Dataset Information

New combined microRNA and protein plasmatic biomarker panel for pancreatic cancer.

ABSTRACT:

Introduction

Lack of diagnostic makers results in loss of operation opportunity in that most patients are diagnosed at the late stage. Pancreatic cancer (PC) has been regarded as a fatal disease with a 5-year survival rate below 10%. Therefore, the development of diagnostic biomarkers for PC is in urgent need to control the mortality of the disease.

Materials and methods

This is a case-control study including 640 plasma samples from healthy controls (HC), patients with benign pancreatic diseases (BPD), patients with PC; and patients with other gastrointestinal (GI) cancers. Eight biomarker candidates, including miR-20a, miR-21, miR-25, miR-155, miR-196a, miR-210, Macrophage Inhibitory Cytokine-1(MIC-1) and CA19-9, were evaluated to establish two diagnostic indexes in this study.

Results

The plasma level of the six miRNAs and MIC-1, CA19-9 were elevated in PC patients compared with those of healthy controls (P<0.001). Among them, miR-20a, miR-21, miR-25, MIC-1 and CA19-9 could distinguish PC patients from those with other GI cancers or BPD. With multivariable logistic regression, we established two specific indexes for diagnosis of PC(Index1 contains miR-21, MIC-1 and CA19-9; Index2 contains miR-25, MIC-1 and CA19-9). In a randomized setting of 260 HC, 168 PC, 132 other GI cancers and 80 BPD patients, both indexes performed not only better sensitivity for PC but also better specificity to distinguish PC from other GI cancers than CA19-9 and individual biomarkers.

Conclusions

These results indicated that combination of biomarkers as a panel could improve diagnostic values compared with using a single marker. Such panels as illustrated in this study could provide novel plasmatic biomarker for PC diagnosis.

SUBMITTER: Yuan W

PROVIDER: S-EPMC5346769 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

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Publications

New combined microRNA and protein plasmatic biomarker panel for pancreatic cancer.

Yuan Wei W Tang Wanyan W Xie Yibin Y Wang Shaoming S Chen Yingtai Y Qi Jun J Qiao Youlin Y Ma Jie J

Oncotarget 20161101 48

<h4>Introduction</h4>Lack of diagnostic makers results in loss of operation opportunity in that most patients are diagnosed at the late stage. Pancreatic cancer (PC) has been regarded as a fatal disease with a 5-year survival rate below 10%. Therefore, the development of diagnostic biomarkers for PC is in urgent need to control the mortality of the disease.<h4>Materials and methods</h4>This is a case-control study including 640 plasma samples from healthy controls (HC), patients with benign panc ...[more]

PMID: 27713117

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Project description:Pancreatic cancer is a lethal disease where specific early detection biomarkers would be very valuable to improve outcomes in patients. Many previous studies have compared biosamples from pancreatic cancer patients with healthy controls to find potential biomarkers. However, a range of related disease conditions can influence the performance of these putative biomarkers, including pancreatitis and diabetes. In this study, quantitative proteomics methods were applied to discover potential serum glycoprotein biomarkers that distinguish pancreatic cancer from other pancreas related conditions (diabetes, cyst, chronic pancreatitis, obstructive jaundice) and healthy controls. Aleuria aurantia lectin (AAL) was used to extract fucosylated glycoproteins and then both TMT protein-level labeling and label-free quantitative analysis were performed to analyze glycoprotein differences from 179 serum samples across the six different conditions. A total of 243 and 354 serum proteins were identified and quantified by label-free and TMT protein-level quantitative strategies, respectively. Nineteen and 25 proteins were found to show significant differences in samples between the pancreatic cancer and other conditions using the label-free and TMT strategies, respectively, with 7 proteins considered significant in both methods. Significantly different glycoproteins were further validated by lectin-ELISA and ELISA assays. Four candidates were identified as potential markers with profiles found to be highly complementary with CA 19-9 (p < 0.001). Obstructive jaundice (OJ) was found to have a significant impact on the performance of every marker protein, including CA 19-9. The combination of α-1-antichymotrypsin (AACT), thrombospondin-1 (THBS1), and haptoglobin (HPT) outperformed CA 19-9 in distinguishing pancreatic cancer from normal controls (AUC = 0.95), diabetes (AUC = 0.89), cyst (AUC = 0.82), and chronic pancreatitis (AUC = 0.90). A marker panel of AACT, THBS1, HPT, and CA 19-9 showed a high diagnostic potential in distinguishing pancreatic cancer from other conditions with OJ (AUC = 0.92) or without OJ (AUC = 0.95).

Project description:BackgroundBlood-based biomarkers for early detection of pancreatic ductal adenocarcinoma (PDAC) are urgently needed. Current biomarkers lack high sensitivity and specificity for population screening. The gold-standard biomarker, CA 19-9, also fails to demonstrate the predictive value necessary for early detection.MethodsTo validate a functional genomics-based plasma migration signature biomarker panel, plasma tissue factor pathway inhibitor (TFPI), tenascin C (TNC-FN III-C), and CA 19-9 levels were measured by enzyme-linked immunosorbent assays in three early-stage PDAC plasma cohorts, including two independent blinded validation cohorts containing a total of 43 stage I, 163 stage II, 86 chronic pancreatitis, 31 acute biliary obstruction, and 108 controls. Logistic regression models developed classification rules combining TFPI and/or TNC-FN III-C with CA 19-9 for patient cases and control subjects, with or without adjustment for age and diabetes status. Model classification performance was evaluated and analyses repeated among subpopulations without diabetes and pancreatitis history. Two-sided P values were calculated using bootstrap method.ResultsThe TFPI/TNC-FN III-C/CA 19-9 panel improved CA 19-9 performance in all early-stage cohorts, including discriminating stage IA/IB/IIA, stage IIB, and all early-stage cancer from healthy controls. Statistical significance was reached for a number of subcohorts, including for all early-stage cancer vs healthy controls (cohort 1 AUC = 0.92, 95% CI = 0.86 to 0.96, P = .04; cohort 3 AUC = 0.83, 95% CI = 0.76 to 0.89, P = .045). Among subcohorts without diabetes and pancreatitis history, the panel approaches potential clinical utility for early detection to discriminate early-stage PDAC from healthy controls including an area under the curve (AUC) of 0.87 (95% CI = 0.77 to 0.95) for stage I/IIA, an AUC of 0.93 (95% CI = 0.87 to 0.98) for stage IIB, and a statistically significant AUC of 0.89 (95% CI = 0.82 to 0.95) for all early-stage cancer ( P = .03).ConclusionsTFPI/TNC-FN III-C migration signature adds statistically significantly to CA 19-9's predictive power to detect early-stage PDAC and may have clinical utility for early detection of surgically resectable PDAC, as well as for enhanced survival from this routinely lethal cancer.

Dataset Information

New combined microRNA and protein plasmatic biomarker panel for pancreatic cancer.

Introduction

Materials and methods

Results

Conclusions

Publications

New combined microRNA and protein plasmatic biomarker panel for pancreatic cancer.

Similar Datasets

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