Noah S. Brown , Matthew A. Firpo , Courtney L. Scaife
{"title":"Pre-operative biomarkers may predict nodal status in pancreatic ductal adenocarcinoma","authors":"Noah S. Brown , Matthew A. Firpo , Courtney L. Scaife","doi":"10.1016/j.soi.2025.100157","DOIUrl":null,"url":null,"abstract":"<div><h3>Introduction</h3><div>The current standard for preoperative nodal staging for pancreatic adenocarcinoma, endoscopic ultrasound, varies widely in its accuracy, with pathologic concurrence as low as 41 %. Patients who are found to have 4 or more pathologically positive lymph nodes are defined as N2 nodal status. These patients experience extremely poor overall survival.</div></div><div><h3>Objective</h3><div>We sought to identify any biomarkers specific to this patient population to better stratify these patients pre-operatively.</div></div><div><h3>Methods</h3><div>We began with an existing database of patients with histologically confirmed pancreatic adenocarcinoma treated at the University of Utah between January 2004 and October 2019. These patients and their biological samples have already been screened using a 31 analyte panel to detect early stage disease. We recategorized these patients using the updated AJCC 8th edition introducing N2 disease. The individual analytes were then screened for their ability to distinguish N2 disease.</div></div><div><h3>Results</h3><div>Basigin (BSG) was significantly elevated in N2 disease (mean 17.45, SD 13.53) compared to N0 disease (mean 12.09, SD 11.47), p = 0.014 by Dunn's test) while Leucine-rich alpha-2-glycoprotein 1 (LRG1) was significantly decreased in N2 disease (mean 3446.21, SD 2719.12) compared to N0 disease (mean 5727.25, SD 3236.40, p = 0.025).</div></div><div><h3>Conclusion</h3><div>BSG and LRG1 could be useful in preoperatively identifying candidates that would benefit most from resection. This offers a foundation for future studies to combine biomarkers and clinical factors into a machine learning algorithm to reliably distinguish N2 disease in the preoperative setting. This may affect the pre-surgical discussion and provide vital prognostic information to patients.</div></div>","PeriodicalId":101191,"journal":{"name":"Surgical Oncology Insight","volume":"2 3","pages":"Article 100157"},"PeriodicalIF":0.0000,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Surgical Oncology Insight","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2950247025000362","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Introduction
The current standard for preoperative nodal staging for pancreatic adenocarcinoma, endoscopic ultrasound, varies widely in its accuracy, with pathologic concurrence as low as 41 %. Patients who are found to have 4 or more pathologically positive lymph nodes are defined as N2 nodal status. These patients experience extremely poor overall survival.
Objective
We sought to identify any biomarkers specific to this patient population to better stratify these patients pre-operatively.
Methods
We began with an existing database of patients with histologically confirmed pancreatic adenocarcinoma treated at the University of Utah between January 2004 and October 2019. These patients and their biological samples have already been screened using a 31 analyte panel to detect early stage disease. We recategorized these patients using the updated AJCC 8th edition introducing N2 disease. The individual analytes were then screened for their ability to distinguish N2 disease.
Results
Basigin (BSG) was significantly elevated in N2 disease (mean 17.45, SD 13.53) compared to N0 disease (mean 12.09, SD 11.47), p = 0.014 by Dunn's test) while Leucine-rich alpha-2-glycoprotein 1 (LRG1) was significantly decreased in N2 disease (mean 3446.21, SD 2719.12) compared to N0 disease (mean 5727.25, SD 3236.40, p = 0.025).
Conclusion
BSG and LRG1 could be useful in preoperatively identifying candidates that would benefit most from resection. This offers a foundation for future studies to combine biomarkers and clinical factors into a machine learning algorithm to reliably distinguish N2 disease in the preoperative setting. This may affect the pre-surgical discussion and provide vital prognostic information to patients.