Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, 211166, China.
BMC Med Genomics. 2023 Jul 5;16(1):157. doi: 10.1186/s12920-023-01574-z.
Smoking is a well-recognized risk factor for esophageal carcinoma, but the underlying molecular mechanism remains unclear. Previous studies have demonstrated the predictive value of non-coding RNA (ncRNA) for the prognosis of esophageal carcinoma; however, the expression of smoking-related ncRNAs has not been systematically characterized. Herein, we comprehensively assessed the hazard of heavy smoking and its impact on ncRNA expression patterns in patients with esophageal carcinoma.
Transcriptome and clinical features of patients with esophageal carcinoma were acquired from The Cancer Genome Atlas (TCGA) database. Cox regression analysis was employed to calculate the hazard ratio (HR) of smoking behavior. Differential expression analysis was conducted with the "edgeR" package. The smoking-related RNA regulatory network was based on lncRNA‒miRNA and miRNA‒mRNA pairs and visualized by Cytoscape 3.7.1. We applied Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses for functional annotation. Univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses were used for model construction. We applied Kaplan‒Meier analysis with a log-rank test for survival analysis, with group comparison by the Wilcoxon signed ranked test.
Heavy smoking contributed to the poor overall survival of esophageal carcinoma, with an HR of 3.167 (95% CI: 1.077-9.312). A total of 195 lncRNAs and 73 miRNAs were differentially expressed between patients with or without smoking behavior. We constructed smoking-related RNA regulatory networks, and functional annotation enriched a series of cancer-related pathways. We generated a smoking-related prognostic risk score and found that patients with a high score had a poor prognosis. Fourteen out of 23 immune cell types differentially infiltrated into a distinct risk group, while no correlation was observed between the risk score and immune cells.
Altogether, we profiled smoking-related ncRNA expression patterns and constructed an RNA regulatory network, providing a landscape of smoking-related molecular mechanisms of esophageal carcinoma. The smoking-related risk score, which was related to prognosis, revealed that tobacco smoking could suppress tumor immunity via the ncRNA mechanism.
吸烟是食管癌的一个公认的危险因素，但潜在的分子机制仍不清楚。先前的研究已经证明了非编码 RNA（ncRNA）对食管癌预后的预测价值；然而，吸烟相关 ncRNA 的表达尚未得到系统的描述。在此，我们全面评估了重度吸烟的危害及其对食管癌患者 ncRNA 表达模式的影响。
从癌症基因组图谱（TCGA）数据库中获取食管癌患者的转录组和临床特征。采用 Cox 回归分析计算吸烟行为的危险比（HR）。采用“edgeR”包进行差异表达分析。基于 lncRNA-miRNA 和 miRNA-mRNA 对构建吸烟相关的 RNA 调控网络，并通过 Cytoscape 3.7.1 可视化。我们应用基因本体论（GO）和京都基因与基因组百科全书（KEGG）通路富集分析进行功能注释。采用单变量和最小绝对值收缩和选择算子（LASSO）Cox 回归分析进行模型构建。采用 Kaplan-Meier 分析和对数秩检验进行生存分析，Wilcoxon 符号秩检验进行组间比较。
重度吸烟导致食管癌总体生存不良，HR 为 3.167（95%CI：1.077-9.312）。有吸烟行为和无吸烟行为的患者之间共有 195 个 lncRNA 和 73 个 miRNA 存在差异表达。我们构建了吸烟相关的 RNA 调控网络，并对富集的一系列癌症相关通路进行了功能注释。我们生成了一个与吸烟相关的预后风险评分，并发现高分患者预后不良。23 种免疫细胞类型中有 14 种不同程度地浸润到一个不同的风险组中，而风险评分与免疫细胞之间没有相关性。
总之，我们分析了吸烟相关的 ncRNA 表达模式，并构建了 RNA 调控网络，提供了食管癌吸烟相关分子机制的全景图。与预后相关的吸烟相关风险评分表明，吸烟可能通过 ncRNA 机制抑制肿瘤免疫。
