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基于甲基化相关长链非编码 RNA 的肺腺癌预测模型。

A methylation-related lncRNA-based prediction model in lung adenocarcinomas.

机构信息

Thoracic Surgery, The Thirteenth People's Hospital of Chongqing City, Chongqing City, China.

Thoracic Surgery, Nuclear Industry 215 Hospital, Xianyang City, Shaanxi Province, China.

出版信息

Clin Respir J. 2024 Aug;18(8):e13753. doi: 10.1111/crj.13753.

Abstract

BACKGROUND

The collaboration between methylation and the lung adenocarcinoma (LUAD) occurrence and development is closes. Long noncoding RNA (lncRNA), as a regulatory factor of various biological functions, can be used for cancer diagnosis. Our study aimed to construct a robust methylation-related lncRNA signature of LUAD.

METHODS

In the Cancer Genome Atlas (TCGA) dataset, we download the RNA expression data and clinical information of LUAD cases. To develop the best prognostic signature based on methylation-related lncRNAs, Cox regression analyses were utilized. Using Kaplan-Meier analysis, overall survival rates were compared between risk category included both low- and high-risk patients. To categorize genes according to their functional significance, GSEA (Subramanian et al, 2005) was used. Single-sample gene set enrichment analysis (ssGSEA) was used to further reveal the potential molecular mechanism of the methylation-related lncRNA prognostic model in immune infiltration. Using TRLnc (http://www.licpathway.net/TRlnc) and lncRNASNP to analyse the SNP sites and TRLnc of these 18 lncRNAs. LncSEA website was used to analyse 18 lncRNA in the process of tumour development and development. Go was used to analyse the enriched pathways enriched by TFs (transcription factors), Cerna networks, and proteins bound to each other of these 18 lncRNAs. The 'prophetic' package was used to analyse the value of this prognostic model in guiding personalized immunotherapy.

RESULTS

In this study, we identified 18 methylation-related lncRNAs (AP002761.1, AL118558.3, CH17-340M24.3, AL353150.1, AC004687.1, LINC00996, AF186192.1, HSPC324, AC087752.3, FAM30A, AC106047.1, AC026355.1, ABALON, LINC01843, AL606489.1, NKILA, AP001453.2, GSEC) to establish a methylation-related lncRNA signature that can detect patients prognosis in LUAD. The enriched pathways enriched by proteins interacting with 18 lncRNAs are mainly EMT, hypoxia, stemness and proliferation, among which LINC00996 and AF186192.1 are regulated by multiple tumour associated transcription factors, such as TP53 and TP63, and fam30a and mRNA form a Cerna network. There are 2319 SNP loci in LINC00996, 36 of which are risk SNP loci and 205 SNP loci in af186192.1; AF186192.1 affects 95 conserved miRNAs and 123 non-conserved miRNAs, promotes the binding of 149 pairs of miRNAs: lncRNAs and inhibits the binding of 95 pairs of miRNAs: lncRNAs. The ROC curve demonstrated that the established methylation-related lncRNA signature was more effective in predicting the prognosis of patients in LUAD than the clinicopathological parameters. Our research has confirmed that patients in the high-risk group which was separated by the risk score model based on methylation-related lncRNA had shorter OS. According to GSEA, the high-risk group had a predominantly tumour- and immune-related pathway enrichment. A significant association was shown by ssGSEA between predictive signature and immune status in LUAD patients. In addition, principal component analysis (PCA) demonstrated the prognostic and predictive value of our signature. The correlation between the predictive signature of methylation-related lncRNA and IC50 of conventional chemotherapy drugs can provide personalized chemotherapy regimens for LUAD patients. Methylation-related lncRNA signature can effectively predict DFS of patients in LUAD.

摘要

背景

甲基化与肺腺癌(LUAD)发生发展的关系密切。长链非编码 RNA(lncRNA)作为多种生物功能的调节因子,可用于癌症诊断。本研究旨在构建稳健的 LUAD 甲基化相关 lncRNA 特征。

方法

在癌症基因组图谱(TCGA)数据集,我们下载了 LUAD 病例的 RNA 表达数据和临床信息。为了基于甲基化相关 lncRNA 开发最佳预后标志物,我们使用了 Cox 回归分析。使用 Kaplan-Meier 分析比较了风险类别中包含的低风险和高风险患者的总生存率。为了根据功能意义对基因进行分类,我们使用了 GSEA(Subramanian 等人,2005)。使用单样本基因集富集分析(ssGSEA)进一步揭示了甲基化相关 lncRNA 预后模型在免疫浸润中的潜在分子机制。使用 TRLnc(http://www.licpathway.net/TRlnc)和 lncRNASNP 分析这些 18 个 lncRNA 的 SNP 位点和 TRLnc。LncSEA 网站用于分析 18 个 lncRNA 在肿瘤发生和发展过程中的作用。GO 用于分析这些 18 个 lncRNA 所富含的转录因子(TFs)、Cerna 网络和彼此结合的蛋白质所富集的通路。使用 'prophetic' 包分析这个预后模型在指导个性化免疫治疗中的价值。

结果

在这项研究中,我们确定了 18 个甲基化相关的 lncRNA(AP002761.1、AL118558.3、CH17-340M24.3、AL353150.1、AC004687.1、LINC00996、AF186192.1、HSPC324、AC087752.3、FAM30A、AC106047.1、AC026355.1、ABALON、LINC01843、AL606489.1、NKILA、AP001453.2、GSEC)来建立一个甲基化相关的 lncRNA 特征,用于检测 LUAD 患者的预后。与 18 个 lncRNA 相互作用的蛋白质所富含的通路主要是 EMT、缺氧、干性和增殖,其中 LINC00996 和 AF186192.1 受多个肿瘤相关转录因子的调节,如 TP53 和 TP63,fam30a 和 mRNA 形成 Cerna 网络。LINC00996 有 2319 个 SNP 位点,其中 36 个是风险 SNP 位点,AF186192.1 有 205 个 SNP 位点;AF186192.1 影响 95 个保守 miRNA 和 123 个非保守 miRNA,促进 149 对 miRNA:lncRNA 的结合,抑制 95 对 miRNA:lncRNA 的结合。ROC 曲线表明,建立的甲基化相关 lncRNA 特征在预测 LUAD 患者的预后方面比临床病理参数更有效。我们的研究证实,基于甲基化相关 lncRNA 构建的风险评分模型将患者分为高危组和低危组后,高危组患者的 OS 更短。根据 GSEA,高危组具有主要的肿瘤和免疫相关通路富集。ssGSEA 显示预测签名与 LUAD 患者的免疫状态之间存在显著相关性。此外,主成分分析(PCA)证明了我们的签名的预后和预测价值。甲基化相关 lncRNA 特征与常规化疗药物 IC50 的相关性可以为 LUAD 患者提供个性化的化疗方案。甲基化相关 lncRNA 特征可以有效地预测 LUAD 患者的 DFS。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/b416/11347386/db040de12428/CRJ-18-e13753-g007.jpg

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