Non-pancreatic Cancer Research Articles

Predictive role of the prognostic nutritional index for long-term prognosis among patients undergoing pancreatoduodenectomy: a meta-analysis

PurposeTo identify the predictive role of the preoperative prognostic nutritional index (PNI) for long-term survival in patients undergoing pancreatoduodenectomy.MethodsThe PubMed, EMBASE, Web of Science, Cochrane Library and CNKI databases were searched up to October 28, 2024. The primary outcomes included overall survival (OS) and disease-free survival (DFS). Hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated, and subgroup analyses by country, type of cancer and source of HR were performed.ResultsFifteen studies with 2106 patients were included. The pooled results demonstrated that a lower preoperative PNI was related to poorer OS (HR = 1.60, 95% CI: 1.38–1.86, P < 0.001) and DFS (HR = 1.44, 95% CI: 1.00–2.07, P = 0.051). Subgroup analysis stratified by country (China vs. non-China), type of cancer (pancreatic cancer vs. nonpancreatic cancer vs. mixed) and source of HR (univariate vs. multivariate analysis) revealed similar results.ConclusionOn the basis of the available evidence, the preoperative PNI might serve as a novel prognostic indicator in patients undergoing pancreatoduodenectomy, with a lower PNI predicting worse survival. However, more high-quality studies are needed to further verify the above findings.

The diagnostic value of combining preoperative serum CA19-9, ALBI score, and 18 F-FDG PET/CT imaging in preoperative resectability of pancreatic cancer.

Pancreatic cancer is an increasing cause of cancer-related mortality, with persistently low survival rates. We investigated the clinical diagnostic value of the combination of preoperative serum carbohydrate antigen 19-9 (CA19-9), albumin-bilirubin (ALBI) score, and 18 F-fluoro-2-deoxy- d -glucose PET integrated with computed tomography ( 18 F-FDG PET/CT) imaging in pancreatic cancer preoperative resectability. This study included 143 pancreatic cancer patients, including 68 preoperative resectable and 75 preoperative unresectable pancreatic cancer patients. Meanwhile, 67 patients with non-pancreatic cancer were included as the control group. The clinical data were collected. Serum CA19-9 level was measured by ELISA. The levels of total bilirubin and albumin were determined using a biochemical analyzer, with the ALBI score calculated. All patients underwent 18 F-FDG PET/CT imaging. The consistency of the diagnosis was evaluated by the Kappa test. Logistic univariate and multivariate regression analyses were performed. The diagnostic efficacy of these parameters was evaluated using receiver operating characteristic (ROC) curves, and the optimal ROC curve thresholds were obtained using the Youden index. The preoperative serum CA19-9 and ALBI score of patients with preoperative resectable pancreatic cancer were increased, which helped diagnose preoperative resectable pancreatic cancer. 18 F-FDG PET/CT imaging had diagnostic value for preoperative resectable pancreatic cancer. Preoperative serum CA19-9, ALBI score, and 18 F-FDG PET/CT imaging were independent influencing factors for pancreatic cancer preoperative resectability, and their combination had higher diagnostic value for preoperative resectable pancreatic cancer than any single of these indexes. The combination of preoperative serum CA19-9, ALBI score, and 18 F-FDG PET/CT imaging had high diagnostic value for pancreatic cancer preoperative resectability.

Effectiveness of data-augmentation on deep learning in evaluating rapid on-site cytopathology at endoscopic ultrasound-guided fine needle aspiration

Rapid on-site cytopathology evaluation (ROSE) has been considered an effective method to increase the diagnostic ability of endoscopic ultrasound-guided fine needle aspiration (EUS-FNA); however, ROSE is unavailable in most institutes worldwide due to the shortage of cytopathologists. To overcome this situation, we created an artificial intelligence (AI)-based system (the ROSE-AI system), which was trained with the augmented data to evaluate the slide images acquired by EUS-FNA. This study aimed to clarify the effects of such data-augmentation on establishing an effective ROSE-AI system by comparing the efficacy of various data-augmentation techniques. The ROSE-AI system was trained with increased data obtained by the various data-augmentation techniques, including geometric transformation, color space transformation, and kernel filtering. By performing five-fold cross-validation, we compared the efficacy of each data-augmentation technique on the increasing diagnostic abilities of the ROSE-AI system. We collected 4059 divided EUS-FNA slide images from 36 patients with pancreatic cancer and nine patients with non-pancreatic cancer. The diagnostic ability of the ROSE-AI system without data augmentation had a sensitivity, specificity, and accuracy of 87.5%, 79.7%, and 83.7%, respectively. While, some data-augmentation techniques decreased diagnostic ability, the ROSE-AI system trained only with the augmented data using the geometric transformation technique had the highest diagnostic accuracy (88.2%). We successfully developed a prototype ROSE-AI system with high diagnostic ability. Each data-augmentation technique may have various compatibilities with AI-mediated diagnostics, and the geometric transformation was the most effective for the ROSE-AI system.

CLASSIFICATION OF PANCREATIC CANCER FROM CT SCAN IMAGES USING DEEP LEARNING TECHNIQUE

Artificial intelligence is being applied in various computer science domains in order to enhance the performance and decision making capability of manual frameworks, Deep learning accompanied with medical imaging is proven to be really fruitful for the detection and classification of various diseases like brain tumor, breast cancer, head and neck cancer, Hepatic and Pulmonary cancer or classification of leukemia and various diseases that can help medical professionals to diagnose and classify. Cancer is also very hard for doctors or medical professionals to spot in the very first place because major cancer symptoms are really similar to other diseases so finding cancer in a patient is a major objective especially in early stages. Pancreatic cancer, a kind of malignant neoplastic disease where the malignant neoplastic disease/malignant cells develops within the tissues of pancreas, the general survival rate of 5 year of pancreatic cancer is 10% while as the pancreas lies behind the stomach and liver so it requires intensive medical imaging. The proposed methodology presents an extensive solution for this problem, the methodology presents a deep learning based system that is able to classify pancreatic and non-pancreatic cancer through CT scan images of human pancreas. The system has been constructed using a custom VGG16 architecture using a collection of 20014 CT scan images for pancreatic and non-pancreatic cancer. The proposed methodology produces training and validation accuracy of 100%, 100% respectively.

Efficacy and safety of uncovered self-expandable metal stents for distal malignant biliary obstruction in unresectable non-pancreatic cancer.

The efficacy of uncovered self-expandable metal stents (UCSEMS) versus fully covered self-expandable metal stents for distal malignant biliary obstruction remains controversial. Additionally, the heterogeneity of the disease conditions has been indicated in previous studies because pancreatic and non-pancreatic cancers have different characteristics in clinical course. Therefore, the etiology of biliary obstruction necessitates investigations stratified by primary disease. This study aimed to evaluate the outcomes of UCSEMS, specifically for non-pancreatic cancer-induced distal malignant biliary obstruction. We conducted a single-center retrospective review to evaluate the time to recurrent biliary obstruction and frequency of adverse events (AEs) in patients receiving UCSEMS for unresectable non-pancreatic cancer-induced malignant biliary obstruction. Overall, 32 patients were enrolled in the study between January 2016 and December 2023. The median time to recurrent biliary obstructionwas 140 days. AE rates were low at 3.1% for both pancreatitis and cholecystitis, suggesting a potential benefit of UCSEMS in reducing post-procedural AEs. UCSEMS may reduce the risk of post-procedural AEs and should be considered in patients at high risk of post-endoscopic retrograde cholangiopancreatography pancreatitis. However, the patency period may be shorter, necessitating future comparative research with fully covered self-expandable metal stentsto determine the optimal stent choice.

MiRNA- and Cell Line-Specific Constraints on Precursor miRNA Processing of Stably Transfected Pancreatic Cancer and Other Mammalian Cells.

MicroRNAs (miRNAs) regulate approximately one-third of all human genes. The dysregulation of miRNAs has been implicated in the development of numerous human diseases, including cancers. In our investigation focusing on altering specific miRNA expression in human pancreatic cancer cells, we encountered an interesting finding. While two expression vector designs effectively enhanced miR-708 levels, they were unable to elevate mature forms of miR-29b, -1290, -2467, and -6831 in pancreatic cancer cell lines. This finding was also observed in a panel of other non-pancreatic cancer cell lines, suggesting that miRNA processing efficiency was cell line specific. Using a step-by-step approach in each step of miRNA processing, we ruled out alternative strand selection by the RISC complex and transcriptional interference at the primary miRNA (pri-miRNA) level. DROSHA processing and pri-miRNA export from the nucleus also appeared to be occurring normally. We observed precursor (pre-miRNA) accumulation only in cell lines where mature miRNA expression was not achieved, suggesting that the block was occurring at the pre-miRNA stage. To further confirm this, synthetic pre-miRNA mimics that bypass DICER processing were processed into mature miRNAs in all cases. This study has demonstrated the distinct behaviours of different miRNAs with the same vector in the same cell line, the same miRNA between the two vector designs, and with the same miRNA across different cell lines. We identified a stable vector pre-miRNA processing block. Our findings on the structural and sequence differences between successful and non-successful vector designs could help to inform future chimeric miRNA design strategies and act as a guide to other researchers on the intricate processing dynamics that can impact vector efficiency. Our research confirms the potential of miRNA mimics to surmount some of these complexities.

Using machine learning to identify risk factors for pancreatic cancer: a retrospective cohort study of real-world data.

This study aimed to identify the risk factors for pancreatic cancer through machine learning. We investigated the relationships between different risk factors and pancreatic cancer using a real-world retrospective cohort study conducted at West China Hospital of Sichuan University. Multivariable logistic regression, with pancreatic cancer as the outcome, was used to identify covariates associated with pancreatic cancer. The machine learning model extreme gradient boosting (XGBoost) was adopted as the final model for its high performance. Shapley additive explanations (SHAPs) were utilized to visualize the relationships between these potential risk factors and pancreatic cancer. The cohort included 1,982 patients. The median ages for pancreatic cancer and nonpancreatic cancer groups were 58.1years (IQR: 51.3-64.4) and 57.5years (IQR: 49.5-64.9), respectively. Multivariable logistic regression indicated that kirsten rats arcomaviral oncogene homolog (KRAS) gene mutation, hyperlipidaemia, pancreatitis, and pancreatic cysts are significantly correlated with an increased risk of pancreatic cancer. The five most highly ranked features in the XGBoost model were KRAS gene mutation status, age, alcohol consumption status, pancreatitis status, and hyperlipidaemia status. Machine learning algorithms confirmed that KRAS gene mutation, hyperlipidaemia, and pancreatitis are potential risk factors for pancreatic cancer. Additionally, the coexistence of KRAS gene mutation and pancreatitis, as well as KRAS gene mutation and pancreatic cysts, is associated with an increased risk of pancreatic cancer. Our findings offered valuable implications for public health strategies targeting the prevention and early detection of pancreatic cancer.

Non-pancreatic cancer tumors: case series from a Tertiary Care Center in Nepal

Non-pancreatic cancer tumors: case series from a Tertiary Care Center in Nepal

High-Fat Mouse Model to Explore the Relationship between Abnormal Lipid Metabolism and Enolase in Pancreatic Cancer.

Malignant tumors have become a major social health problem that seriously threatens human health, among which pancreatic cancer has a high degree of malignancy, difficult diagnosis and treatment, short survival time, and high mortality. More and more attention has been paid to abnormal lipid metabolism as a momentous carcinogenesis mechanism. Here, we explored the relationship between abnormal lipid metabolism, enolase, and pancreatic cancer by clinical data analysis. A high-fat mouse model was constructed, and then, a subcutaneous tumorigenesis mouse model of carcinoma of pancreatic cells and a metastatic neoplasm mouse pattern of pancreatic carcinoma cells injected through the tail vein were constructed to explore whether abnormal lipid metabolism affects the progression of pancreatic cancer in mice. We constructed a high-lipid model of pancreatic carcinoma cell lines and knockdown and overexpressed enolase in pancreatic carcinoma cell lines and investigated whether high lipid regulates epithelial-mesenchymal transition (EMT) by upregulating enolase (ENO), thereby promoting the cells of pancreatic carcinoma to invade and migrate. Triglycerides, total cholesterol, free cholesterin, high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and neuron-specific enolase (NSE) from pancreatic cancer patients and nonpancreatic cancer patients were tested. The differences in blood lipids between patients with and without pancreatic carcinoma were compared, and the correlation between blood lipids and neuron-specific enolase was analyzed. We confirmed that the serum triglyceride level of pancreatic cancer patients at initial diagnosis is overtopping nonpancreatic cancer patients, and the neuron-specific enolase level of patients with pancreatic carcinoma is better than nonpancreatic carcinoma sufferers. Triglyceride level is positively correlated with neuron-specific enolase level, and serum triglyceride level has predictive value for pancreatic cancer. Hyperlipidemia can promote tumor growth and increase the expression levels of ENO1, ENO2, and ENO3 in subcutaneous tumor formation of pancreatic cancer in mice. Additional hyperlipidemia promoted pancreatic carcinoma metastasis in the lung in mice injected through the tail vein, which confirmed that hyperlipidemia accelerated the process of EMT by increasing the expression of ENO1, ENO2, and ENO3, therefore promoting the pancreatic cancer cell metastasis.

Expansion of a Synthesized Library of N-Benzyl Sulfonamides Derived from an Indole Core to Target Pancreatic Cancer.

In an effort to further investigate previously observed activity of indolyl sulfonamides towards pancreatic cancer cell lines, a library of 44 compounds has been synthesized. The biological activity of the compounds has been determined using two different screening assay techniques against 7 pancreatic cancer cell lines and 9 non-pancreatic cancer cell lines. In the first assay, the cytotoxicity of the compounds was evaluated using a traditional (48 hour compound exposure) method. An in silico investigation was conducted to determine if the compounds might be inducing cell death by inhibiting the S100A2-p53 protein-protein interaction. In the second assay, the potential role of the compounds as metabolic inhibitors of ATP production was evaluated using a rapid screening (1-2 hour compound exposure) method. IC50 values of the hit compounds were obtained and four compounds displayed sub-micromolar potency against PANC-1 cells. The investigation has provided several compounds that display selective in vitro activity toward pancreatic cancer that warrant further development.

Outcomes of Intraductal Placement of Covered Metal Stents for Unresectable Distal Malignant Biliary Obstruction

Intraductal self-expandable metal stent (SEMS) placement may prolong stent patency by reducing duodenobiliary reflux. This study aimed to evaluate the efficacy and safety of this biliary drainage method in patients with unresectable distal malignant biliary obstruction (MBO). Consecutive patients with unresectable MBO who underwent initial covered SEMS placement between 2015 and 2022 were retrospectively reviewed. We compared the causes of recurrent biliary obstruction (RBO), time to RBO (TRBO), adverse events (AEs), and reintervention rates between two biliary drainage methods (SEMSs placed above and across the papilla). A total of 86 patients were included (above: 38 and across: 48). Overall RBO rates (24% vs. 44%, p = 0.069) and median TRBO (11.6 months vs. 9.8 months, p = 0.189) were not significantly different between the two groups. The frequency of overall AEs was similar between the two groups in the entire cohort, but was significantly lower in patients with non-pancreatic cancer (6% vs. 44%, p = 0.035). Reintervention was successfully performed in the majority of patients in both groups. Intraductal SEMS placement was not associated with a prolonged TRBO in this study. Larger studies are warranted to further evaluate the benefit of intraductal SEMS placement.

Clinical impact of lactate on postoperative pancreatic fistula after pancreaticoduodenectomy: A single-center retrospective study of 1,043 patients

Background/objectivesSeveral hemodynamic markers have been studied to predict postoperative complication which is a risk factor for poor quality of life and prognosis. The aim of this study was to determine whether postoperative lactate clearance could affect clinical outcome based on complications in one surgical patient group. MethodsWe retrospectively reviewed data from all patients who underwent pancreaticoduodenectomy (PD) at Samsung Medical Center from January 2015 to December 2019. Differences in baseline characteristics of patients, intraoperative outcome, and postoperative outcome were evaluated according to the presence or absence of clinically relevant postoperative pancreatic fistula (CR-POPF). ResultsAmong a total of 1107 patients, 1043 patients were tested for arterial lactate levels immediately after surgery, and the day after surgery. Immediately postoperative hyperlactatemia (lactate ≥2.0 mmol/L) was not related to CR-POPF (P = 0.269). However, immediately postoperative hyperlactatemia with a negative lactic clearance on postoperative day (POD) 1 was related to CR-POPF (P = 0.003). In multivariate analyses, non-pancreatic cancer (hazard ratio (HR): 2.545, P < 0.001), soft pancreatic texture (HR: 1.884, P < 0.001), and postoperative hyperlactatemia with negative lactate clearance on POD 1 (HR: 1.805, P = 0.008) were independent risk factors for CR-POPF. ConclusionsHyperlactatemia with negative lactate clearance after PD, one of the high-risk surgeries requiring postoperative ICU care, is a risk factor for CR-POPF. In case of immediately postoperative hyperlactatemia after PD, lactate clearance with serial lactate level follow-up can be used for achieving the hemodynamic goal to prevent CR-POPF.

The value of artificial intelligence techniques in predicting pancreatic ductal adenocarcinoma with EUS images: A meta-analysis and systematic review.

Conventional EUS plays an important role in identifying pancreatic cancer. However, the accuracy of EUS is strongly influenced by the operator's experience in performing EUS. Artificial intelligence (AI) is increasingly being used in various clinical diagnoses, especially in terms of image classification. This study aimed to evaluate the diagnostic test accuracy of AI for the prediction of pancreatic cancer using EUS images. We searched the Embase, PubMed, and Cochrane Library databases to identify studies that used endoscopic ultrasound images of pancreatic cancer and AI to predict the diagnostic accuracy of pancreatic cancer. Two reviewers extracted the data independently. The risk of bias of eligible studies was assessed using a Deek funnel plot. The quality of the included studies was measured by the QUDAS-2 tool. Seven studies involving 1110 participants were included: 634 participants with pancreatic cancer and 476 participants with nonpancreatic cancer. The accuracy of the AI for the prediction of pancreatic cancer (area under the curve) was 0.95 (95% confidence interval [CI], 0.93-0.97), with a corresponding pooled sensitivity of 93% (95% CI, 0.90-0.95), specificity of 90% (95% CI, 0.8-0.95), positive likelihood ratio 9.1 (95% CI 4.4-18.6), negative likelihood ratio 0.08 (95% CI 0.06-0.11), and diagnostic odds ratio 114 (95% CI 56-236). The methodological quality in each study was found to be the source of heterogeneity in the meta-regression combined model, which was statistically significant (P = 0.01). There was no evidence of publication bias. The accuracy of AI in diagnosing pancreatic cancer appears to be reliable. Further research and investment in AI could lead to substantial improvements in screening and early diagnosis.

Three-dimensional simulation of the pancreatic parenchyma, pancreatic duct and vascular arrangement in pancreatic surgery using a deep learning algorithm.

Three-dimensional surgical simulation, already in use for hepatic surgery, can be used in pancreatic surgery. However, some problems still need to be overcome to achieve more precise pancreatic surgical simulation. The present study evaluates the performance of SYNAPSE VINCENT® (version 6.6, Fujifilm Medical Co., Ltd., Tokyo, Japan) in the semiautomated surgical simulation of the pancreatic parenchyma, pancreatic ducts, and peripancreatic vessels using an artificial intelligence (AI) engine designed with deep learning algorithms. One-hundred pancreatic cancer patients and a control group of 100 nonpancreatic cancer patients were enrolled. The evaluation methods for visualizing the extraction were compared using the Dice coefficient (DC). In the pancreatic cancer patients, tumor size, position, and stagewise correlations with the pancreatic parenchymal DC were analyzed. The relationship between the pancreatic duct diameter and the DC, and between the manually and AI-measured diameters of the pancreatic duct were analyzed. In the pancreatic cancer/control groups, the pancreatic parenchymal DC and pancreatic duct extraction were 0.83/0.86 and 0.84/0.77. The DC of the arteries (portal veins/veins) and associated sensitivity and specificity were 0.89/0.88 (0.89/0.88), 0.85/0.83 (0.85/0.82), and 0.82/0.81 (0.84/0.81), respectively. No correlations were observed between pancreatic parenchymal DC and tumor size, position, or stage. No correlation was observed between the pancreatic duct diameter and the DC. A positive correlation (r = 0.61, p<0.001) was observed between the manually and AI-measured diameters of the pancreatic duct. Extraction of the pancreatic parenchyma, pancreatic duct, and surrounding vessels with the SYNAPSE VINCENT® AI engine assumed to be useful as surgical simulation.

Deep learning for real-time auxiliary diagnosis of pancreatic cancer in endoscopic ultrasonography.

In recent year, many deep learning have been playing an important role in the detection of cancers. This study aimed to real-timely differentiate a pancreatic cancer (PC) or a non-pancreatic cancer (NPC) lesion via endoscopic ultrasonography (EUS) image. A total of 1213 EUS images from 157 patients (99 male, 58 female) with pancreatic disease were used for training, validation and test groups. Before model training, regions of interest (ROIs) were manually drawn to mark the PC and NPC lesions using Labelimage software. Yolov5m was used as the algorithm model to automatically distinguish the presence of pancreatic lesion. After training the model based on EUS images using YOLOv5, the parameters achieved convergence within 300 rounds (GIoU Loss: 0.01532, Objectness Loss: 0.01247, precision: 0.713 and recall: 0.825). For the validation group, the mAP0.5 was 0.831, and mAP@.5:.95 was 0.512. In addition, the receiver operating characteristic (ROC) curve analysis showed this model seemed to have a trend of more AUC of 0.85 (0.665 to 0.956) than the area under the curve (AUC) of 0.838 (0.65 to 0.949) generated by physicians using EUS detection without puncture, although pairwise comparison of ROC curves showed that the AUC between the two groups was not significant (z= 0.15, p = 0.8804). This study suggested that the YOLOv5m would generate attractive results and allow for the real-time decision support for distinction of a PC or a NPC lesion.

A Risk Prediction Model for Post-endoscopic Retrograde Cholangiopancreatography Pancreatitis After Stent Insertion for Malignant Biliary Obstruction: Development and Validation.

Pancreatitis is the most common complication of post-endoscopic retrograde cholangiopancreatography (ERCP). There are currently no prediction models, particularly for post-ERCP pancreatitis (PEP) after biliary stent placement due to malignant biliary obstruction (MBO). To that end, we aim to develop and validate a predictive model for PEP. We retrospectively analyzed the data of patients who underwent ERCP for biliary stent placement due to MBO at the Second Affiliated Hospital of Harbin Medical University from January 1, 2014 to August 31, 2021. The eligible patients were randomly allocated to the development and validation cohorts. A prediction model was built using the development cohort, and the model's effect was validated using a validation cohort. A total of 1524 patients were enrolled, including 1016 in the development cohort and 508 in the validation cohort, with an overall PEP rate of 7.1%. The model's predictors included acute pancreatitis history, the absence of pancreatic duct dilation, nonpancreatic cancer, difficult cannulation, and pancreatic injection. The area under the curve (AUC) in the development cohort was 0.810, and the incidence of PEP in the low-risk, medium-risk, and high-risk groups was 1.53%, 9.12%, and 36.36%, respectively. Meanwhile, the AUC of the validation cohort was 0.781, and the incidence of PEP in the low-risk, medium-risk, and high-risk groups was 4.17%, 8.75%, and 41.67%, respectively. This study was the first to build and validate a risk prediction model, especially for PEP after biliary stent placement due to MBO. Moreover, this model might assist clinicians in identifying high-risk patients and help implement preventive measures in a more timely manner.

CNN Classification of Computed Tomography Images for Pancreatic Tumor Detection

The five-year survival rate for pancreatic cancer (PC) is the lowest of any cancer kind, and it is the fourth greatest cause of cancer-related death, with a growing death rate. When it comes to cancer invasion, the most significant risk factors are: smoking; alcohol usage; diabetes; and prior pancreatitis. By using this method, we will be able to detect our PC, which is equipped with picture handling technology. Researchers used CT images as input in this study and preprocessed them to remove any noise in the images that had been learned using an adaptive Weiner filter. Preprocessing is followed by the use of a region grow ideal to segment the noise-free image. Scale Invariant Feature Transform (SIFT) is utilized once more to extract the tumor limits and principal component analysis (PCA) is used to enhance the retrieved structures to improve the types of pancreatic CT images. In order to activate the picture parameters, a convolutional neural network (CNN) classifier is used. In order to categorize an image as nonpancreatic cancer or pancreatic cancer, the test data were compared to the training data and the classified image was compared. MATLAB then initiates the entire process, and the most recent performance estimation approach is utilized, resulting in outstanding accuracy.

YOD1 serves as a potential prognostic biomarker for pancreatic cancer

BackgroundUbiquitination is a basic post-translational modification of intracellular proteins and can be reversed enzymatically by DUBs (deubiquitinating enzymes). More than 90 DUBs have been identified. Among them, the deubiquitinating enzyme YOD1, a member of the ovarian tumor domain protease (OTUs) subfamily, is involved in the regulation of endoplasmic reticulum (ER)-related degradation pathways. In fact, it is reported that YOD1 is an important proliferation and metastasis-inducing gene, which can stimulate the characteristics of cancer stem cells and maintain circulating tumor cells (CTC). However, the expression level, prognostic effect and biological functional mechanism of YOD1 in pancreatic cancer are still unclear.ResultsIn the GEO and TCGA databases, YOD1 mRNA expression is significantly up regulated in a variety of human pancreatic cancer tissues. Survival analysis showed that the up regulation of YOD1 can predict poor prognosis of pancreatic cancer. Cox analysis showed that high YOD1 expression is an independent prognostic factor of pancreatic cancer. ROC analysis shows that YOD1 has significant diagnostic value. The immunohistochemistry (IHC) results showed that the protein expression level of YOD1 in pancreatic cancer tissue was higher than that in neighboring non-pancreatic cancer tissues (P < 0.001). In addition, we found that YOD1 expression is negatively correlated with the infiltration level of CD8 + T cells, macrophages, neutrophils and dendritic cells (DC) in pancreatic cancer. The expression of YOD1 has a strong correlation with the different immune marker sets in PAAD. Co-expression network and functional enrichment analysis indicate that YOD1 may participate in the development of pancreatic cancer through cell adhesion molecules, p53, Hippo, TGF-β and other pathways. The experimental results of EDU, Transwell, Immunohistochemistry (IHC), Western blot and Flow Cytometry indicate that YOD1 is highly expressed in pancreatic cancer cells and pancreatic cancer tissues, and its overexpression can promote the proliferation and metastasis of pancreatic cancer cells and affect the immune microenvironment.ConclusionOur results indicate that YOD1 may be a useful biomarker for the prognosis of human pancreatic cancer, and it may also be a potential molecular target for the diagnosis and treatment of pancreatic cancer.

Minimally invasive vs. open pancreatoduodenectomy in selected patients with peri-ampullary, non-pancreatic cancer: A systematic review and individual patient data meta-analysis

Minimally invasive vs. open pancreatoduodenectomy in selected patients with peri-ampullary, non-pancreatic cancer: A systematic review and individual patient data meta-analysis

Core homologous recombination mutations and improved survival in nonpancreatic GI cancers.

663 Background: GI malignancies continue to be a major cause of cancer related deaths, despite advances in therapy options. In pancreatic cancer, the presence of homologous recombination mutations (HRM) has been shown to confer increased sensitivity to platinum chemotherapy. However, the role of HRM mutations in other GI cancers remains to be determined. The focus of this study is to evaluate the prognostic nature of core and noncore HRM in nonpancreatic GI cancers. Additionally, we aim to understand the predictive nature of these mutations related to platinum exposure. Methods: This was a retrospective review of patients at Moffitt Cancer Center with a primary stage IV nonpancreatic GI cancer treated with platinum therapy. All patients had next generation sequencing and were in the Clinical Genomics Action Committee database between 1/1/2013 and 11/1/2020. All patients had either a core HRM (BRCA1, BRCA2, PALB2) or noncore HRM (such as ATM, ATR, BARD1, BRIP1, FANCA, NBN, and RAD51). Patients were grouped into core HRM vs. noncore HRM. Response was stratified between responders (complete response and partial response) vs. non responders (stable disease and progressive disease), based on RECIST criteria. Progression free survival and overall survival were estimated using Kaplan-Meier method, and the log-rank test was used to assess the difference in progression free survival (PFS) and overall survival (OS) by HRM status and type of platinum therapy used. Results: There were 72 patients included in the study: the majority of patients were male (65.3%) and Caucasian (87.5%). Twenty-one (29.2%) patients had a core HRM and 51 (70.8%) had a noncore HRM. There was no significant difference in response rate between patients with core HRM and patients with noncore HRM for evaluable patients (n = 66): 20.0% of patients with core HRM vs. 21.7% of patients with noncore HRM demonstrated a response to platinum therapy (p = 0.41). An improved OS was noted for patients with core HRM vs. those with noncore HRM at 68.9 vs. 24.3 months (p = 0.019). An improved PFS was also seen in patients with core HRM at 10.4 months vs. 6.3 months with noncore HRM (p = 0.027). There was no difference in PFS based on type of platinum therapy used (oxaliplatin vs. carboplatin vs. cisplatin), while an improved OS was noted for patients treated with oxaliplatin vs. carboplatin vs. cisplatin at 43.1 vs. 28.2 vs. 16.0 months (p = 0.011). Conclusions: Our study demonstrated that in stage IV nonpancreatic GI malignancies treated with platinum therapy, patients with core HRM had a greater OS and PFS compared to those with noncore HRM, suggesting a potential prognostic and predictive role of these mutations. Further studies are needed to confirm our findings.