Open Access iconOpen Access

ARTICLE

crossmark

The F5 gene predicts poor prognosis of patients with gastric cancer by promoting cell migration identified using a weighted gene co-expression network analysis

MENGYI TANG1,2,3,4,#, BOWEN YANG1,2,3,4,#, CHUANG ZHANG1,2,3,4, CHAOXU ZHANG1,2,3,4, DAN ZANG1,2,3,4, LIBAO GONG1,2,3,4, YUNPENG LIU1,2,3,4, ZHI LI1,2,3,4,*, XIUJUAN QU1,2,3,4,*

1 Department of Medical Oncology, The First Hospital of China Medical University, Shenyang, China
2 Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province, The First Hospital of China Medical University, Shenyang, China
3 Liaoning Province Clinical Research Center for Cancer, Shenyang, China
4 Key Laboratory of Precision Diagnosis and Treatment of Gastrointestinal Tumors, Ministry of Education, Shenyang, China

* Address correspondence to: Zhi Li, email; Xiujuan Qu, email
# Mengyi TANG and Bowen YANG contributed equally to this work

BIOCELL 2021, 45(4), 911-921. https://doi.org/10.32604/biocell.2021.010119

Abstract

Distal gastric cancer (DGC) is a subgroup of gastric cancer (GC), which has different molecular characteristics from proximal gastric cancer (PGC). These differences result in different overall survival (OS) rates; however, data pertaining to the survival rate in PGC or DGC are contradictory. This suggests that the location of GC is not the unique cause of the different survival rates, while the molecular characteristics might be more important factors determining the prognosis of DGC. Therefore, the aim of this study was to discover key prognostic factors in DGC using bioinformatic methods and to explore the potential molecular mechanism. The Cancer Genome Atlas (TCGA) public database was employed to screen data relating to DGC, and we conducted a weighted gene co-expression network analysis (WGCNA) on DGC patient samples to establish co-expression modules. High-weight genes (hub genes) in a dominant color module were identified. In vitro experiments and gene set enrichment analyses (GSEA) were carried out to elucidate the potential molecular mechanism. In this study, 139 DGC samples were enrolled to perform a co-expression analysis. According to the correlation between gene modules and clinical characteristics, the royal blue module related to stage M of DGC was screened, and a survival analysis was conducted to show that highcoagulation-factor V (F5) expression was related to the short OS of patients with GC. In vitro experiments confirmed that F5 could promote the migration of GC cells. GSEA suggested that F5 might have affected the prognosis of GC by modulating the activities of the Wnt and/or the TGF-β signaling pathways. Our results indicated that high F5 expression predicts poor prognosis of patients with DGC, and it functions probably by promoting cell migration through the Wnt and/or the TGF-β signaling pathways.

Keywords


Cite This Article

APA Style
TANG, M., YANG, B., ZHANG, C., ZHANG, C., ZANG, D. et al. (2021). The f5 gene predicts poor prognosis of patients with gastric cancer by promoting cell migration identified using a weighted gene co-expression network analysis. BIOCELL, 45(4), 911-921. https://doi.org/10.32604/biocell.2021.010119
Vancouver Style
TANG M, YANG B, ZHANG C, ZHANG C, ZANG D, GONG L, et al. The f5 gene predicts poor prognosis of patients with gastric cancer by promoting cell migration identified using a weighted gene co-expression network analysis. BIOCELL . 2021;45(4):911-921 https://doi.org/10.32604/biocell.2021.010119
IEEE Style
M. TANG et al., “The F5 gene predicts poor prognosis of patients with gastric cancer by promoting cell migration identified using a weighted gene co-expression network analysis,” BIOCELL , vol. 45, no. 4, pp. 911-921, 2021. https://doi.org/10.32604/biocell.2021.010119



cc Copyright © 2021 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 2407

    View

  • 1440

    Download

  • 0

    Like

Share Link