Objective: Versican (VCAN) has been reported as a potential biomarker in some cancers. However, its role in gastric cancer (GC) is poorly understood. Methods: Associations between clinical variables and VCAN were assessed. The diagnostic value of VCAN expression in GC patients was determined through receiver operating characteristic (ROC) curve analysis. Cox regression and the Kaplan-Meier method were used to explore clinicopathologic factors related to overall survival in GC patients. The Gene Expression Omnibus and the Human Protein Atlas were used for further validation. Gene set enrichment analysis (GSEA) was performed using The Cancer Genome Atlas dataset. Results: High expression of VCAN was associated with high stage and T classification in GC. The area under the ROC curve was 0.853. Patients with high VCAN expression had worse prognoses than those with low VCAN expression. Multivariate analysis showed that VCAN was an independent risk factor for overall survival in both cohorts. GSEA identified pathways involved in cancer, ECM-receptor interaction, Wnt signaling, T cell receptor signaling, and chemokine signaling as differentially enriched in GCs with high VCAN expression. Conclusion: We demonstrated that VCAN is expressed at high levels in GC, and represents a potential independent molecular marker for diagnosis and prognosis of GC.

Background:Differentiation of high-grade gliomas (HGGs) and low-grade gliomas (LGGs) is an important clinical problem because treatment strategies vary greatly. This study was performed to investigate the potential diagnostic value of incoherent intravoxel motion imaging (IVIM) to distinguish HGG from LGG by meta-analysis.Methods:A computerized search of the literature was performed using the free-access PubMed database, Web of Science, and Chinese biomedical database, and relevant articles until September 18, 2018 that used IVIM to distinguish HGG from LGG were included. All analyses were performed using Review Manager 5.3 and Stata. Mean difference (MD) at 95% confidence interval (CI) of the apparent diffusion coefficient (ADC), diffusion coefficient value (D), perfusion fraction value (f), and perfusion coefficient value (D∗) were summarized.Results:Nine studies were used for general data pooling. In the tumor parenchyma (TP) regions, subgroup analysis revealed D∗ in HGG is higher than in LGG (MD = 1.19, P = .002), and D in HGG is lower than in LGG (MD = −1.06, P = .001). However, no significant difference in f (MD = 0.89, P = .056) was detected between HGG and LGG. In the white matter regions, HGG had higher D∗ (MD = 0.76, P = .04) compared with LGG, while no marked differences between the D value (P = .07) and f (P = .09) values.Conclusion:The present meta-analysis shows that the ADC, D, and D∗ values derived from IVIM may be useful in differentiating HGG from LGG. Considering the small sample of this study, we need to be cautious when interpreting the results of this study. Other prospective and large-sample randomized controlled trials were needed to establish the value of IVIM in differentiating HGG from LGG.

No abstract

No abstract

Aim: This study aimed to predict progression-free survival (PFS) in patients with early glottic cancer using radiomic features on dual-energy computed tomography iodine maps. Methods: Radiomic features were extracted from arterial and venous phase iodine maps, and radiomic risk scores were determined by univariate Cox proportional hazards regression analysis and least absolute shrinkage and selection operator regression with tenfold cross-validation. The Kaplan–Meier method was used to evaluate the association between radiomic risk scores and PFS. Results: Patients were stratified into low-risk and high-risk groups using radiomics, the PFS corresponding rates with statistical significance between the two groups. The high-risk group showed better survival, benefiting from laryngectomy. Conclusion: Radiomics could provide a promising biomarker for predicting the PFS of early glottic cancer patients.