Corrigendum: Serum Biomarker Panel for Diagnosis and Prognosis of Pancreatic Ductal Adenocarcinomas.
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ABSTRACT:
SUBMITTER: Mehta S
PROVIDER: S-EPMC8527849 | biostudies-literature | 2021
REPOSITORIES: biostudies-literature
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Corrigendum: Serum Biomarker Panel for Diagnosis and Prognosis of Pancreatic Ductal Adenocarcinomas.
Mehta Shreya S Bhimani Nazim N Gill Anthony J AJ Samra Jaswinder S JS Sahni Sumit S Mittal Anubhav A
Frontiers in oncology 20211006
[This corrects the article DOI: 10.3389/fonc.2021.708963.]. ...[more]
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Project description:BackgroundPatients with pancreatic ductal adenocarcinoma (PDAC) have late diagnosis which results in poor prognosis. Currently, surgical resection is the only option for curative intent. Identifying high-risk features for patients with aggressive PDAC is essential for accurate diagnosis, prognostication, and personalised care due to the disease burden and risk of recurrence despite surgical resection. A panel of three biomarkers identified in tumour tissue (S100A4, Ca125 and Mesothelin) have shown an association with poor prognosis and overall survival. The diagnostic and prognostic value of the serum concentration of this particular biomarker panel for patients with PDAC has not been previously studied.MethodsRetrospectively collected blood samples of PDAC patients (n =120) and healthy controls (n =80) were evaluated for the serum concentration of select biomarkers - S100A4, S100A2, Ca-125, Ca 19-9 and mesothelin. Statistical analyses were performed for diagnostic and prognostic correlation.ResultsA panel of four biomarkers (S100A2, S100A4, Ca-125 and Ca 19-9) achieved high diagnostic potential (AUROC 0.913). Three biomarkers (S100A4, Ca-125 and Ca 19-9) correlated with poor overall survival in a univariable model (p < 0.05). PDAC patients with abnormal levels of 2 or more biomarkers in their serum demonstrated significantly lower survival compared to patients with abnormal levels of one or less biomarker (p < 0.05).Conclusion and impactThe identified biomarker panels have shown the potential to diagnose PDAC patients and stratify patients based on their prognostic outcomes. If independently validated, this may lead to the development of a diagnostic and prognosticating blood test for PDAC.
| S-EPMC8287202 | biostudies-literature
Project description:A single tumor marker has a low diagnostic value in pancreatic cancer. Combinations of multiple biomarkers and unique analysis algorithms can be applied to overcome these limitations. This study sought to develop diagnostic algorithms using multiple biomarker panels and to validate their performance in the diagnosis of pancreatic ductal adenocarcinoma (PDAC). We used blood samples from 180 PDAC patients and 573 healthy controls. Candidate markers consisted of 11 markers that are commonly expressed in various cancers and which have previously demonstrated increased expression in pancreatic cancer. Samples were divided into training and validation sets. Five linear or non-linear classification methods were used to determine the optimal model. Differences were identified in 10 out of the 11 markers tested. We identified 2047 combinations, all of which were applied to 5 separate algorithms. The new biomarker combination consisted of 6 markers (ApoA1, CA125, CA19-9, CEA, ApoA2, and TTR). The area under the curve, specificity, and sensitivity were 0.992, 95%, and 96%, respectively, in the training set. Meanwhile, the measures were 0.993, 96%, and 93% in the validation set. This study demonstrated the utility of multiple biomarker combinations in the early detection of PDAC. A diagnostic panel of 6 biomarkers was developed and validated. These algorithms will assist in the early diagnosis of PDAC.
| S-EPMC7352313 | biostudies-literature
Project description:BackgroundEarlier detection of pancreatic ductal adenocarcinoma (PDAC) is key to improving patient outcomes, as it is mostly detected at advanced stages which are associated with poor survival. Developing non-invasive blood tests for early detection would be an important breakthrough.MethodsThe primary objective of the work presented here is to use a dataset that is prospectively collected, to quantify a set of cancer-associated proteins and construct multi-marker models with the capacity to predict PDAC years before diagnosis. The data used is part of a nested case-control study within the UK Collaborative Trial of Ovarian Cancer Screening and is comprised of 218 samples, collected from a total of 143 post-menopausal women who were diagnosed with pancreatic cancer within 70 months after sample collection, and 249 matched non-cancer controls. We develop a stacked ensemble modelling technique to achieve robustness in predictions and, therefore, improve performance in newly collected datasets.ResultsHere we show that with ensemble learning we can predict PDAC status with an AUC of 0.91 (95% CI 0.75-1.0), sensitivity of 92% (95% CI 0.54-1.0) at 90% specificity, up to 1 year prior to diagnosis, and at an AUC of 0.85 (95% CI 0.74-0.93) up to 2 years prior to diagnosis (sensitivity of 61%, 95% CI 0.17-0.83, at 90% specificity).ConclusionsThe ensemble modelling strategy explored here outperforms considerably biomarker combinations cited in the literature. Further developments in the selection of classifiers balancing performance and heterogeneity should further enhance the predictive capacity of the method.
| S-EPMC9860022 | biostudies-literature
Project description:IntroductionNew biomarkers are urgently needed to detect pancreatic ductal adenocarcinoma (PDAC) at an earlier stage for individualized treatment strategies and to improve outcomes. Autoantibodies (AAbs) in principle make attractive biomarkers as they arise early in disease, report on disease-associated perturbations in cellular proteomes, and are static in response to other common stimuli, yet are measurable in the periphery, potentially well in advance of the onset of clinical symptoms.MethodsHere, we used high-throughput, custom cancer antigen microarrays to identify a clinically relevant autoantibody biomarker combination able to differentially detect PDAC. Specifically, we quantified the serological AAb profiles of 94 PDAC, chronic pancreatitis (CP), other pancreatic- (PC) and prostate cancers (PRC), non-ulcer dyspepsia patients (DYS), and healthy controls (HC).ResultsCombinatorial ROC curve analysis on the training cohort data from the cancer antigen microarrays identified the most effective biomarker combination as CEACAM1-DPPA2-DPPA3-MAGEA4-SRC-TPBG-XAGE3 with an AUC = 85·0% (SE = 0·828, SP = 0·684). Additionally, differential expression analysis on the samples run on the iOme™ array identified 4 biomarkers (ALX1-GPA33-LIP1-SUB1) upregulated in PDAC against diseased and healthy controls. Identified AAbs were validated in silico using public immunohistochemistry datasets and experimentally using a custom PDAC protein microarray comprising the 11 optimal AAb biomarker panel. The clinical utility of the biomarker panel was tested in an independent cohort comprising 223 PDAC, PC, PRC, colorectal cancer (CRC), and HC samples. Combinatorial ROC curve analysis on the validation data identified the most effective biomarker combination to be CEACAM1-DPPA2-DPPA3-MAGEA4-SRC-TPBG-XAGE3 with an AUC = 85·0% (SE = 0·828, SP = 0·684). Subsequently, the specificity of the 11-biomarker panel was validated against other cancers (PDAC vs PC: AUC = 70·3%; PDAC vs CRC: AUC = 84·3%; PDAC vs PRC: AUC = 80·2%) and healthy controls (PDAC vs HC: AUC = 80·9%), confirming that this novel AAb biomarker panel is able to selectively detect PDAC amongst other confounding diseases.ConclusionThis AAb panel may therefore have the potential to form the basis of a novel diagnostic test for PDAC.
| S-EPMC11821970 | biostudies-literature
Project description:BackgroundPancreatic cancer is the most common pancreatic solid malignancy with an aggressive clinical course and low survival rate. There are a limited number of reliable prognostic biomarkers and a need to understand the pathogenesis of pancreatic tumors; neuroendocrine (PNET) and pancreatic ductal adenocarcinomas (PDAC) encouraged us to analyze the serum metabolome of pancreatic tumors and disturbances in the metabolism of PDAC and PNET.MethodsUsing the AbsoluteIDQ® p180 kit (Biocrates Life Sciences AG, Innsbruck, Austria) with liquid chromatography-mass spectrometry (LC-MS), we identified changes in metabolite profiles and disrupted metabolic pathways serum of NET and PDAC patients.ResultsThe concentration of six metabolites showed statistically significant differences between the control group and PDAC patients (p.adj < 0.05). Glutamine (Gln), acetylcarnitine (C2), and citrulline (Cit) presented a lower concentration in the serum of PDAC patients, while phosphatidylcholine aa C32:0 (PC aa C32:0), sphingomyelin C26:1 (SM C26:1), and glutamic acid (Glu) achieved higher concentrations compared to serum samples from healthy individuals. Five of the tested metabolites: C2 (FC = 8.67), and serotonin (FC = 2.68) reached higher concentration values in the PNET serum samples compared to PDAC, while phosphatidylcholine aa C34:1 (PC aa C34:1) (FC = -1.46 (0.68)) had a higher concentration in the PDAC samples. The area under the curves (AUC) of the receiver operating characteristic (ROC) curves presented diagnostic power to discriminate pancreatic tumor patients, which were highest for acylcarnitines: C2 with AUC = 0.93, serotonin with AUC = 0.85, and PC aa C34:1 with AUC = 0.86.ConclusionsThe observations presented provide better insight into the metabolism of pancreatic tumors, and improve the diagnosis and classification of tumors. Serum-circulating metabolites can be easily monitored without invasive procedures and show the present clinical patients' condition, helping with pharmacological treatment or dietary strategies.
| S-EPMC10296343 | biostudies-literature
Project description:BackgroundEvolutionary cancer has a supply mechanism to satisfy higher energy demands even in poor-nutrient conditions. Metabolic reprogramming is essential to supply sufficient energy. The relationship between metabolic reprogramming and the clinical course of pancreatic ductal adenocarcinoma (PDAC) remains unclear. We aimed to clarify the differences in metabolic status among PDAC patients.MethodsWe collected clinical data from 128 cases of resectable PDAC patients undergoing surgery. Sixty-three resected tissues, 15 tissues from the low carbohydrate antigen 19-9 (CA19-9), 38-100 U/mL, and high CA19-9, > 500 U/mL groups, and 33 non-tumor control parts, were subjected to tandem mass spectrometry workflow to systematically explore metabolic status. Clinical and proteomic data were compared on the most used PDAC biomarker, preoperative CA19-9 value.ResultsHigher CA19-9 levels were clearly associated with higher early recurrence (p < 0.001), decreased RFS (p < 0.001), and decreased DSS (p = 0.025). From proteomic analysis, we discovered that cancer evolution-related as well as various metabolism-related pathways were more notable in the high group. Using resected tissue immunohistochemical staining, we learned that high CA19-9 PDAC demonstrated aerobic glycolysis enhancement, yet no decrease in protein synthesis. We found a heterogeneity of various metabolic processes, including carbohydrates, proteins, amino acids, lipids, and nucleic acids, between the low and the high groups, suggesting differences in metabolic adaptive capacity.ConclusionsOur study found metabolic adaptation differences among PDAC cases, pertaining to both cancer evolution and the prognosis. CA19-9 can help estimate the metabolic adaptive capacity of energy supply for PDAC evolution.
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Project description:The poor prognosis of pancreatic ductal adenocarcinoma (PDAC) is largely due to several challenges, such as late diagnosis, early metastasis, limited response to chemotherapy, aggressive tumor biology, and high rates of tumor recurrence. Therefore, the development of a non-invasive and effective method for early detection of PDAC is crucial to improving patient outcomes. Continued research and exploration in this area are essential to enhance early detection methods and ultimately improve the prognosis for individuals with PDAC. In this study, we examined 37 exosomal surface proteins through a multiplex flow cytometry test on peripheral plasma samples from a group of 51 clinical control individuals (including healthy volunteers and non-cancer patients (Cholecystectomy, Hernia, healthy volunteers)), 21 pancreatitis, and 48 patients diagnosed with PDAC. Our research findings revealed that the level of exosomal CD40 expression is significantly lower in patients with PDAC and pancreatitis compared to non-cancer patients (p < 0.0001). Additionally, pancreatitis patients exhibited higher levels of exosomal CD25 expression than PDAC patients (p = 0.0104). PDAC patients with higher exo-CD40 had worse survival than patients with lower exo-CD40 (p = 0.0035). Similarly, PDAC patients with higher exo-CD25 showed worse survival in comparison to patients with lower exo-CD25 (p = 0.04). Statistical analysis revealed that exosomal CD40 achieved an AUC of 0.827 in distinguishing PDAC from clinical controls. Combining exo-CD40 along with exo-CD25 and CA19-9 discriminated PDAC patients from clinical controls with an AUC of 0.92. Exo-CD40 and exo-CD25 proteins found in exosomes isolated from plasma can serve as excellent non-invasive biomarkers for the early diagnosis of PDAC. Further larger scale studies are needed to validate combined exo-CD40 and exo-CD25 as a diagnostic tool for the identification of PDAC patients through non-invasive liquid biopsy.
| S-EPMC11854914 | biostudies-literature
Project description:Background: Nardilysin, (N-arginine dibasic convertase, NRDC) has been reported to play an important role in cancer progression, and is associated with tumor proliferation signals and inflammatory signals, such as tumor necrosis factor-a (TNF-a) and heparin-binding epidermal growth factor-like growth factor (HB-EGF), through the activation of disintegrin and metalloproteinase (ADAM) proteases. NRDC has recently been revealed to be involved in the tumorigenesis of various types of cancer, including intrahepatic cholangiocarcinoma, malignant cerebral infarction, esophageal squamous cell carcinoma, and gastric cancer. However, the expression profiles and biological relevance of NRDC in pancreatic ductal adenocarcinoma have rarely been reported. Methods: We analyzed the NRDC expression profile in pancreatic ductal adenocarcinoma by enzyme-linked immunosorbent assay (ELISA) and identified NRDC as a circulating biomarker in the serum of 112 pancreatic ductal adenocarcinoma patients. The diagnostic value of NRDC was analyzed by the area under the curve (AUC) and the receiver operating characteristic (ROC) test. Results: Our results demonstrated that the clinical prognosis significance of NRDC with the clinical characteristics in pancreatic ductal adenocarcinoma (PDAC). NRDC was notably decreased in PDAC patient serum compared with the control group (p < 0.001). Furthermore, the present study found that the NRDC expression level was correlated with T grade (p < 0.001), metastasis(p < 0.001), differentiation(p < 0.001), and TNM stage (p = 0.011). Further bioinformatics analysis revealed that NRDC correlated with proliferation and migration pathways; in particular, it mediated cell-matrix adhesion-dependent activation in pancreatic ductal adenocarcinoma. Conclusions: Serum NRDC may play a useful diagnostic biomarker to evaluate the aggressive clinical features in PAAD patients.
| S-EPMC9181681 | biostudies-literature
Project description:BackgroundAlthough CA19-9 is an essential blood biomarker of pancreatic cancer (PC), its sensitivity and specificity are limited for early detection.MethodsWe analyzed the serum proprotein convertase subtilisin/kexin type 9 (sPCSK9) in PC patients, benign disease groups (BDG), and healthy controls (HC) by ELISA.ResultsConsistently, sPCSK9 was considerably lower in PC patients than in HC (Z = -2.546, P < 0.05), and sPCSK9 in PC patients was statistically significantly higher than in BDG (Z = -5.457, P < 0.001). sPCSK9 was linked to the invasion of lymph nodes (χ2 = 6.846, P < 0.01). According to ROC curves, combining sPCSK9 with CA19-9 could potentially enhance the diagnostic capability of CA19-9 in early-stage PC patients. Furthermore, the low sPCSK9 group (n = 41) exhibited statistically significantly prolonged overall survival compared to the high sPCSK9 group (n = 15), with median survival times of 27 months (95% CI [17.59-36.41]) and 11 months (95% CI [7.21-14.79]), respectively (P = 0.022).ConclusionThe diagnostic performance of CA19-9 for early-stage PC patients could be improved by combining sPCSK9 with CA19-9. Moreover, the higher sPCSK9 group has a significantly shorter overall survival rate.
| S-EPMC11397121 | biostudies-literature
Project description:Potential biomarkers which include S100 calcium binding protein A9 (S100A9), mucin 5AC (MUC5AC), transforming growth factor β1 (TGF-β1), and angiopoietin-2 have previously been shown to be effective for cholangiocarcinoma (CCA) diagnosis. This study attempted to measure the sera levels of these biomarkers compared with carbohydrate antigen 19-9 (CA19-9). A total of 40 serum cases of CCA, gastrointestinal cancers (non-CCA), and healthy subjects were examined by using an enzyme-linked immunosorbent assay. The panel of biomarkers was evaluated for their accuracy in diagnosing CCA and subsequently used as inputs to construct the decision tree (DT) model as a basis for binary classification. The findings showed that serum levels of S100A9, MUC5AC, and TGF-β1 were dramatically enhanced in CCA patients. In addition, 95% sensitivity and 90% specificity for CCA differentiation from healthy cases, and 70% sensitivity and 83% specificity for CCA versus non-CCA cases was obtained by a panel incorporating all five candidate biomarkers. In CCA patients with low CA19-9 levels, S100A9 might well be a complementary marker for improved diagnostic accuracy. The high levels of TGF-β1 and angiopoietin-2 were both associated with severe tumor stages and metastasis, indicating that they could be used as a reliable prognostic biomarkers panel for CCA patients. Furthermore, the outcome of the CCA burden from the Classification and Regression Tree (CART) algorithm using serial CA19-9 and S100A9 showed high diagnostic efficiency. In conclusion, results have shown the efficacy of CCA diagnosis and prognosis of the novel CCA-biomarkers panel examined herein, which may prove be useful in clinical settings.
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