Aberrant overexpression of cyclooxygenase-2 (COX2) is observed in urothelial carcinoma of the bladder (UCB). Studies evaluating COX2 as a prognostic marker in UCB report contradictory results. We determined the prognostic potential of COX2 expression in UCB and quantitatively summarize the results with those of the literature through a meta-analysis. Newly diagnosed UCB patients recruited between 1998–2001 in 18 Spanish hospitals were prospectively included in the study and followed-up (median, 70.7 months). Diagnostic slides were reviewed and uniformly classified by expert pathologists. Clinical data was retrieved from hospital charts. Tissue microarrays containing non-muscle invasive (n = 557) and muscle invasive (n = 216) tumours were analyzed by immunohistochemistry using quantitative image analysis. Expression was evaluated in Cox regression models to assess the risk of recurrence, progression and disease-specific mortality. Meta-hazard ratios were estimated using our results and those from 11 additional evaluable studies. COX2 expression was observed in 38% (211/557) of non-muscle invasive and 63% (137/216) of muscle invasive tumors. Expression was associated with advanced pathological stage and grade (p<0.0001). In the univariable analyses, COX2 expression - as a categorical variable - was not associated with any of the outcomes analyzed. As a continuous variable, a weak association with recurrence in non-muscle invasive tumors was observed (p-value = 0.048). In the multivariable analyses, COX2 expression did not independently predict any of the considered outcomes. The meta-analysis confirmed these results. We did not find evidence that COX2 expression is an independent prognostic marker of recurrence, progression or survival in patients with UCB.
Background Pancreatic cancer (PC) is a complex disease in which both non-genetic and genetic factors interplay. To date, 40 GWAS hits have been associated with PC risk in individuals of European descent, explaining 4.1% of the phenotypic variance. Methods We complemented a new conventional PC GWAS (1D) with genome spatial autocorrelation analysis (2D) permitting to prioritize low frequency variants not detected by GWAS. These were further expanded via Hi-C map (3D) interactions to gain additional insight into the inherited basis of PC. In silico functional analysis of public genomic information allowed prioritization of potentially relevant candidate variants. Results We identified several new variants located in genes for which there is experimental evidence of their implication in the biology and function of pancreatic acinar cells. Among them is a novel independent variant in NR5A2 (rs3790840) with a meta-analysis p value = 5.91E−06 in 1D approach and a Local Moran’s Index (LMI) = 7.76 in 2D approach. We also identified a multi-hit region in CASC8—a lncRNA associated with pancreatic carcinogenesis—with a lowest p value = 6.91E−05. Importantly, two new PC loci were identified both by 2D and 3D approaches: SIAH3 (LMI = 18.24), CTRB2/BCAR1 (LMI = 6.03), in addition to a chromatin interacting region in XBP1—a major regulator of the ER stress and unfolded protein responses in acinar cells—identified by 3D; all of them with a strong in silico functional support. Conclusions This multi-step strategy, combined with an in-depth in silico functional analysis, offers a comprehensive approach to advance the study of PC genetic susceptibility and could be applied to other diseases.
150 Word count -Abstract: 148 151 Word count -Text: 4,119 152 Word count Methods: 1,963 153 Number of References: 75 154 Number of Figures: 6 155 Number of supplementary tables: 8 156 ABSTRACT 158 Pancreatic cancer (PC) is a complex disease in which both non-genetic and genetic factors 159 interplay. To-date, 40 GWAS hits have been associated with PC risk in individuals of 160 European descent, explaining 4.1% of the phenotypic variance. Here, we complemented 161 PanC4, have been described elsewhere 6-9 . Genotyping for PanScan studies was performed
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.