4.6 Article

Prognostic value of sorting nexin 10 weak expression in stomach adenocarcinoma revealed by weighted gene co-expression network analysis

Journal

WORLD JOURNAL OF GASTROENTEROLOGY
Volume 24, Issue 43, Pages 4906-4919

Publisher

BAISHIDENG PUBLISHING GROUP INC
DOI: 10.3748/wjg.v24.i43.4906

Keywords

Stomach adenocarcinoma; The Cancer Genome Atlas; Weighted gene co-expression network analysis; Sorting nexin 10; Clinicopathological predictors; Disease-free survival; Overall survival

Funding

  1. Liaoning ST Project [2015020269]

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AIM To detect significant clusters of co-expressed genes associated with tumorigenesis that might help to predict stomach adenocarcinoma (SA) prognosis. METHODS The Cancer Genome Atlas database was used to obtain RNA sequences as well as complete clinical data of SA and adjacent normal tissues from patients. Weighted gene co-expression network analysis (WGCNA) was used to investigate the meaningful module along with hub genes. Expression of hub genes was analyzed in 362 paraffin-embedded SA biopsy tissues by immunohistochemical staining. Patients were classified into two groups (according to expression of hub genes): Weak expression and over-expression groups. Correlation of biomarkers with clinicopathological factors indicated patient survival. RESULTS Whole genome expression level screening identified 6,231 differentially expressed genes. Twenty-four co-expressed gene modules were identified using WGCNA. Pearson's correlation analysis showed that the tan module was the most relevant to tumor stage (r = 0.24, P = 7 x 10(-6)). In addition, we detected sorting nexin (SNX)10 as the hub gene of the tan module. SNX10 expression was linked to T category (P = 0.042, chi(2) = 8.708), N category (P = 0.000, chi(2) = 18.778), TNM stage (P = 0.001, chi(2) = 16.744) as well as tumor differentiation (P = 0.000, chi(2) = 251.930). Patients with high SNX10 expression tended to have longer disease-free survival (DFS; 44.97 mo vs 33.85 mo, P = 0.000) as well as overall survival (OS; 49.95 vs 40.84 mo, P = 0.000) in univariate analysis. Multivariate analysis showed that dismal prognosis could be precisely predicted clinicopathologically using SNX10 [DFS: P = 0.014, hazard ratio (HR) = 0.698, 95% confidence interval (CI): 0.524-0.930, OS: P = 0.017, HR = 0.704, 95%CI: 0.528-0.940]. CONCLUSION This study provides a new technique for screening prognostic biomarkers of SA. Weak expression of SNX10 is linked to poor prognosis, and is a suitable prognostic biomarker of SA.

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