lncRNA Profiles Enable Prognosis Prediction and Subtyping for Esophageal Squamous Cell Carcinoma.

Long non-coding RNAs (lncRNAs) have emerged as useful prognostic biomarkers in many cancers. In the present study, we investigated the potential application of lncRNA markers for the prognostic prediction of esophageal squamous cell carcinoma (ESCC). We first identified ESCC-specific lncRNA signatures by comparing ESCC tissues with normal tissues. Subsequently, Kaplan-Meier (KM) method in combination with the univariate Cox proportional hazards regression (UniCox) method was applied to screen prognostic lncRNAs. By combining the differential and prognostic lncRNAs, we developed a prognostic model using cox stepwise regression analysis. The obtained prognostic prediction model could effectively predict the 3-year and 5-year prognosis and survival of ESCC patients by time-dependent receiver operating characteristic (ROC) curves (area under curve = 0.87 and 0.89, respectively). Besides, we generated a lncRNA-based molecular classification of ESCC using an unsupervised clustering method (k-means) and obtained two subgroups of ESCC patients with a significant association with race and Barrett's esophagus (BE) (both P<0.001). Finally, we found that a novel lncRNA AC007128.1 was up-regulated in both ESCC cells and tissues, and it was associated with poor prognosis of ESCC patients. Furthermore, AC007128.1 could promote epithelial-mesenchymal transition (EMT) of ESCC cells by increasing the activation of MAPK/ERK and MAPK/p38 signaling pathways. Collectively, our findings indicated the potentials of lncRNA markers in the prognosis, molecular subtyping, and EMT of ESCC.