Label-free serum proteomics and multivariate data analysis identifies biomarkers and expression trends that differentiate Intraductal papillary mucinous neoplasia from pancreatic adenocarcinoma and healthy controls

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http://hdl.handle.net/10138/301862

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Translational Medicine Communications. 2019 May 14;4(1):6

Title: Label-free serum proteomics and multivariate data analysis identifies biomarkers and expression trends that differentiate Intraductal papillary mucinous neoplasia from pancreatic adenocarcinoma and healthy controls
Author: Saraswat, Mayank; Nieminen, Heini; Joenvaara, Sakari; Tohmola, Tiialotta; Seppänen, Hanna; Ristimäki, Ari; Haglund, Caj; Renkonen, Risto
Publisher: BioMed Central
Date: 2019-05-14
URI: http://hdl.handle.net/10138/301862
Abstract: Abstract Background Intraductal Papillary Mucinous Neoplasia (IPMN) are potentially malignant cystic tumors of the pancreas. IPMN can progress from low to moderate to high grade dysplasia and further to IPMN associated carcinoma. Often the difference between benign and malignant nature of the IPMN is not clear preoperatively. We aim to elucidate molecular expression patterns of various grades of IPMN and pancreatic carcinoma. Additionally we suggest potential novel biomarkers to differentiate IPMN from healthy individuals and pancreatic carcinoma to enable early detection as well as help in differential diagnosis in future. Methods We have performed retrospective label-free proteomic analysis of the serum samples from 44 patients with various grades of benign IPMN or IPMN associated carcinoma and 11 healthy controls. Proteomic data was further analyzed by various multivariate statistical methods. Four groups of samples (low-grade, high-grade IPMN, pancreatic carcinoma and age- and sex-matched healthy controls) were compared with ANOVA. Orthogonal projections to latent structures-discriminant analysis (OPLS-DA) modeling gave S-plot for feature selection. Stringently selected potential markers were further evaluated with ROC curve analysis and area under the curve was calculated. Differentially expressed proteins were used for pathway analysis. Linear trend analysis (Mann Kendall test) was used for identifying significant increasing or decreasing trends from healthy-low grade-high grade IPMN-pancreatic carcinoma. Results Based on protein expression (436 proteins quantified), PCA separated most sample groups from each other. S-Plot selected biomarker panels with moderate to very high AUC values for differentiating controls from Low-, High-Grade IPMN and carcinoma. Linear trend analysis identified 12 proteins which were consistently increasing or decreasing trend among the groups. We found potential biomarkers to differentiate healthy controls from different degrees of dysplasia and pancreatic carcinoma. These biomarkers can classify IPMN, carcinoma and healthy controls from each other which is an unmet clinical need. Data are available via ProteomeXchange with identifier PXD009139. Conclusion Kininogen-1 was able to differentiate healthy persons from low and high-grade IPMN. Retinol binding protein-4 could classify the low-grade IPMN from pancreatic carcinoma. Twelve proteins including apolipoproteins and complement proteins had significantly increasing or decreasing trends from healthy to low to high-grade IPMN to pancreatic carcinoma.
Subject: IPMN
Low-grade dysplasia
Pancreatic carcinoma
Serum proteomics
UDMSE
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