Diagnosis Of Pancreatic Cancer Research Articles

New-onset diabetes mellitus type 2 treated with metformin - is it possible to slow the development of pancreatic cancer?- case report

In this article, we present the case of an 87-year-old patient whose initial symptom of developing pancreatic cancer was newly diagnosed type 2 diabetes mellitus. She was treated with metformin, which, on the one hand, could slow the development of pancreatic cancer and, on the other hand, delayed a key cancer diagnosis. However, before the definitive diagnosis, the patient's symptoms were considered merely as side effects of metformin treatment, delaying the proper diagnosis. We consider that early diagnosis of pancreatic cancer remains a significant challenge. Often, the first symptom is newly diagnosed diabetes mellitus type 2. In this article, we aim to demonstrate the importance of being vigilant when diagnosing and treating diabetes mellitus with metformin. We also present the impact of metformin on the course of pancreatic cancer. Additionally, we emphasize the need to be attentive when patients complain of side effects of this medication, as they may often mask the development of pancreatic cancer.

Abstract B020: DNA methylation and pancreatic cancer in select gene promoter regions: A repeated measures case-control study of US military service members

Abstract Introduction: Pancreatic cancer (PC) is a devastating disease, its etiology is not fully understood, and there is a paucity of non-invasive biomarkers for early diagnosis. While DNA methylation (DNAm) holds promise, associations between DNAm and PC risk are poorly understood, especially regarding temporal changes in associations. Therefore, we aimed to evaluate potential associations between DNAm in pre-diagnostic serum on 9 genes (BNIP3, CDKN1C, CLDN4, LCN2, p16, RASSF1, SFN, SPARC, and TFPI-2) and 2 repetitive elements (Alu and LINE-1) and PC risk in a US Department of Defense cohort. Methods : The study population included 82 PC cases, diagnosed 1991 - 2007, and 229 controls individually- matched on age (within 5 years), sex, race, and serum draw time period. Cases had up to 3 pre- diagnostic serum samples. Controls had 1 serum sample collected 0-10 years before their selection date (date of case diagnosis). DNA was extracted from serum and the percentage of cytosines at a given CpG site that were methylated was quantified via Pyrosequencing. For serum samples within a time window (<2, 2-<5, and 5-10 years pre-diagnosis), we computed odds ratios (ORs) and 95% confidence intervals (95% CIs) for the association of each CpG site with PC risk (66 sites in total across all genes and repetitive elements) with conditional logistic regression models. DNAm was evaluated as both a binary variable and a continuous variable. Results : In samples collected 5-10 years before diagnosis, those with no DNAm (0% DNAm) had increased odds of PC compared to those with DNAm (>0% DNAm) in the promoter region of BNIP3 (Chr10:131982293 OR, 95% CI: 5.63, 1.16 – 27.28) and those with lower DNAm (<median) had increased odds of PC compared to those with higher DNAm (≥median) in the promoter regions of CDKN1C (Chr11:2886398 OR, 95% CI: 4.07, 1.16 – 14.31; Chr11:2886388 OR, 95% CI: 3.51, 1.24 – 9.96) and RASSF1 (Chr3:50340841 OR, 95% CI: 3.98, 1.15 – 17.45). Similarly, in samples collected 2 - <5 years before diagnosis, lower DNAm (<median) was associated with increased odds of PC compared to those with higher DNAm (≥median) in the promoter region of TFPI-2 (Chr7:93890717 OR, 95% CI: 3.82, 1.20 – 12.15). In models with DNAm as a continuous measure and in samples collected <2 years before diagnosis, a 1% increase in DNAm in the promoter region of SFN was associated with increased odds of PC (Chr1:26863188 OR, 95% CI: 1.23, 1.01 – 1.51). There was no evidence of associations between DNAm on Alu, CLDN4, LCN2, LINE-1, p16, or SPARC and PC risk. Conclusions : Our findings of increased risk of PC associated with lower DNAm on BNIP3, CDKN1C and TFPI-2 and higher DNAm on SFN genes suggest there may be molecular patterns occurring prior to PC diagnosis that could be considered further for developing non-invasive biomarkers for early diagnosis of this aggressive carcinoma. The opinions and assertions expressed herein do not reflect the official policy or position of the Uniformed Services University, the Department of Defense, or the National Cancer Institute. Citation Format: Jordan McAdam, Ruth M Pfeiffer, Alexander P Keil, Ann Meyer, Anwar E Ahmed, Dechang Chen, Jennifer Rusiecki. DNA methylation and pancreatic cancer in select gene promoter regions: A repeated measures case-control study of US military service members [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr B020.

Accelerated Exosomal Metabolic Profiling Enabled by Robust On-Target Array Sintering with Metal-Organic Frameworks.

Pancreatic cancer is highly lethal, and survival chances improve only with early detection at a precancerous stage. However, there remains a significant gap in developing tools for large-scale, rapid screening. To this end, a high-throughput On-Target Array Extraction Platform (OTAEP) by direct sintering of a series of metal-organic frameworks (MOFs) for dual in situ extraction, encompassing both exosomes and their metabolic profiles, is developed. Based on the principle of geometry-dependent photothermal conversion efficiency and standard testing, the appropriate MOF functional unit is identified. This unit enables exosome enrichment within 10 min and metabolic fingerprint extraction in under 1 s of laser irradiation, with over five reuse. To further accelerate and enhance the quality of metabolic profile analysis, the application of Surrogate Variable Analysis to eliminate hidden confounding factors within the profiles is proposed, and five biomarkers demonstrated by MS/MS experiments are identified. These biomarkers enable early diagnosis, risk stratification, and staging of pancreatic cancer simultaneously, with sensitivity of 94.1%, specificity of 98.8%, and precision of 94.9%. This work represents a breakthrough for overcoming throughput challenges in large-scale testing and for addressing confounding factors in big data analysis.

Serum PCSK9 is a novel serological biomarker for the diagnosis and prognosis of pancreatic cancer.

Although CA19-9 is an essential blood biomarker of pancreatic cancer (PC), its sensitivity and specificity are limited for early detection. We analyzed the serum proprotein convertase subtilisin/kexin type 9 (sPCSK9) in PC patients, benign disease groups (BDG), and healthy controls (HC) by ELISA. Consistently, sPCSK9 was considerably lower in PC patients than in HC (Z = -2.546, P < 0.05), and sPCSK9 in PC patients was statistically significantly higher than in BDG (Z = -5.457, P < 0.001). sPCSK9 was linked to the invasion of lymph nodes (χ2 = 6.846, P < 0.01). According to ROC curves, combining sPCSK9 with CA19-9 could potentially enhance the diagnostic capability of CA19-9 in early-stage PC patients. Furthermore, the low sPCSK9 group (n = 41) exhibited statistically significantly prolonged overall survival compared to the high sPCSK9 group (n = 15), with median survival times of 27 months (95% CI [17.59-36.41]) and 11 months (95% CI [7.21-14.79]), respectively (P = 0.022). The diagnostic performance of CA19-9 for early-stage PC patients could be improved by combining sPCSK9 with CA19-9. Moreover, the higher sPCSK9 group has a significantly shorter overall survival rate.

Data privacy-aware machine learning approach in pancreatic cancer diagnosis

ProblemPancreatic ductal adenocarcinoma (PDAC) is considered a highly lethal cancer due to its advanced stage diagnosis. The five-year survival rate after diagnosis is less than 10%. However, if diagnosed early, the five-year survival rate can reach up to 70%. Early diagnosis of PDAC can aid treatment and improve survival rates by taking necessary precautions. The challenge is to develop a reliable, data privacy-aware machine learning approach that can accurately diagnose pancreatic cancer with biomarkers.AimThe study aims to diagnose a patient’s pancreatic cancer while ensuring the confidentiality of patient records. In addition, the study aims to guide researchers and clinicians in developing innovative methods for diagnosing pancreatic cancer.MethodsMachine learning, a branch of artificial intelligence, can identify patterns by analyzing large datasets. The study pre-processed a dataset containing urine biomarkers with operations such as filling in missing values, cleaning outliers, and feature selection. The data was encrypted using the Fernet encryption algorithm to ensure confidentiality. Ten separate machine learning models were applied to predict individuals with PDAC. Performance metrics such as F1 score, recall, precision, and accuracy were used in the modeling process.ResultsAmong the 590 clinical records analyzed, 199 (33.7%) belonged to patients with pancreatic cancer, 208 (35.3%) to patients with non-cancerous pancreatic disorders (such as benign hepatobiliary disease), and 183 (31%) to healthy individuals. The LGBM algorithm showed the highest efficiency by achieving an accuracy of 98.8%. The accuracy of the other algorithms ranged from 98 to 86%. In order to understand which features are more critical and which data the model is based on, the analysis found that the features “plasma_CA19_9”, REG1A, TFF1, and LYVE1 have high importance levels. The LIME analysis also analyzed which features of the model are important in the decision-making process.ConclusionsThis research outlines a data privacy-aware machine learning tool for predicting PDAC. The results show that a promising approach can be presented for clinical application. Future research should expand the dataset and focus on validation by applying it to various populations.

Development of gelatin nanoparticles for positron emission tomography diagnosis in pancreatic cancer

Abstract Pancreatic cancer remains difficult to diagnose using currently available imaging probes. Thus, this study aimed to develop a novel imaging agent for the diagnosis of pancreatic cancer using positron emission tomography (PET). Specifically, this study explores gelatin-based nanoparticles fabricated via radiation-induced crosslinking, as gelatin is known for its ability to produce biocompatible materials. Aqueous gelatin solutions were irradiated with γ-rays to produce nanoparticles with average diameters of 5–20 nm via a radiation crosslinking technique. The gelatin nanoparticles were labeled with 64Cu, exhibiting negative surface potentials. Furthermore, the nanoparticles were evaluated in vivo by injecting them into pancreatic tumor-bearing mice. Notably, the nanoparticles accumulated in the tumors. Hence, 64Cu-labeled gelatin nanoparticles show promise as a platform for next-generation PET imaging agents for pancreatic cancer.

Prediction of pancreatic cancer in patients with new onset hyperglycemia: A modified ENDPAC model

Background/Objectives: The Enriching New-Onset Diabetes for Pancreatic Cancer (ENDPAC) model relies primarily on fasting glucose values. Health systems have increasingly shifted practice towards use of glycated hemoglobin (HbA1c) measurement. We modified the ENDPAC model using patients with new onset hyperglycemia. MethodsFour cohorts of patients 50-84 years of age with HbA1c results ≥6.2-6.5% in 2011-2018 were identified. A combine cohort was formed. A widened eligibility criterion was applied to form additional four individual cohorts and one combined cohort. The primary outcome was the diagnosis of pancreatic cancer within 3 years after the first elevated HbA1c testing. The performance of the modified ENDPAC model was evaluated by AUC, sensitivity, positive predictive value, cases detected, and total number of patients screened. ResultsThe individual and combined cohorts consisted of 39,001-79,060 and 69,334-92,818 patients, respectively (mean age 63.5-65.0 years). The three-year PC incidence rates were 0.47%-0.54%. The AUC measures were in the range of 0.75-0.77 for the individual cohorts and 0.75 for the combined cohorts. When the four individual cohorts were combined, more PC cases can be identified (149 by the combined vs. 113-116 by individual cohorts when risk score was 5+). Performance measures were compromised in nonwhites. Asian and Pacific islanders had lower sensitivity compared to other racial and ethnic groups (29% vs. 50-60%) when risk score was 5+. ConclusionsThe modified ENDPAC model targets a broader population and thus identifies more high-risk patients for cancer screening. The differential performance needs to be considered when the model is applied to non-white population.

Hormone replacement therapy and risk of pancreatic cancer in postmenopausal women: Evidence from the US National Inpatient Sample 2008–2018

BackgroundPancreatic cancer is a serious, usually fatal disease and one of the most aggressive malignancies. Research into whether hormone replacement therapy (HRT) might protect against pancreatic cancer has yielded mixed results. This study aimed to investigate the potential association between HRT and the risk of pancreatic cancer in postmenopausal women. MethodsThis population-based, retrospective study extracted data from the US National Inpatient Sample (NIS) 2008–2018. Hospitalized females aged ≥55 years were eligible for inclusion. Associations between HRT, other study variables, and pancreatic cancer diagnosis were determined using univariate and multivariable regression analyses. ResultsAfter 1:4 matching by age, data of postmenopausal women with (n = 35,309) and without (n = 141,236) HRT were included in the analysis. The mean age was 73.4 years. Multivariable analyses showed that women with HRT had significantly decreased odds of pancreatic cancer (adjusted OR [aOR], 0.69, 95 % CI: 0.53–0.90). Compared to patients without HRT, patients with HRT in the 55-64-year-old group (aOR 0.48, 95 % CI: 0.32–0.74), 65-74-year-old group (aOR 0.49, 95 % CI: 0.34–0.71), non-hypertensive group (aOR 0.55, 95 % CI: 0.38–0.79), and non-hyperlipidemia group (aOR 0.59, 95 % CI: 0.42–0.82) had significantly decreased odds of pancreatic cancer. ConclusionsIn US postmenopausal women, HRT is associated with a reduced risk of pancreatic cancer, especially those aged 55–74 year. Further study is needed to clarify the mechanisms underlying the associations.

Long non-coding RNA MIR4435-2HG promotes pancreatic cancer progression by regulating ABHD17C through sponging miR-128-3p.

The recently identified carcinogenic long non-coding RNA (lncRNA) MIR4435-2HG has been validated to contribute to the initiation and progression of several malignancies. Nonetheless, its specific mechanistic function in pancreatic cancer (PC) is yet to be determined. This study aims to investigate the expression and functional role of MIR4435-2HG in PC and to elucidate its potential mechanism. This study employed The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx)-Pancreas datasets for the analysis of MIR4435-2HG expression in PC and normal pancreatic tissues and its relations with prognosis in PC. Moreover, quantitative real-time polymerase chain reaction (qRT-PCR) was employed for analyzing MIR4435-2HG, miR-128-3p, and ABHD17C expressions within cells and tissues. Cell proliferation and apoptosis were detected in vitro through Cell Counting Kit 8 (CCK-8) assay and flow cytometry while utilizing transwell and wound healing assays to assess cell migration and invasion. Predicting miR-128-3p binding sites with MIR4435-2HG or ABHD17C was conducted via the online tool starBase and validated through a dual-luciferase reporter (DLR), RNA pull-down and RNA binding protein immunoprecipitation (RIP) assays. Herein, we deployed Western blot to assess protein expression levels. The in vivo role of MIR4435-2HG was studied using tumor xenografts. MIR4435-2HG overexpression exhibited a correlation with poor prognosis in PC. Knocking down MIR4435-2HG significantly hindered the proliferative, invading, and migrating PC cell abilities, accompanied by apoptosis induction, counteracted via a miR-128-3p inhibitor. Moreover, MIR4435-2HG could directly bind to miR-128-3p. Additionally, miR-128-3p directly targeted ABHD17C. Furthermore, in vitro as well as in vivo experiment results elucidated that knocking down MIR4435-2HG hindered PC progression by suppressing ABHD17C expression via miR-128-3p upregulation. MIR4435-2HG can serve as a dependable target for PC diagnosis and treatment by modulating the miR-128-3p/ABHD17C axis to promote its progression.

Pathology for early diagnosis of pancreatic cancer

Pathology for early diagnosis of pancreatic cancer

Results and future developments of collaboration initiatives between hospitals and clinics for early diagnosis of pancreatic cancer

Results and future developments of collaboration initiatives between hospitals and clinics for early diagnosis of pancreatic cancer

Risk factors to to monitor to facilitate early diagnosis of pancreatic cancer

Risk factors to to monitor to facilitate early diagnosis of pancreatic cancer

Role of endoscopic retrograde cholangiopancreatography in early diagnosis of pancreatic cancer

Role of endoscopic retrograde cholangiopancreatography in early diagnosis of pancreatic cancer

Advances in new biomarkers for early diagnosis of pancreatic cancer

Advances in new biomarkers for early diagnosis of pancreatic cancer

Role of US and EUS in early diagnosis of pancreatic cancer

Role of US and EUS in early diagnosis of pancreatic cancer

Early detection of pancreatic cancer by comprehensive serum miRNA sequencing with automated machine learning.

Pancreatic cancer is often diagnosed at advanced stages, and early-stage diagnosis of pancreatic cancer is difficult because of nonspecific symptoms and lack of available biomarkers. We performed comprehensive serum miRNA sequencing of 212 pancreatic cancer patient samples from 14 hospitals and 213 non-cancerous healthy control samples. We randomly classified the pancreatic cancer and control samples into two cohorts: a training cohort (N = 185) and a validation cohort (N = 240). We created ensemble models that combined automated machine learning with 100 highly expressed miRNAs and their combination with CA19-9 and validated the performance of the models in the independent validation cohort. The diagnostic model with the combination of the 100 highly expressed miRNAs and CA19-9 could discriminate pancreatic cancer from non-cancer healthy control with high accuracy (area under the curve (AUC), 0.99; sensitivity, 90%; specificity, 98%). We validated high diagnostic accuracy in an independent asymptomatic early-stage (stage 0-I) pancreatic cancer cohort (AUC:0.97; sensitivity, 67%; specificity, 98%). We demonstrate that the 100 highly expressed miRNAs and their combination with CA19-9 could be biomarkers for the specific and early detection of pancreatic cancer.

Progress in the diagnosis and treatment of pancreatic cancer with acute pancreatitis as the initial symptom

Pancreatic cancer patients often have complaints such as upper abdominal pain and obstructive jaundice when seeking diagnosis and treatment. However, acute pancreatitis as a rare initial clinical manifestation of pancreatic cancer is often overlooked in clinical practice. This oversight often leads to a delayed diagnosis of pancreatic cancer, uncertainty in treatment strategies, and significantly affects patients' quality of life and prognosis. Therefore, early diagnosis and treatment, and active follow-up are crucial for patients with acute pancreatitis as an initial symptom of pancreatic cancer. Upon admission to such patients, common causes such as gallstones, alcohol abuse, and hyperlipidemia should be initially ruled out. Evaluation with tumor markers, CT and MRI, and endoscopic ultrasound are essential to confirm the diagnosis of pancreatic cancer. For patients with mild pancreatitis, managing peripancreatic inflammation first before radical resection of pancreatic cancer could reduce postoperative complications. Moreover, pancreatitis serves as a high-risk factor for pancreatic cancer, so it is crucial to closely follow up patients with pancreatitis to detect pancreatic cancer early.

IRTIDP: A simple integrated real-time isolation and detection platform for small extracellular vesicles Glypican-1 in pancreatic cancer patients

Glypican-1 (GPC-1) protein-positive small extracellular vesicles (GPC-1+-sEV) have been proposed as potential biomarkers for early diagnosis of pancreatic cancer. In this study, we present an integrated real-time isolation and detection platform (IRTIDP) to capture and analyze GPC-1+-sEV directly from sera of pancreatic cancer patients. First, CD63 antibody-modified metal-organic framework (MOF) materials were utilized to enrich sEVs with a capture efficiency of 93.93 %. Second, a SERS probe was constructed by Raman reporter 4-MBA and GPC-1 antibody modified SERS active silver nanoparticles (AgNPs), which formed a sandwich complex structure of "MOFs@GPC-1+-sEV@AgNPs-4-MBA" with MOFs-enriched sEVs. The IRTSDP can complete the capture and detection process within 35 min, with a detection limit for 1 GPC-1+-sEV/μL, and linear range between 105∼109 GPC-1+-sEV/mL. Furthermore, this approach has been applied to quantify serum sEV GPC-1 in clinical pancreatic cancer patients. Based on the SERS intensity analysis, pancreatic cancer patients can be distinguished from pancreatic cystadenoma patients and healthy individuals effectively using this innovative platform that provides highly specific and sensitive means for early diagnosis of pancreatic cancer as well as other tumor types.

Endoscopic ultrasonography for microvascular imaging without contrast enhancement in the differential diagnosis of pancreatic lesions.

Detective flow imaging endoscopic ultrasonography (DFI-EUS) is a recent imaging modality developed for detecting fine vessels without the need for ultrasound contrast agents. The aim of the present study was to evaluate the utility of DFI-EUS for solid pancreatic lesions and to compare the diagnostic ability for pancreatic cancer (PC) between DFI-EUS, directional power Doppler (eFLOW) EUS, and contrast-enhanced harmonic (CH)-EUS. Patients with a pancreatic lesion who underwent DFI-EUS, eFLOW-EUS, and CH-EUS between March 2019 and November 2023 were retrospectively enrolled. Final diagnoses were confirmed by pathologic examination of EUS-guided tissue acquisition and/or resected specimens. Lesions were categorized into the three patterns of poor, mild, and rich vascularity on DFI-EUS and eFLOW-EUS, and hypo-, iso-, and hypervascular on CH-EUS. PC was defined as a poor pattern on DFI-EUS and eFLOW-EUS, and a hypovascular pattern on CH-EUS. The final diagnoses of 90 examined tumors were PC (n = 57), inflammatory mass (n = 6), autoimmune pancreatitis (n = 13), neuroendocrine tumor (n = 9), and others (n = 5). The sensitivity, specificity, and accuracy for diagnosis of PC were 93%, 82%, and 88%, respectively, on DFI-EUS, 97%, 42%, and 77% on eFLOW-EUS, and 95%, 89%, and 92% on CH-EUS. The accuracy of DFI-EUS was significantly superior to eFLOW-EUS (P = 0.005), but no significant difference was found between DFI-EUS and CH-EUS. DFI-EUS is more sensitive for depicting vasculature than eFLOW-EUS, and has higher diagnostic sensitivity for PC. Evaluation of vascularity on DFI-EUS is useful for the differential diagnosis of pancreatic lesions without the need for intravenous contrast agent.

Advancements in Early Detection and Screening Strategies for Pancreatic Cancer: From Genetic Susceptibility to Novel Biomarkers.

Pancreatic cancer is a rare but lethal cancer due to its biologically aggressive nature, advanced stage at the time of diagnosis, and poor response to oncologic therapies. The risk of pancreatic cancer is significantly higher to 5% in certain high-risk individuals with inherited genetic susceptibility. Screening for pancreatic cancer in these individuals from high-risk groups can help with the early detection of pancreatic cancer as well as the detection of precursor lesions leading to early surgical resection and improved overall outcomes. The advancements in radiological imaging as well as advanced endoscopic procedures has made a significant impact on the early diagnosis, surveillance, and staging of pancreatic cancer. There is also a significant advancement in the development of biomarkers for the early detection of pancreatic cancer, which has also led to the development of liquid biopsy, allowing for microRNA detection in serum and circulating tumor cells. Various societies and organizations have provided guidelines for pancreatic cancer screening and surveillance in high-risk individuals. In this review, we aim to discuss the hereditary risk factors for developing pancreatic cancer, summarize the screening recommendations by different societies, and discuss the development of novel biomarkers and areas for future research in pancreatic cancer screening for high-risk individuals.