Recent epidemiological studies suggested that proton pump inhibitor (PPI) use was associated with an increased risk of biliary tract cancer (BTC), however, confounders were not adequately controlled. Our study aimed to evaluate PPI use and subsequent risk of BTC and its subtypes in three well-established cohorts. We conducted a pooled analysis of the subjects free of cancers in UK Biobank (n = 463 643), Nurses' Health Study (NHS, n = 80 235) and NHS II (n = 95 869). Propensity score weighted Cox models were used to estimate marginal HRs of PPIs use on BTC risk, accounting for potential confounders. We documented 284 BTC cases in UK Biobank (median follow-up: 7.6 years), and 91 cases in NHS and NHS II cohorts (median follow-up: 15.8 years). In UK biobank, PPI users had a 96% higher risk of BTC compared to nonusers in crude model (HR 1.96, 95% CI 1.44-2.66), but the effect was attenuated to null after adjusting for potential confounders (HR 0.95, 95% CI 0.60-1.49). PPI use was not associated with risk of BTC in the pooled analysis of three cohorts (HR 0.93, 95% CI 0.60-1.43). We also observed no associations between PPI use with risk of intrahepatic (HR 1.00, 95% CI 0.49-2.04), extrahepatic bile duct (HR 1.09, 95% CI 0.52-2.27) and gallbladder cancers (HR 0.66, 95% CI 0.26-1.66) in UK Biobank. In summary, regular use of PPIs was not associated with the risk of BTC and its subtypes. 相似文献
Introduction: Collaborative interactions between several diverse biological processes govern the onset and progression of breast cancer. These processes include alterations in cellular metabolism, anti-tumor immune responses, DNA damage repair, proliferation, anti-apoptotic signals, autophagy, epithelial-mesenchymal transition, components of the non-coding genome or onco-mIRs, cancer stem cells and cellular invasiveness. The last two decades have revealed that each of these processes are also directly regulated by a component of the cell cycle apparatus, cyclin D1.
Area covered: The current review is provided to update recent developments in the clinical application of cyclin/CDK inhibitors to breast cancer with a focus on the anti-tumor immune response.
Expert opinion: The cyclin D1 gene encodes the regulatory subunit of a proline-directed serine-threonine kinase that phosphorylates several substrates. CDKs possess phosphorylation site selectivity, with the phosphate-acceptor residue preceding a proline. Several important proteins are substrates including all three retinoblastoma proteins, NRF1, GCN5, and FOXM1. Over 280 cyclin D3/CDK6 substrates have b\een identified. Given the diversity of substrates for cyclin/CDKs, and the altered thresholds for substrate phosphorylation that occurs during the cell cycle, it is exciting that small molecular inhibitors targeting cyclin D/CDK activity have encouraging results in specific tumors. 相似文献
BACKGROUND Postoperative liver failure is the most severe complication in cirrhotic patients with hepatocellular carcinoma(HCC) after major hepatectomy. Current available clinical indexes predicting postoperative residual liver function are not sufficiently accurate.AIM To determine a radiomics model based on preoperative gadoxetic acid-enhanced magnetic resonance imaging for predicting liver failure in cirrhotic patients with HCC after major hepatectomy.METHODS For this retrospective study, a radiomics-based model was developed based on preoperative hepatobiliary phase gadoxetic acid-enhanced magnetic resonance images in 101 patients with HCC between June 2012 and June 2018. Sixty-one radiomic features were extracted from hepatobiliary phase images and selected by the least absolute shrinkage and selection operator method to construct a radiomics signature. A clinical prediction model, and radiomics-based model incorporating significant clinical indexes and radiomics signature were built using multivariable logistic regression analysis. The integrated radiomics-based model was presented as a radiomics nomogram. The performances of clinical prediction model, radiomics signature, and radiomics-based model for predicting post-operative liver failure were determined using receiver operating characteristics curve, calibration curve, and decision curve analyses.RESULTS Five radiomics features from hepatobiliary phase images were selected to construct the radiomics signature. The clinical prediction model, radiomics signature, and radiomics-based model incorporating indocyanine green clearance rate at 15 min and radiomics signature showed favorable performance for predicting postoperative liver failure(area under the curve: 0.809-0.894). The radiomics-based model achieved the highest performance for predicting liver failure(area under the curve: 0.894; 95%CI: 0.823-0.964). The integrated discrimination improvement analysis showed a significant improvement in the accuracy of liver failure prediction when radiomics signature was added to the clinical prediction model(integrated discrimination improvement = 0.117, P =0.002). The calibration curve and an insignificant Hosmer-Lemeshow test statistic(P = 0.841) demonstrated good calibration of the radiomics-based model. The decision curve analysis showed that patients would benefit more from a radiomics-based prediction model than from a clinical prediction model and radiomics signature alone.CONCLUSION A radiomics-based model of preoperative gadoxetic acid–enhanced MRI can be used to predict liver failure in cirrhotic patients with HCC after major hepatectomy. 相似文献