New-onset diabetes: a potential clue to the early diagnosis of pancreatic cancer

Macrophages and T cells in metabolic disorder-associated cancers

Cancer and metabolic disorders have emerged as major global health challenges, reaching epidemic levels in recent decades. Often viewed as separate issues, metabolic disorders are shown by mounting evidence to heighten cancer risk and incidence. The intricacies underlying this connection are still being unraveled and encompass a complex interplay between metabolites, cancer cells and immune cells within the tumour microenvironment (TME). Here, we outline the interplay between metabolic and immune cell dysfunction in the context of three highly prevalent metabolic disorders, namely obesity; two associated liver diseases, metabolic dysfunction-associated steatotic liver disease (MASLD) and metabolic dysfunction-associated steatohepatitis (MASH); and type 2 diabetes. We focus primarily on macrophages and T cells, the critical roles of which in dictating inflammatory response and immune surveillance in metabolic disorder-associated cancers are widely reported. Moreover, considering the ever-increasing number of patients prescribed with metabolism disorder-altering drugs and diets in recent years, we discuss how these therapies modulate systemic and local immune phenotypes, consequently impacting cancer malignancy. Collectively, unraveling the determinants of metabolic disorder-associated immune landscape and their role in fuelling cancer malignancy will provide a framework essential to therapeutically address these highly prevalent diseases.

Using Data to Improve Healthcare: A Case Study of Pancreatic Enzyme Replacement in Pancreatic Cancer

OBJECTIVES

In the UK, guidelines recommend pancreatic enzyme replacement therapy (PERT) to all people with unresectable pancreatic cancer. In 2023, we published a national audit of PERT which showed suboptimal prescribing and wide regional variation in England. The aim of this manuscript was to describe how we used the PERT audit to drive improvements in healthcare.

METHODS

Building on the PERT audit, we deployed an online dashboard which will deliver ongoing updates of the PERT audit. We developed a collaborative intervention with cancer nurse specialists (CNS) to improve care delivered to people with pancreatic cancer. The intervention called Creating a natiOnAL CNS pancrEatic cancer network to Standardise and improve CarE (COALESCE) will use the dashboard to evaluate improvements in prescribing of PERT.

RESULTS

We demonstrated how large databases of electronic healthcare records (EHRs) can be used to improve cancer care. The PERT audit was implemented into a dashboard for tracking the progress of COALESCE. We will measure improvements in PERT prescribing as the intervention with CNS progresses.

CONCLUSIONS

Improving healthcare is an ongoing and iterative process. By implementing the PERT dashboard, we created a resource-efficient, automated evaluation method enabling COALESCE to deliver a sustainable change. National-scale databases of EHRs enable rapid cycles of audits, providing regular feedback to interventions, working systematically to deliver change. Here, the focus is on pancreatic cancer. However, this methodology is transferable to other areas of healthcare.

IMPLICATIONS FOR NURSING PRACTICE

Nurses play a key role in collecting good quality data which are needed in clinical audits to identify shortcomings in healthcare. Nurse-driven interventions can be designed to improve healthcare. In this study, we capitalize on the unique role of CNS coordinating care for every patient with cancer. COALESCE is the first national collaborative study which uses CNS as researchers and change agents.

Exocrine Pancreas in Type 1 and Type 2 Diabetes: Different Patterns of Fibrosis, Metaplasia, Angiopathy, and Adiposity

The endocrine and exocrine compartments of the pancreas are spatially related but functionally distinct. Multiple diseases affect both compartments, including type 1 diabetes (T1D), pancreatitis, cystic fibrosis, and pancreatic cancer. To better understand how the exocrine pancreas changes with age, obesity, and diabetes, we performed a systematic analysis of well-preserved tissue sections from the pancreatic head, body, and tail of organ donors with T1D (n = 20) or type 2 diabetes (T2D) (n = 25) and donors with no diabetes (ND; n = 74). Among ND donors, we found that the incidence of acinar-to-ductal metaplasia (ADM), angiopathy, and pancreatic adiposity increased with age, and ADM and adiposity incidence also increased with BMI. Compared with age- and sex-matched ND organs, T1D pancreata had greater rates of acinar atrophy and angiopathy, with fewer intralobular adipocytes. T2D pancreata had greater rates of ADM and angiopathy and a higher total number of T lymphocytes, but no difference in adipocyte number, compared with ND organs. Although total pancreatic fibrosis was increased in both T1D and T2D, the patterns were different, with periductal and perivascular fibrosis occurring more frequently in T1D pancreata and lobular and parenchymal fibrosis occurring more frequently in T2D. Thus, the exocrine pancreas undergoes distinct changes as individuals age or develop T1D or T2D.

ARTICLE HIGHLIGHTS

The complex landscape of intracellular signalling in protein modification under hyperglycaemic stress leading to metabolic disorders

Hyperglycaemia is a life-threatening risk factor that occurs in both chronic and acute phases and has been linked to causing injury to many organs. Protein modification was triggered by hyperglycaemic stress, which resulted in pathogenic alterations such as impaired cellular function and tissue damage. Dysregulation in cellular function increases the condition associated with metabolic disorders, including cardiovascular diseases, nephropathy, retinopathy, and neuropathy. Hyperglycaemic stress also increases the proliferation of cancer cells. The major areas of experimental biomedical research have focused on the underlying mechanisms involved in the cellular signalling systems involved in diabetes-associated chronic hyperglycaemia. Reactive oxygen species and oxidative stress generated by hyperglycaemia modify many intracellular signalling pathways that result in insulin resistance and β-cell function degradation. The dysregulation of post translational modification in β cells is clinically associated with the development of diabetes mellitus and its associated diseases. This review will discuss the effect of hyperglycaemic stress on protein modification and the cellular signalling involved in it. The focus will be on the significant molecular changes associated with severe metabolic disorders.

Bidirectional relationship between pancreatic cancer and diabetes mellitus: a comprehensive literature review

Pancreatic cancer (PC) is a fatal malignant disease. It is well known that the relationship between PC and type 2 diabetes mellitus (T2DM) is a complicated bidirectional relationship. The most important factors causing increased risks of pancreatic cancer are hyperglycaemia, hyperinsulinemia, pancreatitis, and dyslipidemia. Genetics and the immune system also play an important role in the relationship between diabetes mellitus and pancreatic cancer. The primary contributors to this association involve insulin resistance and inflammatory processes within the tumour microenvironment. The combination of diabetes and obesity can contribute to PC by inducing hyperinsulinemia and influencing leptin and adiponectin levels. Given the heightened incidence of pancreatic cancer in diabetes patients compared to the general population, early screening for pancreatic cancer is recommended. Diabetes negatively impacts the survival of pancreatic cancer patients. Among patients receiving chemotherapy, it reduced their survival. The implementation of a healthy lifestyle, including weight management, serves as an initial preventive measure to mitigate the risk of disease development. The role of anti-diabetic drugs on survival is controversial; however, metformin may have a positive impact, especially in the early stages of cancer, while insulin therapy increases the risk of PC.

Predicting the risk of pancreatic cancer in women with new-onset diabetes mellitus

BACKGROUND AND AIM

People with new-onset diabetes mellitus (diabetes) could be a possible target population for pancreatic cancer surveillance. However, distinguishing diabetes caused by pancreatic cancer from type 2 diabetes remains challenging. We aimed to develop and validate a model to predict pancreatic cancer among women with new-onset diabetes.

METHODS

We conducted a retrospective cohort study among Australian women newly diagnosed with diabetes, using first prescription of anti-diabetic medications, sourced from administrative data, as a surrogate for the diagnosis of diabetes. The outcome was a diagnosis of pancreatic cancer within 3 years of diabetes diagnosis. We used prescription medications, severity of diabetes (i.e., change/addition of medication within 2 months after first medication), and age at diabetes diagnosis as potential predictors of pancreatic cancer.

RESULTS

Among 99 687 women aged ≥ 50 years with new-onset diabetes, 602 (0.6%) were diagnosed with pancreatic cancer within 3 years. The area under the receiver operating curve for the risk prediction model was 0.73. Age and diabetes severity were the two most influential predictors followed by beta-blockers, acid disorder drugs, and lipid-modifying agents. Using a risk threshold of 50%, sensitivity and specificity were 69% and the positive predictive value (PPV) was 1.3%.

CONCLUSIONS

Our model doubled the PPV of pancreatic cancer in women with new-onset diabetes from 0.6% to 1.3%. Age and rapid progression of diabetes were important risk factors, and pancreatic cancer occurred more commonly in women without typical risk factors for type 2 diabetes. This model could prove valuable as an initial screening tool, especially as new biomarkers emerge.

Challenges of Managing Type 3c Diabetes in the Context of Pancreatic Resection, Cancer and Trauma

Type 3c diabetes mellitus (T3cDM), also known as pancreatogenic or pancreoprivic diabetes, is a specific type of DM that often develops as a result of diseases affecting the exocrine pancreas, exhibiting an array of hormonal and metabolic characteristics. Several pancreatic exocrine diseases and surgical procedures may cause T3cDM. Diagnosing T3cDM remains difficult as the disease characteristics frequently overlap with clinical presentations of type 1 DM (T1DM) or type 2 DM (T2DM). Managing T3cDM is likewise challenging due to numerous confounding metabolic dysfunctions, including pancreatic endocrine and exocrine insufficiencies and poor nutritional status. Treatment of pancreatic exocrine insufficiency is of paramount importance when managing patients with T3cDM. This review aims to consolidate the latest information on surgical etiologies of T3cDM, focusing on partial pancreatic resections, total pancreatectomy, pancreatic cancer and trauma.

Glucose-to-Lymphocyte Ratio (GLR) as an Independent Prognostic Factor in Patients with Resected Pancreatic Ductal Adenocarcinoma-Cohort Study

Background: We retrospectively evaluated the usefulness of an elevated glucose-to-lymphocyte ratio (GLR) as a sensitive prognostic biomarker of disease-specific survival in 338 patients who underwent surgical resection of pancreatic ductal adenocarcinoma (PDAC). Methods: The optimal GLR cutoff value was determined using the method of Contal and O'Quigley. Patient demographics, clinical information, and imaging data were analyzed to identify preoperative predictors of long-term survival outcomes. Results: Elevated GLR correlated significantly with aggressive tumor biologic behaviors, such as a high carbohydrate antigen (CA) 19-9 level (p = 0.003) and large tumor size (p = 0.011). Multivariate analysis identified (1) GLR > 92.72 [hazard ratio (HR) = 2.475, p < 0.001], (2) CA 19-9 level > 145.35 (HR = 1.577, p = 0.068), and (3) symptoms (p = 0.064) as independent predictors of long-term, cancer-specific survival. These three risk factors were used to group patients into groups 1 (0 factors), 2 (1-2 factors), and 3 (3 factors), which corresponded to significantly different 5-year overall survival rates (50.2%, 34.6%, and 11.7%, respectively; p < 0.001). Conclusions: An elevated preoperative GLR is associated with aggressive tumor characteristics and is an independent predictor of poor postoperative prognosis in patients with PDAC. Further prospective studies are required to verify these findings.

Decoding the role of microRNA dysregulation in the interplay of pancreatic cancer and type 2 diabetes

This study examines the complex relationship between pancreatic cancer (PC) and type 2 diabetes (T2D) by focusing on the role of microRNAs (miRNAs). miRNAs are small non-coding RNAs that regulate gene expression and have been implicated in many diseases, including T2D and cancer. To begin, we conducted a literature review to identify miRNAs associated with the PC-T2D link. However, we found limited research on this specific association, with most studies focusing on the antitumor effects of metformin. Furthermore, we performed a bioinformatics analysis to identify new potential miRNAs that might be relevant in the context of PC-T2D. First, we identified miRNAs and gene expression alterations common to both diseases using publicly available datasets. Subsequently, we performed an integrative analysis between the identified miRNAs and genes alterations. As a result, we identified nine miRNAs that could potentially play an important role in the interplay between PC and T2D. These miRNAs have the potential to influence nearby cells and distant tissues, affecting critical processes like extracellular matrix remodeling and cell adhesion, ultimately contributing to the development of T2D or PC. Taken together, these analyses underscore the importance of further exploring the role of miRNAs in the complex interplay of PC and T2D.

Development and Validation of Case-Finding Algorithms to Identify Pancreatic Cancer in the Veterans Health Administration

BACKGROUND

Survival in pancreatic ductal adenocarcinoma (PDAC) remains poor due to late diagnosis. Electronic Health Records (EHRs) can be used to study this rare disease, but validated algorithms to identify PDAC in the United States EHRs do not currently exist.

AIMS

To develop and validate an algorithm using Veterans Health Administration (VHA) EHR data for the identification of patients with PDAC.

METHODS

We developed two algorithms to identify patients with PDAC in the VHA from 2002 to 2023. The algorithms required diagnosis of exocrine pancreatic cancer in either ≥ 1 or ≥ 2 of the following domains: (i) the VA national cancer registry, (ii) an inpatient encounter, or (iii) an outpatient encounter in an oncology setting. Among individuals identified with ≥ 1 of the above criteria, a random sample of 100 were reviewed by three gastroenterologists to adjudicate PDAC status. We also adjudicated fifty patients not qualifying for either algorithm. These patients died as inpatients and had alkaline phosphatase values within the interquartile range of patients who met ≥ 2 of the above criteria for PDAC. These expert adjudications allowed us to calculate the positive and negative predictive value of the algorithms.

RESULTS

Of 10.8 million individuals, 25,533 met ≥ 1 criteria (PPV 83.0%, kappa statistic 0.93) and 13,693 individuals met ≥ 2 criteria (PPV 95.2%, kappa statistic 1.00). The NPV for PDAC was 100%.

CONCLUSIONS

An algorithm incorporating readily available EHR data elements to identify patients with PDAC achieved excellent PPV and NPV. This algorithm is likely to enable future epidemiologic studies of PDAC.

Clinical development of a blood biomarker using apolipoprotein-A2 isoforms for early detection of pancreatic cancer

BACKGROUND

We have previously reported apolipoprotein A2-isoforms (apoA2-is) as candidate plasma biomarkers for early-stage pancreatic cancer. The aim of this study was the clinical development of apoA2-is.

METHODS

We established a new enzyme-linked immunosorbent sandwich assay for apoA2-is under the Japanese medical device Quality Management System requirements and performed in vitro diagnostic tests with prespecified end points using 2732 plasma samples. The clinical equivalence and significance of apoA2-is were compared with CA19-9.

RESULTS

The point estimate of the area under the curve to distinguish between pancreatic cancer (n = 106) and healthy controls (n = 106) was higher for apoA2-ATQ/AT [0.879, 95% confidence interval (CI): 0.832-0.925] than for CA19-9 (0.849, 95% CI 0.793-0.905) and achieved the primary end point. The cutoff apoA2-ATQ/AT of 59.5 μg/mL was defined based on a specificity of 95% in 2000 healthy samples, and the reliability of specificities was confirmed in two independent healthy cohorts as 95.3% (n = 106, 95% CI 89.4-98.0%) and 95.8% (n = 400, 95% CI 93.3-97.3%). The sensitivities of apoA2-ATQ/AT for detecting both stage I (47.4%) and I/II (50%) pancreatic cancers were higher than those of CA19-9 (36.8% and 46.7%, respectively). The combination of apoA2-ATQ/AT (cutoff, 59.5 μg/mL) and CA19-9 (37 U/mL) increased the sensitivity for pancreatic cancer to 87.7% compared with 69.8% for CA19-9 alone. The clinical performance of apoA2-is was blindly confirmed by the National Cancer Institute Early Detection Research Network.

CONCLUSIONS

The clinical performance of ApoA2-ATQ/AT as a blood biomarker is equivalent to or better than that of CA19-9.

New-onset diabetes is a predictive risk factor for pancreatic lesions in high-risk individuals: An observational cohort study

Background and Objectives

Pancreatic cancer (PC) is the third cause of cancer-related deaths. Early detection and interception of premalignant pancreatic lesions represent a promising strategy to improve outcomes. We evaluated risk factors of focal pancreatic lesions (FPLs) in asymptomatic individuals at hereditary high risk for PC.

Methods

This is an observational single-institution cohort study conducted over a period of 5 years. Surveillance was performed through imaging studies (EUS or magnetic resonance imaging/magnetic resonance cholangiopancreatography) and serum biomarkers. We collected demographic characteristics and used univariate and multivariate logistic regression models to evaluate associations between potential risk factors and odd ratios (ORs) for FPL development.

Results

A total of 205 patients completed baseline screening. Patients were followed up to 53 months. We detected FPL in 37 patients (18%) at baseline; 2 patients had lesions progression during follow-up period, 1 of them to PC. Furthermore, 13 patients developed new FPLs during the follow-up period. Univariate and multivariate analyses revealed that new-onset diabetes (NOD) is strongly associated with the presence of FPL (OR, 10.94 [95% confidence interval, 3.01-51.79; P < 0.001]; OR, 9.98 [95% confidence interval, 2.15-46.33; P = 0.003]). Follow-up data analysis revealed that NOD is also predictive of lesions progression or development of new lesions during screening (26.7% vs. 2.6%; P = 0.005).

Conclusions

In a PC high-risk cohort, NOD is significantly associated with presence of FPL at baseline and predictive of lesions progression or new lesions during surveillance.

Association Between Age at Diabetes Diagnosis and Subsequent Incidence of Cancer: A Longitudinal Population-Based Cohort

OBJECTIVE

Diabetes presenting at a younger age has a more aggressive nature. We aimed to explore the association of age at type 2 diabetes mellitus (T2DM) diagnosis with subsequent cancer incidence in a large Chinese population.

RESEARCH DESIGN AND METHODS

The prospective population-based longitudinal cohort included 428,568 newly diagnosed T2DM patients from 2011 to 2018. Participants were divided into six groups according to their age at diagnosis: 20-54, 55-59, 60-64, 65-69, 70-74, and ≥75 years. The incidence of overall and 14 site-specific cancers was compared with the Shanghai general population including 100,649,346 person-years.

RESULTS

A total of 18,853 and 582,643 overall cancer cases were recorded in the T2DM cohort and the general population. The age-standardized rate of overall cancer in T2DM patients was 501 (95% CI: 491, 511) per 100,000 person-years, and the standardized incidence ratio (SIR) was 1.10 (1.09, 1.12). Younger age at T2DM diagnosis was associated with higher incidence of overall and site-specific cancers. SIRs for overall cancer with T2DM diagnosis at ages 20-54, 55-59, 60-64, 65-69, 70-74, and ≥75 years were 1.48 (1.41, 1.54), 1.30 (1.25, 1.35), 1.19 (1.15, 1.23), 1.16 (1.12, 1.20), 1.06 (1.02, 1.10), and 0.86 (0.84, 0.89), respectively. Similar trends were observed for site-specific cancers, including respiratory, colorectum, stomach, liver, pancreatic, bladder, central nervous system, kidney, and gallbladder cancer and lymphoma among both males and females.

CONCLUSIONS

Our findings highlight the necessity of stratifying management for T2DM according to age of diagnosis. As with a range of vascular outcomes, age-standardized cancer risks are greater in earlier compared with later onset T2DM.

Pancreatic Adenocarcinoma: Imaging Modalities and the Role of Artificial Intelligence in Analyzing CT and MRI Images

Pancreatic ductal adenocarcinoma (PDAC) stands out as the predominant malignant neoplasm affecting the pancreas, characterized by a poor prognosis, in most cases patients being diagnosed in a nonresectable stage. Image-based artificial intelligence (AI) models implemented in tumor detection, segmentation, and classification could improve diagnosis with better treatment options and increased survival. This review included papers published in the last five years and describes the current trends in AI algorithms used in PDAC. We analyzed the applications of AI in the detection of PDAC, segmentation of the lesion, and classification algorithms used in differential diagnosis, prognosis, and histopathological and genomic prediction. The results show a lack of multi-institutional collaboration and stresses the need for bigger datasets in order for AI models to be implemented in a clinically relevant manner.

Prediction of pancreatic cancer risk in patients with new-onset diabetes using a machine learning approach based on routine biochemical parameters

OBJECTIVE

To develop a machine-learning model that can predict the risk of pancreatic ductal adenocarcinoma (PDAC) in people with new-onset diabetes (NOD).

METHODS

From a population-based sample of individuals with NOD aged >50 years, patients with pancreatic cancer-related diabetes (PCRD), defined as NOD followed by a PDAC diagnosis within 3 years, were included (n = 716). These PCRD patients were randomly matched in a 1:1 ratio with individuals having NOD. Data from Danish national health registries were used to develop a random forest model to distinguish PCRD from Type 2 diabetes. The model was based on age, gender, and parameters derived from feature engineering on trajectories of routine biochemical variables. Model performance was evaluated using receiver operating characteristic curves (ROC) and relative risk scores.

RESULTS

The most discriminative model included 20 features and achieved a ROC-AUC of 0.78 (CI:0.75-0.83). Compared to the general NOD population, the relative risk for PCRD was 20-fold increase for the 1 % of patients predicted by the model to have the highest cancer risk (3-year cancer risk of 12 % and sensitivity of 20 %). Age was the most discriminative single feature, followed by the rate of change in haemoglobin A1c and the latest plasma triglyceride level. When the prediction model was restricted to patients with PDAC diagnosed six months after diabetes diagnosis, the ROC-AUC was 0.74 (CI:0.69-0.79).

CONCLUSION

In a population-based setting, a machine-learning model utilising information on age, sex and trajectories of routine biochemical variables demonstrated good discriminative ability between PCRD and Type 2 diabetes.

Determining the feasibility of calculating pancreatic cancer risk scores for people with new-onset diabetes in primary care (DEFEND PRIME): study protocol

INTRODUCTION

Worldwide, pancreatic cancer has a poor prognosis. Early diagnosis may improve survival by enabling curative treatment. Statistical and machine learning diagnostic prediction models using risk factors such as patient demographics and blood tests are being developed for clinical use to improve early diagnosis. One example is the Enriching New-onset Diabetes for Pancreatic Cancer (ENDPAC) model, which employs patients' age, blood glucose and weight changes to provide pancreatic cancer risk scores. These values are routinely collected in primary care in the UK. Primary care's central role in cancer diagnosis makes it an ideal setting to implement ENDPAC but it has yet to be used in clinical settings. This study aims to determine the feasibility of applying ENDPAC to data held by UK primary care practices.

METHODS AND ANALYSIS

This will be a multicentre observational study with a cohort design, determining the feasibility of applying ENDPAC in UK primary care. We will develop software to search, extract and process anonymised data from 20 primary care providers' electronic patient record management systems on participants aged 50+ years, with a glycated haemoglobin (HbA1c) test result of ≥48 mmol/mol (6.5%) and no previous abnormal HbA1c results. Software to calculate ENDPAC scores will be developed, and descriptive statistics used to summarise the cohort's demographics and assess data quality. Findings will inform the development of a future UK clinical trial to test ENDPAC's effectiveness for the early detection of pancreatic cancer.

ETHICS AND DISSEMINATION

This project has been reviewed by the University of Surrey University Ethics Committee and received a favourable ethical opinion (FHMS 22-23151 EGA). Study findings will be presented at scientific meetings and published in international peer-reviewed journals. Participating primary care practices, clinical leads and policy makers will be provided with summaries of the findings.

Advances in the management of pancreatic cancer

Pancreatic cancer remains among the malignancies with the worst outcomes. Survival has been improving, but at a slower rate than other cancers. Multimodal treatment, including chemotherapy, surgical resection, and radiotherapy, has been under investigation for many years. Because of the anatomical characteristics of the pancreas, more emphasis on treatment selection has been placed on local extension into major vessels. Recently, the development of more effective treatment regimens has opened up new treatment strategies, but urgent research questions have also become apparent. This review outlines the current management of pancreatic cancer, and the recent advances in its treatment. The review discusses future treatment pathways aimed at integrating novel findings of translational and clinical research.

[Pancreatic cancer-screening or surveillance?]

BACKGROUND

Despite continuous improvement of diagnostic and therapeutic procedures, the number of new pancreatic ductal adenocarcinoma (PDAC) cases diagnosed annually almost equals the number of PDAC-related deaths. Prerequisite for curative treatment is a resectable tumor at the time of diagnosis. Individuals with genetic and/or familial risk profiles should therefore be screened and included in structured surveillance programs.

OBJECTIVES

Description of the status quo and usefulness of current PDAC screening and surveillance concepts.

METHODS

A selective literature search of current national and international guidelines including underlying literature was performed.

RESULTS

Nearly half of pancreatic cancer cases are missed by currently available surveillance programs, even in high-risk cohorts. Magnetic resonance imaging and endoscopic ultrasound supplemented by CA19‑9 (± HbA1c) are not accurate enough to ensure robust earlier pancreatic cancer detection. Complementary biomarker panels will take on a crucial diagnostic role in the future.

Large cystic lymphangioma of the pancreas: unusual finding with differential diagnosis and therapeutic considerations

Lymphangiomas are rare benign tumours of lymphatic vascular origin. They are more common in the paediatric population and manifest mainly in the neck and axillary region. Retroperitoneal lymphangiomas are <1% and pancreatic origin is even rarer. We present a case of a pancreatic cystic lymphangioma in a 60-year-old woman with chronic diffuse symptoms, diagnosed because of newly onset of diabetes mellitus. She was successfully managed with distal pancreatectomy and spleenectomy en-bloc with the cystic mass without any complications. Cystic lymphangioma of the pancreas is a rare entity presenting with a challenging preoperative diagnosis as imaging modalities may provide ambiguous information. The clinician should be aware of its complicated differential diagnosis and its persistent and subtle symptomatology.

Therapeutic Strategies for Pancreatic-Cancer-Related Type 2 Diabetes Centered around Natural Products

Pancreatic ductal adenocarcinoma (PDAC), a highly malignant neoplasm, is classified as one of the most severe and devastating types of cancer. PDAC is a notable malignancy that exhibits a discouraging prognosis and a rising occurrence. The interplay between diabetes and pancreatic cancer exhibits a reciprocal causation. The identified metabolic disorder has been observed to possess noteworthy consequences on health outcomes, resulting in elevated rates of morbidity. The principal mechanisms involve the suppression of the immune system, the activation of pancreatic stellate cells (PSCs), and the onset of systemic metabolic disease caused by dysfunction of the islets. From this point forward, it is important to recognize that pancreatic-cancer-related diabetes (PCRD) has the ability to increase the likelihood of developing pancreatic cancer. This highlights the complex relationship that exists between these two physiological states. Therefore, we investigated into the complex domain of PSCs, elucidating their intricate signaling pathways and the profound influence of chemokines on their behavior and final outcome. In order to surmount the obstacle of drug resistance and eliminate PDAC, researchers have undertaken extensive efforts to explore and cultivate novel natural compounds of the next generation. Additional investigation is necessary in order to comprehensively comprehend the effect of PCRD-mediated apoptosis on the progression and onset of PDAC through the utilization of natural compounds. This study aims to examine the potential anticancer properties of natural compounds in individuals with diabetes who are undergoing chemotherapy, targeted therapy, or immunotherapy. It is anticipated that these compounds will exhibit increased potency and possess enhanced pharmacological benefits. According to our research findings, it is indicated that naturally derived chemical compounds hold potential in the development of PDAC therapies that are both safe and efficacious.

Blood-based Migration Signature Biomarker Panel Discriminates Early Stage New Onset Diabetes related Pancreatic Ductal Adenocarcinoma from Type 2 Diabetes

BACKGROUND AND AIMS

While type 2 diabetes is a well-known risk factor for pancreatic ductal adenocarcinoma (PDAC), PDAC-induced new-onset diabetes (PDAC-NOD) is a manifestation of underlying PDAC. In this study, we sought to identify potential blood-based biomarkers for distinguishing PDAC-NOD from type 2 diabetes (T2DM) without PDAC.

MATERIALS AND METHODS

By ELISA analysis, a migration signature biomarker panel comprising tissue factor pathway inhibitor (TFPI), tenascin C (TNC-FNIII-C) and CA 19-9 was analyzed in plasma samples from 50 PDAC-NOD and 50 T2DM controls.

RESULTS

Both TFPI (area under the curve (AUC) 0.71) and TNC-FNIII-C (AUC 0.69) outperformed CA 19-9 (AUC 0.60) in distinguishing all stages of PDAC-NOD from T2DM controls. The combined panel showed an AUC of 0.82 (95% CI = 0.73-0.90) (p = 0.002). In the PDAC-NOD early stage II samples, the three biomarkers had an AUC of 0.84 (95% CI = 0.73-0.93) vs CA 19-9, AUC = 0.60, (95% CI = 0.45-0.73), which also improved significance (p = 0.0123).

CONCLUSION

The migration signature panel adds significantly to CA 19-9 to discriminate PDAC-NOD from T2DM controls and warrants further validation for high-risk group stratification.

Protein Advanced Glycation End Products and Their Implications in Pancreatic Cancer

Protein advanced glycation end products (AGE) formed by nonenzymatic glycation can disrupt the normal structure and function of proteins, and stimulate the receptor for AGEs (RAGE), triggering intricate mechanisms that are etiologically related to various chronic diseases, including pancreatic cancer. Many common risk factors of pancreatic cancer are the major sources for the formation of protein AGEs and glycative stress in the human body. Abnormal accumulation of protein AGEs can impair the cellular proteome and promote AGE-RAGE driven pro-inflammatory signaling cascades, leading to increased oxidative stress, protease resistance, protein dysregulation, transcription activity of STAT, NF-κB, and AP-1, aberrant status in ubiquitin-proteasome system and autophagy, as well as other molecular events that are susceptible for the carcinogenic transformation towards the development of neoplasms. Here, we review studies to highlight our understanding in the orchestrated molecular events in bridging the impaired proteome, dysregulated functional networks, and cancer hallmarks initiated upon protein AGE formation and accumulation in pancreatic cancer.

Pancreatic Cancer-Derived Exosomal miR-Let-7b-5p Stimulates Insulin Resistance in Skeletal Muscle Cells Through RNF20/STAT3/FOXO1 Axis Regulation

Background

Cancers trigger systemic metabolic disorders usually associated with glucose intolerance, which is an initially apparent phenomenon. One of the features of pancreatic cancer (PC) metabolic reprogramming is the crosstalk between PC and peripheral tissues (skeletal muscle and adipose tissues), emphasized by insulin resistance (IR). Our previous study reported that mice pancreatic cancer-derived exosomes could induce skeletal muscle cells (C2C12) IR, and exosomal microRNAs (miRNAs) may exert an important effect. However, the underlying mechanism remains to be further elucidated.

Methods

qPCR was used to determine the expression of let-7b-5p in normal pancreatic islet cells and PC cells. Exosomes were purified from PC cell culture medium by ultracentrifugation. The role let-7b-5p on IR-mediated by PC cells-derived exosomes was asses by Oil Red O staining using miRNA inhibitor. Western blot assay was performed to examine the expression of IR-related genes and the activation of signaling pathways. A Luciferase experiment was applied to confirm how let-7b-5p regulated the expression of RNF20. IP/WB analysis further determined whether RNF20 promoted STAT3 ubiquitination. Rescue experiment using RNF20 overexpression plasmid was performed to confirm the role of RNF20 on IR-mediated using PC cell-derived exosomes in C2C12 myotube cells.

Results

miRNA-let-7b-5p was identified as the key exosomal miRNA, which could promote the IR in C2C12 myotube cells supported the lipid accumulation, the activation of STAT3/FOXO1 axis, and the decreased expression of IRS-1 and GLUT4. RNF20, an E3 ubiquitin ligase, was confirmed as the target gene of let-7b-5p and was found to improve IR by downregulating STAT3 protein expression via ubiquitination-mediated protein degradation. The ectopic expression of RNF20 could effectively attenuate the IR mediated by the pancreatic cancer-derived exosomes in C2C12 myotube cells.

Conclusion

Our data suggest that exosomal miRNA-let-7b-5p may promote IR in C2C12 myotube cells by targeting RNF20 to activate the STAT3/FOXO1 axis.

A blood-based metabolomic signature predictive of risk for pancreatic cancer

Emerging evidence implicates microbiome involvement in the development of pancreatic cancer (PaCa). Here, we investigate whether increases in circulating microbial-related metabolites associate with PaCa risk by applying metabolomics profiling to 172 sera collected within 5 years prior to PaCa diagnosis and 863 matched non-subject sera from participants in the Prostate, Lung, Colorectal, and Ovarian (PLCO) cohort. We develop a three-marker microbial-related metabolite panel to assess 5-year risk of PaCa. The addition of five non-microbial metabolites further improves 5-year risk prediction of PaCa. The combined metabolite panel complements CA19-9, and individuals with a combined metabolite panel + CA19-9 score in the top 2.5th percentile have absolute 5-year risk estimates of >13%. The risk prediction model based on circulating microbial and non-microbial metabolites provides a potential tool to identify individuals at high risk of PaCa that would benefit from surveillance and/or from potential cancer interception strategies.

Pancreatic Cancer and Detection Methods

The pancreas is a vital organ with exocrine and endocrine functions. Pancreatitis is an inflammation of the pancreas caused by alcohol consumption and gallstones. This condition can heighten the risk of pancreatic cancer (PC), a challenging disease with a high mortality rate. Genetic and epigenetic factors contribute significantly to PC development, along with other risk factors. Early detection is crucial for improving PC outcomes. Diagnostic methods, including imagining modalities and tissue biopsy, aid in the detection and analysis of PC. In contrast, liquid biopsy (LB) shows promise in early tumor detection by assessing biomarkers in bodily fluids. Understanding the function of the pancreas, associated diseases, risk factors, and available diagnostic methods is essential for effective management and early PC detection. The current clinical examination of PC is challenging due to its asymptomatic early stages and limitations of highly precise diagnostics. Screening is recommended for high-risk populations and individuals with potential benign tumors. Among various PC screening methods, the N-NOSE plus pancreas test stands out with its high AUC of 0.865. Compared to other commercial products, the N-NOSE plus pancreas test offers a cost-effective solution for early detection. However, additional diagnostic tests are required for confirmation. Further research, validation, and the development of non-invasive screening methods and standardized scoring systems are crucial to enhance PC detection and improve patient outcomes. This review outlines the context of pancreatic cancer and the challenges for early detection.

Earlier Diagnosis of Pancreatic Cancer: Is It Possible?

Pancreatic ductal adenocarcinoma has a very high mortality rate which has been only minimally improved in the last 30 years. This high mortality is closely related to late diagnosis, which is usually made when the tumor is large and has extensively infiltrated neighboring tissues or distant metastases are already present. This is a paradoxical situation for a tumor that requires nearly 15 years to develop since the first founding mutation. Response to chemotherapy under such late circumstances is poor, resistance is frequent, and prolongation of survival is almost negligible. Early surgery has been, and still is, the only approach with a slightly better outcome. Unfortunately, the relapse percentage after surgery is still very high. In fact, early surgery clearly requires early diagnosis. Despite all the advances in diagnostic methods, the available tools for improving these results are scarce. Serum tumor markers permit a late diagnosis, but their contribution to an improved therapeutic result is very limited. On the other hand, effective screening methods for high-risk populations have not been fully developed as yet. This paper discusses the difficulties of early diagnosis, evaluates whether the available diagnostic tools are adequate, and proposes some simple and not-so-simple measures to improve it.

Diabetes Status and Pancreatic Cancer Survival in the Nationwide Veterans Affairs Healthcare System

BACKGROUND

Long-standing type 2 diabetes is a known risk factor for developing pancreatic cancer, however, its influence on cancer-associated outcomes is understudied.

AIMS

To examine the associations between diabetes status and pancreatic cancer outcomes.

METHODS

We identified patients diagnosed with pancreatic adenocarcinoma in the national Veterans Administration System from 2010 to 2018. We classified each patient by pre-cancer diagnosis diabetes status: no diabetes, new-onset diabetes (NOD) of ≤ 3 years duration, or long-standing diabetes of > 3 years duration. We used Cox proportional hazards models to examine the association between diabetes status and survival. We adjusted the models for age, race, sex, body mass index, tobacco, and alcohol use, coronary artery disease, hypertension, chronic kidney disease, year of cancer diagnosis, and cancer stage and treatment.

RESULTS

We identified 6342 patients diagnosed with pancreatic adenocarcinoma. Most had long-standing diabetes (45.7%) prior to their cancer diagnosis, 14.5% had NOD, and 39.8% had no diabetes. Patients with long-standing diabetes had 10% higher mortality risk compared to patients without diabetes after adjusting for sociodemographic factors and medical comorbidities (adjusted HR 1.10; 95% CI 1.03-1.16). This difference in mortality remained statistically significant after additionally adjusting for cancer stage and receipt of potentially curative treatment (adjusted HR 1.09; 95% CI 1.02-1.15). There was no significant difference in mortality between patients with NOD compared to those without diabetes.

CONCLUSIONS

Long-standing but not new-onset diabetes is independently associated with increased mortality among patients with pancreatic cancer. This information has implication for prognostication and risk stratification among pancreatic cancer patients.

Proposing new early detection indicators for pancreatic cancer: Combining machine learning and neural networks for serum miRNA-based diagnostic model

Background

Pancreatic cancer (PC) is a lethal malignancy that ranks seventh in terms of global cancer-related mortality. Despite advancements in treatment, the five-year survival rate remains low, emphasizing the urgent need for reliable early detection methods. MicroRNAs (miRNAs), a group of non-coding RNAs involved in critical gene regulatory mechanisms, have garnered significant attention as potential diagnostic and prognostic biomarkers for pancreatic cancer (PC). Their suitability stems from their accessibility and stability in blood, making them particularly appealing for clinical applications.

Methods

In this study, we analyzed serum miRNA expression profiles from three independent PC datasets obtained from the Gene Expression Omnibus (GEO) database. To identify serum miRNAs associated with PC incidence, we employed three machine learning algorithms: Support Vector Machine-Recursive Feature Elimination (SVM-RFE), Least Absolute Shrinkage and Selection Operator (LASSO), and Random Forest. We developed an artificial neural network model to assess the accuracy of the identified PC-related serum miRNAs (PCRSMs) and create a nomogram. These findings were further validated through qPCR experiments. Additionally, patient samples with PC were classified using the consensus clustering method.

Results

Our analysis revealed three PCRSMs, namely hsa-miR-4648, hsa-miR-125b-1-3p, and hsa-miR-3201, using the three machine learning algorithms. The artificial neural network model demonstrated high accuracy in distinguishing between normal and pancreatic cancer samples, with verification and training groups exhibiting AUC values of 0.935 and 0.926, respectively. We also utilized the consensus clustering method to classify PC samples into two optimal subtypes. Furthermore, our investigation into the expression of PCRSMs unveiled a significant negative correlation between the expression of hsa-miR-125b-1-3p and age.

Conclusion

Our study introduces a novel artificial neural network model for early diagnosis of pancreatic cancer, carrying significant clinical implications. Furthermore, our findings provide valuable insights into the pathogenesis of pancreatic cancer and offer potential avenues for drug screening, personalized treatment, and immunotherapy against this lethal disease.

Diabetes Mellitus in Pancreatic Cancer: A Distinct Approach to Older Subjects with New-Onset Diabetes Mellitus

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is associated with a very poor prognosis, with near-identical incidence and mortality. According to the World Health Organization Globocan Database, the estimated number of new cases worldwide will rise by 70% between 2020 and 2040. There are no effective screening methods available so far, even for high-risk individuals. The prognosis of PDAC, even at its early stages, is still mostly unsatisfactory. Impaired glucose metabolism is present in about 3/4 of PDAC cases.

METHODS

Available literature on pancreatic cancer and diabetes mellitus was reviewed using a PubMed database. Data from a national oncology registry (on PDAC) and information from a registry of healthcare providers (on diabetes mellitus and a number of abdominal ultrasound investigations) were obtained.

RESULTS

New-onset diabetes mellitus in subjects older than 60 years should be an incentive for a prompt and detailed investigation to exclude PDAC. Type 2 diabetes mellitus, diabetes mellitus associated with chronic non-malignant diseases of the exocrine pancreas, and PDAC-associated type 3c diabetes mellitus are the most frequent types. Proper differentiation of particular types of new-onset diabetes mellitus is a starting point for a population-based program. An algorithm for subsequent steps of the workup was proposed.

CONCLUSIONS

The structured, well-differentiated, and elaborately designed approach to the elderly with a new onset of diabetes mellitus could improve the current situation in diagnostics and subsequent poor outcomes of therapy of PDAC.

Hypoglycemic agents and incidence of pancreatic cancer in diabetic patients: a meta-analysis

Background and aims: Hypoglycemic agents are the primary therapeutic approach for the treatment of diabetes and have been postulated to impact pancreatic cancer (PC) incidence in diabetic patients. We conducted a meta-analysis to further evaluate and establish the associations between four common types of hypoglycemic agents [metformin, sulfonylureas, thiazolidinediones (TZDs), and insulin] and PC incidence in individuals with diabetes mellitus (DM). Methods: A comprehensive literature search of PubMed, Web of Science, Embase, and the Cochrane Library identified studies that analyzed the relationship between hypoglycemic agents and PC published between January 2012 and September 2022. Randomized control trials (RCTs), cohorts, and case-control studies were included if there was clear and evaluated defined exposure to the involved hypoglycemic agents and reported PC outcomes in patients with DM. Furthermore, reported relative risks or odds ratios (ORs) or other provided data were required for the calculation of odds ratios. Summary odds ratio estimates with a 95% confidence interval (CI) were estimated using the random-effects model. Additionally, subgroup analysis was performed to figure out the source of heterogeneity. Sensitivity analysis and publication bias detection were also performed. Results: A total of 11 studies were identified that evaluated one or more of the hypoglycemic agents, including three case-control studies and eight cohort studies. Among these, nine focused on metformin, six on sulfonylureas, seven on TZDs, and seven on insulin. Meta-analysis of the 11 observational studies reported no significant association between metformin (OR = 1.04, 95% CI 0.73-1.46) or TZDs (OR = 1.13, 95% CI 0.73-1.75) and PC incidence, while the risk of PC increased by 79% and 185% with sulfonylureas (OR = 1.79, 95% CI 1.29-2.49) and insulin (OR = 2.85, 95% CI 1.75-4.64), respectively. Considerable heterogeneity was observed among the studies and could not be fully accounted for by study design, region, or adjustment for other hypoglycemic agents. Conclusion: Sulfonylureas and insulin may increase the incidence of pancreatic cancer in diabetic patients, with varying effects observed among different ethnicities (Asian and Western). Due to significant heterogeneity across studies, further interpretation of the relationship between hypoglycemic agents and pancreatic cancer incidence in diabetic patients requires well-adjusted data and better-organized clinical trials.

The association of new-onset diabetes with subsequent diagnosis of pancreatic cancer-novel use of a large administrative database

BACKGROUND

Screening options for pancreatic ductal adenocarcinoma (PDAC) are limited. New-onset type 2 diabetes (NoD) is associated with subsequent diagnosis of PDAC in observational studies and may afford an opportunity for PDAC screening. We evaluated this association using a large administrative database.

METHODS

Patients were identified using claims data from the OptumLabs® Data Warehouse. Adult patients with NoD diagnosis were matched 1:3 with patients without NoD using age, sex and chronic obstructive pulmonary disease (COPD) status. The event of PDAC diagnosis was compared between cohorts using the Kaplan-Meier method. Factors associated with PDAC diagnosis were evaluated with Cox's proportional hazards modeling.

RESULTS

We identified 640 421 patients with NoD and included 1 921 263 controls. At 3 years, significantly more PDAC events were identified in the NoD group vs control group (579 vs 505; P < 0.001). When controlling for patient factors, NoD was significantly associated with elevated risk of PDAC (HR 3.474, 95% CI 3.082-3.920, P < 0.001). Other factors significantly associated with PDAC diagnosis were increasing age, increasing age among Black patients, and COPD diagnosis (P ≤ 0.05).

CONCLUSIONS

NoD was independently associated with subsequent diagnosis of PDAC within 3 years. Future studies should evaluate the feasibility and benefit of PDAC screening in patients with NoD.

Presence of PD-1 similarity genes in monocytes may promote the development of type 1 diabetes mellitus and poor prognosis of pancreatic cancer

INTRODUCTION

To identify proteins and corresponding genes that share sequential and structural similarity with programmed cell death protein-1 (PD-1) in patients with type 1 diabetes mellitus (T1DM) via bioinformatics analysis.

RESEARCH DESIGN AND METHODS

All proteins with immunoglobulin V-set domain were screened in the human protein sequence database, and the corresponding genes were obtained in the gene sequence database. GSE154609 was downloaded from the GEO database, which contained peripheral blood CD14+ monocyte samples from patients with T1DM and healthy controls. The difference result and the similar genes were intersected. Analysis of gene ontology and Kyoto encyclopedia of genes and genomes pathways was used to predict potential functions using the R package 'cluster profiler'. The expression differences of intersected genes were analyzed in The Cancer Genome Atlas pancreatic cancer dataset and GTEx database using t-test. The correlation between the overall survival and disease-free progression of patients with pancreatic cancer was analyzed using Kaplan-Meier survival analysis.

RESULTS

2068 proteins with immunoglobulin V-set domain similar to PD-1 and 307 corresponding genes were found. 1705 upregulated differentially expressed genes (DEGs) and 1335 downregulated DEGs in patients with T1DM compared with healthy controls were identified. A total of 21 genes were overlapped with the 307 PD-1 similarity genes, including 7 upregulated and 14 downregulated. Of these, mRNA levels of 13 genes were significantly increased in patients with pancreatic cancer. High expression of MYOM3 and HHLA2 was significantly correlated with shorter overall survival of patients with pancreatic cancer, while high expression of FGFRL1, CD274, and SPEG was significantly correlated with shorter disease-free survival of patients with pancreatic cancer.

CONCLUSIONS

Genes encoding immunoglobulin V-set domain similar to PD-1 may contribute to the occurrence of T1DM. Of these genes, MYOM3 and SPEG may serve as potential biomarkers for the prognosis of pancreatic cancer.

Prediction of Pathologic Change Development in the Pancreas Associated with Diabetes Mellitus Assessed by NMR Metabolomics

Nuclear magnetic resonance (NMR) metabolomics was used for identification of metabolic changes in pancreatic cancer (PC) blood plasma samples when compared to healthy controls or diabetes mellitus patients. An increased number of PC samples enabled a subdivision of the group according to individual PC stages and the construction of predictive models for finer classification of at-risk individuals recruited from patients with recently diagnosed diabetes mellitus. High-performance values of orthogonal partial least squares (OPLS) discriminant analysis were found for discrimination between individual PC stages and both control groups. The discrimination between early and metastatic stages was achieved with only 71.5% accuracy. A predictive model based on discriminant analyses between individual PC stages and the diabetes mellitus group identified 12 individuals out of 59 as at-risk of development of pathological changes in the pancreas, and four of them were classified as at moderate risk.

Correlations of demographical and clinicopathological features with patient outcome of pancreatic ductal adenocarcinoma: A retrospective study (2010-2018) from a Libyan Cohort

Objective

The aim of the study was to study the correlations of demographical and clinicopathological variables of patients with pancreatic ductal adenocarcinoma (PDAC) and evaluate the association of these variables with patients' survival outcomes.

Patients and Methods

A retrospective analysis of 123 patients with PDAC were diagnosed and treated at the National Cancer Institute, Misurata, Libya during the 2010-2108 period. Data for demographics, clinicopathological, biological variables, risk factors, presentation, treatment, and survival-related data were collected from the patients' medical records.

Results

The mean age of patient was 61.2 years (range: 19-90 years) and most of patients (80.5%) were aged >50 years. For gender distribution, PDAC was more frequent in males (59.3%). Abdominal pain was the most frequent presenting symptom (84.6%) and 78% (96 patients) among them had head tumors. Most patients (80.5%) presented with unresectable tumor at diagnosis. Disease-free survival was better in patients with early stage (P < 0.0001), low-grade tumor (P = 0.001), resectable tumor (P < 0.0001), and with carcinoembryonic antigen levels <5 ng/ml (P = 0.004). Multivariate Cox's regression analysis showed that tumor stage is an independent poor survival factor (P = 0.002). Age at diagnosis, gender, family history, and position of tumor did not show any significant associations with patient outcome.

Conclusion

Libyan patients with PDAC had different demographics, clinicopathological, and biological variables. Typically, they presented with unresectable tumor, advanced stages, and had very short survival times. These results urge us to conduct in-depth biomolecular research studies to identify effective early diagnostics and therapeutics biomarkers in order to fight this disease before it escalates.

Insulin promotes the proliferation and migration of pancreatic cancer cells by up-regulating the expression of PLK1 through the PI3K/AKT pathway

Pancreatic cancer has a particularly poor prognosis compared to other tumors. The peculiar hyperinsulin microenvironment of the pancreas is formed due to the endocrine secretion of islets in the pancreas. This study focused on the effect of insulin on the migration and proliferation of pancreatic cancer cells and its molecular mechanisms. We found that insulin promotes the proliferation and migration of pancreatic cancer cells. At the same time, it can up-regulate the expression of PLK1 in pancreatic cancer cells. Knocking down the expression of PLK1 in pancreatic cancer cells can inhibit the effect of insulin on the biological behavior of pancreatic cancer. In addition, we found that insulin activates the PI3K/AKT pathway in pancreatic cancer cells, and that inhibition of this pathway suppresses PLK1 expression. The PI3K/AKT inhibitor LY294002 inhibits the effects of insulin on the proliferation of pancreatic cancer cells. This study shows that insulin up-regulates PLK1 expression in pancreatic cancer cells via the PI3K/AKT pathway, which in this way enhances the migration and proliferation of pancreatic cancer cells. This may be one of the important reasons for the poor prognosis of pancreatic cancer.

The next "sweet" spot for pancreatic ductal adenocarcinoma: Glycoprotein for early detection

Pancreatic ductal adenocarcinoma (PDAC) is the most common neoplastic disease of the pancreas, accounting for more than 90% of all pancreatic malignancies. As a highly lethal malignancy, PDAC is the fourth leading cause of cancer-related deaths worldwide with a 5-year overall survival of less than 8%. The efficacy and outcome of PDAC treatment largely depend on the stage of disease at the time of diagnosis. Surgical resection followed by adjuvant chemotherapy remains the only possibly curative therapy, yet 80%-90% of PDAC patients present with nonresectable PDAC stages at the time of clinical presentation. Despite our advancing knowledge of PDAC, the prognosis remains strikingly poor, which is primarily due to the difficulty of diagnosing PDAC at the early stages. Recent advances in glycoproteomics and glycomics based on mass spectrometry have shown that aberrations in protein glycosylation plays a critical role in carcinogenesis, tumor progression, metastasis, chemoresistance, and immuno-response of PDAC and other types of cancers. A growing interest has thus been placed upon protein glycosylation as a potential early detection biomarker for PDAC. We herein take stock of the advancements in the early detection of PDAC that were carried out with mass spectrometry, with special focus on protein glycosylation.

Tissue clearing and 3D reconstruction of digitized, serially sectioned slides provide novel insights into pancreatic cancer

Pancreatic cancer is currently the third leading cause of cancer death in the United States. The clinical hallmarks of this disease include abdominal pain that radiates to the back, the presence of a hypoenhancing intrapancreatic lesion on imaging, and widespread liver metastases. Technologies such as tissue clearing and three-dimensional (3D) reconstruction of digitized serially sectioned hematoxylin and eosin-stained slides can be used to visualize large (up to 2- to 3-centimeter cube) tissues at cellular resolution. When applied to human pancreatic cancers, these 3D visualization techniques have provided novel insights into the basis of a number of the clinical characteristics of this disease. Here, we describe the clinical features of pancreatic cancer, review techniques for clearing and the 3D reconstruction of digitized microscope slides, and provide examples that illustrate how 3D visualization of human pancreatic cancer at the microscopic level has revealed features not apparent in 2D microscopy and, in so doing, has closed the gap between bench and bedside. Compared with animal models and 2D microscopy, studies of human tissues in 3D can reveal the difference between what can happen and what does happen in human cancers.

Genetic medicine in companion diagnostics of germline BRCA testing of Japanese pancreatic cancer patients

In 2021, Japan's national health insurance made germline BRCA (g.BRCA) testing available to unresectable pancreatic cancer (PC) patients as a companion diagnostic (CD) of the PARP inhibitor. This study investigated the incidence of the g.BRCA variant (g.BRCAv.) and the status of the genetic medicine associated with its testing. A total of 110 PC patients underwent the testing, five of whom (4.5%) had a deleterious g.BRCA2v. (all truncations) but no g.BRCA1v. The turnaround time (TAT) to the doctors was 13 days, and to the patients, 17 days. A higher incidence of a BRCA-related family history and a shorter TAT were seen in the g.BRCAv. patients, but they were insignificant (p = 0.085 and p = 0.059, respectively). Genetic counseling was not performed for three g.BRCA2v. patients because two of them had no accessible relatives and one died of the cancer before the genetic report was completed. Two families underwent generic counseling and testing based on the patient's genetic data. g.BRCAv. is recognized in a small fraction of PC cases, and the following genetic counseling is done more for the relatives than for the patients. TAT was constant and did not affect much on the genetic counseling, but the earlier testing is expected for patients with a deadly cancer.

Research advances and treatment perspectives of pancreatic adenosquamous carcinoma

BACKGROUND

As a malignant tumor, pancreatic cancer has an extremely low overall 5-year survival rate. Pancreatic adenosquamous carcinoma (PASC), a rare pancreatic malignancy, owns clinical presentation similar to pancreatic ductal adenocarcinoma (PDAC), which is the most prevalent pancreatic cancer subtype. PASC is generally defined as a pancreatic tumor consisting mainly of adenocarcinoma tissue and squamous carcinoma tissue. Compared with PDAC, PASC has a higher metastatic potential and worse prognosis, and lacks of effective treatment options to date. However, the pathogenesis and treatment of PASC are not yet clear and are accompanied with difficulties.

CONCLUSION

The present paper systematically summarizes the possible pathogenesis, diagnosis methods, and further suggests potential new treatment directions through reviewing research results of PASC, including the clinical manifestations, pathological manifestation, the original hypothesis of squamous carcinoma and the potential regulatory mechanism. In short, the present paper provides a systematic review of the research progress and new ideas for the development mechanism and treatment of PASC.

Identification of individuals at high-risk for pancreatic cancer using a digital patient-input tool combining family cancer history screening and new-onset diabetes

Capturing family history might be a valuable tool for identification of individuals at increased risk of pancreatic cancer, which would allow enrollment into pancreatic surveillance programs. In addition, weight loss and concurrent new-onset diabetes may be utilized as an early marker for pancreatic cancer. This study evaluates the yield of combining family history and the Enriching New-Onset Diabetes for Pancreatic Cancer (ENDPAC) model to identify individuals who could benefit from pancreatic surveillance. A novel questionnaire and digital input tool was created that combined questions on family cancer history and criteria of the ENDPAC model. Individuals meeting ENDPAC criteria were enrolled directly in the high-risk pancreatic clinic. Individuals who met the criteria for a significant family history of cancer were offered referral to a genetic counselor. The questionnaire was completed by 453 patients. Of those, 25.8% (117/453) had significant familial risk factors. Eighteen individuals (15.4%) completed genetic testing previously, of whom five had a pathogenic variant. Thirty-four (29.9%) out of 117 individuals with a strong family history - flagged by the questionnaire - underwent genetic testing. Four (11.8%) of these patients harbored a pathogenic variant. Additionally, through cascade family testing, two siblings were found to carry pathogenic variants. Four (0.9%) of the 453 patients matched ENDPAC criteria. Two were diagnosed with pancreatic cancer and the others were enrolled in the surveillance program. In conclusion, identification of high-risk individuals for pancreatic cancer can be achieved by combining family history screening and the ENDPAC model to facilitate referral to genetic counseling and high-risk clinics.

Nutraceuticals as Supportive Therapeutic Agents in Diabetes and Pancreatic Ductal Adenocarcinoma: A Systematic Review

The correlation between pancreatic ductal adenocarcinoma (PDAC) and diabetes-related mechanisms support the hypothesis that early therapeutic strategies targeting diabetes can contribute to PDAC risk reduction and treatment improvement. A systematic review was conducted, using PubMed, Embase and Cochrane Library databases, to evaluate the current evidence from clinical studies qualitatively examining the efficacy of four natural products: Curcumin-Curcuma longa L.; Thymoquinone-Nigella sativa L.; Genistein-Glycine max L.; Ginkgo biloba L.; and a low-carbohydrate ketogenic diet in type 2 diabetes (T2D) and PDAC treatment. A total of 28 clinical studies were included, showing strong evidence of inter-study heterogeneity. Used as a monotherapy or in combination with chemo-radiotherapy, the studied substances did not significantly improve the treatment response of PDAC patients. However, pronounced therapeutic efficacy was confirmed in T2D. The natural products and low-carbohydrate ketogenic diet, combined with the standard drugs, have the potential to improve T2D treatment and thus potentially reduce the risk of cancer development and improve multiple biological parameters in PDAC patients.

Structured deep embedding model to generate composite clinical indices from electronic health records for early detection of pancreatic cancer

The high-dimensionality, complexity, and irregularity of electronic health records (EHR) data create significant challenges for both simplified and comprehensive health assessments, prohibiting an efficient extraction of actionable insights by clinicians. If we can provide human decision-makers with a simplified set of interpretable composite indices (i.e., combining information about groups of related measures into single representative values), it will facilitate effective clinical decision-making. In this study, we built a structured deep embedding model aimed at reducing the dimensionality of the input variables by grouping related measurements as determined by domain experts (e.g., clinicians). Our results suggest that composite indices representing liver function may consistently be the most important factor in the early detection of pancreatic cancer (PC). We propose our model as a basis for leveraging deep learning toward developing composite indices from EHR for predicting health outcomes, including but not limited to various cancers, with clinically meaningful interpretations.

Screening for pancreatic cancer has the potential to save lives, but is it practical?

INTRODUCTION

Most patients with pancreatic cancer present with advanced stage, incurable disease. However, patients with high-grade precancerous lesions and many patients with low-stage disease can be cured with surgery, suggesting that early detection has the potential to improve survival. While serum CA19.9 has been a long-standing biomarker used for pancreatic cancer disease monitoring, its low sensitivity and poor specificity have driven investigators to hunt for better diagnostic markers.

AREAS COVERED

This review will cover recent advances in genetics, proteomics, imaging, and artificial intelligence, which offer opportunities for the early detection of curable pancreatic neoplasms.

EXPERT OPINION

From exosomes, to circulating tumor DNA, to subtle changes on imaging, we know much more now about the biology and clinical manifestations of early pancreatic neoplasia than we did just five years ago. The overriding challenge, however, remains the development of a practical approach to screen for a relatively rare, but deadly, disease that is often treated with complex surgery. It is our hope that future advances will bring us closer to an effective and financially sound approach for the early detection of pancreatic cancer and its precursors.

Association between age at diabetes onset or diabetes duration and subsequent risk of pancreatic cancer: Results from a longitudinal cohort and mendelian randomization study

BACKGROUND

The aim of the study is to estimate the incidence of pancreatic cancer among individuals with new-onset type 2 Diabetes (T2DM) and evaluate the relationship of pancreatic cancer risk with age at diabetes onset and diabetes duration.

METHODS

This longitudinal cohort study included 428,362 new-onset T2DM patients in Shanghai and Mendelian randomization (MR) in the east-Asian population were used to investigate the association. Incidence rates of pancreatic cancer in all patients and by subgroups were calculated and compared to the general population.

FINDINGS

A total of 1056 incident pancreatic cancer cases were identified during eight consecutive years of follow-up. The overall pancreatic cancer annual incidence rate was 55·28/100,000 person years in T2DM patients, higher than that in the general population, with a standardized incidence ratio (SIR) of 1·54 (95% confidence interval [CI], 1·45-1·64). The incidence of pancreatic cancer increased with age and a significantly higher incidence was observed in the older groups with T2DM. However, the relative pancreatic cancer risk was inversely related to age of T2DM onset, and a higher SIR of 5·73 (95%CI, 4·49-7·22) was observed in the 20-54 years old group. The risk of pancreatic cancer was elevated at any diabetes duration. Fasting blood glucose ≥10·0 mmol/L was associated with increased risk of pancreatic cancer. MR analysis indicated a positive association between T2DM and pancreatic cancer risk.

INTERPRETATION

Efforts toward early and close follow-up programs, especially in individuals with young-onset T2DM, and the improvement of glucose control might represent effective strategies for improving the detection and results of treatment of pancreatic cancer.

FUNDING

Chinese National Natural Science Foundation.

Difference of risk of pancreatic cancer in new-onset diabetes and long-standing diabetes: population-based cohort study

CONTEXT

Considering the absence of methods to find pancreatic cancer early, surveillance of high-risk groups is needed for early diagnosis.

OBJECTIVE

The study aimed to investigate the effect in the incidence of pancreatic cancer and the differences between new-onset DM (NODM) and long-standing DM (LSDM) since NODM group is a representative high-risk group.

METHODS

The Korean National Health Insurance Service-National Sample Cohort between 2002 and 2013 data was used. Regarding 88,396 people with DM (case group), we conducted a 1:1 propensity score matching to select a matched non-DM population (control group). To investigate the interaction between DM and the time variable distinguishing NODM and LSDM, we performed a multi-variable time-dependent Cox regression analysis.

RESULTS

The incidence of pancreatic cancer was higher in the DM group compared to the non-DM group (0.52% vs. 0.16%, P < 0.001). The DM group had shown different risk of pancreatic cancer development according to the duration since the DM diagnosis (NODM hazard ratio (HR): 3.81, 95% confidence interval (CI): 2.97-4.88, P < 0.001; LSDM HR: 1.53, 95% CI: 1.11-2.11, P < 0.001). When the NODM and the LSDM groups were compared, the risk of pancreatic cancer was higher in the NODM group than LSDM group (HR: 1.55, P = 0.020). In subgroup analysis, NODM group showed that men (HR = 4.42 95% CI: 3.15-6.19, P < 0.001) and patients who were in their 50 s (HR = 7.54, 95% CI: 3.24-17.56, P < 0.001) were at a higher risk of developing pancreatic cancer than matched same sex or age control group (non-DM population), respectively.

CONCLUSION

The risk of pancreatic cancer was greater in people with DM than non-DM population. Among people with DM, NODM showed a higher risk of pancreatic cancer than long standing DM.

The association between diabetes mellitus of different durations and risk of pancreatic cancer: an Australian national data-linkage study in women

AIMS

The bidirectional association between diabetes mellitus (DM) and pancreatic cancer (PC) is established; however, the strength of association between duration of DM and risk of PC needs further investigation.

METHODS

We conducted a case-control study nested within a population-based cohort of Australian women established using record linkage. Women diagnosed with PC from July 2007 to December 2013, were matched to five controls based on age and state of residence. DM was defined according to prescription of anti-diabetic medication from administrative prescription data. We used conditional logistic regression to calculate odds ratios (OR) and 95% confidence intervals (CI), adjusted for area-level socioeconomic status, rurality of residence, weighted comorbidity score, and predicted probability of obesity.

RESULTS

The analyses included 7,267 cases and 35,978 controls. The mean age at the time of DM diagnosis was 71 years whereas the mean age at the time of diagnosis of PC was 76 years. A history of DM of any duration was associated with a 2-fold increase in risk of PC (OR=2.12; 95%CI:1.96-2.29) compared to having no history of DM. The risk decreased with increasing duration of DM. The highest risk was in those who had recent-onset DM (OR=8.08; 95%CI:6.88-9.50 for <12 months of DM), but the risk remained elevated with ≥5 years of DM (OR=1.40; 95%CI:1.27-1.55).

CONCLUSION

The markedly increased risk of PC in those with recent-onset DM emphasises the need for further research to distinguish patients for whom new-onset DM is a manifestation of PC from those with type-2 DM. The elevated risk associated with long-standing DM suggests that preventing DM may contribute to a reduction in the incidence of PC.

Diets, Dietary Patterns, Single Foods and Pancreatic Cancer Risk: An Umbrella Review of Meta-Analyses

Pancreatic cancer (PC) represents the third leading cause of cancer death in 2020. Despite the fact that, in 2018, the World Cancer Research Fund report concluded that there is still a lack of evidence on the role of foods or diets and risk for PC, a flourishing body of evidence has been published and needs to be analyzed. For this reason, we conducted an umbrella review on the association between different dietary patterns/food components and PC. Data sources PubMed/MEDLINE, Scopus, Web of Science, EMBASE, and the Cochrane Collaboration were searched. The Joanna Briggs Institute Umbrella Review Methodology was used. The protocol was registered in PROSPERO. A total of 23 articles were included, covering a wide range of dietary patterns/food components: healthy/prudent dietary patterns (n = 4), Mediterranean diets (MedDiet) (n = 1), plant-based diets (n = 2), the Dietary Inflammatory Index (DII) (n = 2), western diets (n = 2), and, lastly, unhealthy diets (n = 2). Regarding dietary components, the following were assessed: total fruit (n = 2), citrus fruit (n = 1), total vegetables (n = 2), cruciferous vegetables (n = 1), red meat (n = 6), processed meat (n = 4), poultry (n = 2), eggs (n = 1), fish (n = 5), whole grain (n = 2), potato (n = 1), and nuts (n = 2). The methodological quality of the included meta-analyses was generally low or critically low. Although the strength of evidence was generally weak, convincing or suggestive evidence was found for a healthy/prudent, plant-based diet, fruit and vegetables, and lower risk of PC, whereas a high intake of red meat was associated with a higher risk of PC at a convincing level of evidence. Further studies are needed to confirm the role of the other dietary patterns/food components and the risk of PC.

Risk prediction of pancreatic cancer using AI analysis of pancreatic subregions in computed tomography images

Early detection of Pancreatic Ductal Adenocarcinoma (PDAC) is complicated as PDAC remains asymptomatic until cancer advances to late stages when treatment is mostly ineffective. Stratifying the risk of developing PDAC can improve early detection as subsequent screening of high-risk individuals through specialized surveillance systems reduces the chance of misdiagnosis at the initial stage of cancer. Risk stratification is however challenging as PDAC lacks specific predictive biomarkers. Studies reported that the pancreas undergoes local morphological changes in response to underlying biological evolution associated with PDAC development. Accurate identification of these changes can help stratify the risk of PDAC. In this retrospective study, an extensive radiomic analysis of the precancerous pancreatic subregions was performed using abdominal Computed Tomography (CT) scans. The analysis was performed using 324 pancreatic subregions identified in 108 contrast-enhanced abdominal CT scans with equal proportion from healthy control, pre-diagnostic, and diagnostic groups. In a pairwise feature analysis, several textural features were found potentially predictive of PDAC. A machine learning classifier was then trained to perform risk prediction of PDAC by automatically classifying the CT scans into healthy control (low-risk) and pre-diagnostic (high-risk) classes and specifying the subregion(s) likely to develop a tumor. The proposed model was trained on CT scans from multiple phases. Whereas using 42 CT scans from the venous phase, model validation was performed which resulted in ~89.3% classification accuracy on average, with sensitivity and specificity reaching 86% and 93%, respectively, for predicting the development of PDAC (i.e., high-risk). To our knowledge, this is the first model that unveiled microlevel precancerous changes across pancreatic subregions and quantified the risk of developing PDAC. The model demonstrated improved prediction by 3.3% in comparison to the state-of-the-art method that considers the global (whole pancreas) features for PDAC prediction.