Relationship between ENO1 and Metastasis Risk and Prognosis in Esophageal squamous cell carcinoma

Objective: Esophageal cancer (EC), a common gastrointestinal malignancy, ranks as the sixth leading cause of cancer death worldwide. Esophageal squamous cell carcinoma (ESCC) is the predominant histological subtype of EC. Lack of potential biomarkers for treatment and prognosis is a limitation for early diagnosis and treatment of ESCC. Methods Based on The Cancer Genome Atlas (TCGA) database, the weighted gene co-expression network analysis (WGCNA) was constructed to identify gene modules associated with the metastasis in ESCC. The LASSO algorithm was used to construct a prognosis genes model. After the collinearity test, all independent prognostic parameters and important clinical parameters were included in the prognostic nomogram constructed by the Cox regression model. The nomogram was used to evaluate the prognostic significance of these parameters in ESCC. Finally, we used biological experiments to preliminary investigate the impact of ENO1 on the metastasis risk of ESCC. Results A total of 16 genetic modules were identified, and the brown module is considered the most relevant to tumor metastasis. (P = 0.02, R 2  = 0.30). Protein-protein interaction (PPI) network was performed to identify the hub nodes in the brown module. The univariate and multivariate Cox regression analysis indicated that ENO1 and SUPT5H were independent prognostic factors. Expression data of ENO1 was pipelined along with patients’ clinic pathological data for nomogram construction 1-, and 2-OS forecasting. ENO1 gene was regarded as "real" hub genes for cancer metastasis risk. Low-expressed ENO1 inhibited migration and invasion of human ESCC cells in vitro. The mechanism may involve the Wnt signaling pathway and the influence of EMT. Conclusion ENO1 might be a novel metastasis risk biomarker for ESCC.