Esophageal squamous cell carcinoma (ESCC) is one of the six most commonly diagnosed tumor types in the Chinese population. Gene expression profiles help to predict the prognosis of patients with ESCC. Disease recurrence as the survival endpoint has been analyzed in the majority of previous studies; therefore, the aim of the present study was to construct a robust gene signature in order to determine the overall survival (OS) of patients with ESCC. The gene expression and clinical data of patients with ESCC were downloaded from The Cancer Genome Atlas (TCGA) database. Of the selected data (172 samples from surviving patients), 72 samples were randomly selected as modeling data, and verification was conducted using the entire dataset. Data from the Gene Expression Omnibus was analyzed simultaneously, and a venn diagram was constructed to determine the intersection between these two sets of results; a total of 97 genes were found to be associated with OS. Kyoto Encyclopedia of Genes and Genomes analysis demonstrated that these genes were primarily associated with specific pathways (Homo sapiens), including DNA replication, protein processing in endoplasmic reticulum and influenza A. A five-gene signature was identified with a robust likelihood-based survival modeling approach. Using regression coefficient modeling, a prognostic model consisting of the C-X-C motif chemokine ligand 8, DNA damage inducible transcript 3, RAB27A, member RAS oncogene family, replication factor C subunit 2 and elongation factor for RNA polymerase II 2 genes was constructed and validated. Based on these results, patients were subdivided into high and low-risk groups. Compared with the high-risk group, the OS time of patients in the low-risk group was significantly increased. Furthermore, it was determined that the five genes were all differentially expressed in ESCC tissues compared with normal tissues, indicating the potential role of these genes in ESCC initiation and progression. In another independent cohort, this five-gene signature was further confirmed and was considered as an independent prognostic biomarker for OS prediction in patients with ESCC. In conclusion, the OS of patients with ESCC may be predicted using this five-gene signature, which may be useful in identifying patients with high-risk ESCC.