Recent experimental evidence support the model in which the simultaneous induction of BMI-1 and USP22 is critical during cancer progression. Whether this model may affect gastric cancer (GC) progression is worthy of additional study. In this study, we examined the significance of the USP22 and BMI-1 expression in GC (n = 219), non-cancerous mucosa (n = 37), and lymph node metastasis (n = 37). The protein expression level of USP22 and BMI-1 were concomitantly up-regulated from non-cancerous mucosa to primary carcinoma and from carcinomas to lymph node metastasis (P < 0.001). A statistical correlation was observed between USP22 and BMI-1 expression in GC tissues (n = 219, r = 0.634, P < 0.001) and in lymph node metastasis (n = 37, r = 0.689, P < 0.001). The incidence of positive expression was 57.08% for USP22, 49.32% for BMI-1, and 45.21% for USP22/BMI-1 in 219 GC tissues, respectively. Co-positive of USP22/BMI-1 was significantly correlated with gross features (x(2) = 14.256, P < 0.001), differentiation (x(2) = 5.872, P = 0.015), pT classification (x(2) = 18.486, P < 0.001), pN classification (x(2) = 9.604, P = 0.002), pM classification (x(2) = 32.766, P < 0.001), and AJCC stage (x(2) = 58.278, P < 0.001). Notably, high USP22/BMI-1 expression was significantly associated with shorter disease-specific survival (P < 0.001). By Cox regression analysis, co-positive of USP22/BMI-1 was found to be an independent prognostic factor (P = 0.002). Our results indicated the simultaneous activation of USP22 and BMI-1 may associate with GC progression and therapy failure.

BackgroundThere is no study accessible now assessing the prognostic aspect of radiomics for anti-PD-1 therapy for patients with HCC.AimThe aim of this study was to develop and validate a radiomics nomogram by incorporating the pretreatment contrast-enhanced Computed tomography (CT) images and clinical risk factors to estimate the anti-PD-1 treatment efficacy in Hepatocellular Carcinoma (HCC) patients.MethodsA total of 58 patients with advanced HCC who were refractory to the standard first-line of therapy, and received PD-1 inhibitor treatment with Toripalimab, Camrelizumab, or Sintilimab from 1st January 2019 to 31 July 2020 were enrolled and divided into two sets randomly: training set (n = 40) and validation set (n = 18). Radiomics features were extracted from non-enhanced and contrast-enhanced CT scans and selected by using the least absolute shrinkage and selection operator (LASSO) method. Finally, a radiomics nomogram was developed based on by univariate and multivariate logistic regression analysis. The performance of the nomogram was evaluated by discrimination, calibration, and clinical utility.ResultsEight radiomics features from the whole tumor and peritumoral regions were selected and comprised of the Fusion Radiomics score. Together with two clinical factors (tumor embolus and ALBI grade), a radiomics nomogram was developed with an area under the curve (AUC) of 0.894 (95% CI, 0.797–0.991) and 0.883 (95% CI, 0.716–0.998) in the training and validation cohort, respectively. The calibration curve and decision curve analysis (DCA) confirmed that nomogram had good consistency and clinical usefulness.ConclusionsThis study has developed and validated a radiomics nomogram by incorporating the pretreatment CECT images and clinical factors to predict the anti-PD-1 treatment efficacy in patients with advanced HCC.

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