基于N6-甲基腺苷相关长链非编码核糖核酸表达的喉鳞状细胞癌预后分析

Prognostic analysis of laryngeal squamous cell carcinoma based on the expression of N6-methyladenosine-related lncRNAs

摘要:

目的 探索N6-甲基腺苷(m6A)相关长链非编码核糖核酸(lncRNA)与喉鳞状细胞癌(LSCC)的预后关系及其临床意义。方法 获取癌症基因组图谱 (TCGA)数据库LSCC的转录组数据和临床数据，共表达分析筛选m6A基因相关的lncRNA，单变量Cox分析m6A相关lncRNA与LSCC预后关系、套索回归及交叉验证法迭代分析构建LSCC预后模型。采用实时荧光定量聚合酶链式反应 (qPCR) 验证构建预后模型的lncRNA在LSCC中的转录水平。通过预后模型计算风险评分，将LSCC区分为高、低风险患者，高、低风险患者之间进行基因集富集分析(GSEA)。风险评分与浸润LSCC的免疫细胞进行免疫相关性分析。结果 m6A相关基因与lncRNA的共表达分析筛选出169个与m6A基因相关的lncRNA(相关系数>0.4，P<0.001)，通过单变量Cox分析确定了LSCC预后相关lncRNA：ALOX12-AS1(P<0.05)、LINC00528(P<0.05)、STAG3L5P-PVRIG2P-PILRB(P<0.05)、MNX1-AS1(P<0.01)和LINC02043(P<0.05)。套索回归、交叉验证迭代分析结果：变量为4时模型的均方根误差最小，即5个预后相关lncRNA仅有ALOX12-AS1、LINC00528、STAG3L5P-PVRIG2P-PILRB与MNX1-AS1可作为模型变量。预后模型：风险评分=(0.176723096228585×MNX1-AS1表达量)+(-0.614916717648596×ALOX12-AS1表达量)+(-0.814201385798827×LINC00528表达量)+(-0.436537752110547×STAG3L5P-PVRIG2P-PILRB表达量)。qPCR 结果ALOX12-AS1(P<0.0001)、LINC00528(P<0.01)、STAG3L5P-PVRIG2P-PILRB(P<0.0001)、MNX1-AS1(P<0.0001)较于癌旁组织，在LSCC组织表达水平上调。GSEA分析结果：高风险患者在细胞-基质粘附(KEGG_FOCAL_ADHESION)(P<0.05)与细胞外基质受体相互作用(KEGG_ECM_RECEPTOR_INTERACTION)(P<0.05)的通路下调，低风险患者在碱基切除修复(KEGG_SPLICEOSOME)(P<0.05)、剪接体(KEGG_SPLICEOSOME)(P<0.05)通路上调。风险评分与免疫浸润细胞相关性分析，风险评分与效应B细胞(R=-0.24,P=0.017)、细胞毒性T细胞(R=-0.26,P=0.0091)和T滤泡辅助细胞(R=-0.22,P=0.028)呈负相关。结论 基于m6A相关LncRNA表达量构建的LSCC预后模型可作为预测LSCC患者预后的有效工具。

Abstract:

Objective To explore the relationship between m6A-related lncRNAs and the prognosis of laryngeal squamous cell carcinoma (LSCC) and its clinical significance.Methods Transcriptome data from the Cancer Genome Atlas (TCGA) database and clinical data of LSCC were obtained. The lncRNAs associated with the m6A gene were screened by co-expression analysis, a prognostic model of LSCC was constructed by lasso regression analysis and iterative analysis of cross-validated methods, and the relationship between m6A-associated lncRNA and LSCC survival was explored by univariate cox analysis. Real-time polymerase chain reaction (RT-qPCR) was used to validate the transcript levels of lncRNA in the prognostic model. Risk scores were calculated by the prognostic model to distinguish LSCC into high-and low-risk groups, and gene set enrichment analysis (GSEA) was performed between the two groups. Tumor tissues were detected for immune infiltration, and the correlation between the risk score value and the degree of immune cell infiltration was evaluated by correlation analysis.Results A total of 169 lncRNA associated with m6A genes were screened out by co-expression analysis (correlation coefficient >0.4, P<0.001), and univariate cox analysis determined the prognosis-associated lncRNAs in LSCC including ALOX12-AS1 (P<0.05), LINC00528 (P<0.05), STAG3L5P-PVRIG2P-PILRB (P<0.05), MNX1-AS1 (P<0.01), and LINC02043 (P<0.05). Lasso regression and cross-validation iterative analysis revealed that the root-mean-square error of the model was the smallest when the variable was 4, i.e., only ALOX12-AS1, LINC00528, STAG3L5P-PVRIG2P-PILRB and MNX1-AS1 could be used as model variables. In the prognostic model, the risk score=(0.176723096228585MNX1-AS1 expression)+(-0.614916717648596ALOX12-AS1 expression)+(-0.814201385798827LINC00528 expression)+(-0.436537752110547STAG3L5P-PVRIG2P-PILRB expression). RT-qPCR showed that ALOX12-AS1 (P<0.0001), LINC00528 (P<0.01), STAG3L5P-PVRIG2P-PILRB (P<0.0001), and MNX1-AS1 (P<0.0001) were up-regulated in LSCC tissues compared with para-carcinoma tissues. GSEA analysis indicated KEGGFOCALADHESION (P<0.05) and KEGGECMRECEPTORINTERACTION (P<0.05) pathways were down-regulated in the high-risk group, and KEGGSPLICEOSOME pathway (P<0.05) and KEGGSPLICSOME pathway (P<0.05) were up-regulated in the low-risk group. Correlation analysis between risk score and immune infiltrating cells disclosed that risk score was negatively correlated with plasma cell (R=-0.24, P<0.05), CD8 T cell (R=-0.26, P<0.01) and T follicular helper cell (R=-0.22, P<0.05).Conclusion The prognostic model of LSCC constructed based on m6A-associated lncRNA expression can be used as an effective tool to predict the prognosis of patients with this disease.