Prediction of radiosensitive patients with gastric cancer by developing gene signature

Abstract

Adjuvant radiotherapy is an important clinical treatment for the majority of gastric cancer, a common cancer. However, radiotherapy is a double-edged sword. It is necessary to develop a method to predict radiosensitive patients who are most likely to benefit from radiotherapy. Using the publicly available data of gastric cancer from The Cancer Genome Atlas (TCGA), we developed a gene signature that predicts radiosensitive patients through estimating a new index, nominal HR (nHR) (HR product of sensitive genes), for each patient. In this study, we provided several results to validate our prediction. Cross-validation results showed that the predicted radiosensitive patients who received radiotherapy had significantly better survival than predicted radiosensitive patients who did not receive radiotherapy. After adjusting for other clinical factors, including age, sex, target therapy, histologic diagnosis, tumor stage, the benefit of radiotherapy on predicted radiosensitive patient remained significant. In addition, predicted radiosensitive patients who received radiotherapy had a significantly reduced rate of disease progression. Taken together, we have obtained a set of genes, to identify radiosensitive patients with gastric cancer. These genes may be potential biomarkers for diagnosis and treatment of gastric cancer, which could give new insight for revealing the underlying mechanism of radiosensitivity of gastric cancer.

Figure 1

The −log₁₀ (p-values) profile by log-rank tests between radiotherapy and non-radiotherapy groups for predicted radiosensitive patients. The figure shows gene signatures including the top 11 significant genes with a threshold nHR=0.065 that can provide a powerful prediction with the smallest p-values (p=3.810E-09).

Figure 2

The survival curves under radiotherapy and non-radiotherapy for both predicted radiosensitive and non-radiosensitive patients. The colored areas denote the 95% confidence intervals for survival rate. The number of deaths and total patients for each group appear in brackets.

Figure 3

The HR estimation for radiotherapy (RT) vs. non-radiotherapy (NRT) and predicted radiosensitive (RS) vs. non-radiosensitive (NRS). The p-values are estimated by a Wald test. The adjusted factors are sex, age, target therapy, histologic diagnosis, tumor stage, T, N and M stage.

Figure 4

The comparison of the rate of the new tumor event and progressive disease among different groups. The rates for different groups are compared using the Fisher exact test. RT, radiotherapy; NRT, non-radiotherapy; RS, radiosensitive; NRS, non-radiosensitive.

Figure 5

The survival curves under radiotherapy and non-radiotherapy for predicted radiosensitive and non-radiosensitive patients with different T stage. Patients in T3 and T4 stages are combined for a log-rank test because of small sample sizes.

Figure 6

The survival curves under radiotherapy and non-radiotherapy for predicted radiosensitive and non-radiosensitive patients with different M stage.

Figure 7

Hierarchical clustering analyses. Hierarchical clustering was used to determine the expression pattern of the 11 selected genes. The top blue and yellow bands denote the predicted radiosensitive and non-radiosensitive patients, respectively.