Title:Integrated Differential Regulatory Analysis Reveals a Novel Prognostic 36-Gene Signature for Gastric Cancer in Asian Population
Volume: 20
Issue: 2
Author(s): Junyi Li, Sujuan Wu, Liguang Yang, Yi-Xue Li, Bing-Ya Liu*Yuan-Yuan Li*
Affiliation:
- Shanghai Key Laboratory of Gastric Neoplasms, Shanghai Institute of Digestive Surgery, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200025,China
- Shanghai Engineering Research Center of Pharmaceutical Translation, 1278 Keyuan Road, Shanghai 201203,China
Keywords:
Differential regulatory analysis, differential co-expression analysis, co-expression network, prognostic biomarker,
gastric cancer carcinogenesis, precision medicine.
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.
