ArticleAnalytical chemistry2026
High-Sensitive Spatial Proteomics for Pancreatic Cancer Progression Analysis.
Article in Analytical chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- Mass spectrometry-based proteomics delivers in-depth proteome profiling of FFPE lung cancer biopsies from single glass slides.NPJ precision oncology · 2026Article
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12 authors.
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Abstract
Pancreatic cancer remains one of the most challenging malignancies to diagnose and treat due to the late development of symptoms and limited early diagnostic options. Intraductal papillary mucinous neoplasms (IPMNs) are noninvasive precursors to invasive pancreatic ductal adenocarcinoma (PDAC), and an understanding of the changes in patterns of protein expression that accompany the progression from normal ductal (ND) cells to IPMN and PDAC may provide avenues for improved earlier detection. In this study, we present an optimized spatial tissue proteomics workflow, termed SP-Max (Spatial Proteomics Optimized for Maximum Sensitivity and Reproducibility in Minimal Sample), designed to maximize protein recovery and quantification from limited laser microdissected (LMD) samples. Our workflow enabled the identification of more than 6000 proteins and the quantification of over 5200 protein groups from FFPE tissue contours of pancreatic tissues. Comparative analyses across ND, IPMN, and PDAC revealed critical molecular differences in protein pathways and potential markers of progression. SP-Max provides a systematic, reproducible approach that markedly enhances our ability to study precancerous lesions and cancer progression in pancreatic tissues at high resolution.
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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.