ArticleBMC cancer2026
Development of a multiplex assay for early detection of pancreatic cancer in high-risk groups.
Article in BMC cancer, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundPancreatic cancer ranks as the third leading cause of cancer-related deaths in the United States, with a dismal 5-year survival rate of only 13%. This poor prognosis is largely due to the challenge of early detection, which is crucial for effective treatment. Given the low prevalence of pancreatic ductal adenocarcinoma (PDAC), widespread screening is impractical and economically unfeasible. We present a novel, multiplex blood-based assay aimed to improve early detection for PDAC in high-risk populations.
methodsUtilizing comprehensive proteomic data, we established a mass spectrometry-based assay that simultaneously detects a panel of five protein biomarkers (APOA4, SERPING1, LRG1, C3, ORM1) in whole plasma. Machine learning was applied to construct a composite biomarker readout, and a “direct sampling” approach was employed to establish a reference threshold for PDAC detection. Characterized through Receiver Operating Characteristic (ROC) analysis with case-control cohorts from multiple medical centers, the assay’s efficacy was further evaluated in high-risk groups.
resultsThe assay achieved an area under the curve (AUC) of 0.87 and an overall classification accuracy of 75% in distinguishing early-stage PDAC cases from controls. Its efficacy was further assessed in high-risk groups, including adults aged 50 and older with new-onset diabetes, individuals with familial risks, and patients with chronic pancreatitis. Across all high-risk groups, the assay demonstrated a positive predictive value (PPV) exceeding 18% and a diagnostic odds ratio (DOR) above 67 - sufficient to identify potential PDAC cases for further clinical evaluation.
conclusionsThis straightforward multiplex assay enables direct detection of early-stage PDAC from whole plasma without the need for immunodepletion, enrichment, or prefractionation, demonstrating high efficacy and strong clinical applicability. It significantly outperforms the FDA-approved CA19-9 in distinguishing early-stage PDAC from control samples and in detecting PDAC among high-risk populations. The improved performance is attributable to the diverse biological functions and pathways represented by the selected biomarkers, which are actively involved in PDAC development. Although the assay is not predictive in nature, its diagnostic strength stems from the functional relevance of these biomarkers to disease-associated processes. Overall, the novel assay demonstrates robust efficacy in identifying PDAC in high-risk groups.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.