Abstract
Aim and Objective: Gastric cancer is one of the most common cancers and has very high incidence and mortality rate in Asian population. To tackle the problems of infiltration and heterogeneity, more accurate biomarkers for diagnosis and prognosis as well as effective targets for treatment are needed to achieve better outcomes of gastric cancer patients. Recently, methods and algorithms for analyzing high-throughput sequencing data have greatly facilitated the molecular profiling of gastric cancer. Nevertheless, prognostic biomarkers for gastric cancer that can be potentially applied in clinic are still lacking.
Materials and Methods: In this study, we performed differential regulatory analysis based on gene co-expression network for four different cohorts of Asian gastric cancer samples and their clinical data. Results: We identified a 36-gene prognostic signature specific for gastric cancer, particularly for Asian population. We further analyzed differential regulatory patterns related to these featured genes, such as C1S, and suggested hypotheses for investigating their roles in gastric cancer pathogenesis. Conclusion: Findings from present study suggest a 36-gene signature which is based on differential regulatory analysis and can predict the prognosis of gastric cancer. Our research explores molecular mechanism of gastric cancer at transcriptional regulation level and provides potential drug targets. This integrated biomarker searching scheme is extendable to other cancer study for not only prognostic prediction, but also pathogenesis.Keywords: Differential regulatory analysis, differential co-expression analysis, co-expression network, prognostic biomarker, gastric cancer carcinogenesis, precision medicine.
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