Potential Biomarker For Pancreatic Cancer Research Articles

Imaging Techniques and Biochemical Biomarkers: New Insights into Diagnosis of Pancreatic Cancer.

Pancreatic cancer (PaC) incidence is increasing, but our current screening and diagnostic strategies are not very effective. However, screening could be helpful in the case of PaC, as recent evidence shows that the disease progresses gradually. Unfortunately, there is no ideal screening method or program for detecting PaC in its early stages. Conventional imaging techniques, such as abdominal ultrasound, CT, MRI, and EUS, have not been successful in detecting early-stage PaC. On the other hand, biomarkers may be a more effective screening tool for PaC and have greater potential for further evaluation compared to imaging. Recent studies on biomarkers and artificial intelligence (AI)-enhanced imaging have shown promising results in the early diagnosis of PaC. In addition to proteins, non-coding RNAs are also being studied as potential biomarkers for PaC. This review consolidates the current literature on PaC screening modalities to provide an organized framework for future studies. While conventional imaging techniques have not been effective in detecting early-stage PaC, biomarkers and AI-enhanced imaging are promising avenues of research. Further studies on the use of biomarkers, particularly non-coding RNAs, in combination with imaging modalities may improve the accuracy of PaC screening and lead to earlier detection of this deadly disease.

The Expression Analysis of Long Non-coding RNAs Related to Wnt/β-Catenin Signaling in Pancreatic Cancer Patients.

Background The oncogenic Wnt/β-catenin signaling plays a critical role in carcinogenesis, prognosis, and resistance to therapy. Pancreatic cancer (PC) has high mortality because of its poor prognosis. Several studies have suggested that lncRNAs are directly involved in the development and progression of PC as well as in Wnt/β-catenin signaling. In this study, we investigated and compared the expression of Wnt/β-catenin signaling-related ZFAS1 and HCG11 lncRNAs, and their targets, CTNNB1 and IGF2BP1 genes in the blood of patients with PC and healthy individuals. A total of 47 PC patients and 50 healthy individuals participated in this study. RNA was extracted from the peripheral blood samples of participants, and cDNA was synthesized. The expression level of the selected genes was quantified by real-time PCR. The expression of HCG11 lncRNA and CTNNB1 genes in patients with PC was significantly upregulated compared to healthy individuals, and the expression of the ZFAS1 lncRNA was significantly downregulated. According to the analysis of the ROC curve, the diagnostic powers of ZFAS1 and CTNNB1 in PC were 0.67 and 0.69, respectively. Altogether, the present study suggests a role for ZFAS1 and HCG11 lncRNAs and CTNNB1 and IGF2BP1 in the pathogenesis of pancreatic cancer. Moreover, the peripheral expression of these lncRNAs may be useful as potential biomarkers for PC.

Screening and identification of potential biomarkers for pancreatic cancer: An integrated bioinformatics analysis

Pancreatic cancer is one of the highly invasive and the seventh most common cause of death among cancers worldwide. To identify essential genes and the involved mechanisms in pancreatic cancer, we used bioinformatics analysis to identify potential biomarkers for pancreatic cancer management. Gene expression profiles of pancreatic cancer patients and normal tissues were screened and downloaded from The Cancer Genome Atlas (TCGA) bioinformatics database. The Differentially expressed genes (DEGs) were identified among gene expression signatures of normal and pancreatic cancer, using R software. Then, enrichment analysis of the DEGs, including Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, was performed by an interactive and collaborative HTML5 gene list enrichment analysis tool (enrichr) and ToppGene. The protein-protein interaction (PPI) network was also constructed using the Search Tool for the Retrieval of Interacting Genes (STRING) database and ToppGenet web based tool followed by identifying hub genes of the top 100 DEGs in pancreatic cancer using Cytoscape software. Over 2000 DEGs with variable log2 fold (LFC) were identified among 34,706 genes. Principal component analysis showed that the top 20 DEGs, including H1–4, H1–5, H4C3, H4C2, RN7SL2, RN7SL3, RN7SL4P, RN7SKP80, SCARNA12, SCARNA10, SCARNA5, SCARNA7, SCARNA6, SCARNA21, SCARNA9, SCARNA13, SNORA73B, SNORA53, SNORA54 might distinguish pancreatic cancer from normal tissue. GO analysis showed that the top DEGs have more enriched in the negative regulation of gene silencing, negative regulation of chromatin organization, negative regulation of chromatin silencing, nucleosome positioning, regulation of chromatin silencing, and nucleosomal DNA binding. KEGG analysis identified an association between pancreatic cancer and systemic lupus erythematosus, alcoholism, neutrophil extracellular trap formation, and viral carcinogenesis. In PPI network analysis, we found that the different types of histone-encoding genes are involved as hub genes in the carcinogenesis of pancreatic cancer. In conclusion, our bioinformatics analysis identified genes that were significantly related to the prognosis of pancreatic cancer patients. These genes and pathways could serve as new potential prognostic markers and be used to develop treatments for pancreatic cancer patients.

Mucins as Potential Biomarkers for Early Detection of Cancer.

Early detection significantly correlates with improved survival in cancer patients. So far, a limited number of biomarkers have been validated to diagnose cancers at an early stage. Considering the leading cancer types that contribute to more than 50% of deaths in the USA, we discuss the ongoing endeavors toward early detection of lung, breast, ovarian, colon, prostate, liver, and pancreatic cancers to highlight the significance of mucin glycoproteins in cancer diagnosis. As mucin deregulation is one of the earliest events in most epithelial malignancies following oncogenic transformation, these high-molecular-weight glycoproteins are considered potential candidates for biomarker development. The diagnostic potential of mucins is mainly attributed to their deregulated expression, altered glycosylation, splicing, and ability to induce autoantibodies. Secretory and shed mucins are commonly detected in patients' sera, body fluids, and tumor biopsies. For instance, CA125, also called MUC16, is one of the biomarkers implemented for the diagnosis of ovarian cancer and is currently being investigated for other malignancies. Similarly, MUC5AC, a secretory mucin, is a potential biomarker for pancreatic cancer. Moreover, anti-mucin autoantibodies and mucin-packaged exosomes have opened new avenues of biomarker development for early cancer diagnosis. In this review, we discuss the diagnostic potential of mucins in epithelial cancers and provide evidence and a rationale for developing a mucin-based biomarker panel for early cancer detection.

Integrated Analysis of ECT2 and COL17A1 as Potential Biomarkers for Pancreatic Cancer.

Background Pancreatic cancer (PC) is a malignant tumor of the digestive tract. It presents with atypical clinical symptoms and lacks specific diagnostic indicators. This study is aimed at exploring the potential biomarkers of PC. Methods TCGA database pancreatic cancer dataset was normalized and used to identify differentially expressed genes (DEGs). Survival, independent prognostic, and clinical correlation analyses were performed on DEGs to screen for key genes. DNA methylation, mutation, and copy number variation (CNV) analyses were used to analyze genetic variants in key genes. GSEA was performed to explore the functional enrichment of the key genes. Based on the expression of key genes, construction of a competing endogenous RNA (ceRNA) network, analysis of the tumor microenvironment (TME), and prediction of chemotherapeutic drug sensitivity were performed. Furthermore, the GEO database was used to validate the reliability of key genes. Results Two key genes (ECT2 and COL17A1) were identified, which were highly expressed in PC. The mRNA expression of ECT2 and COL17A1 was associated with DNA methylation and CNV. The cell cycle, proteasome, and pathways in cancer were enriched in the high-COL17A1 and ECT2 groups. The TME results showed that immune scores were decreased in the high-ECT2 group. CeRNA network results showed that there were eleven miRNAs were involved in the regulation of ECT2 and COL17A1. Moreover, pRRophetic analysis showed that 20 chemotherapeutic drugs were associated with ECT2 and COL17A1 expression. Conclusions Collectively, ECT2 and COL17A1 may be potential biomarkers for PC, providing a new direction for clinical diagnosis and treatment.

Highly Sensitive Exosome Detection for Early Diagnosis of Pancreatic Cancer Using Immunoassay Based on Hierarchical Surface‐Enhanced Raman Scattering Substrate

Exosomes have emerged as potential biomarkers for pancreatic cancer (PaC). However, it is still challenging to get quantitative detection of exosomes with the specific surface receptors. In this study, a highly sensitive detection system is first constructed for the direct quantitation of specific exosomes in real samples using hierarchical surface-enhanced Raman scattering substrate (H-SERS substrate) and rapid enrichment strategy magnetic beads @ exosomes @ SERS detection probes (MEDP). It is found that the detection system (MEDP @ H-SERS substrate) could provide a 3.5 times higher SERS intensity compared with MEDP sandwich immunocomplex only. Moreover, LRG1-positive exosomes (LRG1-Exos) and GPC1-positive exosomes (GPC1-Exos) are chosen to distinguish PaC through exosome proteomics and database screening. The lower limit of detection (LOD) is 15 particles µL-1 using the MEDP @ H-SERS substrate. Significantly, the detection in clinical samples shows that the innovative combination of LRG1-Exos and GPC1-Exos could improve the diagnostic efficiency of PaC, with an area under the operating characteristic curve (AUC) of 0.95. Even for the early-stage PaC, the diagnostic accuracy is still high (AUC = 0.95). Collectively, the findings indicate that the MEDP @ H-SERS substrate has great potential for the early diagnosis of PaC.

Integrated bioinformatics analysis of potential biomarkers for pancreatic cancer.

BackgroundPancreatic cancer, particularly pancreatic ductal adenocarcinoma (PDA), is an aggressive malignancy associated with a low 5‐year survival rate. Poor outcomes associated with PDA are attributable to late detection and inoperability. Most patients with PDA are diagnosed with locally advanced and metastatic disease. Such cases are primarily treated with chemotherapy and radiotherapy. Because of the lack of effective molecular targets, early diagnosis and successful therapies are limited. The purpose of this study was to screen key candidate genes for PDA using a bioinformatic approach and to research their potential functional, pathway mechanisms associated with PDA progression. It may help to understand the role of associated genes in the development and progression of PDA and identify relevant molecular markers with value for early diagnosis and targeted therapy.Materials and methodsTo identify novel genes associated with carcinogenesis and progression of PDA, we analyzed the microarray datasets GSE62165, GSE125158, and GSE71989 from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified, and the Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. A protein‐protein interaction (PPI) network was constructed using STRING, and module analysis was performed using Cytoscape. Gene Expression Profiling Interactive Analysis (GEPIA) was used to evaluate the differential expression of hub genes in patients with PDA. In addition, we verified the expression of these genes in PDA cell lines and normal pancreatic epithelial cells.ResultsA total of 202 DEGs were identified and these were found to be enriched for various functions and pathways, including cell adhesion, leukocyte migration, extracellular matrix organization, extracellular region, collagen trimer, membrane raft, fibronectin‐binding, integrin binding, protein digestion, and absorption, and focal adhesion. Among these DEGs, 12 hub genes with high degrees of connectivity were selected. Survival analysis showed that the hub genes (HMMR, CEP55, CDK1, UHRF1, ASPM, RAD51AP1, DLGAP5, KIF11, SHCBP1, PBK, and HMGB2) may be involved in the tumorigenesis and development of PDA, highlighting their potential as diagnostic and therapeutic factors in PDA.ConclusionsIn summary, the DEGs and hub genes identified in the present study not only contribute to a better understanding of the molecular mechanisms underlying the carcinogenesis and progression of PDA but may also serve as potential new biomarkers and targets for PDA.

Metabolomics study reveals systematic metabolic dysregulation and early detection markers associated with incident pancreatic cancer.

Biomarkers for early detection of pancreatic cancer are in urgent need. To explore systematic circulating metabolites unbalance and identify potential biomarkers for pancreatic cancer in prospective Chinese cohorts, we conducted an untargeted metabolomics study in subjects with incident pancreatic cancer and matched controls (n=192) from the China Cardiometabolic Disease and Cancer Cohort (4C) Study. We characterized 998 metabolites in baseline serum and calculated 156 product-to-precursor ratios based on the KEGG database. The identified metabolic profiling revealed systematic metabolic network disorders before pancreatic cancer diagnosis. Forty-Five metabolites or product-to-precursor ratios showed significant associations with pancreatic cancer (P < .05 and FDR < 0.1), revealing abnormal metabolism of amino acids (especially alanine, aspartate and glutamate), lipids (especially steroid hormones), vitamins, nucleotides and peptides. A novel metabolite panel containing aspartate/alanine (OR [95% CI]: 1.97 [1.31-2.94]), androstenediol monosulfate (0.69 [0.49-0.97]) and glycylvaline (1.68 [1.04-2.70]) was significantly associated with risk of pancreatic cancer. Area under the receiver operating characteristic curves (AUCs) was improved from 0.573 (reference model of CA 19-9) to 0.721. The novel metabolite panel was validated in an independent cohort with AUC improved from 0.529 to 0.661. These biomarkers may have a potential value in early detection of pancreatic cancer.

HIST3H2A is a potential biomarker for pancreatic cancer: A study based on TCGA data.

The family of histone H2A proved that there are a lot of variants associated with cancer development. The link between HIST3H2A and pancreatic cancer has never been noted before. Our research suggests that HIST3H2A affects pancreatic tumor immune process and prognosis of patients, through the JAK STAT pathway, so it is expected to become the biomarker of pancreatic cancer.Gene expression profiles and clinical data of pancreatic cancer patients were downloaded from The Cancer Genome Atlas database (TCGA) and The Genotype Tissue Expression (GETx) project. R software (Rx64 3.6.0) was utilized to analyze. Gene set enrichment analysis (GSEA) was used to analyze HIST3H2A related signaling pathways in pancreatic cancer. CIBERSORT is applied to estimate the compositional patterns of the 22 types of immune cell fraction based on bulk expression data.HIST3H2A was expressed at higher in pancreatic cancer tissues than normal pancreatic tissues. Kaplan–Meier survival analysis suggested that the level of HIST3H2A expression affect prognosis of pancreatic cancer patients. Univariate Cox analysis and Multivariate Cox analysis suggested that HIST3H2A expression is a prognostic factor of pancreatic cancer. Cor expression analysis indicated that the genes positively correlated with HIST3H2A expression trend were DCST1-AS1, HIST1H2BD, SLC12A9-AS1. GSEA showed that the JAK-STAT signaling pathway was enriched in the HIST3H2A high expression phenotype, whereas intestinal network for IgA production, Asthma and Chemokine signaling pathway were enriched in the HIST3H2A low expression phenotype. In additional, results showed that CD8 T cells (P = .007), activated CD4 memory T cells (P = .001), and monocytes (P = .002) were more abundant in lower HIST3H2A expression groups.HIST3H2A is a promising biomarker for predicting prognosis of pancreatic cancer, and it could be a potential therapeutic target. HIST3H2A might regulate the progression of tumor immune in pancreatic cancer through modulating the JAK-STAT pathway. In addition, the role HIST3H2A in pancreatic cancer may be related to DCST1-AS1, HIST1H2B, SLC12A9-AS1. However, more research is necessary to validate findings.

Rapid sample preparation workflow based on enzymatic nanoreactors for potential serum biomarker discovery in pancreatic cancer

Mass spectrometry (MS)-based proteomics have been extensively applied in clinical practice to discover potential protein and peptide biomarkers. However, the traditional sample pretreatment workflow remains labor-intensive and time-consuming, which limits the application of MS-based proteomic biomarker discovery studies in a high throughput manner. In the current work, we improved the previously reported procedure of the simple and rapid sample preparation methods (RSP) by introducing macroporous ordered siliceous foams (MOSF), namely RSP-MOSF. With the aid of MOSF, we further reduced the digestion time to 10 min, facilitating the whole sample handling process within 30 min. Combining with 30 min direct data independent acquisition (DIA) of LC-MS/MS, we accomplished a serum sample analysis in 1 h. Comparing with the RSP method, the performance of protein and peptide identification, quantitation, as well as the reproducibility of RSP-MOSF is comparable or even outperformed the RSP method. We further applied this workflow to analyze serum samples for potential candidate biomarker discovery of pancreatic cancer. Overall, 576 serum proteins were detected with 41 proteins significantly changed, which could serve as potential biomarkers for pancreatic cancer. Additionally, we evaluated the performance of RSP-MOSF method in a 96-well plate format which demonstrated an excellent reproducibility of the analysis. These results indicated that RSP-MOSF method had the potential to be applied on an automatic platform for further scaled analysis.

Liquid biopsy approach to pancreatic cancer.

Pancreatic cancer (PC) continues to pose a major clinical challenge. There has been little improvement in patient survival over the past few decades, and it is projected to become the second leading cause of cancer mortality by 2030. The dismal 5-year survival rate of less than 10% after the diagnosis is attributable to the lack of early symptoms, the absence of specific biomarkers for an early diagnosis, and the inadequacy of available chemotherapies. Most patients are diagnosed when the disease has already metastasized and cannot be treated. Cancer interception is vital, actively intervening in the malignization process before the development of a full-blown advanced tumor. An early diagnosis of PC has a dramatic impact on the survival of patients, and improved techniques are urgently needed to detect and evaluate this disease at an early stage. It is difficult to obtain tissue biopsies from the pancreas due to its anatomical position; however, liquid biopsies are readily available and can provide useful information for the diagnosis, prognosis, stratification, and follow-up of patients with PC and for the design of individually tailored treatments. The aim of this review was to provide an update of the latest advances in knowledge on the application of carbohydrates, proteins, cell-free nucleic acids, circulating tumor cells, metabolome compounds, exosomes, and platelets in blood as potential biomarkers for PC, focusing on their clinical relevance and potential for improving patient outcomes.

The research of potential biomarkers of pancreatic cancer

The research of potential biomarkers of pancreatic cancer

Circular RNAs as novel potential biomarkers for pancreatic cancer.

Pancreatic cancer (PaCa) is the fourth leading cause of cancer-related deaths in the United States, and the vast majority of these malignancies are pancreatic ductal adenocarcinomas (PDAC), but there is still a lack of early detection biomarkers for PaCa. Unlike linear RNAs, circRNAs form covalently closed continuous loops and can act as mammalian gene regulators. They may be diagnostic or predictive biomarkers for some tumors, also be novel potential therapeutic targets in different diseases. This review focuses on (1) the biogenesis of circRNAs, RNA binding proteins (RBPs) and complementary sequences of circRNAs; (2) the characteristics of circRNAs which allow them to interact with miRNAs; (3) the roles of circRNAs playing in the regulation of gene expression, cell behavior and cancer, and their potential role as novel biomarkers and therapeutic targets in pancreatic cancer.

Rapid Sample Preparation Workflow for Serum Sample Analysis with Different Mass Spectrometry Acquisition Strategies.

Fast, robust, and high-throughput mass spectrometry-based serum proteomic pipelines have great potential to yield information for biomarker discovery and daily clinical practice. Here, we developed a simple and rapid sample preparation (RSP) workflow by reducing the classical pretreatment time from overnight to less than 1.5 h in an ordinary system. In HeLa cell lysates and serum samples, the number of proteins and tryptic peptides generated using the RSP was comparable to that generated using conventional methods. For fast scanning of the serum proteome, the RSP-supported pipeline could complete a test in less than 2 h with 30 min of LC-MS/MS analysis. Nearly 390 proteins spanning 8 magnitudes of abundance range were identified with high reproducibility, containing over 90 cancer-associated proteins and over 50 FDA-approved biomarkers. For fast assay development, eight candidate biomarker peptides for cardiovascular disease (CVD) were quantified by MRM with high accuracy (CV% <10). After a simple highly abundant protein removal, a deep serum proteome of over 1400 proteins was reached. By analyzing the depleted serum in DIA acquisition mode, over 700 proteins were quantified. The differentially expressed proteins could help us unambiguously distinguish the serum samples from healthy people and patients with pancreatic cancer (PC). Potential biomarkers for PC were also found. The new RSP method, which is rapid and simple, meets the demands of both deep mining and fast analysis of serum proteins. We believe that it will be widely used in serum protein studies and accelerate the transformation from biomarker discovery to clinical application.

Search for Early Pancreatic Cancer Blood Biomarkers in Five European Prospective Population Biobanks Using Metabolomics.

Most patients with pancreatic cancer present with advanced disease and die within the first year after diagnosis. Predictive biomarkers that signal the presence of pancreatic cancer in an early stage are desperately needed. We aimed to identify new and validate previously found plasma metabolomic biomarkers associated with early stages of pancreatic cancer. Prediagnostic blood samples from individuals who were to receive a diagnosis of pancreatic cancer between 1 month and 17 years after sampling (N = 356) and age- and sex-matched controls (N = 887) were collected from five large population cohorts (HUNT2, HUNT3, FINRISK, Estonian Biobank, Rotterdam Study). We applied proton nuclear magnetic resonance–based metabolomics on the Nightingale platform. Logistic regression identified two interesting hits: glutamine (P = 0.011) and histidine (P = 0.012), with Westfall–Young family-wise error rate adjusted P values of 0.43 for both. Stratification in quintiles showed a 1.5-fold elevated risk for the lowest 20% of glutamine and a 2.2-fold increased risk for the lowest 20% of histidine. Stratification by time to diagnosis suggested glutamine to be involved in an earlier process (2 to 5 years before diagnosis), and histidine in a process closer to the actual onset (<2 years). Our data did not support the branched-chain amino acids identified earlier in several US cohorts as potential biomarkers for pancreatic cancer. Thus, although we identified glutamine and histidine as potential biomarkers of biological interest, our results imply that a study at this scale does not yield metabolomic biomarkers with sufficient predictive value to be clinically useful per se as prognostic biomarkers.

Detection and proteomic characterization of extracellular vesicles in human pancreatic juice

AimsThe prognosis of patients with pancreatic cancer has remained virtually unchanged with a high mortality rate compared to other types of cancers. An earlier detection would provide a time window of opportunity for treatment and prevention of deaths.In the present study we investigated extracellular vesicle (EV)-associated potential biomarkers for pancreatic cancer by directly assessing EV size-based subpopulations in pancreatic juice samples of patients with chronic pancreatitis or pancreatic cancer. In addition, we also studied blood plasma and pancreatic cancer cell line-derived EVs. MethodsComparative proteomic analysis was performed of 102 EV preparations from human pancreatic juices, blood, and pancreatic cancer cell lines Capan-1 and MIA PaCa-2. EV preparations were also characterized by electron microscopy, tunable resistive pulse sensing, and flow cytometry. ResultsHere we describe the presence of EVs in human pancreatic juice samples. Pancreatic juice EV-associated proteins that we identified as possible candidate markers for pancreatic cancer included mucins, such as MUC1, MUC4, MUC5AC, MUC6 and MUC16, CFTR, and MDR1 proteins. These candidate biomarkers could also be detected by flow cytometry in EVs found in pancreatic juice and those secreted by pancreatic cancer cell lines. ConclusionsTogether our data show that detection and characterization of EVs directly in pancreatic juice is feasible and may prove to be a valuable source of potential biomarkers of pancreatic cancer.

Expression and prognostic significance of TRPV6 in the development and progression of pancreatic cancer.

In this study, we aimed to clarify the expression and biological functions of TRPV6 in human pancreatic cancer (PC) tissue and pancreatic cancer (PC) cell lines. TRPV6 was up-regulated in the primary cancer tissues from pancreatic cancer (PC) patients. TRPV6 may regulate multiple proteins related to apoptosis, cell cycle and metastasis pathways. Silencing TRPV6 significantly inhibited invasion, proliferation and migration and induced apoptosis and cell cycle arrest. TRPV6 plays a promising role of an oncogene in pancreatic carcinogenesis and represents the new potential biomarker for PC.

Diagnosis of pancreatic cancer via1H NMR metabolomics of human plasma.

Metabolic changes induced by pancreatic cancer were investigated by 1H NMR spectroscopy of plasma samples of patients and healthy controls. The acquired data were submitted to multivariate statistical analysis providing clear discrimination between both groups. The most significant differences were found in levels of 3-hydroxybutyrate and lactate. The obtained results (100% sensitivity, 90% specificity) clearly show the potential of 1H NMR spectroscopy in pancreatic cancer diagnosis. Therefore, the NMR-based metabolomics may contribute to the early diagnosis, prevention and/or therapy of diseases in the future. On the other hand, the number of samples in the presented pilot study is limited and has to be significantly increased in the future in order to obtain solid statistical models and to confirm the current findings.

Reevaluation of Pholiota squarrosa lectin-reactive haptoglobin as a pancreatic cancer biomarker using an improved ELISA system

An increase in Lewis- and core-type fucosylation of haptoglobin has been reported in patients with pancreatic cancer (PC), suggesting that fucosylated haptoglobin is a candidate PC biomarker. Previously, we developed a Pholiota squarrosa lectin antibody enzyme-linked immunosorbent assay (PhoSL-ELISA) system for the detection of core-fucosylated haptoglobin. However, with this methodology, positive results were only obtained for some patients with PC, demonstrating the need for a more sensitive detection system. In the current study, we developed an improved PhoSL-ELISA system with higher sensitivity to detect core-fucosylated haptoglobin using high-concentration urea as a denaturing agent with lectin to facilitate detection. We then reevaluated the performance of PhoSL reactive-core-fucosylated haptoglobin (PhoSL-HP) as a PC biomarker using the improved PhoSL-ELISA system. PhoSL-HP levels in the sera of patients with PC were significantly higher than those in healthy volunteers, with an area under the curve (AUC) value of 0.753. Furthermore, the AUC value of CA19–9 improved from 0.793 to 0.907 when combined with PhoSL-HP. Additionally, several CA19–9-negative cases among the patients with PC were diagnosed as positive for PhoSL-HP. In conclusion, PhoSL-HP detection using our improved ELISA system might allow PhoSL-HP to serve as a potential biomarker for PC and thus might be useful to complement the detection of CA19–9 in PC diagnosis.

Upregulated exosomic miR‑23b‑3p plays regulatory roles in the progression of pancreatic cancer.

Pancreatic cancer (PC) is one of the most lethal malignances. Identification of biomarkers for early diagnosis of PC is a key imperative. MicroRNAs (miRNAs) have been shown to be valuable biomarkers in the context of several cancers. Exosomes refer to vesicles released by the tumor cells at the early stage of disease. Thus, detection of miRNA in exosomes can be used as a potential biomarker for PC. In this study, we profiled serum levels of miRNAs in patients with chronic pancreatitis (CP) and PC; the role of miR-23b-3p in PC progression was assessed in vitro. Additionally, we assessed, the expression of miR-23b-3p in exosomes isolated from serum samples and assessed the correlation between the expression of miR-23b-3p and carbohydrate antigen 19-9 (CA19-9). Three serum samples each were randomly selected from healthy controls (n=20), and patients with CP (n=18) and PC (n=16) for miRNA microarray profiling. The dysregulated miRNAs were confirmed using qRT-PCR. Four dysregulated miRNAs common to patients with CP and PC were identified on miRNA microarray analysis and confirmed by qRT-PCR. miR-23b-3p level was consistently higher in serum samples from PC patients as compared to those from healthy controls and CP patients (p<0.05). Overexpression of miR-23b-3p promoted proliferation, migration, and invasion capability of PC cells in vitro (p<0.05). Furthermore, miR-23b-3p was upregulated in exosomes of PC serum samples and the supernatant of pancreatic cancer cells (PANC-1), and the expression levels of miR-23b-3p were associated with those of serum CA19-9 levels. This study provides insights into the potential role of miR-23b-3p as a novel biomarker and target for treatment of PC.