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一种基于N7-甲基鸟苷相关长链非编码RNA的创新模型,用于预测膀胱癌的预后和肿瘤免疫格局。

An innovative model based on N7-methylguanosine-related lncRNAs for forecasting prognosis and tumor immune landscape in bladder cancer.

作者信息

Ren Lei, Yang Xu, Liu Jinwen, Wang Weifeng, Liu Zixiong, Lin Qingyuan, Huang Bin, Pan Jincheng, Mao Xiaopeng

机构信息

Department of Urology, The First Affiliated Hospital of Sun Yat-Sen University, Sun Yat-Sen University, No.58 Zhongshan 2nd Road, Guangzhou, 510080, Guangdong, China.

Department of Urology, The Seventh Affiliated Hospital of Sun Yat-Sen University, Sun Yat-Sen University, Shenzhen, China.

出版信息

Cancer Cell Int. 2023 May 8;23(1):85. doi: 10.1186/s12935-023-02933-7.

DOI:10.1186/s12935-023-02933-7
PMID:37158958
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10165842/
Abstract

BACKGROUND

As a novel type of the prevalent post-transcriptional modifications, N7-methylguanosine (m7G) modification is essential in the tumorigenesis, progression, and invasion of many cancers, including bladder cancer (BCa). However, the integrated roles of m7G-related lncRNAs in BCa remain undiscovered. This study aims to develop a prognostic model based on the m7G-related lncRNAs and explore its predictive value of the prognosis and anti-cancer treatment sensitivity.

METHODS

We obtained RNA-seq data and corresponding clinicopathological information from the TCGA database and collected m7G-related genes from previous studies and GSEA. Based on LASSO and Cox regression analysis, we developed a m7G prognostic model. The Kaplan-Meier (K-M) survival analysis and ROC curves were performed to evaluate the predictive power of the model. Gene set enrichment analysis (GSEA) was conducted to explore the molecular mechanisms behind apparent discrepancies between the low- and high-risk groups. We also investigated immune cell infiltration, TIDE score, TMB, the sensitivity of common chemotherapy drugs, and the response to immunotherapy between the two risk groups. Finally, we validated the expression levels of these ten m7G-related lncRNAs in BCa cell lines by qRT-PCR.

RESULTS

We developed a m7G prognostic model (risk score) composed of 10 m7G-related lncRNAs that are significantly associated with the OS of BCa patients. The K-M survival curves revealed that the high-risk group patients had significantly worse OS than those in the low-risk group. The Cox regression analysis confirmed that the risk score was a significant independent prognostic factor for BCa patients. We found that the high-risk group had higher the immune scores and immune cell infiltration. Furthermore, the results of the sensitivity of common anti-BCa drugs showed that the high-risk group was more sensitive to neoadjuvant cisplatin-based chemotherapy and anti-PD1 immunotherapy. Finally, qRT-PCR revealed that AC006058.1, AC073133.2, LINC00677, and LINC01338 were significantly downregulated in BCa cell lines, while the expression levels of AC124312.2 and AL158209.1 were significantly upregulated in BCa cell lines compared with normal cell lines.

CONCLUSION

The m7G prognostic model can be applied to accurately predict the prognosis and provide robust directions for clinicians to develop better individual-based and precise treatment strategies for BCa patients.

摘要

背景

作为一种新型的普遍存在的转录后修饰,N7-甲基鸟苷(m7G)修饰在包括膀胱癌(BCa)在内的许多癌症的发生、发展和侵袭中至关重要。然而,m7G相关lncRNAs在BCa中的综合作用仍未被发现。本研究旨在建立一种基于m7G相关lncRNAs的预后模型,并探讨其对预后和抗癌治疗敏感性的预测价值。

方法

我们从TCGA数据库中获取RNA测序数据和相应的临床病理信息,并从先前的研究和基因集富集分析(GSEA)中收集m7G相关基因。基于LASSO和Cox回归分析,我们建立了一个m7G预后模型。进行Kaplan-Meier(K-M)生存分析和ROC曲线以评估该模型的预测能力。进行基因集富集分析以探索低风险组和高风险组之间明显差异背后的分子机制。我们还研究了两组风险组之间的免疫细胞浸润、TIDE评分、肿瘤突变负荷(TMB)、常用化疗药物的敏感性以及对免疫治疗的反应。最后,我们通过qRT-PCR验证了这10种m7G相关lncRNAs在BCa细胞系中的表达水平。

结果

我们建立了一个由10种m7G相关lncRNAs组成的m7G预后模型(风险评分),这些lncRNAs与BCa患者的总生存期(OS)显著相关。K-M生存曲线显示,高风险组患者的OS明显低于低风险组。Cox回归分析证实,风险评分是BCa患者的一个显著独立预后因素。我们发现高风险组具有更高的免疫评分和免疫细胞浸润。此外,常用抗BCa药物敏感性的结果表明,高风险组对基于顺铂的新辅助化疗和抗PD1免疫治疗更敏感。最后,qRT-PCR显示AC006058.1、AC073133.2、LINC00677和LINC01338在BCa细胞系中显著下调,而与正常细胞系相比,AC124312.2和AL158209.1在BCa细胞系中的表达水平显著上调。

结论

m7G预后模型可用于准确预测预后,并为临床医生为BCa患者制定更好的个体化和精准治疗策略提供有力指导。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a33/10165842/e3f57d80424f/12935_2023_2933_Fig11_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a33/10165842/0e8ecfda32af/12935_2023_2933_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a33/10165842/db5478775e43/12935_2023_2933_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a33/10165842/eae889ed3a7e/12935_2023_2933_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/7a33/10165842/dddbd46fa5ff/12935_2023_2933_Fig9_HTML.jpg
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