Zheng Ze-Jun, Li Yan-Shang, Zhu Jun-De, Zou Hai-Ying, Fang Wang-Kai, Cui Yi-Yao, Xie Jian-Jun
Department of Biochemistry and Molecular Biology, Shantou University Medical College, Shantou, China.
Department of Pathology, Medical College of Jiaying University, Meizhou, China.
Front Genet. 2022 Mar 31;13:839589. doi: 10.3389/fgene.2022.839589. eCollection 2022.
Esophageal squamous cell carcinoma (ESCC) is a common malignant gastrointestinal tumor threatening global human health. For patients diagnosed with ESCC, determining the prognosis is a huge challenge. Due to their important role in tumor progression, long non-coding RNAs (lncRNAs) may be putative molecular candidates in the survival prediction of ESCC patients. Here, we obtained three datasets of ESCC lncRNA expression profiles (GSE53624, GSE53622, and GSE53625) from the Gene Expression Omnibus (GEO) database. The method of statistics and machine learning including survival analysis and LASSO regression analysis were applied. We identified a six-lncRNA signature composed of AL445524.1, AC109439.2, LINC01273, AC015922.3, LINC00547, and PSPC1-AS2. Kaplan-Meier and Cox analyses were conducted, and the prognostic ability and predictive independence of the lncRNA signature were found in three ESCC datasets. In the entire set, time-dependent ROC curve analysis showed that the prediction accuracy of the lncRNA signature was remarkably greater than that of TNM stage. ROC and stratified analysis indicated that the combination of six-lncRNA signature with the TNM stage has the highest accuracy in subgrouping ESCC patients. Furthermore, experiments subsequently confirmed that one of the lncRNAs LINC01273 may play an oncogenic role in ESCC. This study suggested the six-lncRNA signature could be a valuable survival predictor for patients with ESCC and have potential to be an auxiliary biomarker of TNM stage to subdivide ESCC patients more accurately, which has important clinical significance.
食管鳞状细胞癌(ESCC)是一种威胁全球人类健康的常见胃肠道恶性肿瘤。对于被诊断为ESCC的患者来说,确定其预后是一项巨大的挑战。由于长链非编码RNA(lncRNA)在肿瘤进展中发挥着重要作用,它们可能是ESCC患者生存预测中潜在的分子候选指标。在此,我们从基因表达综合数据库(GEO)中获取了三个ESCC lncRNA表达谱数据集(GSE53624、GSE53622和GSE53625)。应用了包括生存分析和LASSO回归分析在内的统计学和机器学习方法。我们鉴定出了一个由AL445524.1、AC109439.2、LINC01273、AC015922.3、LINC00547和PSPC1-AS2组成的六lncRNA特征。进行了Kaplan-Meier分析和Cox分析,并在三个ESCC数据集中发现了lncRNA特征的预后能力和预测独立性。在整个数据集中,时间依赖性ROC曲线分析表明,lncRNA特征的预测准确性显著高于TNM分期。ROC和分层分析表明,六lncRNA特征与TNM分期相结合在ESCC患者亚组划分中具有最高的准确性。此外,随后的实验证实,lncRNA之一LINC01273可能在ESCC中发挥致癌作用。本研究表明,六lncRNA特征可能是ESCC患者有价值的生存预测指标,并有潜力成为TNM分期的辅助生物标志物,以更准确地对ESCC患者进行细分,具有重要的临床意义。