Evaluation of Serum Exosomal lncRNAs as Diagnostic and Prognostic Biomarkers for Esophageal Squamous Cell Carcinoma.
Cancer Manag Res. 2020 ;12:9753-9763. Epub 2020 Oct 7. PMID: 33116835
Background: Exosomal long non-coding RNAs (lncRNAs) have been recognised as promising stable biomarkers in cancers. The aim of this study was to identify an exosomal lncRNA panel for diagnosis and prognosis of esophageal squamous cell carcinoma (ESCC).
Materials and Methods: Exosomes were isolated from serum by ExoQuick Solution. To validate the exosomes, exosomal markers and characterization of nanoparticle were performed. Quantitative real-time PCR was used to measure the levels of lncRNAs in exosomes from ESCC patients and healthy subjects. In the training set, exosomal lncRNA profiles from 404 samples were conducted and established new models by multivariate logistic regression. In the validation set, the diagnostic performance of the panel was further validated in 222 additional individuals with a receiver operating characteristic curve (ROC). Kaplan-Meier and multivariate Cox proportional hazards analysis were applied to assess the correlation between lncRNAs and survival rate of ESCC patients.
Results: A 4-lncRNA panel (UCA1, POU3F3, ESCCAL-1 and PEG10) in exosomes for ESCC diagnosis was developed by logistic regression model. The diagnostic accuracy of panel was evaluated with AUC value of 0.844 and 0.853 for training and validation stage, respectively. The corresponding AUCs for patients with TNM stage I-II and III were 0.820 and 0.935, significantly higher than squamous cell carcinoma antigen (<0.001), which were 0.652 and 0.642, respectively. Kaplan-Meier analysis indicated that patients with higher level of UCA1 and POU3F3 had lower survival rate (<0.001). Additionally, POU3F3 might be as an independent prognostic factor for ESCC patients (=0.004).
Conclusion: These findings suggested that serum exosomal 4-lncRNA panel has considerable value for ESCC diagnosis, and POU3F3 may serve as a novel and independent prognostic predictor in clinical applications.