Pancreatic ductal adenocarcinoma (PDAC) is frequently diagnosed in its late stages when treatment options are limited. Unlike other common cancers, there are no population-wide screening programmes for PDAC. Thus, early disease detection, although urgently needed, remains elusive. Individuals in certain high-risk groups are, however, offered screening or surveillance. Here we explore advances in understanding high-risk groups for PDAC and efforts to implement biomarker-driven detection of PDAC in these groups. We review current approaches to early detection biomarker development and the use of artificial intelligence as applied to electronic health records (EHRs) and social media. Finally, we address the cost-effectiveness of applying biomarker strategies for early detection of PDAC.

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy with limited early diagnostic methods and therapeutic options, contributing to its poor prognosis. Recent advances in high-throughput sequencing have highlighted the critical roles of noncoding RNAs (ncRNAs), particularly PIWI-interacting RNAs (piRNAs), in cancer biology. In this review, we systematically summarize the emerging roles of piRNAs and their associated PIWI proteins in PDAC pathogenesis, progression, and prognosis. We provide a comprehensive analysis of the molecular mechanisms by which piRNAs/PIWIs regulate gene expression and cellular signaling pathways in PDAC. Furthermore, we discuss their potential as novel biomarkers for early diagnosis and therapeutic targets. Importantly, this review identifies key piRNAs/PIWIs involved in PDAC and proposes innovative strategies for improving diagnosis and treatment outcomes. Our work not only consolidates current knowledge but also offers new perspectives for future research and clinical applications in PDAC management.

In previous studies, methylated DNA markers (MDMs) have been identified in pancreatic juice (PJ) for detecting pancreatic ductal adenocarcinoma (PDAC). In this prospective multicenter study, the sensitivity and specificity characteristics of this panel of PJ-MDMs was evaluated standalone and in combination with plasma carbohydrate antigen 19-9 (CA 19-9).

Paired PJ and plasma were assayed from 88 biopsy-proven treatment-naïve PDAC cases and 134 controls (53 with normal pancreas, 23 with chronic pancreatitis [CP], 58 with intraductal papillary mucinous neoplasm). Bisulfite-converted DNA from buffered PJ was analyzed using long-probe quantitative amplified signal assay targeting 14 MDMs (NDRG4, BMP3, TBX15, C13orf18, PRKCB, CLEC11A, CD1D, ELMO1, IGF2BP1, RYR2, ADCY1, FER1L4, EMX1, and LRRC4) and a reference gene (methylated B3GALT6). Logistic regression was used to fit the previously identified 3-MDM PJ panel (FER1L4, C13orf18, and BMP3). Discrimination accuracy was summarized using area under the receiver-operating characteristic curve (AUROC) with corresponding 95% confidence interval (CI).

Methylated FER1L4 had the highest individual AUROC of 0.83 (95% CI, 0.78-0.89). The AUROC for the 3-MDM PJ + plasma CA 19-9 model (0.95; 95% CI, 0.92-0.98) was higher than both the 3-MDM PJ panel (0.87; 95% CI, 0.82-0.92)) and plasma CA 19-9 alone (0.91; 95% CI, 0.87-0.96) (P = .0002 and .0135, respectively). At a specificity of 88% (95% CI, 81%-93%), the sensitivity of this model was 89% (95% CI, 80%-94%) for all PDAC stages and 83% (95% CI, 64%-94%) for stage I/II PDAC.

A panel combining PJ-MDMs and plasma CA 19-9 discriminates PDAC from both healthy and disease control groups with high accuracy. This provides support for combining PJ and blood-based biomarkers for enhancing diagnostic sensitivity and successful early PDAC detection.

Classification and risk stratification of pancreatic cysts are challenging because of limited radiographic and cytomorphologic features. Although molecular profiling has emerged as an ancillary test for pancreatic cyst fluid (PCF), additional high-sensitivity and -specificity biomarkers are still needed for improved classification.

In this study, PCF from 93 patients, including intraductal papillary mucinous neoplasms (n = 65), mucinous cystic neoplasms (n = 9), serous cystadenomas (n = 9), pancreatic cyst not otherwise specified (n = 8), and pseudocysts (n = 2), were evaluated for biomarkers. Molecular profiling by next-generation sequencing was performed, and a subset of the cases (n = 32) were interrogated with 2083 microRNAs (miRNAs) to evaluate their use for pancreatic cyst risk stratification.

As independent PCF biomarkers in 32 cases with histologic diagnoses, three miRNAs performed significantly better than mutant KRAS, mutant GNAS, carcinoembryonic antigen (CEA), and serum carbohydrate antigen 19-9 (CA19-9) in discriminating high-risk from low-risk cysts. The three elevated miRNAs in combination with mutant KRAS, mutant GNAS, and serum CA19-9 displayed similar diagnostic performance (miR-4461: area under the curve [AUC], 0.950; 95% confidence interval [CI], 0.800-1; miR-6723-5p: AUC, 0.958; 95% CI, 0.850-1; miR-6755-3p: AUC, 0.942; 95% CI, 0.816-1) in discriminating high-risk from low-risk cysts, when compared to mutant KRAS, mutant GNAS, CEA, and serum CA19-9 (AUC, 0.950; 95% CI, 0.825-1). In the absence of CA19-9, the three-marker panel of KRAS, GNAS, and miRNAs showed marginally improved performance compared with KRAS, GNAS, and CEA, which highlights the potential utility of miRNAs as biomarkers in PCF analysis.

These findings demonstrate that a multiomics biomarker approach with elevated PCF miRNAs with mutant KRAS, mutant GNAS, and serum CA19-9 may help in better detecting high-risk cysts for early clinical intervention.

Pancreatic cancer ranks among the most lethal digestive malignancies, exhibiting a steadily increasing incidence and mortality worldwide. Despite significant advances in cancer research, the 5-year survival rate remains below 10%, predominantly due to delayed diagnosis and limited therapeutic options. Concurrently, the gut microbiota-an integral component of host physiology-has emerged as a crucial player in the pathogenesis of pancreatic cancer. Mounting evidence indicates that alterations in gut microbial composition and function may influence tumor initiation, progression, and response to therapy. This review provides an in-depth examination of the intricate interplay between the gut microbiome and pancreatic cancer, highlighting potential diagnostic biomarkers and exploring microbiome-targeted therapeutic strategies to improve patient outcomes.

Pancreatic ductal adenocarcinoma (PDAC) tumor heterogeneity impedes the development of biomarker assays for early disease detection. We hypothesized that PDAC cell subpopulations could be identified by aberrant glycan signatures in both tumor tissue and blood samples. We used multiplexed glycan immunofluorescence to distinguish between PDAC and noncancer cell subpopulations within tumor tissue, and we developed hybrid glycan sandwich assays to determine whether the aberrant glycan signatures could be detected in blood samples. We found that PDAC cells were identified by signatures of glycans detected by four glycan-binding proteins (VVL, CA19-9, sTRA, and GM2) and that there are three types of glycan-defined PDAC tumors: sTRA type, CA19-9 type, and intermixed. In patient-matched tumor and blood samples, the PDAC tumor type could be determined by the aberrant glycans in the blood. As a result, the combined assays of aberrant glycan signatures were more sensitive and specific than any individual assay. Our results demonstrate a methodology to detect and stratify PDAC.

In the current study, we assessed whether repeated measurements of a panel of protein biomarkers with relevance to pancreatic ductal adenocarcinoma (PDAC) improves lead time performance for earlier detection over a single timepoint measurement. Specifically, CA125, CEA, LRG1, REG3A, THBS2, TIMP1, TNRFSF1A as well as CA19-9 were assayed in serially collected pre-diagnostic plasma from 242 PDAC cases and 242 age- and sex-matched non-case control participants in the PLCO cohort. We compared performance estimates of a parametric empirical Bayes (PEB) algorithm, which incorporates participant biomarker history, to that of a single-threshold (ST) method. We demonstrated improvements in AUC estimates (2-13 %) for all biomarkers when considering the PEB approach compared to ST. For CA19-9, the PEBCA19-9 yielded an AUC of 0.88 when at least one repeat measurement was within 3 years of clinical diagnosis. At a specificity of 98.5 %, the PEBCA19-9 identified 15 of the 41 PDAC cases and signaled positive at an average lead-time of 1.09 years whereas the ST approach captured 11 of the 41 PDAC cases with an average positive signal at 0.48 years. Among CA19-9 low individuals, a PEB algorithm based on repeat measurements of TIMP1 yielded an additional 14 % sensitivity at 98.5 % specificity. An adaptive algorithm that considers repeated CA19-9 measurements improves sensitivity and lead-time detection of PDAC compared to a single-threshold method. Additional protein biomarkers may improve sensitivity for earlier detection of PDAC among cases with low CA19-9.

Camptothecin (CPT), an effective topoisomerase I inhibitor used in colorectal cancer chemotherapy, often faces limitations due to severe toxicities. Addressing this, we developed OCENM, a nanomedicine featuring the CPT-ET conjugate─comprising a cathepsin B-sensitive linker and CPT linked to the ET peptide targeting leucine-rich alpha-2-glycoprotein 1 (LRG1) and encapsulating Olaparib, a potent poly ADP-ribose polymerase (PARP) inhibitor. OCENM aims for precise CPT delivery to colorectal cancer sites overexpressing LRG1, while Olaparib disrupts compromised DNA repair pathways, enhancing DNA damage and promoting increased tumor cell apoptosis. Our results show OCENM accumulates preferentially in colorectal cancer models through LRG1 targeting and triggers synergistic tumor cell apoptosis through the dual action of enhanced DNA damage and inhibited repair mechanisms. This study not only highlights the potential of LRG1 as a strategic target for nanomedicine delivery but also underscores the therapeutic promise of combining CPT with Olaparib for colorectal cancer treatment.

Currently, those recommended to undergo pancreatic cancer (PC) surveillance include appropriately aged individuals at high risk of PC due to an identifiable genetic susceptibility or those without identifiable genetic susceptibility who nonetheless have a strong family history of PC. With increases in identification of individuals at high risk for PC and increased use of PC surveillance in clinical practice, there has been increasing debate about who should undergo surveillance as well as how surveillance should be performed including use of imaging and blood-based testing. Furthermore, there is increasing interest in the outcomes of PC surveillance in high-risk individuals with some studies demonstrating that surveillance leads to downstaging of PC and improvements in survival. In this review, we summarize the current state of PC surveillance in high-risk individuals, providing an overview of the risk factors associated with PC, selection of high-risk individuals for PC surveillance, and the current, but non-uniform, recommendations for performing PC surveillance. Additionally, we review approaches to apply various imaging and blood-based tests to surveillance and the outcomes of PC surveillance.

Cholangiocarcinoma (CHOL) is the second most common primary liver malignancy, characterized by high aggressiveness and heterogeneity. It is typically diagnosed at an advanced stage, leading to a poor prognosis. Although Peptidyl Proline Isomerase H (PPIH) has been implicated in various tumors, its role in CHOL remains unexplored. This study aims to investigate the diagnostic value and potential function of PPIH in CHOL.

We analyzed the expression levels, prognostic significance, and diagnostic efficiency of PPIH in CHOL using data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, coupled with gene enrichment analyses. The CIBERSORT database was employed to assess the correlation between PPIH expression and immune cell infiltration in CHOL. Additionally, immunohistochemical experiments were conducted to validate PPIH expression levels in CHOL tissues and to explore its correlation with TP53 gene mutations.

Our findings indicate that overexpression of PPIH mRNA in CHOL is associated with poor prognosis, with increased PPIH protein levels observed in CHOL tissues. Furthermore, PPIH expression showed a positive correlation with TP53 mutations. PPIH demonstrated strong diagnostic value for CHOL. Moreover, PPIH may influence tumor progression through its involvement in cell cycle regulation and spliceosome pathways, and is associated with immune cell infiltration levels.

The results of this study suggest that PPIH is a potential novel biomarker with significant diagnostic value for patients with CHOL. PPIH may also play a role in modulating the immune microenvironment, contributing to poor prognosis.

Carbohydrate antigen 19-9 is a well-known malignancy biomarker, and its sensitive detection is particularly crucial in the diagnosis and assessment of pancreatic cancer. In this study, an ultrasensitive CA19-9 immunosensor was constructed using the Zn2+-regulated CdSySe1-y (Zn-CdSySe1-y) nanospheres (NSs) as the electrochemiluminescence (ECL) emitter and FeCoS2 nano octahedrons (NOs) as a coreactant enhancer. The microstructure of ternary transition metal chalcogenide CdSySe1-y was precisely tuned by Zn2+ doping to avoid aggregation and thus enable stable and efficient cathodic ECL responses. The bimetallic sulfide FeCoS2 was synthesized using a metal organic framework (MOF) as the template by ion permeation. It was able to catalyze the coreactant efficiently due to the synergistic effect of the Fe2+ and Co2+. The immunosensor exhibited low detection limit (7.6 × 10-5 U mL-1) in the wide linear range of 0.0001-100 U mL-1, offering a sensitive CA19-9 detection method.

Aberrant glycosylation is one of the hallmarks of cancer. The profile of glycoprotein expression caused by abnormal glycosylation has been revealed, while abnormal glycogenes that may disturb the structure of glycans have not yet been identified in esophageal squamous cell carcinoma (ESCC).

Genomic alterations driven by differentially expressed glycogenes in ESCC were compared with matched normal tissues by multi-omics analysis. Immunohistochemistry, MTT, colony formation, transwell assays, subcutaneous tumor formation experiments and tail vein injection were used to study the expression and the effect on the proliferation and metastasis of the differentially expressed glycogenes POFUT1 and RPN1 in ESCC. In the alkyne fucose labeling experiment, AAL lectin affinity chromatography and immunoprecipitation were used to explore the mechanism of POFUT1 in ESCC.

The expression of the POFUT1 and RPN1 glycogenes were upregulated, as determined by genomic copy number gain and proteomics analysis. The overexpression of POFUT1 or RPN1 was associated with poor prognosis in ESCC patients and affected the proliferation and metastasis of ESCC in vivo and in vitro. The overexpression of POFUT1 increased the overall fucosylation level and activated the Notch signaling pathway, which partially mediated POFUT1 induced pro-migration in ESCC. The regulation of malignant progression of ESCC by RPN1 may be related to the TNF signaling pathway, p53 signaling pathway, etc. CONCLUSIONS: Our study fills a gap in the study of abnormal glycogenes and highlights the potential role of the POFUT1/Notch axis in ESCC. Moreover, our study identifies POFUT1 and RPN1 as promising anticancer targets in ESCC.

Pancreatic cancer is an increasing cause of cancer-related mortality, with persistently low survival rates. We investigated the clinical diagnostic value of the combination of preoperative serum carbohydrate antigen 19-9 (CA19-9), albumin-bilirubin (ALBI) score, and 18 F-fluoro-2-deoxy- d -glucose PET integrated with computed tomography ( 18 F-FDG PET/CT) imaging in pancreatic cancer preoperative resectability.

This study included 143 pancreatic cancer patients, including 68 preoperative resectable and 75 preoperative unresectable pancreatic cancer patients. Meanwhile, 67 patients with non-pancreatic cancer were included as the control group. The clinical data were collected. Serum CA19-9 level was measured by ELISA. The levels of total bilirubin and albumin were determined using a biochemical analyzer, with the ALBI score calculated. All patients underwent 18 F-FDG PET/CT imaging. The consistency of the diagnosis was evaluated by the Kappa test. Logistic univariate and multivariate regression analyses were performed. The diagnostic efficacy of these parameters was evaluated using receiver operating characteristic (ROC) curves, and the optimal ROC curve thresholds were obtained using the Youden index.

The preoperative serum CA19-9 and ALBI score of patients with preoperative resectable pancreatic cancer were increased, which helped diagnose preoperative resectable pancreatic cancer. 18 F-FDG PET/CT imaging had diagnostic value for preoperative resectable pancreatic cancer. Preoperative serum CA19-9, ALBI score, and 18 F-FDG PET/CT imaging were independent influencing factors for pancreatic cancer preoperative resectability, and their combination had higher diagnostic value for preoperative resectable pancreatic cancer than any single of these indexes.

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

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal disease that is challenging to detect at an early stage. Biomarkers are needed that can detect PDAC early in the course of disease when interventions lead to the best outcomes. We highlight study design and statistical considerations that inform pancreatic cancer early detection biomarker evaluation.

We describe experimental design strategies in this setting useful for streamlining biomarker evaluation at each Early Detection Research Network (EDRN) phase of biomarker development. We break the early EDRN phases into sub-phases, proposing objectives, study design strategies, and biomarker performance benchmarks.

The goal of early detection in populations at high-risk of PDAC is described. Evaluating biomarker behavior in patients under surveillance without disease can winnow candidate biomarkers. Potential resources for biomarker validation studies are described.

Multisite and multidisciplinary collaboration can facilitate study design strategies in this lethal but low incidence disease and streamline the path from biomarker discovery to clinical use. Improvements in analytical and experimental design methods could help accelerate biomarker evaluation through the phases of biomarker development.

Neuroendocrine neoplasms (NENs) arise from neuroendocrine cells in a wide variety of organs. One of the most affected disease sites is the gastrointestinal system, which originates the gastro-entero-pancreatic NENs (GEP-NENs), a heterogenous group of malignancies that are rapidly increasing in incidence. These tumors can be functioning, with secretory activity leading to identifiable clinical syndromes, or non-functioning, with no secretory activity but with local symptoms of tumor growth and metastasis. A limitation in biomarkers is a crucial unmet need in non-secretory NEN management, as clinical decision-making is made more difficult by obstacles in tumor classification, prognostic evaluation, assessment of treatment response and surveillance. The objective of this review is to present existing and novel biomarkers for NENs that can function as prognostic factors and monitor disease progression or regression longitudinally, with a special emphasis on innovative research into novel multianalyte biomarkers.

A blood test that enables surveillance for early-stage pancreatic ductal adenocarcinoma (PDAC) is an urgent need. Independent laboratories have reported PDAC biomarkers that could improve biomarker performance over CA19-9 alone, but the performance of the previously reported biomarkers in combination is not known. Therefore, we conducted a coordinated case/control study across multiple laboratories using common sets of blinded training and validation samples (132 and 295 plasma samples, respectively) from PDAC patients and non-PDAC control subjects representing conditions under which surveillance occurs. We analyzed the training set to identify candidate biomarker combination panels using biomarkers across laboratories, and we applied the fixed panels to the validation set. The panels identified in the training set, CA19-9 with CA199.STRA, LRG1, TIMP-1, TGM2, THSP2, ANG, and MUC16.STRA, achieved consistent performance in the validation set. The panel of CA19-9 with the glycan biomarker CA199.STRA improved sensitivity from 0.44 with 0.98 specificity for CA19-9 alone to 0.71 with 0.98 specificity (p < 0.001, 1000-fold bootstrap). Similarly, CA19-9 combined with the protein biomarker LRG1 and CA199.STRA improved specificity from 0.16 with 0.94 sensitivity for CA19-9 to 0.65 with 0.89 sensitivity (p < 0.001, 1000-fold bootstrap). We further validated significantly improved performance using biomarker panels that did not include CA19-9. This study establishes the effectiveness of a coordinated study of previously discovered biomarkers and identified panels of those biomarkers that significantly increased the sensitivity and specificity of early-stage PDAC detection in a rigorous validation trial.

Pancreatic ductal adenocarcinoma (PDAC) continues to remain one of the leading causes of cancer-related death. Unlike other malignancies where universal screening is recommended, the same cannot be said for PDAC. The purpose of this study is to review which patients are at high risk of developing PDAC and therefore candidates for screening, methods/frequency of screening, and risk for these groups of patients.

Using the prostate, lung, colorectal, and ovarian (PLCO) Cancer Screening Trial samples, we identified cell-free DNA (cfDNA) candidate biomarkers bearing the epigenetic mark 5-hydroxymethylcytosine (5hmC) that detected occult colorectal cancer (CRC) up to 36 months before clinical diagnosis.

We performed the 5hmC-seal assay and sequencing on ≤8 ng cfDNA extracted from PLCO study participant plasma samples, including n = 201 cases (diagnosed with CRC within 36 months of blood collection) and n = 401 controls (no cancer diagnosis on follow-up). We conducted association studies and machine learning modeling to analyze the genome-wide 5hmC profiles within training and validation groups that were randomly selected at a 2:1 ratio.

We successfully obtained 5hmC profiles from these decades-old samples. A weighted Cox model of 32 5hmC-modified gene bodies showed a predictive detection value for CRC as early as 36 months before overt tumor diagnosis (training set AUC, 77.1% [95% CI, 72.2 to 81.9] and validation set AUC, 72.8% [95% CI, 65.8 to 79.7]). Notably, the 5hmC-based predictive model showed comparable performance regardless of sex and race/ethnicity, and significantly outperformed risk factors such as age and obesity (assessed as BMI). Finally, when splitting cases at median weighted prediction scores, Kaplan-Meier analyses showed significant risk stratification for CRC occurrence in both the training set (hazard ratio, [HR], 3.3 [95% CI, 2.6 to 5.8]) and validation set (HR, 3.1 [95% CI, 1.8 to 5.8]).

Candidate 5hmC biomarkers and a scoring algorithm have the potential to predict CRC occurrence despite the absence of clinical symptoms and effective predictors. Developing a minimally invasive clinical assay that detects 5hmC-modified biomarkers holds promise for improving early CRC detection and ultimately patient outcomes.

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.

Pancreatic ductal adenocarcinoma (PDAC) tumor heterogeneity impedes the development of biomarker assays suitable for early disease detection that would improve patient outcomes. The CA19-9 glycan is currently used as a standalone biomarker for PDAC. Furthermore, previous studies have shown that cancer cells may display aberrant membrane-associated glycans. We therefore hypothesized that PDAC cancer cell subpopulations could be distinguished by aberrant glycan signatures. We used multiplexed glycan immunofluorescence combined with pathologist annotation and automated image processing to distinguish between PDAC cancer cell subpopulations within tumor tissue. Using a training-set/test-set approach, we found that PDAC cancer cells may be identified by signatures comprising 4 aberrant glycans (VVL, CA19-9, sTRA, and GM2) and that there are three glycan-defined PDAC tumor types: sTRA type, CA19-9 type, and intermixed. To determine whether the aberrant glycan signatures could be detected in blood samples, we developed hybrid glycan sandwich assays for membrane-associated glycans. In both patient-matched tumor and blood samples, the proportion of aberrant glycans detected was consistent. Furthermore, our multiplexed glycan immunofluorescent approach proved to be more sensitive and more specific than CA19-9 alone. Our results provide proof of concept for a novel methodology to improve early PDAC detection and patient outcomes.

Early detection is critical for improving pancreatic cancer prognosis. Our study aims to identify circulating microRNAs (miRNAs) associated with pancreatic cancer risk. The two-stage study used plasma samples collected ≤5 years prior to cancer diagnosis, from case-control studies nested in five prospective cohort studies. The discovery stage included 185 case-control pairs from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. Replication stage samples comprised 277 pairs from Shanghai Women's Health Study/Shanghai Men's Health Study, Southern Community Cohort Study, and Multiethnic Cohort Study. Seven hundred and ninety-eight miRNAs were measured using the NanoString nCounter Analysis System. Odds ratios (OR) and 95% confidence intervals (CI) for per 10% change in miRNAs in association with pancreatic cancer risk were derived from conditional logistic regression analysis in discovery and replication studies, separately, and then meta-analyzed. Stratified analysis was conducted by age at diagnosis (<65/≥65 years) and time interval between sample collection and diagnosis (≤2/>2 years). In the discovery stage, 120 risk associated miRNAs were identified at p < .05. Three were validated in the replication stage: hsa-miR-199a-3p/hsa-miR-199b-3p, hsa-miR-767-5p, and hsa-miR-191-5p, with respective ORs (95% CI) being 0.89 (0.84-0.95), 1.08 (1.02-1.13), and 0.90 (0.85-0.95). Five additional miRNAs, hsa-miR-640, hsa-miR-874-5p, hsa-miR-1299, hsa-miR-22-3p, and hsa-miR-449b-5p, were validated among patients diagnosed at ≥65 years, with OR (95% CI) of 1.23 (1.09-1.39), 1.33 (1.16-1.52), 1.25 (1.09-1.43), 1.28 (1.12-1.46), 0.76 (0.65-0.89), and 1.22 (1.07-1.39), respectively. The miRNA targets were enriched in pancreatic carcinogenesis/progression-related pathways. Our study suggests that circulating miRNAs may identify individuals at high risk for pancreatic cancer ≤5 years prior to diagnosis, indicating its potential utility in cancer screening and surveillance.

Pancreatic ductal adenocarcinoma (PDAC) is a deadly disease with increasing incidence. The majority of PDACs are incurable at presentation, but population-based screening is not recommended. Surveillance of high-risk individuals for PDAC may lead to early detection, but the survival benefit is unproven.

To compare the survival of patients with surveillance-detected PDAC with US national data.

This comparative cohort study was conducted in multiple US academic medical centers participating in the Cancer of the Pancreas Screening program, which screens high-risk individuals with a familial or genetic predisposition for PDAC. The comparison cohort comprised patients with PDAC matched for age, sex, and year of diagnosis from the Surveillance, Epidemiology, and End Results (SEER) program. The Cancer of the Pancreas Screening program originated in 1998, and data collection was done through 2021. The data analysis was performed from April 29, 2022, through April 10, 2023.

Endoscopic ultrasonography or magnetic resonance imaging performed annually and standard-of-care surgical and/or oncologic treatment.

Stage of PDAC at diagnosis, overall survival (OS), and PDAC mortality were compared using descriptive statistics and conditional logistic regression, Cox proportional hazards regression, and competing risk regression models. Sensitivity analyses and adjustment for lead-time bias were also conducted.

A total of 26 high-risk individuals (mean [SD] age at diagnosis, 65.8 [9.5] years; 15 female [57.7%]) with PDAC were compared with 1504 SEER control patients with PDAC (mean [SD] age at diagnosis, 66.8 [7.9] years; 771 female [51.3%]). The median primary tumor diameter of the 26 high-risk individuals was smaller than in the control patients (2.5 [range, 0.6-5.0] vs 3.6 [range, 0.2-8.0] cm, respectively; P < .001). The high-risk individuals were more likely to be diagnosed with a lower stage (stage I, 10 [38.5%]; stage II, 8 [30.8%]) than matched control patients (stage I, 155 [10.3%]; stage II, 377 [25.1%]; P < .001). The PDAC mortality rate at 5 years was lower for high-risk individuals than control patients (43% vs 86%; hazard ratio, 3.58; 95% CI, 2.01-6.39; P < .001), and high-risk individuals lived longer than matched control patients (median OS, 61.7 [range, 1.9-147.3] vs 8.0 [range, 1.0-131.0] months; 5-year OS rate, 50% [95% CI, 32%-80%] vs 9% [95% CI, 7%-11%]).

These findings suggest that surveillance of high-risk individuals may lead to detection of smaller, lower-stage PDACs and improved survival.

The aim of the present study was to evaluate the diagnostic value of plasma human cystatin-S (CST4) in patients with digestive system malignant tumors. CST4 and tumor markers, such as α-fetoprotein (AFP), carcinoembryonic antigen (CEA), carbohydrate antigen (CA)199, CA125, CA153 and CA724, were detected in blood samples from 100 patients with a digestive system malignant tumor and 100 patients with benign digestive system diseases. The tumor markers AFP, CEA, CA199, CA125, CA153 and CA724 were detected using an electrochemiluminescence immunoassay, and CST4 levels were detected using a human CST4 ELISA kit. The results demonstrated that the sensitivities of AFP and CA153 (both 5.00%) were significantly lower than that of CST4 (38.00%) in the diagnosis of digestive system malignancy (P<0.001), and CA724 (18.00%) was also less sensitive than CST4 (P<0.05). The sensitivities of CA199 (26.00%), CEA (31.00%) and CA125 (25.00%) were similar to that of CST4 (P>0.05). There was no significant difference in the CEA, CA125, CA724 and CST4 specificities (P>0.05), which were 91.00, 95.00, 94.00 and 83.00%, respectively. The specificities of AFP (99.00%), CA199 (98.00%) and CA153 (100.00%) were significantly higher than that of CST4 (P<0.01). By constructing a receiver operating characteristic curve and comparing the area under the curve as well as sensitivity, the findings of the present study demonstrated that combining CST4 with AFP, CEA, CA199, CA125, CA153 and CA724 can significantly enhance the diagnostic sensitivity for malignancies of the digestive system. However, the introduction of CST4 into the traditional diagnostic groups (CEA + AFP, CA199 + CA125 + CA153 + CA724 and AFP + CEA + CA199 + CA125 + CA153 + CA724) resulted in an increased sensitivity and loss of specificity, thereby not offering significant advantages in terms of comprehensive diagnostic efficiency compared with the traditional diagnostic groups. In conclusion, CST4 detection may be a promising diagnostic tool. Nonetheless, the potential false positive results in tumor diagnosis should be taken into consideration when developing new diagnostic groups involving CST4.

Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer-related deaths and is associated with a poor prognosis. The majority of these cancers are detected at a late stage, contributing to the bad prognosis. This underscores the need for novel, enhanced early detection strategies to improve the outcomes. While population-based screening is not recommended due to the relatively low incidence of PDAC, surveillance is recommended for individuals at high risk for PDAC due to their increased incidence of the disease. However, the outcomes of pancreatic cancer surveillance in high-risk individuals are not sorted out yet. In this review, we will address the identification of individuals at high risk for PDAC, discuss the objectives and targets of surveillance, outline how surveillance programs are organized, summarize the outcomes of high-risk individuals undergoing pancreatic cancer surveillance, and conclude with a future perspective on pancreatic cancer surveillance and novel developments.

Pancreatic surveillance can detect early-stage pancreatic cancer and achieve long-term survival, but currently involves annual endoscopic ultrasound and MRI/MRCP, and is recommended only for individuals who meet familial/genetic risk criteria. To improve upon current approaches to pancreatic cancer early detection and to expand access, more accurate, inexpensive, and safe biomarkers are needed, but finding them has remained elusive. Newer approaches to early detection, such as using gene tests to personalize biomarker interpretation, and the increasing application of artificial intelligence approaches to integrate complex biomarker data, offer promise that clinically useful biomarkers for early pancreatic cancer detection are on the horizon.

Pancreatic cancer (PC) is a clinically challenging tumor to combat due to its advanced stage at diagnosis as well as its resistance to currently available therapies. The absence of early symptoms and known detectable biomarkers renders this disease incredibly difficult to detect/manage. Recent advances in the understanding of PC biology have highlighted the importance of cancer-immune cell interactions, not only in the tumor micro-environment but also in distant systemic sites, like the bone marrow, spleen and circulating immune cells, the so-called macro-environment. The response of the macro-environment is emerging as a determining factor in tumor development by contributing to the formation of an increasingly immunogenic micro-environment promoting tumor homeostasis and progression. We will summarize the key events associated with the feedback loop between the tumor immune micro-environment (TIME) and the tumor immune macroenvironment (TIMaE) in pancreatic precancerous lesions along with how it regulates disease development and progression. In addition, liquid biopsy biomarkers capable of diagnosing PC at an early stage of onset will also be discussed. A clearer understanding of the early crosstalk between micro-environment and macro-environment could contribute to identifying new molecular therapeutic targets and biomarkers, consequently improving early PC diagnosis and treatment.

The purpose of this study was to assess the influence and the clinical effectiveness of the short stature homeobox 2 (SHOX2) and ras association domain family 1A (RASSF1A) genes by tissue sampling through ultrasound endoscopy-guided fine-needle aspiration (EUS-FNA) as auxiliary diagnostic tools for pancreatic cancer (PC). Methylation markers were detected in 96 patients using real-time fluorescence quantitative PCR (qPCR), and the performance of this diagnostic assay was compared with CA19-9, CEA, and puncture fluid-based exfoliative cytology using receiver operating characteristic curve (ROC) analysis. The PC group exhibited higher methylation rates for SHOX2, RASSF1A, and the combined assay of both genes compared to the control group (95.7 % vs. 54.0 %, 78.3 % vs. 36.0 %, and 73.9 % vs. 16.0 %, P < 0.05). The areas under the ROC curve (AUC) for CA19-9, CEA, liquid-based exfoliative cytology, SHOX2, RASSF1A, the combination of SHOX2 and RASSF1A, the combination assay with CEA, CA19-9, and liquid-based exfoliative cytology were 0.827, 0.692, 0.767, 0.770, 0.732, 0.870, 0.870, 0.933, and 0.900, respectively. Therefore, the methylation assay based on the combined SHOX2 and RASSF1A genes in EUS-FNA puncture fluid is more effective than using a single gene, liquid-based exfoliative cytology, or intravenous tumor markers for diagnosing PC. Combining the conventional marker CA19-9 enhances the diagnostic value, making it a promising approach to complement histology and cytology.

Early detection of pancreatic adenocarcinoma (PAAD) remains a pressing clinical problem. Information on the clinical prognostic value of mitochondrial fusion-related genes in PAAD remains limited. In this study, we investigated mitochondrial fusion-related genes of PAAD to establish an optimal signature plate for the early diagnosis and prognosis of PAAD. The cancer genome atlas database was used to integrate the Fragments Per Kilobase Million data and related clinical data for patients with PAAD. Least absolute shrinkage and selection operator regression, cox regression, operating characteristic curves, and cBioPortal database was used to evaluate model performance, assess the prognostic ability and sensitivity. The levels of immune infiltration were compared by CIBERSORT, QUANTISEQ, and EPIC. Chemotherapy sensitivity between the different risk groups was compared by the Genomics of Drug Sensitivity in Cancer database and the "pRRophetic" R package. At last, a total of 4 genes were enrolled in multivariate Cox regression analysis. The risk-predictive signature was constructed as: (0.5438 × BAK1) + (-1.0259 × MIGA2) + (1.1140 × PARL) + (-0.4300 × PLD6). The area under curve of these 4 genes was 0.89. Cox regression analyses indicates the signature was an independent prognostic indicator (P < .001, hazard ratio [HR] = 1.870, 95% CI = 1.568-2.232). Different levels of immune cell infiltration in the 2 risk groups were observed using the 3 algorithms, with tumor mutation load (P = .0063), tumor microenvironment score (P = .01), and Tumor Immune Dysfunction and Exclusion score (P = .0012). The chemotherapeutic sensitivity analysis also revealed that the half-maximal inhibitory concentration of 5-fluorouracil (P = .0127), cisplatin (P = .0099), docetaxel (P < .0001), gemcitabine (P = .0047), and pacilataxel (P < .0001) were lower in the high-risk groups, indicating that the high-risk group patients had a greater sensitivity to chemotherapy. In conclude, we established a gene signature plate comprised of 4 mitochondrial fusion related genes to facilitate early diagnosis and prognostic prediction of PAAD.

Pancreatic ductal adenocarcinoma (PDAC) is currently the fourth leading cause of death in the United States and is expected to be ranked second in the next 10 years due to poor prognosis and a rising incidence. Distant metastatic PDAC is associated with the worst prognosis among the different phases of PDAC. The diagnostic options for PDAC are convenient and available for staging, tumor response evaluation, and management of resectable or borderline resectable PDAC. However, imaging is crucial in PDAC diagnosis, monitoring, resectability appraisal, and response evaluation. The advancement of medical technologies is evolving, hence the use of imaging in PDAC treatment options has grown as well as the utilization of ctDNA as a tumor marker. Treatment options for metastatic PDAC are minimal with the primary goal of therapy limited to symptom relief or palliation, especially in patients with low functional capacity at the point of diagnosis. Molecular profiling has shown promising potential solutions that would push the treatment boundaries for patients with PDAC. In this review, we will discuss the latest updates from evidence-based guidelines regarding diagnosis, therapy response evaluation, prognosis, and surveillance, as well as illustrating novel therapies that have been recently investigated for PDAC, in addition to discussing the molecular profiling advances in PDAC.

There is growing interest in multicancer detection tests, which identify molecular signals in the blood that indicate a potential preclinical cancer. A key stage in evaluating these tests is a prediagnostic performance study, in which investigators store specimens from asymptomatic individuals and later test stored specimens from patients with cancer and a random sample of controls to determine predictive performance. Performance metrics include rates of cancer-specific true-positive and false-positive findings and a cancer-specific positive predictive value, with the latter compared with a decision-analytic threshold. The sample size trade-off method, which trades imprecise targeting of the true-positive rate for precise targeting of a zero-false-positive rate can substantially reduce sample size while increasing the lower bound of the positive predictive value. For a 1-year follow-up, with ovarian cancer as the rarest cancer considered, the sample size trade-off method yields a sample size of 163 000 compared with a sample size of 720 000, based on standard calculations. These design and analysis recommendations should be considered in planning a specimen repository and in the prediagnostic evaluation of multicancer detection tests.

Pancreatic cancer is a highly lethal malignant tumor, which has the characteristics of occult onset, low early diagnosis rate, rapid development and poor prognosis. The reason for the high mortality is partly that pancreatic cancer is usually found in the late stage and missed the best opportunity for surgical resection. As a promising detection technology, liquid biopsy has the advantages of non-invasive, real-time and repeatable. In recent years, the continuous development of liquid biopsy has provided a new way for the detection and screening of pancreatic cancer. The update of biomarkers and detection tools has promoted the development of liquid biopsy. Circulating tumor cells (CTCs), circulating tumor DNA (ctDNA), circulating tumor RNA (ctRNA) and extracellular vesicles (EVs) provide many biomarkers for liquid biopsy of pancreatic cancer, and screening tools around them have also been developed. This review aims to report the application of liquid biopsy technology in the detection of pancreatic cancer patients, mainly introduces the biomarkers and some newly developed tools and platforms. We have also considered whether liquid biopsy technology can replace traditional tissue biopsy and the challenges it faces.

Small extracellular vesicles (sEVs) are cell-derived vesicles evolving as important elements involved in all stages of cancers. sEVs bear unique protein signatures that may serve as biomarkers. Pancreatic cancer (PC) records a very poor survival rate owing to its late diagnosis and several cancer cell-derived proteins have been reported as candidate biomarkers. However, given the pivotal role played by stellate cells (PSCs, which produce the collagenous stroma in PC), it is essential to also assess PSC-sEV cargo in biomarker discovery. Thus, this study aimed to isolate and characterise sEVs from mouse PC cells and PSCs cultured alone or as co-cultures and performed proteomic profiling and pathway analysis. Proteomics confirmed the enrichment of specific markers in the sEVs compared to their cells of origin as well as the proteins that are known to express in each of the culture types. Most importantly, for the first time it was revealed that PSC-sEVs are enriched in proteins (including G6PI, PGAM1, ENO1, ENO3, and LDHA) that mediate pathways related to development of diabetes, such as glucose metabolism and gluconeogenesis revealing a potential role of PSCs in pancreatic cancer-related diabetes (PCRD). PCRD is now considered a harbinger of PC and further research will enable to identify the role of these components in PCRD and may develop as novel candidate biomarkers of PC.

A blood test that enables surveillance for early-stage pancreatic ductal adenocarcinoma (PDAC) is an urgent need. Independent laboratories have reported PDAC biomarkers that could improve biomarker performance over CA19-9 alone, but the performance of the previously reported biomarkers in combination is not known. Therefore, we conducted a coordinated case/control study across multiple laboratories using common sets of blinded training and validation samples (132 and 295 plasma samples, respectively) from PDAC patients and non-PDAC control subjects representing conditions under which surveillance occurs. We analyzed the training set to identify candidate biomarker combination panels using biomarkers across laboratories, and we applied the fixed panels to the validation set. The panels identified in the training set, CA19-9 with CA199.STRA, LRG1, TIMP-1, TGM2, THSP2, ANG, and MUC16.STRA, achieved consistent performance in the validation set. The panel of CA19-9 with the glycan biomarker CA199.STRA improved sensitivity from 0.44 with 0.98 specificity for CA19-9 alone to 0.71 with 0.98 specificity (p < 0.001, 1000-fold bootstrap). Similarly, CA19-9 combined with the protein biomarker LRG1 and CA199.STRA improved specificity from 0.16 with 0.94 sensitivity for CA19-9 to 0.65 with 0.89 sensitivity (p < 0.001, 1000-fold bootstrap). We further validated significantly improved performance using biomarker panels that did not include CA19-9. This study establishes the effectiveness of a coordinated study of previously discovered biomarkers and identified panels of those biomarkers that significantly increased the sensitivity and specificity of early-stage PDAC detection in a rigorous validation trial.

Uridine diphosphate glucuronosyltransferase 1A1 (UGT1A1) is expressed ubiquitously in cancer cells and can metabolize exogenous substances. Studies show higher UGT1A1 levels in pancreatic cancer cells than normal cells. Therefore, we need a method to monitor the activity level of UGT1A1 in pancreatic cancer cells and in vivo. Here, we report a fluorescent probe, BCy-panc, for UGT1A1 imaging in cells and in vivo. Compared with other molecular probes, this probe is readily prepared, with high selectivity and sensitivity for the detection of UGT1A1. Our results show that BCy-panc rapidly detects UGT1A1 in pancreatic cancer. In addition, there is an urgent need for evidence to clarify the relationship between UGT1A1 and pancreatic cancer development. The present investigation found that the increase of UGT1A1 by chrysin was effective in inducing apoptosis in pancreatic cancer cells. These results indicate that the synergistic effect of chrysin and cisplatin at the cellular level is superior to that of cisplatin alone. The UGT1A1 level may be a biomarker for early diagnosis of cancer. Meanwhile, UGT1A1 plays a crucial role in pancreatic cancer, and the combination of chrysin and cisplatin may provide effective ideas for pancreatic cancer treatment.