Identification of Key Gene and Pathways for the Prediction of Peritoneal Metastasis of Gastric Cancer by Co-expression Analysis

Zhang, Simeng; Zang, Dan; Cheng, Yu; Li, Zhi; Yang, Bowen; Guo, Tianshu; Liu, Yunpeng; Qu, Xiujuan; Che, Xiaofang

doi:10.7150/jca.39645



International Journal of Biological Sciences

International Journal of Medical Sciences

Theranostics

Journal of Genomics

Nanotheranostics

Global reach, higher impact

Full Text | PDF

J Cancer 2020; 11(10):3041-3051. doi:10.7150/jca.39645 This issue Cite

Research Paper

Identification of Key Gene and Pathways for the Prediction of Peritoneal Metastasis of Gastric Cancer by Co-expression Analysis

Simeng Zhang^1,2,3*, Dan Zang^1,2,3*, Yu Cheng^1,2,3, Zhi Li^1,2,3, Bowen Yang^2,3, Tianshu Guo^1,2,3, Yunpeng Liu^1,2,3, Xiujuan Qu^1,2,3, Xiaofang Che^1,2,3✉

1. Department of Medical Oncology, the First Hospital of China Medical University, Shenyang 110001, China;
2. Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, the First Hospital of China Medical University, Shenyang 110001, China;
3. Liaoning Province Clinical Research Center for Cancer, Shenyang 110001, China
^*These authors contributed equally to this study.

Citation:

Zhang S, Zang D, Cheng Y, Li Z, Yang B, Guo T, Liu Y, Qu X, Che X. Identification of Key Gene and Pathways for the Prediction of Peritoneal Metastasis of Gastric Cancer by Co-expression Analysis. J Cancer 2020; 11(10):3041-3051. doi:10.7150/jca.39645. https://www.jcancer.org/v11p3041.htm

Other styles

Abstract

Peritoneal metastasis is the most common pattern in advanced gastric cancer and can predict poor disease prognosis. Early detection of peritoneal tumor dissemination is restricted by small peritoneal deposits. Therefore, it is critical to identify a novel predictive marker and to explore the potential mechanism associated with this process. In the present study, one module that correlated with peritoneal metastasis was identified. Enrichment analysis indicated that the Focal adhesion and the PI3K-Akt signaling pathway were the most significant pathways. Following network and Molecular Complex Detection (MCODE) analysis, the hub-gene cluster that consisted of 19 genes was selected. Methionine sulfoxide reductase B3 (MSRB3) was identified as a seed gene. Survival analysis indicated that high expression levels of MSRB3 were independent predictors of peritoneal disease-free survival (pDFS) as determined by univariate (HR 8.559, 95% CI; 3.339-21.937; P<.001) and multivariate Cox analysis (HR 3.982, 95% CI; 1.509-10.509; P=.005). Furthermore, patients with high levels of MSRB3 exhibited a significantly lower Overall Survival (OS) (log-rank P = 0.007). The external validation was performed by the (The Cancer Genome Atlas (TCGA)) (log-rank P = 0.037) and Kaplan Meier-plotter (KMplotter) (log-rank P = 0.031) data. In vitro experiments confirmed that MSRB3 was a critical protein in regulating gastric cancer cell proliferation and migration. In conclusion, High expression levels of MSRB3 in GC can predict peritoneal metastasis and recurrence as well as poor prognosis. Furthermore, MSRB3 was involved in the regulation of the proliferation and migration of GC cells.

Keywords: gastric cancer, peritoneal metastasis, WGCNA, MSRB3, PI3K-Akt

Citation styles

APA

Zhang, S., Zang, D., Cheng, Y., Li, Z., Yang, B., Guo, T., Liu, Y., Qu, X., Che, X. (2020). Identification of Key Gene and Pathways for the Prediction of Peritoneal Metastasis of Gastric Cancer by Co-expression Analysis. Journal of Cancer, 11(10), 3041-3051. https://doi.org/10.7150/jca.39645.

ACS

Zhang, S.; Zang, D.; Cheng, Y.; Li, Z.; Yang, B.; Guo, T.; Liu, Y.; Qu, X.; Che, X. Identification of Key Gene and Pathways for the Prediction of Peritoneal Metastasis of Gastric Cancer by Co-expression Analysis. J. Cancer 2020, 11 (10), 3041-3051. DOI: 10.7150/jca.39645.

NLM

CSE

Zhang S, Zang D, Cheng Y, Li Z, Yang B, Guo T, Liu Y, Qu X, Che X. 2020. Identification of Key Gene and Pathways for the Prediction of Peritoneal Metastasis of Gastric Cancer by Co-expression Analysis. J Cancer. 11(10):3041-3051.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.