Prognosis Of Pancreatic Cancer Research Articles

MicroRNAs in Pancreatic Cancer: Advances in Biomarker Discovery and Therapeutic Implications.

Pancreatic cancer remains a formidable malignancy characterized by high mortality rates, primarily attributable to late-stage diagnosis and a dearth of effective therapeutic interventions. The identification of reliable biomarkers holds paramount importance in enhancing early detection, prognostic evaluation, and targeted treatment modalities. Small non-coding RNAs, particularly microRNAs, have emerged as promising candidates for pancreatic cancer biomarkers in recent years. In this review, we delve into the evolving role of cellular and circulating miRNAs, including exosomal miRNAs, in the diagnosis, prognosis, and therapeutic targeting of pancreatic cancer. Drawing upon the latest research advancements in omics data-driven biomarker discovery, we also perform a case study using public datasets and address commonly identified research discrepancies, challenges, and limitations. Lastly, we discuss analytical approaches that integrate multimodal analyses incorporating clinical and molecular features, presenting new insights into identifying robust miRNA-centric biomarkers.

Revolutionizing Pancreatic Cancer Diagnosis and Prognosis through Machine Learning Technology

Abstract: Pancreatic cancer presents significant challenges in terms of early detection and treatment, resulting in poor patient outcomes. This research article explores the application of random forest, a powerful machine learning algorithm, to enhance predictive modeling in pancreatic cancer research. Leveraging a dataset comprising clinical and molecular features, we trained and evaluated random forest models to predict key outcomes such as tumor progression, treatment response, and overall survival. Our findings demonstrate the effectiveness of random forest in accurately stratifying patients based on their prognosis and treatment outcomes. Furthermore, we identified key biomarkers and clinical variables contributing to predictive accuracy, providing valuable insights into the underlying biological mechanisms of pancreatic cancer progression. The integration of random forest into pancreatic cancer research holds promise for improving patient stratification, guiding treatment decisions, and ultimately, advancing personalized medicine approaches in the management of this challenging disease

Emerging Trends In Immunotherapy Management For Pancreatic Cancer: A Literature Review

The prognosis for pancreatic cancer (PC) is poor making it a substantial treatment challenge. A potentially effective treatment option is immunotherapy. To better understand the changing field of immunotherapy in PC management, this analysis will focus on important tactics and their consequences. To find relevant research published between 2019 and 2024, a thorough literature search was carried out using Scopus, Google Scholar, and PubMed. Tumor cells may be targeted and immune responses can be modulated using passive immunotherapeutic techniques such as CAR T-cell therapy and programmed death checkpoint inhibitors. Preclinical and clinical studies show enhanced effectiveness of combination therapy, such as anti-PD-1/anti-PD-L1 plus CTLA-4 inhibitors. Although their efficacy varies, active immunotherapeutic strategies, in particular cancer vaccines, seek to promote immune recognition of tumor antigens. Subpar response rates and treatment-related toxicity are obstacles. Checkpoint inhibitors, CAR T-cell therapy, and cancer vaccines are a few possible therapeutic options in the exciting field of immunotherapy for PC treatment. Nevertheless, further study is required to enhance these strategies and enhance patient.

Metformin boosts antitumor immunity and improves prognosis in upfront resected pancreatic cancer: an observational study.

Beyond demographic and immune factors, metabolic considerations, particularly metformin's recognized impact in oncology, warrant exploration in treating pancreatic cancer. This study aimed to investigate the influence of metformin on patient survival and its potential correlation with distinct immune profiles in pancreatic ductal adenocarcinoma (PDAC) tumors. We included 82 upfront resected and 66 gemcitabine-based neoadjuvant chemoradiotherapy (nCRT)-treated patients from the PREOPANC randomized controlled trial (RCT). Transcriptomic NanoString immunoprofiling was performed for a subset of 96 available resected specimens. Disparities in survival outcomes and immune profiles were apparent between metformin and non-metformin users in upfront resected patients but lacking in nCRT-treated patients. Compared to non-metformin users, upfront resected metformin users showed a higher median overall survival (OS) of 29 vs 14 months and a better 5-year OS rate of 19% vs 5%. Furthermore, metformin use was a favorable prognostic factor for OS in the upfront surgery group (HR = 0.56; 95% CI = 0.32 to 0.99). Transcriptomic data revealed that metformin users significantly underexpressed genes related to pro-tumoral immunity, including monocyte to M2 macrophage polarization and activation. Furthermore, the relative abundance of anti-inflammatory CD163+ MRC1+ M2 macrophages in non-metformin users and immune-activating CD1A+ CD1C+ dendritic cells in metformin users was heightened (P < .001). This study unveils immune profile changes resulting from metformin use in upfront resected pancreatic cancer patients, possibly contributing to prolonged survival outcomes. Specifically, metformin use may decrease the abundance and activity of pro-tumoral M2 macrophages and increase the recruitment and function of tumor-resolving DCs, favoring antitumor immunity.[PREOPANC trial EudraCT: 2012-003181-40].

Metabolic responses to the occurrence and chemotherapy of pancreatic cancer: biomarker identification and prognosis prediction

As the most malignant tumor, the prognosis of pancreatic cancer is not ideal even in the small number of patients who can undergo radical surgery. As a highly heterogeneous tumor, chemotherapy resistance is a major factor leading to decreased efficacy and postoperative recurrence of pancreatic cancer. In this study, nuclear magnetic resonance (NMR)-based metabolomics was applied to identify serum metabolic characteristics of pancreatic ductal adenocarcinoma (PDAC) and screen the potential biomarkers for its diagnosis. Metabolic changes of patients with different CA19-9 levels during postoperative chemotherapy were also monitored and compared to identify the differential metabolites that may affect the efficacy of chemotherapy. Finally, 19 potential serum biomarkers were screened to serve the diagnosis of PDAC, and significant metabolic differences between the two CA19-9 stratifications of PDAC were involved in energy metabolism, lipid metabolism, amino acid metabolism, and citric acid metabolism. Enrichment analysis of metabolic pathways revealed six shared pathways by PDAC and chemotherapy such as alanine, aspartate and glutamate metabolism, arginine biosynthesis, glutamine and glutamate metabolism, citrate cycle, pyruvate metabolism, and glycogolysis/gluconeogeneis. The similarity between the metabolic characteristics of PDAC and the metabolic responses to chemotherapy provided a reference for clinical prediction of benefits of postoperative chemotherapy in PDAC patients.

Abstract 7289: PancreaAlert: Intelligent nanoengineered biosensor for pancreatic cancer

Abstract Introduction: Pancreatic cancer accounts for 3% of cancer cases and 7% of cancer deaths in the United States. It is currently in the third position with respect to cancer-related deaths, and it is expected to move to second place as of 2040. This is mainly due to its diagnosis in the later stage. If detected in the first or second stage surgery can be performed thus increasing the patient’s survival rate. The current methods of diagnosis cannot do early stage or asymptomatic detection. So, detecting cancer-specific biomarkers can aid in the prognosis. Serum protein-based biomarkers carbohydrate antigen CA242, CA19-9, and carcinoembryonic antigen CEA are specific to pancreatic cancer. Thus, in this preliminary study, we aim to develop a nanoengineered electrochemical biosensor combined with machine learning to detect pancreatic cancer-specific biomarkers CA242, CA19-9, and CEA. Methodology: Nanoengineering the biosensor: The gold working electrode of the gold- biosensor (DRP-250AT) was coated with graphene oxide nano colloidal solution and kept in UV overnight for the graphene oxide nanosheet to crosslink with the working electrode. Detection of biomarkers using an electrochemical biosensor: First, a Crosslinker (DSP) was added to ethanol cleaned biosensor to bind the analytes. Following that antibody specific to each biomarker were added to the biosensors. The addition of TBS superblock prevented non-specific binding. Then different concentrations of biomarkers were added, and the corresponding sensor was subjected to various electrochemical analyses such as electrochemical impedance spectroscopy and voltammetry. SEM-EDAX analysis: The antigen-antibody binding on the biosensor's surface was confirmed. Automation and machine learning: Python code was developed to automate the analysis process and machine learning models (support vector machine and neural network) were used to classify the concentrations into various risk levels. The sensitivity of the developed tool was high when compared with conventional methods like ELISA and confocal microscopy. Results: From the electrochemical studies we found that with increasing concentration of biomarkers, there was a trend in output parameters: capacitance, resistance, impedance and area under the curve. By analyzing this trend, the concentrations of biomarkers can be detected. Using this data as input the machine learning model (accuracy greater than 80%) classified the concentrations into risk levels (normal, low risk, and high risk). Significance: Nanoengineering with graphene oxide nanosheets enhances the sensitivity of electrochemical biosensors. Automation and machine learning models help in the analysis of large datasets produced by this sensor. Overall, this device aids in the prognosis of pancreatic cancer. Our preliminary results are encouraging but more thorough research is required to make this diagnostic tool a working application. Citation Format: Meenal Karunanidhi, Hemalatha Kanniyappan, Edith Zhan, Ruth Mathew, Yani Sun, Junyi Wu, Yan Yan, Gnanasekar Munirathinum, Mathew T. Mathew. PancreaAlert: Intelligent nanoengineered biosensor for pancreatic cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7289.

Identification and Validation of T-Cell Exhaustion Signature for Predicting Prognosis and Immune Response in Pancreatic Cancer by Integrated Analysis of Single-Cell and Bulk RNA Sequencing Data.

Pancreatic cancer (PACA) is one of the most fatal malignancies worldwide. Immunotherapy is largely ineffective in patients with PACA. T-cell exhaustion contributes to immunotherapy resistance. We investigated the prognostic potential of T-cell exhaustion-related genes (TEXGs). A single-cell RNA (scRNA) sequencing dataset from Tumor Immune Single-Cell Hub (TISCH) and bulk sequencing datasets from the Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) were used to screen differentially expressed TEXGs. Kaplan-Meier survival, LASSO regression, and univariate/multivariate Cox regression analyses were performed to construct a TEXG risk model. This model was used to predict the prognosis, tumor immune microenvironment, and immunotherapy response. The PACA cohorts from the ICGC and GSE71729 datasets were used to validate the risk model. Pan-cancer expression of SPOCK2 was determined using the TISCH database. A six-gene (SPOCK2, MT1X, LIPH, RARRES3, EMP1, and MEG3) risk model was constructed. Patients with low risk had prolonged survival times in both the training (TCGA-PAAD, n = 178) and validation (ICGC-PACA-CA, ICGC-PAAD-US, and GSE71729, n = 412) datasets. Multivariate Cox regression analysis demonstrated that the risk score was an independent prognostic variable for PACA. High-risk patients correlated with their immunosuppressive status. Immunohistochemical staining confirmed the changes in TEXGs in clinical samples. Moreover, pan-cancer scRNA sequencing datasets from TISCH analysis indicated that SPOCK2 may be a novel marker of exhausted CD8+ T-cells. We established and validated a T-cell exhaustion-related prognostic signature for patients with PACA. Moreover, our study suggests that SPOCK2 is a novel marker of exhausted CD8+ T cells.

Mitochondrial energy metabolism-related gene signature as a prognostic indicator for pancreatic adenocarcinoma.

Background: Pancreatic adenocarcinoma (PAAD) is a highly malignant gastrointestinal tumor and is associated with an unfavorable prognosis worldwide. Considering the effect of mitochondrial metabolism on the prognosis of pancreatic cancer has rarely been investigated, we aimed to establish prognostic gene markers associated with mitochondrial energy metabolism for the prediction of survival probability in patients with PAAD. Methods: Gene expression data were obtained from The Cancer Genome Atlas and Gene Expression Omnibus databases, and the mitochondrial energy metabolism-related genes were obtained from the GeneCards database. Based on mitochondrial energy metabolism score (MMs), differentially expressed MMRGs were established for MMs-high and MMs-low groups using ssGSEA. After the univariate Cox and least absolute and selection operator (LASSO) analyses, a prognostic MMRG signature was used in the multivariate Cox proportional regression model. Survival and immune cell infiltration analyses were performed. In addition, a nomogram based on the risk model was used to predict the survival probability of patients with PAAD. Finally, the expression of key genes was verified using quantitative polymerase chain reaction and immunohistochemical staining. Intro cell experiments were performed to evaluated the proliferation and invasion of pancreatic cancer cells. Results: A prognostic signature was constructed consisting of two mitochondrial energy metabolism-related genes (MMP11, COL10A1). Calibration and receiver operating characteristic (ROC) curves verified the good predictability performance of the risk model for the survival rate of patients with PAAD. Finally, immune-related analysis explained the differences in immune status between the two subgroups based on the risk model. The high-risk score group showed higher estimate, immune, and stromal scores, expression of eight checkpoint genes, and infiltration of M0 macrophages, which might indicate a beneficial response to immunotherapy. The qPCR results confirmed high expression of MMP11 in pancreatic cancer cell lines, and IHC also verified high expression of MMP11 in clinical pancreatic ductal adenocarcinoma tissues. In vitro cell experiments also demonstrated the role of MMP11 in cell proliferation and invasion. Conclusion: Our study provides a novel two-prognostic gene signature-based on MMRGs-that accurately predicted the survival of patients with PAAD and could be used for mitochondrial energy metabolism-related therapies in the future.

The roles of lncRNAs and miRNAs in pancreatic cancer: a focus on cancer development and progression and their roles as potential biomarkers.

Pancreatic ductal adenocarcinoma (PDAC) is among the most penetrative malignancies affecting humans, with mounting incidence prevalence worldwide. This cancer is usually not diagnosed in the early stages. There is also no effective therapy against PDAC, and most patients have chemo-resistance. The combination of these factors causes PDAC to have a poor prognosis, and often patients do not live longer than six months. Because of the failure of conventional therapies, the identification of key biomarkers is crucial in the early diagnosis, treatment, and prognosis of pancreatic cancer. 65% of the human genome encodes ncRNAs. There are different types of ncRNAs that are classified based on their sequence lengths and functions. They play a vital role in replication, transcription, translation, and epigenetic regulation. They also participate in some cellular processes, such as proliferation, differentiation, metabolism, and apoptosis. The roles of ncRNAs as tumor suppressors or oncogenes in the growth of tumors in a variety of tissues, including the pancreas, have been demonstrated in several studies. This study discusses the key roles of some lncRNAs and miRNAs in the growth and advancement of pancreatic carcinoma. Because they are involved not only in the premature identification, chemo-resistance and prognostication, also their roles as potential biomarkers for better management of PDAC patients.

Morphology of the Dorsal Pancreatic Artery and Its Oncological Significance in Pancreatic Cancer.

Although the dorsal pancreatic artery (DPA) is an important artery that supplies the pancreas, its morphology has not been sufficiently studied. We investigated the morphology of the DPA and the progression of pancreatic cancer along this vessel. Overall, 142 patients with pancreatic cancer who underwent surgical resection at Kanazawa University Hospital between 2004 and 2015 were enrolled. We examined the morphology of the DPA using preoperative computed tomography and cancer progression along the DPA using resected specimens. We investigated the anatomical structures surrounding the DPA through cadaveric examination. The analysis of computed tomography images revealed the presence of the DPA in 141 patients. In typical cases, the DPA divides into a head and a body branch. Histopathological examination revealed cancer progression along the DPA in 32 patients. Cancer progression along the DPA was identified as a factor associated with a poor prognosis in pancreatic head or body cancer. Cadaveric examination showed the presence of abundant nerve and lymphatic tissues along the DPA. It is important to remove the soft tissue surrounding the DPA during surgery for pancreatic head or body cancer because it may serve as an important route for cancer progression.

Adjuvant Gemcitabine Versus Neoadjuvant/Adjuvant FOLFIRINOX in Resectable Pancreatic Cancer: The Randomized Multicenter Phase II NEPAFOX Trial

BackgroundAlthough addition of adjuvant chemotherapy is the current standard, the prognosis of pancreatic cancers still remains poor. The NEPAFOX trial evaluated perioperative treatment with FOLFIRINOX in resectable pancreatic cancer.Patients and MethodsThis multicenter phase II trial randomized patients with resectable or borderline resectable pancreatic cancer without metastases into arm (A,) upfront surgery plus adjuvant gemcitabine, or arm (B,) perioperative FOLFIRINOX. The primary endpoint was overall survival (OS).ResultsOwing to poor accrual, recruitment was prematurely stopped after randomization of 40 of the planned 126 patients (A: 21, B: 19). Overall, approximately three-quarters were classified as primarily resectable (A: 16, B: 15), and the remaining patients were classified as borderline resectable (A: 5, B: 4). Of the 12 evaluable patients, 3 achieved partial response under neoadjuvant FOLFIRINOX. Of the 21 patients in arm A and 19 patients in arm B, 17 and 7 underwent curative surgery, and R0-resection was achieved in 77% and 71%, respectively. Perioperative morbidity occurred in 72% in arm A and 46% in arm B, whereas non-surgical toxicity was comparable in both arms. Median RFS/PFS was almost doubled in arm B (14.1 months) compared with arm A (8.4 months) in the population with surgical resection, whereas median OS was comparable between both arms.ConclusionsAlthough the analysis was only descriptive owing to small patient numbers, no safety issues regarding surgical complications were observed in the perioperative FOLFIRINOX arm. Thus, considering the small number of patients, perioperative treatment approach appears feasible and potentially effective in well-selected cohorts of patients. In pancreatic cancer, patient selection before initiation of neoadjuvant therapy appears to be critical.

Development of a Serum-Based MicroRNA Signature for Early Detection of Pancreatic Cancer: A Multicenter Cohort Study

BackgroundA grim prognosis of pancreatic cancer (PCa) was attributed to the difficulty in early diagnosis of the disease.AimsIdentifying novel biomarkers for early detection of PCa is thus urgent to improve the overall survival rates of patients.MethodsThe study was performed firstly by identification of candidate microRNAs (miRNAs) in formalin-fixed, paraffin-embedded tissues using microarray profiles, and followed by validation in a serum-based cohort study to assess clinical utility of the candidates. In the cohorts, a total of 1273 participants from four centers were retrospectively recruited as two cohorts including training and validation cohort. The collected serum specimens were analyzed by real-time polymerase chain reaction.ResultsWe identified 27 miRNAs expressed differentially in PCa tissues as compared to the benign. Of which, the top-four was selected as a panel whose diagnostic efficacy was fully assessed in the serum specimens. The panel exhibited superior to CA19-9, CA125, CEA and CA242 in discriminating patients with early stage PCa from healthy controls or non-PCa including chronic pancreatitis as well as pancreatic cystic neoplasms, with the area under the curves (AUC) of 0.971 (95% CI 0.956–0.987) and 0.924 (95% CI 0.899–0.949), respectively. Moreover, the panel eliminated interference from other digestive tumors with a specificity of 90.2%.ConclusionsA panel of four serum miRNAs was developed showing remarkably discriminative ability of early stage PCa from either healthy controls or other pancreatic diseases, suggesting it may be developed as a novel, noninvasive approach for early screening of PCa in clinic.Graphical

Clinical value of endoscopic ultrasound sound speed in differential diagnosis of pancreatic solid lesion and prognosis of pancreatic cancer.

Differential diagnosis of pancreatic solid lesion (PSL) and prognosis of pancreatic cancer (PC) is a clinical challenge. We aimed to explore the differential diagnostic value of sound speed (SS) obtained from endoscopic ultrasonography (EUS) in PSL and the prognostic value of SS in PC. Patients with PSL in The Third Xiangya Hospital of Central South University from March 2019 to October 2019 were prospectively enrolled, who obtained SS from PSL. Patients were divided into the PC group and the pancreatic benign lesion (PBL) group. SS1 is the SS of lesions and SS2 is the SS of normal tissues adjacent to lesions. Ratio1 is equal to SS1 divided by SS2 of PSL (ratio1 = SS1/SS2). Eighty patients were enrolled (24 PBL patients, 56 PC patients). SS1 and ratio1 in PC group were higher compared with PBL group (SS1:1568.00 vs. 1550.00, Z = -2.066, p = 0.039; ratio1: 1.0110 vs. 1.0051, Z = -3.391, p = 0.001). The SS1 in PC (Z = -6.503, p < 0.001) was higher compared to SS2. In the nonsurgical group of PC, low ratio1 predicted high overall survival (OS) (7.000 months vs. 4.000 months; p = 0.039). In the surgical group of PC, low SS1 was associated with low median OS (4.000 months vs. 12.000 months; p = 0.033). SS plays a vital role in distinguishing between PBL and PC. Higher SS1 and ratio1 obtained by EUS are more related to PC than PBL. In PC patients, high SS1 may predict pancreatic lesions. In the nonsurgical group of PC, low ratio1 may predict high OS. However, in the surgical group of PC, low SS1 may predict low OS.

Screening of genes characteristic of pancreatic cancer by LASSO regression combined with support vector machine and recursive feature elimination, and immune correlation analysis.

Pancreatic cancer is a malignant tumor of the digestive tract that shows increased mortality, recurrence, and morbidity year on year. Differentially expressed genes between pancreatic cancer and healthy tissues were first analyzed from four datasets within the Gene Expression Omnibus (GEO). Gene ontology, disease ontology, and gene set enrichment analysis of differentially expressed genes were performed, and genes identified as characteristic of pancreatic cancer were screened using LASSO regression combined with support vector machine and recursive feature elimination (SVM-RFE). Differential analysis and receiver operating characteristic curve analysis were performed on the identified eigengenes, and validation was carried out using another dataset from the GEO database. Differences and correlations between characteristic pancreatic cancer genes and immune cells were analyzed. A total of 90 differentially expressed genes were identified by screening, and six genes characteristic of pancreatic cancer were obtained by taking the intersection of two characteristic genes identified by machine learning. Immunoassays yielded multiple immune cells associated with pancreatic cancer signature genes. The six characteristic genes screened by a combination of LASSO regression and SVM-RFE are potential new biomarkers for the early diagnosis and prognosis of pancreatic cancer, and could be a novel therapeutic target.

Discovering ferroptosis-associated tumor antigens and ferroptosis subtypes in pancreatic adenocarcinoma to facilitate mRNA vaccine development

Pancreatic adenocarcinoma (PAAD) is an aggressive, heterogeneous malignancy. We studied the potential of ferroptosis-related tumor vaccines for PAAD treatment. Ferroptosis-related genes, gene expression profiles, and clinical information were extracted from the FerrDB, UCSC Xena, and International Cancer Genome Consortium databases. Differential expression levels and prognostic indices were calculated, genetic alterations and correlations with immune-infiltrating cells were explored, and consensus clustering analysis was performed to identify ferroptosis subtypes and gene modules. Immune enrichment scores were calculated using gene set enrichment analysis, and gene modules were screened using weighted gene co-expression network analysis. The ferroptosis subtype distribution was visualized using graph learning-based dimensionality reduction analysis of the Monocle package with a Gaussian distribution. We identified four ferroptosis-related tumor antigens, AGPS, KDM5A, NRAS, and OSBPL9, which were associated with pancreatic cancer prognosis and antigen-presenting cell infiltration. We determined three minor ferroptosis subtypes, with different clinical prognosis and tumor immune status. Of the subtypes, FS3 may be more suitable for mRNA therapy. We constructed a PAAD ferroptosis landscape to identify the ferroptosis status of patients and predict their prognosis. Finally, we found that the eigengene of the green module was an independent prognostic factor, with a significantly better prognosis in the high-score group than in the low-score group. In conclusion, we identified four ferroptosis-related genes as targets for mRNA vaccines and three ferroptosis subtypes, providing a theoretical basis for the anti-PAAD mRNA vaccine and defining suitable patients for vaccination.

Modeling of new markers for the diagnosis and prognosis of pancreatic cancer based on the transition from inflammation to cancer.

Pancreatic adenocarcinoma (PAAD) is a lethal disease with a poor prognosis. Genes involved in acute pancreatitis (AP) or chronic pancreatitis (CP) might be important for PAAD development. This study sought to identify potential PAAD diagnosis markers and to establish a PAAD prognosis prediction model based on AP- and CP-related genes. The significantly differentially expressed genes in both AP or CP and PAAD were obtained by a bioinformatics analysis. A risk-score model for predicting survival was constructed based on The Cancer Genome Atlas (TCGA) data and validated using an International Cancer Genome Consortium (ICGC) cohort. Protein expression and the effects of the genes in the risk models were validated by immunohistochemistry, or Cell Counting Kit-8 (CCK-8) and transwell assays. The study sample data included six AP tissue samples and five normal pancreatic tissue samples, six CP tissue samples and six normal pancreatic tissue samples from the Gene Expression Omnibus (GEO) expression profiling microarrays GSE109227 and GSE41418 data sets, respectively, and fragments per kilobase per million mapped fragments (FPKM) data from four normal controls and 150 PAAD cases from TCGA database, and 182 cancer patient samples with complete survival prognostic data from the ICGC database. In total, 508 significantly differentially expressed genes were found in both AP or CP and PAAD. Trefoil factor 2 (TFF2), tubulointerstitial nephritis antigen (TINAG), trefoil factor 1 (TFF1), aquaporin 5 (AQP5), SAM pointed domain containing ETS transcription factor (SPDEF), anterior gradient protein 2 (AGR2), apolipoprotein B messenger RNA editing enzyme catalytic subunit 1 (APOBEC1), kallikrein-related peptidase 6 (KLK6), dopa decarboxylase (DDC), mucin 13 (MUC13), claudin 18 (CLDN18), annexin A10 (ANXA10), and tetraspanin 1 (TSPAN1) were found to be present in PAAD and had the largest fold change. A risk-score model, comprising 19 genes, was constructed for prognostic prediction. A high-risk score indicated a poor prognosis. TINAG, DDC, SPDEF, and APOBEC1 proteins were increased in PAAD, while TINAG and DDC were correlated with the pathologic grade. Decreased TINAG, APOBEC1, transmembrane protein 94 (TMEM94), and kelch like family member 36 (KLHL36) expression inhibited PAAD cell proliferation, while decreased SPDEF, TMEM94, and KLHL36 expression significantly inhibited PAAD cell migration. The AP and CP co-related genes were significantly correlated with PAAD. TINAG, DDC, SPDEF, and APOBEC1 could serve as new PAAD predictors. The risk model developed in this study could be used to predict the prognosis of PAAD patients.

Comparison of endoscopic ultrasound-guided fine-needle aspiration and fine-needle biopsy to generate pancreatic cancer organoids: Randomized trial.

Background and study aims The prognosis for pancreatic cancer remains poor. Molecular diagnostics and customized therapies are becoming increasingly important in clinical routine. Patient-derived, predictive model systems such as organoids have the potential to substantially increase the depth of information from biopsy material by functional and molecular characterization. We compared the extent to which the use of fine-needle aspiration needles (FNA, 22G) or fine-needle biopsy needles (FNB, 22G) influences the generation of pancreatic cancer patient-derived organoids (PDOs) to establish endoscopic standards of organoid technology. Patients and methods Endoscopic ultrasound (EUS)-guided punctures by EUS-FNA and EUS-FNB of pancreatic masses highly suspicious for adenocarcinoma (detected by computed tomography and/or magnetic resonance imaging) were prospectively evaluated. Consecutive patients received EUS-FNA and EUS-FNB in a randomized order without the need to exchange the needle shaft (only the inner needle type (FNA/-B) was exchanged) between the passes. With each needle type, the specimens for histological analysis and for PDOs were obtained separately. Results Fifty patients were enrolled in the study. Histology revealed malignancy in 42 of 50 cases (84%). In total PDOs were generated from 17 patients (34%). Of these, nine were established by FNB only, two by FNA only, and six by both FNA and FNB. Histology revealed malignancy in 13 of 17 PDO cases (76%). In two histologically false-negative cases, PDOs could be established. Conclusions EUS-FNB was superior to EUS-FNA in terms of successful generation of PDOs, although it failed to show statistical significance.

Impact of time from diagnosis to chemotherapy on prognosis in advanced pancreatic cancer.

Due to the aggressive nature and poor prognosis of advanced pancreatic cancer, prompt initiation of treatment is critical. We investigated the effect of the interval between cancer diagnosis and initiation of chemotherapy on survival in patients with advanced pancreatic cancer. In this retrospective, single-centre study, consecutive patients with advanced pancreatic cancer between April 2013 and March 2022 were analyzed. Data were extracted from the electronic medical records of patients who received chemotherapy for metastatic, locally advanced or resectable pancreatic cancer or who received chemotherapy due to either being intolerant of or declining surgery. We compared overall survival between two groups: the early waiting time group (waiting time≤30days from diagnosis to chemotherapy initiation) and the elective waiting time group (waiting time≥31days). Prognostic factors, including biliary drainage, were considered. The impact of waiting time on survival was assessed by univariate and multivariate analyses with Cox proportional hazard models. A 1:1 propensity score matching approach was used to balance bias, accounting for significant poor prognosis factors, age and sex. The study involved 137 patients. Overall survival exhibited no statistically significant difference between the early and elective waiting time groups (207 and 261days, P=0.2518). Univariate and multivariate analyses identified poor performance status and metastasis presence as predictors of worse prognosis. This finding persisted post propensity score matching (275 and 222days, P=0.8223). Our study revealed that initiating chemotherapy ˃30days later does not significantly affect treatment efficacy compared to within 30days of diagnosis.

Identify novel inflammation-related prognostic signature in pancreatic cancer patients.

Pancreatic cancer (PC) is a malignant tumor of the digestive system with a poor prognosis. PC patients with pancreatitis have a worse prognosis. But nobody reported the relationship between inflammation and prognosis in PC. Based on this, we are going to explore inflammation-related prognostic signature to predict patients' survival and potential therapeutic target. We screened gene expression profile and corresponding clinical information of patients from The Cancer Genome Atlas (TCGA) database. Gene set enrichment analysis (GSEA) was performed to identify differentially expressed genes (DEGs) between tumor and normal tissues with P value < .05. Univariate and multivariate Cox regression analysis was applied to identify possible prognostic inflammation genes and establish an inflammation-related risk score system, which was validated by Kaplan-Meier and Receiver operating characteristic (ROC) curves. Finally, we used the TISIDB database to predict targeted drugs for up-regulated gene hepatocyte growth factor receptor (MET) and used AUTODOCK software for molecular docking. We built a prognostic model consisted of 3 inflammation-related genes (tumor necrosis factor receptor associated factor 1/TFAR1, tyrosine kinase 2/TYK2, MET). According to the median value of those genes' risk score, PC patients were ranked into high- (88) and low-risk (89) groups. Then, the results of the Kaplan-Meier curves and the area under the curve (AUC) of the ROC curves showed this model had a good predictive power (P < .001, AUC = 0.806). The result of human protein atlas (HPA) database showed the expression of TRAF1 and TYK2 were low in pancreatic cancer, the expression of MET was high. TISIDB database founded brigatinib could target to MET. And AUTODOCK showed brigatinib had a nice docking with MET. Taken together, our study suggested that inflammation-associated prognostic signature might be used as novel biomarkers for predicting prognosis in PC patients and potential therapeutic target of the disease.

Gap junction beta-4 accelerates cell cycle progression and metastasis through MET-AKT activation in pancreatic cancer.

Despite continuing advances in the development of effective new therapies, including immunotherapies, the prognosis of pancreatic cancer remains extremely poor. Gap junction proteins have become attractive targets for potential cancer therapy. However, the role of gap junction beta-4 (GJB4) protein remains unexplored in pancreatic cancer. Through bioinformatic analyses we discovered pancreatic cancer tissues showed higher levels of GJB4 transcripts compared to normal pancreatic tissues and this had a negative effect on overall survival in patients that had pancreatic cancer. The high expression of nuclear GJB4 was identified as a negative prognostic factor in such patients. Knockdown of GJB4 in cultured pancreatic cancer cells resulted in G0/G1 arrest followed by decreased cell proliferation and suppression of metastatic potential. The overexpression of GJB4 accelerated cell proliferation, migration, and invasion in a SUIT-2 cell line, whereas MET inhibitor canceled the acceleration. GJB4 suppression with siRNA significantly inhibited tumor growth in a mouse xenograft model. Mechanistically, suppression of GJB4 inhibited MET-AKT activities. Such data suggest that targeting the GJB4-MET axis could represent a promising new therapeutic strategy for pancreatic cancer.