Prognostic value of gastric cancer‐associated gene signatures: Evidence based on a meta‐analysis using integrated bioinformatics methods

Abstract

Selecting differentially expressed genes (DEGs) based on integrated bioinformatics analyses has been used in previous studies to explore potential biomarkers in gastric cancer (GC) with microarray and RNA sequencing data. However, the genes obtained may be inaccurate because of noisy data and errors, as well as insufficient clinical sample sizes. Thus, we aimed to find robust and strong DEGs with prognostic value for GC, where the robust rank aggregation method was employed to select significant DEGs from eight Gene Expression Omnibus data sets with a total of 140 up‐regulated and 206 down‐regulated genes. Network data mining was then used to screen hub genes, and 11 genes were filtered using Fisher's exact test. Based on these results, we built a prognostic signature with seven genes (FBN1,MMP1,PLAU,SPARC,COL1A2,COL2A1 and ATP4A) using stepwise multivariate Cox proportional hazard regression. According to the risk score for each patient, we found that high‐risk group patients had significantly worse survival results compared with those in the low‐risk group (log‐rank test P‐value < 0.001). This seven‐gene signature was then validated with an external data set. Thus, we established a signature based on seven DEGs with prognostic value for GC patients using multi‐steps bioinformatics methods, which may provide novel insights and potential biomarkers for prognosis, as well as possibly serving as new therapeutic targets in clinical applications.

Identification of New Therapeutic Targets for Gastric Cancer With Bioinformatics

“…In our study, we combined the GDSC, CCLE, and TCGA database to identify six new target genes (FBN1, FN1, HGF, MMP9, THBS1 , and VCAN ). A few of articles reflected that some indexes could affect the proliferation and invasion of GC by regulating HGF or MMP9 ( Appleby et al, 2017 ; Matsumoto et al, 2017 ; Zhang et al, 2017 ; Ding et al, 2018 ; Wang R. et al, 2018 ), but the function of these six genes in GC was still not well-known, especially FBN1, FN1 , and VCAN ( Lee et al, 2016 ; Sakai et al, 2016 ; Wang J. et al, 2018 ; Jiang et al, 2019 ). By observing the relationships between target genes and important factors that have proven to affect the treatment and prognosis of GC, such as immune infiltration, tumor purity, TMB, TME score, and oncogenic signaling pathways, we expect that the six genes could be considered as new prognostic targets in GC.…”

Identification of Key Genes and Circular RNAs in Human Gastric Cancer

“…Secreted protein acidic and rich in cysteine (SPARC) is regarded as a collagen chaperone because it binds to the procollagens and its existence is essential for collagen deposition in tissues [29,48]. SPARC overexpression has been demonstrated in some cancers, such as pancreatic cancer [49], esophageal squamous cell carcinoma [50], and GC [51]. In contrast, some studies have shown that SPARC expression is reduced in bladder cancer [52] and acute leukemia [53].…”

Systematic Review of Prognostic Gene Signature in Gastric Cancer Patients

“…Elaborate search results of prognostic signatures in GC were shown in Table 1 and Figure 2 (Chen et al, 2005 ; Motoori et al, 2005 ; Xu et al, 2009 ; Takeno et al, 2010 ; Cho et al, 2011 ; Bauer et al, 2012 ; Kim et al, 2012 ; Wang et al, 2013 , 2017b , 2018 ; Lee et al, 2014 ; Pasini et al, 2014 ; Li et al, 2016 ; Zhao et al, 2016 , 2019 ; Hou et al, 2017 ; Kuang et al, 2017 ; Lafrenie et al, 2017 ; Liu et al, 2018 , 2019 ; Peng et al, 2018 , 2020 ; Smyth et al, 2018 ; Wu et al, 2018 ; Yuzhalin et al, 2018 ; Chang and Lai, 2019 ; Chang et al, 2019 ; Dai et al, 2019 ; Jiang et al, 2019 , 2020 ; Song et al, 2019 ; Bai et al, 2020 ; Guan et al, 2020 ). Briefly, we got 39 literatures in NCBI PubMed Database following the above procedure ( Figure 1 ).…”