Quantitative proteomic analysis of pancreatic cyst fluid proteins associated with malignancy in intraductal papillary mucinous neoplasms

Abstract Background The application of advanced imaging technologies for identifying pancreatic cysts has become widespread. However, accurately differentiating between low-grade dysplasia (LGD), high-grade dysplasia (HGD), and invasive intraductal papillary mucinous neoplasms (IPMNs) remains a diagnostic challenge with current biomarkers, necessitating the development of novel biomarkers that can distinguish IPMN malignancy. Methods Cyst fluid samples were collected from nine IPMN patients (3 LGD, 3 HGD, and 3 invasive IPMN) during their pancreatectomies. An integrated proteomics approach that combines filter-aided sample preparation, stage tip-based high-pH fractionation, and high-resolution MS was applied to acquire in-depth proteomic data of pancreatic cyst fluid and discover marker candidates for IPMN malignancy. Biological processes of differentially expressed proteins that are related to pancreatic cysts and aggressive malignancy were analyzed using bioinformatics tools such as gene ontology analysis and Ingenuity pathway analysis. In order to confirm the validity of the marker candidates, 19 cyst fluid samples were analyzed by western blot. Results A dataset of 2992 proteins was constructed from pancreatic cyst fluid samples. A subsequent analysis found 2963 identified proteins in individual samples, 2837 of which were quantifiable. Differentially expressed proteins between histological grades of IPMN were associated with pancreatic diseases and malignancy according to ingenuity pathway analysis. Eighteen biomarker candidates that were differentially expressed across IPMN histological grades were discovered—7 DEPs that were upregulated and 11 that were downregulated in more malignant grades. HOOK1 and PTPN6 were validated by western blot in an independent cohort, the results of which were consistent with our proteomic data. Conclusions This study demonstrates that novel biomarker candidates for IPMN malignancy can be discovered through proteomic analysis of pancreatic cyst fluid

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Additional file 8. Table S7. Demographic and clinical characteristics of the study population for validation. A total of 19 pancreatic cyst fluid samples were used to confirm the credibility of marker candidates

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Additional file 1. Supplementary Figures S1–S9

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Additional file 4. Table S3. Coefficient of variation (CV%) values of the sums of logarithm base two-transformed LFQ intensities of technical triplicates. The CV% values were calculated in each pancreatic cyst fluid sample (Technical replicate 1: TR1, Technical replicate 2: TR2, Technical replicate 3: TR3)

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Additional file 10. Table S9. Ponceau S intensity. Densitometric analysis of Ponceau S intensities, used as an alternative loading control to actin in western blots (LGD: Low-grade dysplasia, HGD: High-grade dysplasia, INV: invasive IPMN)

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Additional file 7. Table S6. Gene ontology analysis results. GO annotation was performed using the DAVID bioinformatics tool. The p value (modified Fisher exact p value) cutoff for the GO annotation was set to < 0.05. Genes that were involved in each GO term are provided as official gene symbols. ‘GO FAT’ filters broad GO terms, based on the measured specificity of each term

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Additional file 6. Table S5. List of exclusively upregulated, exclusively downregulated, and up- or downregulated proteins in all comparison groups. Of the 243 DEPs, 142 were upregulated and 91 were downregulated in at least one comparison group; 10 proteins were up- or downregulated by Student’s t test (INV: invasive IPMN)

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Additional file 3. Table S2. The number of identified and quantified proteins in individual samples searched with and without the peptide library. The number of identified and quantified proteins increased by 129 and 83, respectively, on average per individual samples when searched with and without the peptide library. In the same manner, the number of identified peptides rose by 752 on average in individual samples with HGD1 presenting the greatest improvement of 2109

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Additional file 2. Table S1. List of identified proteins. MS information on identified and quantitated proteins is listed consecutively. SignalP and SecretomeP were used to identify the secreted proteins, whereas TMHMM was used to identify transmembrane proteins. The Human Plasma Proteome Database confirmed the association between the proteins that were identified in human plasma and the identified proteins of this study. Identified proteins in our dataset were crossreferenced with pancreatic expression in the Human Protein Atlas. LFQ intensities of individual samples were used for further statistical analysis. The additional proteins searched exclusively in the presence of peptide library were highlighted as the additional column in this table