Photo Credit: Rasi Bhadramani

The following is a summary of “An integrative multi-omics analysis reveals a multi-analyte signature of pancreatic ductal adenocarcinoma in serum,” published in the December 2024 issue of Gastroenterology by Balaya et al.      

Pancreatic ductal adenocarcinoma (PDAC) poses a significant health challenge due to late detection and a lack of reliable biomarkers for early diagnosis. Carbohydrate antigen 19-9 (CA 19-9) levels are often used alongside imaging tests to help confirm the diagnosis.  

Researchers conducted a retrospective study to identify a multi-analyte molecular signature for early detection in individuals with PDAC.  

They analyzed serum samples from 88 individuals, 58 individuals diagnosed with PDAC and 30 individuals without PDAC, using proximity extension technology for cytokines and related proteins and tandem mass spectrometry for lipidomics and metabolomics. Statistical analysis identified molecular alterations, and a machine-learning model was used to develop a biomarker signature.  

The results showed 1,462 circulatory proteins, 873 lipids, and 1,001 metabolites. Among individuals with PDAC, 505 proteins, 186 metabolites, and 33 lipids, including bone marrow stromal antigen 2 (BST2), keratin 18 (KRT18), and cholesteryl ester (20:5), were significantly altered. Markers of glycosphingolipid and galactose metabolism, such as sphinganine, urobilinogen, and lactose, showed significant differences between groups. Elevated levels of diacylglycerols and decreased levels of cholesteryl esters were observed in individuals diagnosed with PDAC. A machine learning model identified a 38-biomarker signature, including 21 proteins, 4 lipids, and 13 metabolites, to differentiate individuals with PDAC from those without the disease.  

They concluded that a multi-analyte biomarker signature, incorporating proteins, lipids, and metabolites, showed promise for improving early detection of individuals with PDAC.  

Source: link.springer.com/article/10.1007/s00535-024-02197-6