A gene expression-based risk model reveals prognosis of gastric cancer

Abstract

BackgroundThe prognosis of gastric cancer is difficult to determine, although clinical indicators provide valuable evidence.MethodsIn this study, using screened biomarkers of gastric cancer in combination with random forest variable hunting and multivariable Cox regression, a risk score model was developed to predict the survival of gastric cancer. Survival difference between high/low-risk groups were compared. The relationship between risk score and other clinicopathological indicators was evaluated. Gene set enrichment analysis (GSEA) was used to identify pathways associated with risk scores.ResultsThe patients with high risk scores (median overall survival: 20.2 months, 95% CI [16.9–26.0] months) tend to exhibit early events compared with those with low risk scores (median survival: 70.0 months, 95% CI [46.9–101] months, p = 1.80e–5). Further validation was implemented in another three independent datasets (, , ). Correlation analyses between clinical observations and risk scores were performed, and the results indicated that the risk score was not significantly associated with gender, age and primary tumor size but was significantly associated with grade and tumor stage. In addition, the risk score was also not influenced by radiation therapy. Cox multivariate regression and three-year survival nomogram suggest that the risk score is an important indicator of gastric cancer prognosis. GSEA was used to identified KEGG pathways significantly associated with risk score, and signaling pathways involved in focal adhesion and the TGF-beta signaling pathway were identified.ConclusionThe risk score model successfully predicted the survival of 1,294 gastric cancer samples from four independent datasets and is among the most important indicators in clinical clinicopathological information for the prognosis of gastric cancer. To our knowledge, it is the first report to predict the survival of gastric cancer using optimized expression panel.

Exploring TCGA database for identification of potential prognostic genes in stomach adenocarcinoma

“…Extracellular region ANGPTL1 [27], BCHE, C7, CARTPT [28], CPXM2 [29], PRG4, VIP Extracellular matrix OMD [30], MGP [31], KERA, SFRP2 [32], SPARCL1 [33] Cytoplasm PDLIM3 [34], MYL9 [35], FHL1 [36], DES, SYNC, CPED1…”

Integrated Analysis Identifies an Immune-Based Prognostic Signature for the Mesenchymal Identity in Gastric Cancer

“…However, some patients with advanced GC remained stable for several years after surgery, while some patients with early GC progressed rapidly [31]. At present, various multigene prognostic markers have been developed [7][8][9][10], but their prediction efficiencies were still uncertain. Therefore, a new signature that can accurately recognize patients with poor GC prognosis is urgently needed to give more rigorous treatments.…”

“…Large-scale public cohorts with tumor gene expression data provide a broader opportunity to search for reliable prognostic markers for gastric cancer. Several studies have developed markers based on gene expression for GC prognosis prediction [7][8][9][10]. However, due to the heterogeneity of GC, most of the markers have low prognostic efficacy and cannot be directly used in clinical practice.…”

Single-center versus multi-center data sets for molecular prognostic modeling: a simulation study

“…Conceptually, the approach of using molecular information for prognostic modeling is backed by the finding that many cancer types are tremendously heterogeneous and form subgroups of different prognosis or different therapeutic accessibility [13][14][15]. Consequently, highdimensional measurements at the genome, transcriptome, post-transcriptome and protein levels, individually or in combination, were used to generate signatures for the stratification of breast carcinomas [13,[16][17][18][19][20], glioblastoma [11,21], gastric cancer [22,23], lung adenocarcinomas [24], squamous cell cervical carcinoma [25] and head and neck squamous cell carcinomas [10,26,27].…”