• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

鉴定用于预测肺腺癌预后和免疫治疗疗效的N7-甲基鸟苷相关特征

Identification of N7-methylguanosine related signature for prognosis and immunotherapy efficacy prediction in lung adenocarcinoma.

作者信息

Li Zhouhua, Wang Wenjun, Wu Juan, Ye Xiaoqun

机构信息

Department of Respiratory Diseases, The Second Affiliated Hospital of Nanchang University, Nanchang, China.

出版信息

Front Med (Lausanne). 2022 Aug 24;9:962972. doi: 10.3389/fmed.2022.962972. eCollection 2022.

DOI:10.3389/fmed.2022.962972
PMID:36091687
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9449120/
Abstract

BACKGROUND

Lung adenocarcinoma (LUAD) is one of the most frequent causes of tumor-related mortality worldwide. Recently, the role of N7-methylguanosine (mG) in tumors has begun to receive attention, but no investigation on the impact of mG on LUAD. This study aims to elucidate the significance of mG on the prognosis and immunotherapy in LUAD.

METHODS

Consensus clustering was employed to determine the molecular subtype according to mG-related regulators extracted from The Cancer Genome Atlas (TCGA) database. Survival, clinicopathological features and tumor mutational burden (TMB) analysis were applied to research molecular characteristics of each subtype. Subsequently, "limma" package was used to screen differentially expressed genes (DEGs) between subtypes. In the TCGA train cohort ( = 245), a prognostic signature was established by univariate Cox regression, lasso regression and multivariate Cox regression analysis according to DEGs and survival analysis was employed to assess the prognosis. Then the prognostic value of the signature was verified by TCGA test cohort ( = 245), TCGA entire cohort ( = 490) and GSE31210 cohort ( = 226). Moreover, the association among immune infiltration, clinical features and the signature was investigated. The immune checkpoints, TMB and tumor immune dysfunction and exclusion (TIDE) were applied to predict the immunotherapy response.

RESULTS

Two novel molecular subtypes (C1 and C2) of LUAD were identified. Compared to C2 subtype, C1 subtype had poorer prognosis and higher TMB. Subsequently, the signature (called the "mG score") was constructed according to four key genes (, , , and ). The distribution of mG score were significantly different between two molecular subtypes. The patients with lower mG score had better prognosis in TCGA train cohort and three verification cohort. The mG score was intensively related to immune infiltration. Compared with the lower score, the higher mG score was related to remarkable upregulation of the PD-1 and PD-L1, the higher TMB and the lower TIDE score.

CONCLUSION

This study established a mG-related signature for predicting prognosis and immunotherapy in LUAD, which may contribute to the development of new therapeutic strategies for LUAD.

摘要

背景

肺腺癌(LUAD)是全球肿瘤相关死亡的最常见原因之一。最近,N7-甲基鸟苷(mG)在肿瘤中的作用开始受到关注,但尚未有关于mG对LUAD影响的研究。本研究旨在阐明mG对LUAD预后和免疫治疗的意义。

方法

采用共识聚类法,根据从癌症基因组图谱(TCGA)数据库中提取的mG相关调节因子确定分子亚型。应用生存分析、临床病理特征和肿瘤突变负荷(TMB)分析来研究各亚型的分子特征。随后,使用“limma”软件包筛选亚型之间的差异表达基因(DEG)。在TCGA训练队列(n = 245)中,根据DEG通过单变量Cox回归、套索回归和多变量Cox回归分析建立预后特征,并采用生存分析评估预后。然后通过TCGA测试队列(n = 245)、TCGA完整队列(n = 490)和GSE31210队列(n = 226)验证该特征的预后价值。此外,还研究了免疫浸润、临床特征与该特征之间的关联。应用免疫检查点、TMB和肿瘤免疫功能障碍与排除(TIDE)来预测免疫治疗反应。

结果

鉴定出LUAD的两种新分子亚型(C1和C2)。与C2亚型相比,C1亚型预后较差且TMB较高。随后,根据四个关键基因(,,,和)构建了特征(称为“mG评分”)。两种分子亚型之间mG评分的分布有显著差异。在TCGA训练队列和三个验证队列中,mG评分较低的患者预后较好。mG评分与免疫浸润密切相关。与较低评分相比,较高的mG评分与PD-1和PD-L1的显著上调、较高的TMB和较低的TIDE评分相关。

结论

本研究建立了一种与mG相关的特征,用于预测LUAD的预后和免疫治疗,这可能有助于开发LUAD的新治疗策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bddf/9449120/a281ff451009/fmed-09-962972-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bddf/9449120/175653acffd0/fmed-09-962972-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bddf/9449120/7246c3f539ad/fmed-09-962972-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bddf/9449120/09b8f4e9690d/fmed-09-962972-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bddf/9449120/c388fd60b146/fmed-09-962972-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bddf/9449120/7a181408be64/fmed-09-962972-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bddf/9449120/1efc5aa144ee/fmed-09-962972-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bddf/9449120/7560690d4050/fmed-09-962972-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bddf/9449120/c0c1ae65d3a7/fmed-09-962972-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bddf/9449120/1df6addc13db/fmed-09-962972-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bddf/9449120/73f1a3fb1492/fmed-09-962972-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bddf/9449120/8e786c07e479/fmed-09-962972-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bddf/9449120/717885fb4e82/fmed-09-962972-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bddf/9449120/a281ff451009/fmed-09-962972-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bddf/9449120/175653acffd0/fmed-09-962972-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bddf/9449120/7246c3f539ad/fmed-09-962972-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bddf/9449120/09b8f4e9690d/fmed-09-962972-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bddf/9449120/c388fd60b146/fmed-09-962972-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bddf/9449120/7a181408be64/fmed-09-962972-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bddf/9449120/1efc5aa144ee/fmed-09-962972-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bddf/9449120/7560690d4050/fmed-09-962972-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bddf/9449120/c0c1ae65d3a7/fmed-09-962972-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bddf/9449120/1df6addc13db/fmed-09-962972-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bddf/9449120/73f1a3fb1492/fmed-09-962972-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bddf/9449120/8e786c07e479/fmed-09-962972-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bddf/9449120/717885fb4e82/fmed-09-962972-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/bddf/9449120/a281ff451009/fmed-09-962972-g013.jpg

相似文献

1
Identification of N7-methylguanosine related signature for prognosis and immunotherapy efficacy prediction in lung adenocarcinoma.鉴定用于预测肺腺癌预后和免疫治疗疗效的N7-甲基鸟苷相关特征
Front Med (Lausanne). 2022 Aug 24;9:962972. doi: 10.3389/fmed.2022.962972. eCollection 2022.
2
Pan-cancer analysis identifies proteasome 26S subunit, ATPase (PSMC) family genes, and related signatures associated with prognosis, immune profile, and therapeutic response in lung adenocarcinoma.泛癌分析确定了蛋白酶体26S亚基、ATP酶(PSMC)家族基因,以及与肺腺癌预后、免疫特征和治疗反应相关的特征。
Front Genet. 2023 Jan 9;13:1017866. doi: 10.3389/fgene.2022.1017866. eCollection 2022.
3
Combination of tumor mutation burden and immune infiltrates for the prognosis of lung adenocarcinoma.肿瘤突变负荷与免疫浸润联合用于肺腺癌的预后评估。
Int Immunopharmacol. 2021 Sep;98:107807. doi: 10.1016/j.intimp.2021.107807. Epub 2021 Jun 25.
4
Anoikis-related subtype and prognosis analyses based on bioinformatics, and an expression verification of ANGPTL4 based on experiments of lung adenocarcinoma.基于生物信息学的失巢凋亡相关亚型及预后分析,以及基于肺腺癌实验的ANGPTL4表达验证。
J Thorac Dis. 2024 Aug 31;16(8):5361-5378. doi: 10.21037/jtd-24-1123. Epub 2024 Aug 28.
5
A cuproptosis-related lncRNA signature for predicting prognosis and immunotherapy response of lung adenocarcinoma.一个与铜死亡相关的 lncRNA 特征,可预测肺腺癌的预后和免疫治疗反应。
Hereditas. 2023 Jul 24;160(1):31. doi: 10.1186/s41065-023-00293-w.
6
Identification of novel gene signature for lung adenocarcinoma by machine learning to predict immunotherapy and prognosis.基于机器学习的肺腺癌新型基因特征识别,预测免疫治疗和预后。
Front Immunol. 2023 Jul 31;14:1177847. doi: 10.3389/fimmu.2023.1177847. eCollection 2023.
7
Comprehensive analysis of the immunogenic cell death-related signature for predicting prognosis and immunotherapy efficiency in patients with lung adenocarcinoma.全面分析免疫细胞死亡相关特征,预测肺腺癌患者的预后和免疫治疗效果。
BMC Med Genomics. 2023 Aug 8;16(1):184. doi: 10.1186/s12920-023-01604-w.
8
Identification of immune activation-related gene signature for predicting prognosis and immunotherapy efficacy in lung adenocarcinoma.鉴定免疫激活相关基因特征,以预测肺腺癌的预后和免疫治疗疗效。
Front Immunol. 2023 Jul 7;14:1217590. doi: 10.3389/fimmu.2023.1217590. eCollection 2023.
9
Clinical Significance and Immunologic Landscape of a Five-IL(R)-Based Signature in Lung Adenocarcinoma.肺腺癌中基于五个免疫检查点基因(IL(R))的表达signature 的临床意义和免疫图谱
Front Immunol. 2021 Aug 23;12:693062. doi: 10.3389/fimmu.2021.693062. eCollection 2021.
10
Single-cell sequencing analysis and transcriptome analysis constructed the macrophage related gene-related signature in lung adenocarcinoma and verified by an independent cohort.单细胞测序分析和转录组分析构建了肺腺癌中与巨噬细胞相关基因相关的特征,并通过独立队列进行了验证。
Genomics. 2022 Nov;114(6):110520. doi: 10.1016/j.ygeno.2022.110520. Epub 2022 Nov 11.

引用本文的文献

1
Prognostic analysis and identification of M7G immune-related genes in lung squamous cell carcinoma.肺鳞状细胞癌中M7G免疫相关基因的预后分析与鉴定
Front Immunol. 2025 Mar 3;16:1515838. doi: 10.3389/fimmu.2025.1515838. eCollection 2025.
2
Leveraging Bioinformatics and Machine Learning for Identifying Prognostic Biomarkers and Predicting Clinical Outcomes in Lung Adenocarcinoma.利用生物信息学和机器学习识别肺腺癌的预后生物标志物并预测临床结果。
Genes (Basel). 2024 Nov 21;15(12):1497. doi: 10.3390/genes15121497.
3
RNA modifications in cancer immune therapy: regulators of immune cells and immune checkpoints.

本文引用的文献

1
DNA methylation molecular subtypes for prognosis prediction in lung adenocarcinoma.肺腺癌预后预测的 DNA 甲基化分子亚型。
BMC Pulm Med. 2022 Apr 7;22(1):133. doi: 10.1186/s12890-022-01924-0.
2
N-methylguanosine tRNA modification promotes esophageal squamous cell carcinoma tumorigenesis via the RPTOR/ULK1/autophagy axis.N-甲基鸟苷转移 RNA 修饰通过 RPTOR/ULK1/自噬轴促进食管鳞状细胞癌发生。
Nat Commun. 2022 Mar 18;13(1):1478. doi: 10.1038/s41467-022-29125-7.
3
Identification of a Four-Gene Signature Associated with the Prognosis Prediction of Lung Adenocarcinoma Based on Integrated Bioinformatics Analysis.
癌症免疫治疗中的 RNA 修饰:免疫细胞和免疫检查点的调节剂。
Front Immunol. 2024 Sep 20;15:1463847. doi: 10.3389/fimmu.2024.1463847. eCollection 2024.
4
Prognostic Value and Immune Landscapes of Four Types of RNA Modification Writer-Related LncRNAs Signature in Lung Adenocarcinoma.四种RNA修饰书写相关长链非编码RNA特征在肺腺癌中的预后价值及免疫景观
J Cancer. 2024 Jul 9;15(15):4818-4837. doi: 10.7150/jca.96755. eCollection 2024.
5
Bioinformatics Identification and Experimental Validation of a Prognostic Model for the Survival of Lung Squamous Cell Carcinoma Patients.肺鳞状细胞癌患者生存预后模型的生物信息学鉴定与实验验证
Biochem Genet. 2024 May 28. doi: 10.1007/s10528-024-10828-z.
6
Integrated analysis reveals critical cisplatin-resistance regulators E2F7 contributed to tumor progression and metastasis in lung adenocarcinoma.综合分析显示,关键的顺铂耐药调节因子E2F7促成了肺腺癌的肿瘤进展和转移。
Cancer Cell Int. 2024 May 17;24(1):173. doi: 10.1186/s12935-024-03366-6.
7
M7G-related tumor immunity: novel insights of RNA modification and potential therapeutic targets.M7G 相关的肿瘤免疫:RNA 修饰的新见解及潜在治疗靶点。
Int J Biol Sci. 2024 Jan 27;20(4):1238-1255. doi: 10.7150/ijbs.90382. eCollection 2024.
基于整合生物信息学分析的与肺腺癌预后预测相关的四个基因标志物的鉴定。
Genes (Basel). 2022 Jan 27;13(2):238. doi: 10.3390/genes13020238.
4
Aberrant translation regulated by METTL1/WDR4-mediated tRNA N7-methylguanosine modification drives head and neck squamous cell carcinoma progression.METTL1/WDR4 介导的 tRNA N7-甲基鸟苷修饰调控的异常翻译驱动头颈部鳞状细胞癌进展。
Cancer Commun (Lond). 2022 Mar;42(3):223-244. doi: 10.1002/cac2.12273. Epub 2022 Feb 18.
5
A Novel Inflammatory-Related Gene Signature Based Model for Risk Stratification and Prognosis Prediction in Lung Adenocarcinoma.一种基于新型炎症相关基因特征的肺腺癌风险分层及预后预测模型
Front Genet. 2022 Jan 5;12:798131. doi: 10.3389/fgene.2021.798131. eCollection 2021.
6
Neoantigen-driven B cell and CD4 T follicular helper cell collaboration promotes anti-tumor CD8 T cell responses.新抗原驱动的 B 细胞和 CD4+T 滤泡辅助细胞协同作用促进抗肿瘤 CD8+T 细胞反应。
Cell. 2021 Dec 9;184(25):6101-6118.e13. doi: 10.1016/j.cell.2021.11.007. Epub 2021 Nov 30.
7
METTL1/WDR4-mediated mG tRNA modifications and mG codon usage promote mRNA translation and lung cancer progression.METTL1/WDR4 介导的 mG tRNA 修饰和 mG 密码子使用促进 mRNA 翻译和肺癌进展。
Mol Ther. 2021 Dec 1;29(12):3422-3435. doi: 10.1016/j.ymthe.2021.08.005. Epub 2021 Aug 8.
8
Deep computational analysis details dysregulation of eukaryotic translation initiation complex eIF4F in human cancers.深度计算分析详细说明了真核翻译起始复合物 eIF4F 在人类癌症中的失调。
Cell Syst. 2021 Sep 22;12(9):907-923.e6. doi: 10.1016/j.cels.2021.07.002. Epub 2021 Aug 5.
9
Development and validation of a novel epigenetic-related prognostic signature and candidate drugs for patients with lung adenocarcinoma.开发和验证一种新型与表观遗传学相关的肺腺癌患者预后标志和候选药物。
Aging (Albany NY). 2021 Jul 20;13(14):18701-18717. doi: 10.18632/aging.203315.
10
Molecular subtypes based on CNVs related gene signatures identify candidate prognostic biomarkers in lung adenocarcinoma.基于与 CNVs 相关基因特征的分子亚型鉴定肺腺癌的候选预后生物标志物。
Neoplasia. 2021 Jul;23(7):704-717. doi: 10.1016/j.neo.2021.05.006. Epub 2021 Jun 14.