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, 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.

Figure 1.

Gene functional and pathway enrichment analysis of the 184 genes, which encode messenger RNAs significantly associated with gastric cancer death at P < 5 × 10⁻³ in discovery stage. P < 0.05 was considered as threshold values of significant difference in enrichment analysis. (a) Significantly enriched GO terms of the 184 genes using the online tool DAVID. (b) Significantly enriched pathways of the 184 genes using the online tool KOBAS 3.0. Four databases were utilized for analyses, including “KEGG pathway,” “Reactome,” “BioCyc,” and “PANTHER.”

Figure 2.

Protein-protein interaction network and subnetwork analysis. (a) Protein–protein interaction network constructed by the 184 genes, which encode messenger RNAs significantly associated with gastric cancer death at P < 5 × 10⁻³ in the discovery set. (b) The key subnetwork module extracted from the protein–protein interaction network through the plugin software ClusterONE (minimum size = 5, minimum density = 0.6, and edge weights = unweighted).

Figure 3.

Kaplan–Meier survival curves for patients in the discovery (the Cancer Genome Atlas-STAD) and validation (GSE84437) sets. The risk scores based on 13 messenger RNAs were categorized into tertiles. The green curve represents low risk score group. The blue curve represents median risk score group. The pink curve represents high risk score group. Log-rank tests were conducted to compare survival curves among subgroups in each dataset. (a) Kaplan–Meier survival curves for patients in the discovery set. (b) Kaplan–Meier survival curves for patients in the validation set.