CT-detected extramural venous invasion-related gene signature for the overall survival prediction in patients with gastric cancer.

BACKGROUND Computed tomography (CT)-detected extramural venous invasion (EMVI) has been identified as an independent factor that can be used for risk stratification and prediction of prognosis in patients with gastric cancer (GC). Overall survival (OS) is identified as the most important prognostic indicator for GC patients. However, the molecular mechanism of EMVI development and its potential relationship with OS in GC are not fully understood. In this radiogenomics-based study, we sought to investigate the molecular mechanism underlying CT-detected EMVI in patients with GC, and aimed to construct a genomic signature based on EMVI-related genes with the goal of using this signature to predict the OS. MATERIALS AND METHODS Whole mRNA genome sequencing of frozen tumor samples from 13 locally advanced GC patients was performed to identify EMVI-related genes. EMVI-prognostic hub genes were selected based on overlapping EMVI-related differentially expressed genes and OS-related genes, using a training cohort of 176 GC patients who were included in The Cancer Genome Atlas database. Another 174 GC patients from this database comprised the external validation cohort. A risk stratification model using a seven-gene signature was constructed through the use of a least absolute shrinkage and selection operator Cox regression model. RESULTS Patients with high risk score showed significantly reduced OS (training cohort, p = 1.143e-04; validation cohort, p = 2.429e-02). Risk score was an independent predictor of OS in multivariate Cox regression analyses (training cohort, HR = 2.758; 95% CI: 1.825-4.169; validation cohort, HR = 2.173; 95% CI: 1.347-3.505; p < 0.001 for both). Gene functions/pathways of the seven-gene signature mainly included cell proliferation, cell adhesion, regulation of metal ion transport, and epithelial to mesenchymal transition. CONCLUSIONS A CT-detected EMVI-related gene model could be used to predict the prognosis in GC patients, potentially providing clinicians with additional information regarding appropriate therapeutic strategy and medical decision-making.