Global gene expression and functional network analysis of gastric cancer identify extended pathway maps and GPRC5A as a potential biomarker
Highlights
► GPRC5A was significantly elevated in gastric cancer tissues. ► The integrative network analysis of deregulated genes generated eight subnetworks. ► WNT, RTK-Ras-PI3K-AKT, NF-κB, PLAU-JAK-STAT pathways showed changes in gene level.
Introduction
Gastric cancer is still one of the most common malignant diseases, despite its steady declining trend worldwide. Overall, a total of 989,600 new stomach cancer cases and 738,000 deaths are estimated to have occurred in 2008, accounting for 8% of the total cases and 10% of total deaths [1]. The high mortality rate of gastric cancer is explained by the fact that the majority of the tumors in the stomach are malignant gastric adenocarcinomas, detected often at an advanced stage and manifested by lymph node invasion and metastasis [2]. Therapeutic interventions to treat such late stage carcinomas are usually restricted to non-curative gastrectomy, lymphadenectomy and postoperative chemoradiotherapy. Thus, five-year relative survival rates of gastric cancer patients barely reach below 30% in most countries [3]. Because early gastric cancer is typically small and asymptomatic [4], it is of great clinical importance to identify new molecular biomarkers for early detection, diagnosis and treatment in gastric cancer.
Understanding of the molecular alterations behind the initiation and progression of gastric carcinogenesis is crucial in finding novel markers for early diagnosis, targeted treatment and prognosis evaluation in gastric cancer. Gene expression profiling is a powerful tool for identifying differentially expressed genes in studies of various disease status [5], [6], [7], [8]. Although much has been learned about the molecular basis of gastric cancer, the detailed mechanisms of gastric cancer development and progression remain unclear.
With an increasing awareness, gene expression profile gives important cues in the contextual molecular network involved in complex biological processes such as carcinogenesis and progression. To identify differentially expressed genes, as well as core networks in gastric cancer, we performed gene expression analysis of 25 pairs of gastric tissues. We applied laser capture microdissection (LCM) to reduce the contamination of cancer cells by non-cancer cells. Our study used Affymetrix Human Exon 1.0 ST Array to screen for expression profiling. With the combination of advanced microarray and LCM, we are able to procure more accurate assessments of gene expression.
Section snippets
Tissue samples and laser capture microdissection
A total of 25 gastric cancer tissues and matched adjacent noncancerous tissues were obtained from surgical resection and snap-frozen in liquid nitrogen till later use. Informed consent was provided from each participating patient. Ethics approval for this study was granted by the Human Research Ethics Committee of Shanghai Jiaotong University School of Medicine. All tissue samples were double examined with hematoxylin and eosin (H & E) staining method by two individual pathologists. All tumors
Differential expression genes in gastric cancer
With SAM analysis, 260 genes showed >2-fold changes in significantly differential expression (FDR < 0.01, frequency > 50% samples) (see Supplementary Table 3). Of these, 220 were over-expressed and 40 under-expressed genes. Altogether those included genes showing significant enrichment (Score > 1.3) in basic functions (Fig. 1) such as angiogenesis (MMP14, TNFRSF12A, CTNNB1, UNQ473, JAG1, ANXA2, and THBS1), inflammatory response (CD44, CD55, FN1, ANXA1, C1QC, C1QA, LIPA, CXCL1, TFRC, CD14, and THBS1),
Discussion
In this study, we applied Affymetrix Human Exon 1.0 ST microarray for gene expression analysis of 25 pairs of gastric tissues. Based on bioinformatics methods, we identified differentially expressed genes, as well as important networks in gastric cancer and found some potential molecular markers with biological roles in gastric carcinogenesis.
We utilized Expression Console software v1.0 to obtain raw signal data and DABG P-value for each probe set of the samples by default method RMA at gene
Acknowledgements
This research got supports by the National Basic Research Program of China, 973 Program, 2006CB910402, 2012CB22308. The authors give thanks to Junsong Han, Xiaona Zhang, Libo Lin in SBC for their helps in microarray experiment performs, and Zhidong Zhu and Yi Zhang in data analysis.
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These authors contributed equally to this work.