以预测预后。诺模图证明了该模型预测预后的准确性。通过ssGSEA计算每个样本的缺氧评分，发现风险评分越高的患者缺氧评分越高，缺氧评分高是一个危险因素。结果显示，两组间共21种微环境细胞（如中央记忆性CD 4 T细胞、中性粒细胞、调节性T细胞等）及基质评分有显著性差异。四种免疫检查点基因（CD 274，LAG 3，VTCN 1和CD 47）在两组中显示出不同的表达水平。此外，16个m6 A调节因子和126个铁蛋白沉积相关基因在低危组中表达较高。GSEA显示风险组与肿瘤相关通路相关。两个风险组被证实对几种化疗药物敏感，低风险组的患者对ICB治疗更敏感。网络的基础上共表达，ceRNA，顺式和反式相互作用提供了深入了解的监管机制GRL.ConclusionsOur确定和验证的风险模型的基础上5 GRL是一个独立的预后因素OC患者。通过综合分析，我们的研究结果揭示了基于GRL的风险模型的潜在生物标志物和治疗靶点。
BackgroundNot only glycolysis but also lncRNAs play a significant role in the growth, proliferation, invasion and metastasis of of ovarian cancer (OC). However, researches about glycolysis -related lncRNAs (GRLs) remain unclear in OC. Herein, we first constructed a GRL-based risk model for patients with OC.MethodsThe processed RNA sequencing (RNA-seq) profiles with clinicopathological data were downloaded from TCGA and glycolysis-related genes (GRGs) were obtained from MSigDB. Pearson correlation coefficient between glycolysis-related genes (GRGs) and annotated lncRNAs (|r| > 0.4 andp< 0.05) were calculated to identify GRLs. After screening prognostic GRLs, a risk model based on five GRLs was constructed using Univariate and Cox regression. The identified risk model was validated by two validation sets. Further, the differences in clinicopathology, biological function, hypoxia score, immune microenvironment, immune checkpoint, immune checkpoint blockade, chemotherapy drug sensitivity, N6-methyladenosine (m6A) regulators, and ferroptosis-related genes between risk groups were explored by abundant algorithms. Finally, we established networks based on co-expression, ceRNA, cis and trans interaction.ResultsA total of 535 GRLs were gained and 35 GRLs with significant prognostic value were identified. The prognostic signature containing five GRLs was constructed and validated and can predict prognosis. The nomogram proved the accuracy of the model for predicting prognosis. After computing hypoxia score of each sample by ssGSEA, we found patients with higher risk scores exhibited higher hypoxia score and high hypoxia score was a risk factor. It was revealed that a total of 21 microenvironment cells (such as Central memory CD4 T cell, Neutrophil, Regulatory T cell and so on) and Stromal score had significant differences between the two groups. Four immune checkpoint genes (CD274, LAG3, VTCN1, and CD47) showed disparate expression levels in the two groups. Besides, 16 m6A regulators and 126 ferroptosis-related genes were expressed higher in the low-risk group. GSEA revealed that the risk groups were associated with tumor-related pathways. The two risk groups were confirmed to be sensitive to several chemotherapeutic agents and patients in the low-risk group were more sensitive to ICB therapy. The networks based on co-expression, ceRNA, cis and trans interaction provided insights into the regulatory mechanisms of GRLs.ConclusionsOur identified and validated risk model based on five GRLs is an independent prognostic factor for OC patients. Through comprehensive analyses, findings of our study uncovered potential biomarker and therapeutic target for the risk model based on the GRLs.
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影响因子：
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作者：
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通讯作者：
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通讯作者：
Hubbard TJ
影响因子：
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作者：
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通讯作者：
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