Identification of novel hub genes associated with liver metastasis of gastric cancer

Abstract

Understanding hub genes involved in gastric cancer (GC) metastasis could lead to effective approaches to diagnose and treat cancer metastasis. In this study, 272 differentially expressed genes between synchronous liver metastasis and the paired GC were selected from microarray assays. KEGG pathway analysis indicated that of 13 enriched pathways, 8 were involved in cancer metastasis. Literature-based annotations showed that the differentially expressed genes significantly enriched known metastasisrelated genes. With the use of protein-protein interaction network, we found a subnetwork significantly enriching the metastasis-related genes and hubs. Unannotated hubs in this subnetwork were predicted to be novel metastasis-associated genes. Nine hubs in this subnetwork were validated by using quantitative RT-PCR, and 4 hubs were further validated by immunohistochemistry. NR4A2 was significantly down-regulated in synchronous liver metastasis compared with the paired GC at both transcriptional and translational levels. NR4A2 immunostaining was apparent in the mesenchymal cells of pathologically normal gastric mucosa and in the epithelial cells of primary GC. HSP90AA1 was not only up-regulated in the metastatic GC compared with primary GC at both transcriptional and translational levels, but also up-regulated in primary GC compared with the normal mucosa at the translational level. NR4A2, NR3C1, ARF3, XAB2, and alternatively spliced variants of NR4A2, SP8 and SP-novel, were significantly down-regulated, whereas CCNE1 significantly up-regulated, in primary GC compared with the normal gastric mucosa. Conclusively, NR4A2 and HSP90AA1 stand out as promising diagnostic markers and therapeutic targets for liver metastasis of GC. CCNE1 and NR3C1 indicate primary GC, rather than distant metastasis. ' 2009 UICC

Expression of orphan nuclear receptor NR4A2 in gastric cancer cells confers chemoresistance and predicts an unfavorable postoperative survival of gastric cancer patients with chemotherapy

“…This was consistent with our previous finding. 15 Immunohistochemistry of the 28 paired FFPE specimens indicated that the ratio of positive NR4A2 immunostaining in adjacent mucosa was 64.3% in lymphocyte nuclei, 39.3% in epithelial cytoplasm, and 25.0% in epithelial nuclei; the ratio in tumors was 14.3% in lymphocyte nuclei, 0% in cancer cytoplasm, and 53.6% in cancer cell nuclei.…”

“…Cell invasive ability and anchorageindependent growth of the transfected cells were examined as described. 21 Real-Time Quantitative Reverse-Transcription PCR and Western Blot Total RNA was isolated and subjected for quantitative real-time reverse-transcription polymerase chain reaction (qRT-PCR) as described, 15 and primers for NR4A2 were described. 15 …”

“…24 Polyclonal antibody against NR4A2 (ab60149, 1:50; Abcam) was used for immunostaining. 15 Immunostains were independently examined by 4 researchers (Y.H., H.C., Y.Y., and G.C.) who were blind to clinical information.…”

“…15 Wnt/b-catenin signaling and bcatenin target molecule cyclin D1 are important in promoting GC progression and chemoresistance. [16][17][18] We hypothesize that NR4A2 is activated in an inflammatory microenvironment, participates in an evolutionary process from inflammation to cancer via affecting PKA and/ or OPN-b-catenin-cyclin D1 pathways, and promotes the progression of GC.…”

Protein-protein Interactions: Network Analysis and Applications in Drug Discovery

“…Integrating PPI networks with expression data, for example, led to the identification of cancer susceptibility genes and oncogenes in breast carcinomas [122], B-cell acute myeloid lymphomas [123,124]; the identification of markers for metastasis in breast [125,126], colorectal [127,128] and gastric [129] cancer; the prediction of disease outcome [130] and response to chemotherapy [131,132] and the determination of therapy-resistance genes [133,134]. By combining protein-protein and protein-DNA interaction studies Kim et al identified a Myc-centered regulatory network in embryonic stem cells, and showed that the Myc module is active in various cancers and predicts cancer outcome [135].…”