4.6 Article

Construction of the Six-lncRNA Prognosis Signature as a Novel Biomarker in Esophageal Squamous Cell Carcinoma

期刊

FRONTIERS IN GENETICS
卷 13, 期 -, 页码 -

出版社

FRONTIERS MEDIA SA
DOI: 10.3389/fgene.2022.839589

关键词

esophageal squamous cell carcinoma; long non-coding RNAs; prognosis; machine learning; LASSO; LINC01273

资金

  1. National Natural Science Foundation of China [81871921, 81773138]
  2. Natural Science Foundation of Guangdong Province-Outstanding Youth Project [2019B151502059]
  3. Basic and Applied Basic Research Programs of Guangdong Province [2018KZDXM033, 2018KTSCX065]
  4. Wu Jie-Ping Medical Foundation [320.6750.2020-17-4]

向作者/读者索取更多资源

This study identified a six-lncRNA signature that can serve as a valuable survival predictor for ESCC patients. When combined with TNM staging, the accuracy of subgrouping ESCC patients was significantly improved. Furthermore, one of the lncRNAs, LINC01273, was found to play an oncogenic role in ESCC.
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.

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