It is challenging to predict patient's prognosis in oesophageal squamous cell carcinoma (abbreviated to OSCC) or perform early intervention and prevention for this disease. With the development of clinical genomics, genetic heterogeneity in tumors was found to be relevant to OSCC tumorigenesis in precision medicine studies. Over the past decade, global microarray expression profiling has been used to investigate biomarkers for human diseases. In this study, we proposed two computational strategies for identifying RNA biomarkers from microarray expression profiles of OSCC. Firstly, the logistic regression model with a least absolute shrinkage and selection operator (LASSO) regularization was used to analyze RNA expression profiles from OSCC tissues. Secondly, differential expression analysis for lncRNA profiles from both tumor and the corresponding para-cancerous tissues was performed to identify biomarkers related with lymph node metastasis, which might contribute to the early diagnosis and treatment of the disease.
Keywords: Biomarkers; Esophageal Squamous Cell Carcinoma; RNA.