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 (GSE15459, GSE26253, GSE62254). 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.
Highlights
Gastric cancer is among the most lethal of cancers worldwide
Differential genes between normal and tumor tissues were identified, and the expression levels of genes that were not significantly different between normal and tumor tissues were excluded from gene list 1
Considering that redundant information exists in these genes and excessive genes may hinder the utilization of the model, a machine learning method called random forest variable hunting was Coefficient −0.05 0 0.05 0.10 0.15 0.20
Summary
Gastric cancer is among the most lethal of cancers worldwide. According to most recent statistical reports in 2015, in China, 679,100 new cases and 498,000 deaths were estimated (Chen et al, 2016). Molecular biomarkers were needed to predict the survival of gastric cancer patients. The prognosis of gastric cancer is difficult to determine, clinical indicators provide valuable evidence. In 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. Cox multivariate regression and three-year survival nomogram suggest that the risk score is an important indicator of gastric cancer prognosis. The 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. It is the first report to predict the survival of gastric cancer using optimized expression panel
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