Abstract
BackgroundAs one of the most common malignant tumors worldwide, the morbidity and mortality of gastric carcinoma (GC) are gradually increasing. The aim of this study was to construct a signature according to immune-relevant genes to predict the survival outcome of GC patients using The Cancer Genome Altas (TCGA).MethodsUnivariate Cox regression analysis was used to assess the relationship between immune-relevant genes regarding the prognosis of patients with GC. The least absolute shrinkage and selection operator (LASSO) Cox regression model was used to select prognostic immune-relevant genes and to establish the signature for the prognostic evaluation of patients with GC. Multivariate Cox regression analysis and Kaplan–Meier survival analysis were used to assess the independent prognostic ability of the immune-relevant gene signature.ResultsA total of 113 prognostic immune-relevant genes were identified using univariate Cox proportional hazards regression analysis. A signature of nine immune-relevant genes was constructed using the LASSO Cox regression. The GC samples were assigned to two groups (low- and high risk) according to the optimal cutoff value of the signature score. Compared with the patients in the high-risk group, patients in the low-risk group had a significantly better prognosis in the TCGA and GSE84437 cohorts (log-rank test P < 0.001). Multivariate Cox regression analysis demonstrated that the signature of nine immune-relevant genes might serve as an independent predictor of GC.ConclusionsOur results showed that the signature of nine immune-relevant genes may potentially serve as a prognostic prediction for patients with GC, which may contribute to the decision-making of personalized treatment for the patients.
Highlights
According to estimates from the GLOBOCAN 2018, approximately 18.1 million new cancer cases and 9.6 million deaths were reported worldwide [1]
Our results showed that the signature of nine immune-relevant genes may potentially serve as a prognostic prediction for patients with Gastric carcinoma (GC), which may contribute to the decision-making of personalized treatment for the patients
Based on the optimal cutoff value of 1.273 for the risk score, samples were further divided into low- and highrisk groups (Figure 1a)
Summary
According to estimates from the GLOBOCAN 2018, approximately 18.1 million new cancer cases and 9.6 million deaths were reported worldwide [1]. Gastric carcinoma (GC) is one of the most common malignant tumors and the third leading cause of cancer-related deaths worldwide [2]. Gastric adenocarcinoma, which accounts for 90% of all GCs, is the most common histological type [3]. Despite significant development in the treatment of GC during the past decade, the prognosis of patients with GC remains unsatisfactory, with a 5-year relative survival rate of less than 40% [4,5]. Identifying effective potential diagnostic markers and therapeutic targets to combat GC and thereby contribute to improving survival outcomes in patients with GC is urgently needed
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