Skip to content
JournalsWorldThe Global Research Discovery Platform
SCHOLARLY PUBLICATION ✓ Open Access

Oncogenic Pathway Combinations Predict Clinical Prognosis in Gastric Cancer

Chia-Huey Ooi, Tatiana Ivanova, Jeanie Wu, Ming‐Hui Lee, Iain Beehuat Tan, Jiong Tao, Lindsay C. Ward, Jun Hao Koo, Veena Gopalakrishnan, Yansong Zhu, Lai Ling Cheng, Julian Lee, Sun Young Rha, Hyun Cheol Chung, Kumaresan Ganesan, Jimmy Bok Yan So, Khee Chee Soo, Dennis Teck Hock Lim, Weng Hoong Chan, Wai Keong Wong, David D.L. Bowtell, Khay Guan Yeoh, Heike Irmgard Grabsch, Alex Boussioutas, Patrick B. O. Tan

📄 Abstract

Many solid cancers are known to exhibit a high degree of heterogeneity in their deregulation of different oncogenic pathways. We sought to identify major oncogenic pathways in gastric cancer (GC) with significant relationships to patient survival. Using gene expression signatures, we devised an in silico strategy to map patterns of oncogenic pathway activation in 301 primary gastric cancers, the second highest cause of global cancer mortality. We identified three oncogenic pathways (proliferation/stem cell, NF-kappaB, and Wnt/beta-catenin) deregulated in the majority (>70%) of gastric cancers. We functionally validated these pathway predictions in a panel of gastric cancer cell lines. Patient stratification by oncogenic pathway combinations showed reproducible and significant survival differences in multiple cohorts, suggesting that pathway interactions may play an important role in influencing disease behavior. Individual GCs can be successfully taxonomized by oncogenic pathway activity into biologically and clinically relevant subgroups. Predicting pathway activity by expression signatures thus permits the study of multiple cancer-related pathways interacting simultaneously in primary cancers, at a scale not currently achievable by other platforms.

📤 Share this page

Found this useful? Share it with your network.

✓ Link copied! Paste it on ResearchGate / Academia.edu