Search

Background: To investigate the association between computed tomography (CT)-detected extramural venous invasion (EMVI)-related genes and immunotherapy resistance and immune escape in patients with gastric cancer (GC). Methods: Thirteen patients with pathologically proven locally advanced GC who had undergone preoperative abdominal contrast-enhanced CT and radical resection surgery were included in this study. Transcriptome sequencing was multidetector performed on the cancerous tissue obtained during surgery, and EMVI-related genes (P value for association < 0.001) were selected. A single-sample gene set enrichment analysis algorithm was also used to divide all GC samples (n = 377) in The Cancer Genome Atlas (TCGA) database into high and low EMVI-immune related groups based on immune-related differential genes. Cluster analysis was used to classify EMVI-immune-related genotypes, and survival among patients was validated in TCGA and Gene Expression Omnibus (GEO) cohorts. The EMVI scores were calculated using principal component analysis (PCA), and GC samples were divided into high and low EMVI score groups. Microsatellite instability (MSI) status, tumor mutation burden (TMB), response rate to immune checkpoint inhibitors (ICIs), immune escape were compared between the high and low EMVI score groups. Hub gene of the model in pan-cancer analysis was also performed. Results: There were 17 EMVI-immune-related genes used for cluster analysis. PCA identified 8 genes (PCH17, SEMA6B, GJA4, CD34, ACVRL1, SOX17, CXCL12, DYSF) that were used to calculate EMVI scores. High EMVI score groups had lower MSI, TMB and response rate of ICIs, status but higher immune escape status. Among the 8 genes used for EMVI scores, CXCL12 and SOX17 were at the core of the protein–protein interaction (PPI) network and had a higher priority in pan-cancer analysis. Immunohistochemical analysis showed that the expression of CXCL12 and SOX17 was significantly higher in CT-detected EMVI-positive samples than in EMVI-negative samples (P < 0.0001). Conclusion: A CT-detected EMVI gene signature could be a potential negative biomarker for ICIs treatment, as the signature is negatively correlated with TMB, and MSI, resulting in poorer prognosis. [ABSTRACT FROM AUTHOR]

Purpose: To develop and evaluate a preoperative risk stratification model for predicting disease-free survival (DFS) based on contrast-enhanced multidetector computed tomography (ceMDCT) images in patients with gastric cancer (GC) undergoing radical surgery. Methods: We retrospectively enrolled patients with GC who underwent ceMDCT followed by radical surgery. A preoperative risk stratification model was constructed (including risk factor selection, risk status scoring, and risk level assignment) using Cox proportional hazard regression and log-rank analyses in the training cohort; the model was tested in the validation cohort. A nomogram was used to compare the preoperative risk stratification model with a postoperative DFS prediction model. Results: A total of 462 patients (training/validation: 271/191) were included. The ceMDCT features of T category (score of 0 or 2), N category (0, 1, 2, or 3), extramural vessel invasion (0 or 2), and tumor location (0 or 1) were selected to construct the preoperative risk stratification model, with 4 risk levels defined based on risk score. There were significant differences in DFS among the risk levels in both cohorts (p < 0.001). The predictive value of the preoperative model was similar to that of the postoperative model, with concordance indices of 0.791 (95% CI, 0.743–0.837) and 0.739 (95% CI, 0.666–0.812), respectively, in the training cohort and 0.762 (95% CI, 0.696–0.828) and 0.738 (95% CI, 0.684–0.792), respectively, in the validation cohort. Conclusion: A preoperative risk stratification model based on ceMDCT images could be used to predict DFS and thus classify GC cases into various risk levels. [ABSTRACT FROM AUTHOR]