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BackgroundThe nuanced relationship between inflammatory bowel disease (IBD) and pancreatic cancer is noticed in recent years. However, the underlying causal effects of these two diseases are still unclear.MethodsThe two-sample mendelian randomization (MR) was conducted to explore the causal effect of IBD condition on pancreatic cancer. Methods of Wald ratio, inverse variance weighted (IVW), MR-Egger, weighted median, and weighted mode were used to investigate the causal relationship between IBD and pancreatic cancer. Besides, Cochrane’s Q test, MR-Egger, and leave-one-out method were further conducted to detect heterogeneity, stability, and pleiotropy of MR results.ResultsIn the MR analysis, we found Crohn’s disease had a significant causal effect on pancreatic cancer. Specifically, Crohn’s disease would increase 11.1% the risk of pancreatic cancer by the IVW method (p= 0.022), 33.8% by MR Egger (p= 0.015), by 35.3% by the Weighted model (p= 0.005). Regarding ulcerative colitis, there was no statistically significant causal effect observed on pancreatic cancer (p>0.05). Additionally, the pleiotropic test and Leave-one-out analysis both proved the validity and reliability of the present two-sample MR analyses.ConclusionThis study indicates that IBD, particularly Crohn’s disease, is causality associated with increased risk of pancreatic cancer. Our results may help public health managers to make better follow-up surveillance of IBD patients.

Background: Statin use for cancer prevention has raised wide attention but the conclusions are still controversial. Whether statins use have exact causal effects on cancer prevention remains unclear.Methods: Based on the Genome-Wide Association Studies (GWAS) datasets from the large prospective UK Biobank and other consortium databases, two-sample mendelian randomization (MR) analysis was conducted to explore the causal effects of statins use on varied site-specific cancer risks. Five MR methods were applied to investigate the causality. The stability, heterogeneity, and pleiotropy of MR results were also evaluated.Results: The atorvastatin use could increase the risk of colorectal cancer (odd ratio (OR) = 1.041, p = 0.035 by fixed-effects inverse variance weighted (IVW) method (IVWFE), OR = 1.086, p = 0.005 by weighted median; OR = 1.101, p = 0.048 by weighted mode, respectively). According to the weighted median and weighted mode, atorvastatin could modestly decrease the risk of liver cell cancer (OR = 0.989, p = 0.049, and OR = 0.984, p = 0.004, respectively) and head and neck cancer (OR = 0.972, p = 0.020). Besides, rosuvastatin use could reduce the bile duct cancer risk by 5.2% via IVWEF method (OR = 0.948, p = 0.031). No significant causality was determined in simvastatin use and pan-cancers via the IVWFE or multiplicative random-effects IVW (IVWMRE) method if applicable (p > 0.05). There was no horizontal pleiotropy observed in the MR analysis and the leave-one-out analysis proved the stability of the results.Conclusion: The causalities between statin use and cancer risk were only observed in colorectal cancer and bile duct cancer in the European ancestry population. Future works are warranted to provide more robust evidence for supporting statin repurposing for cancer prevention.

The evidence for chronic hepatitis B virus (HBV) infection and hepatocellular carcinoma (HCC) occurrence is well established. The hepatocyte epithelium carcinogenesis caused by HBV has been investigated and reviewed in depth. Nevertheless, recent findings from preclinical and observational studies suggested that chronic HBV infection is equally important in extrahepatic cancer occurrence and survival, specifically gastrointestinal system-derived cancers. Immune microenvironment changes (immune-suppressive cytokine infiltration), epigenetic modification (N6-methyladenosine), molecular signaling pathways (PI3K–Akt and Wnt), and serum biomarkers such as hepatitis B virus X (HBx) protein are potential underlying mechanisms in chronic HBV infection-induced extrahepatic cancers. This narrative review aimed to comprehensively summarize the most recent advances in evaluating the association between chronic HBV infection and extrahepatic cancer risk and explore the potential underlying molecular mechanisms in the carcinogenesis induction of extrahepatic cancers in chronic HBV conditions.

PurposeCurrent deep learning methods for dose prediction require manual delineations of planning target volume (PTV) and organs at risk (OARs) besides the original CT images. Perceiving the time cost of manual contour delineation, we expect to explore the feasibility of accelerating the radiotherapy planning by leveraging only the CT images to produce high-quality dose distribution maps while generating the contour information automatically.Materials and MethodsWe developed a generative adversarial network (GAN) with multi-task learning (MTL) strategy to produce accurate dose distribution maps without manually delineated contours. To balance the relative importance of each task (i.e., the primary dose prediction task and the auxiliary tumor segmentation task), a multi-task loss function was employed. Our model was trained, validated and evaluated on a cohort of 130 rectal cancer patients.ResultsExperimental results manifest the feasibility and improvements of our contour-free method. Compared to other mainstream methods (i.e., U-net, DeepLabV3+, DoseNet, and GAN), the proposed method produces the leading performance with statistically significant improvements by achieving the highest HI of 1.023 (3.27E-5) and the lowest prediction error with ΔD95 of 0.125 (0.035) and ΔDmean of 0.023 (4.19E-4), respectively. The DVH differences between the predicted dose and the ideal dose are subtle and the errors in the difference maps are minimal. In addition, we conducted the ablation study to validate the effectiveness of each module. Furthermore, the results of attention maps also prove that our CT-only prediction model is capable of paying attention to both the target tumor (i.e., high dose distribution area) and the surrounding healthy tissues (i.e., low dose distribution areas).ConclusionThe proposed CT-only dose prediction framework is capable of producing acceptable dose maps and reducing the time and labor for manual delineation, thus having great clinical potential in providing accurate and accelerated radiotherapy. Code is available at https://github.com/joegit-code/DoseWithCT

ObjectiveThis study aimed to investigate the prognostic factors of penile cancer and establish a comprehensive predictive model for clinical application.MethodsA total of 581 patients from the Surveillance, Epidemiology, and End Results (SEER) program (2000–2018) were used to develop the prognostic model. The multivariate Cox proportional hazards regression was performed to identify independent prognostic factors to develop the nomogram. The performance of this model was validated internally by a cohort with 143 patients from the SEER database and validated externally by a cohort with 70 patients from the West China Hospital, Sichuan University (2010–2020).ResultsAge, marital status, size of the primary lesion, primary tumor (T), regional lymph nodes status, distant metastasis (M), and the surgery of regional lymph node (LND) were the independent prognostic factors for overall survival (OS) and were incorporated in the prognostic model. The prognostic nomogram showed a good risk stratification ability for OS in the development cohort, internal validation cohort, and external validation cohort.ConclusionThis study incorporates the clinical, pathological, and therapeutic features comprehensively to develop a novel and clinically effective prognostic model for patients with penile cancer.