Abstract
Esophageal squamous cell carcinoma (ESCC) is a prevalent aggressive malignant tumor with poor prognosis. Investigations into the molecular changes that occur as a result of the disease, as well as identification of novel biomarkers for its diagnosis and prognosis, are urgently required. Long non‑coding RNAs (lncRNAs) have been reported to play a critical role in tumor progression. The present study performed data mining analyses for ESCC via an integrated study of accumulated datasets and identification of the differentially expressed lncRNAs from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. The identified intersection of differentially expressed genes (lncRNAs, miRNAs and mRNAs) in ESCC tissues between the GEO and TCGA datasets was investigated. Based on these intersected lncRNAs, the present study constructed a competitive endogenous RNA (ceRNA) network of lncRNAs in ESCC. A total of 81 intersection lncRNAs were identified; 67 of these were included in the ceRNA network. Functional analyses revealed that these 67 key lncRNAs primarily dominated cellular biological processes. The present study then analyzed the associations between the expression levels of these 67 key lncRNAs and the clinicopathological characteristics of the ESCC patients, as well as their survival time using TCGA. The results revealed that 31 of these lncRNAs were associated with tumor grade, tumor‑node‑metastasis (TNM) stage and lymphatic metastasis status (P<0.05). In addition, 15 key lncRNAs were demonstrated to be associated with survival time (P<0.05). Finally, 5 key lncRNAs were selected for validation of their expression levels in 30 patients newly diagnosed with ESCC via reverse transcription‑quantitative PCR (RT‑qPCR). The results suggested that the fold changes in the trends of up‑ and downregulation between GEO, TCGA and RT‑qPCR were consistent. In addition, it was also demonstrated that a select few of these 5 key lncRNAs were significantly associated with TNM stage and lymph node metastasis (P<0.05). The results of the clinically relevant analysis and the aforementioned bioinformatics were similar, hence proving that the bioinformatics analysis used in the present study is credible. Overall, the results from the present study may provide further insight into the functional characteristics of lncRNAs in ESCC through bioinformatics integrative analysis of the GEO and TCGA datasets, and reveal potential diagnostic and prognostic biomarkers for ESCC.