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一种用于预测胶质瘤预后和免疫特征的m6A/m5C/m1A/m7G相关长链非编码RNA特征

An m6A/m5C/m1A/m7G-Related Long Non-coding RNA Signature to Predict Prognosis and Immune Features of Glioma.

作者信息

Shao Dongqi, Li Yu, Wu Junyong, Zhang Binbin, Xie Shan, Zheng Xialin, Jiang Zhiquan

机构信息

Department of Neurosurgery, The First Affiliated Hospital of Bengbu Medical College, Bengbu, China.

出版信息

Front Genet. 2022 May 26;13:903117. doi: 10.3389/fgene.2022.903117. eCollection 2022.

DOI:10.3389/fgene.2022.903117
PMID:35692827
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9178125/
Abstract

Gliomas are the most common and fatal malignant type of tumor of the central nervous system. RNA post-transcriptional modifications, as a frontier and hotspot in the field of epigenetics, have attracted increased attention in recent years. Among such modifications, methylation is most abundant, and encompasses N6-methyladenosine (m6A), 5-methylcytosine (m5C), N1 methyladenosine (m1A), and 7-methylguanosine (m7G) methylation. RNA-sequencing data from healthy tissue and low-grade glioma samples were downloaded from of The Cancer Genome Atlas database along with clinical information and mutation data from glioblastoma tumor samples. Forty-nine m6A/m5C/m1A/m7G-related genes were identified and an m6A/m5C/m1A/m7G-lncRNA signature of co-expressed long non-coding RNAs selected. Least absolute shrinkage and selection operator Cox regression analysis was used to identify 12 m6A/m5C/m1A/m7G-related lncRNAs associated with the prognostic characteristics of glioma and their correlation with immune function and drug sensitivity analyzed. Furthermore, the Chinese Glioma Genome Atlas dataset was used for model validation. A total of 12 m6A/m5C/m1A/m7G-related genes (AL080276.2, AC092111.1, SOX21-AS1, DNAJC9-AS1, AC025171.1, AL356019.2, AC017104.1, AC099850.3, UNC5B-AS1, AC006064.2, AC010319.4, and AC016822.1) were used to construct a survival and prognosis model, which had good independent prediction ability for patients with glioma. Patients were divided into low and high m6A/m5C/m1A/m7G-LS groups, the latter of which had poor prognosis. In addition, the m6A/m5C/m1A/m7G-LS enabled improved interpretation of the results of enrichment analysis, as well as informing immunotherapy response and drug sensitivity of patients with glioma in different subgroups. In this study we constructed an m6A/m5C/m1A/m7G-LS and established a nomogram model, which can accurately predict the prognosis of patients with glioma and provides direction toward promising immunotherapy strategies for the future.

摘要

神经胶质瘤是中枢神经系统中最常见且致命的恶性肿瘤类型。RNA转录后修饰作为表观遗传学领域的前沿和热点，近年来受到了越来越多的关注。在这些修饰中，甲基化最为丰富，包括N6-甲基腺苷（m6A）、5-甲基胞嘧啶（m5C）、N1-甲基腺苷（m1A）和7-甲基鸟苷（m7G）甲基化。从癌症基因组图谱数据库下载了来自健康组织和低级别神经胶质瘤样本的RNA测序数据，以及胶质母细胞瘤肿瘤样本的临床信息和突变数据。鉴定了49个与m6A/m5C/m1A/m7G相关的基因，并选择了共表达的长链非编码RNA的m6A/m5C/m1A/m7G长链非编码RNA特征。使用最小绝对收缩和选择算子Cox回归分析来鉴定12个与神经胶质瘤预后特征相关的m6A/m5C/m1A/m7G相关长链非编码RNA，并分析它们与免疫功能和药物敏感性的相关性。此外，中国神经胶质瘤基因组图谱数据集用于模型验证。总共使用12个与m6A/m5C/m1A/m7G相关的基因（AL080276.2、AC092111.1、SOX21-AS1、DNAJC9-AS1、AC025171.1、AL356019.2、AC017104.1、AC099850.3、UNC5B-AS1、AC006064.2、AC010319.4和AC016822.1）构建了一个生存和预后模型，该模型对神经胶质瘤患者具有良好的独立预测能力。患者被分为低m6A/m5C/m1A/m7G-LS组和高m6A/m5C/m1A/m7G-LS组，后者预后较差。此外，m6A/m5C/m1A/m7G-LS能够更好地解释富集分析的结果，并为不同亚组的神经胶质瘤患者的免疫治疗反应和药物敏感性提供信息。在本研究中，我们构建了m6A/m5C/m1A/m7G-LS并建立了列线图模型，该模型可以准确预测神经胶质瘤患者的预后，并为未来有前景的免疫治疗策略提供方向。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6ca/9178125/50b9cc9c0c07/fgene-13-903117-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6ca/9178125/59b73fefba10/fgene-13-903117-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6ca/9178125/00019ba4c1da/fgene-13-903117-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6ca/9178125/87f79ee18154/fgene-13-903117-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6ca/9178125/b079e4d243e2/fgene-13-903117-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6ca/9178125/5dc2ab9c1732/fgene-13-903117-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6ca/9178125/ce641fae0126/fgene-13-903117-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6ca/9178125/50b9cc9c0c07/fgene-13-903117-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6ca/9178125/59b73fefba10/fgene-13-903117-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6ca/9178125/4a47da349929/fgene-13-903117-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6ca/9178125/71d4046f2341/fgene-13-903117-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6ca/9178125/5b7db86ac85f/fgene-13-903117-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6ca/9178125/00019ba4c1da/fgene-13-903117-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6ca/9178125/87f79ee18154/fgene-13-903117-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6ca/9178125/b079e4d243e2/fgene-13-903117-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6ca/9178125/5dc2ab9c1732/fgene-13-903117-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6ca/9178125/ce641fae0126/fgene-13-903117-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/c6ca/9178125/50b9cc9c0c07/fgene-13-903117-g010.jpg

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