Whole Genome Messenger RNA Profiling Identifies a Novel Signature to Predict Gastric Cancer Survival

Abstract

OBJECTIVES: Molecular prognostic biomarkers for gastric cancer (GC) are still limited. We aimed to identify potential messenger RNAs (mRNAs) associated with GC prognosis and further establish an mRNA signature to predict the survival of GC based on the publicly accessible databases. METHODS: Discovery of potential mRNAs associated with GC survival was undertaken for 441 patients with GC based on the Cancer Genome Atlas (TCGA), with information on clinical characteristics and vital status. Gene ontology functional enrichment analysis and pathway enrichment analysis were conducted to interrogate the possible biological functions. We narrowed down the list of mRNAs for validation study based on a significance level of 1.00 × 10 −4 , also integrating the information from the methylation analysis and constructing the protein–protein interaction network for elucidating biological processes. A total of 54 mRNAs were further studied in the validation stage, using the Gene Expression Omnibus (GEO) database (GSE84437, n = 433). The validated mRNAs were used to construct a risk score model predicting the prognosis of GC. RESULTS: A total of 13 mRNAs were significantly associated with survival of GC, after the validation stage, including DCLK1 , FLRT2 , MCC , PRICKLE1 , RIMS1 , SLC25A15 , SLCO2A1 , CDO1 , GHR , CD109 , SELP , UPK1B , and CD36 . Except CD36 , DCLK1 , and SLCO2A1 , other mRNAs are newly reported to be associated with GC survival. The 13 mRNA-based risk score had good performance on distinguishing GC prognosis, with a higher score indicating worse survival in both TCGA and GEO datasets. CONCLUSIONS: We established a 13-mRNA signature to potentially predict the prognosis of patients with GC, which might be useful in clinical practice for informing patient stratification.

Analysis of retrotransposon subfamily DNA methylation reveals novel early epigenetic changes in chronic lymphocytic leukemia

“…In contrast, the function of DCLK2 in CLL biology is less clear. This gene encodes a regulator of microtubule polymerisation, and while family members have been identified as potential prognostic markers in gastric and colorectal cancers, 49 , 50 DCLK2 has not previously been reported as differentially methylated or expressed in CLL. Further work is required to establish the functional role of this gene in the disease.…”

A Prognostic Nomogram Model Based on mRNA Expression of DNA Methylation-Driven Genes for Gastric Cancer

“…GHR was recently reported to mediate cell progression and apoptosis via the BRAF/MEK/ERK signaling pathway in breast cancer (26). According to the literature, GHR was elevated in GC serum samples and high expression of GHR mRNA was associated with a poor outcome in GC patients (27,28), which suggested that GHR may serve as novel biomarkers for the early diagnosis and prognosis determination of GC.…”

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 ).…”

“…In Strategy I, authors established gene signatures based on DEGs with high prognostic values. Based on DEGs derived from Cancer Genome Atlas (TCGA), Dai et al constructed a 13-mRNA signature to predict the prognosis of GC patients (Dai et al, 2019 ). Liu et al identified nine hub genes and constructed a 9-gene signature based on gene expression profiling datasets by integrated bioinformatics analysis (Liu et al, 2018 ).…”