Integrative Analysis of Angiogenesis-Related Long Non-Coding RNA and Identification of a Six-DEARlncRNA Signature Associated with Prognosis and Therapeutic Response in Esophageal Squamous Cell Carcinoma

Simple Summary Esophageal squamous cell carcinoma (ESCC) is a familiar lethal malignance. Increasing evidence has disclosed that lncRNA is involved in tumorgenesis and progression in various tumor types. However, the current understanding of angiogenesis-related lncRNAs (ARlncRNAs) involved in ESCC remains evasive. We developed and validated a six-DEARlncRNA risk score system among the GSE53624 and GSE53622 set, aiming to identify the novel prognostic targets for ESCC. Our results showed that the six-DEARlncRNA could be used as an effective independent prognostic factor of ESCC. Furthermore, the six-DEARlncRNA biomarkers mainly participated in regulation of the skin and epidermis development, and these processes protected the body from environmental insults and may have been involved in the progression of ESCC. In conclusion, this study showed that the six-DEARlncRNA signature could be used as an independent prognostic factor and may be a valuable target for treatment options in ESCC. Esophageal squamous cell carcinoma (ESCC) is a lethal gastrointestinal malignancy worldwide. We aimed to identify an angiogenesis-related lncRNAs (ARlncRNAs) signature that could predict the prognosis in ESCC. The GSE53624 and GSE53622 datasets were derived from the GEO database. The differently expressed ARlncRNAs (DEARlncRNAs) were retrieved by the weighted gene co-expression network analysis (WGCNA), differential expression analysis, and correlation analysis. Optimal lncRNA biomarkers were screened from the training set and the six-DEARlncRNA signature comprising AP000696.2, LINC01711, RP11-70C1.3, AP000487.5, AC011997.1, and RP11-225N10.1 could separate patients into high- and low-risk groups with markedly different survival. The validation of the reliability of the risk model was performed by the Kaplan-Meier test, ROC curves, and risk curves in the test set and validation set. Predictive independence analysis indicated that risk score is an independent prognostic biomarker for predicting the prognosis of ESCC patients. Subsequently, a ceRNA regulatory network and functional enrichment analysis were performed. The IC50 test revealed that patients in the high-risk group were resistant to Gefitinib and Lapatinib. Finally, the six DEARlncRNAs were detected by qRT-PCR. In conclusion, we demonstrated a novel ARlncRNA signature as an independent prognostic factor to distinguish the risk of ESCC patients and benefit the personalized clinical applications.