CLDN18 as a novel junctional biomarker enhancing multi-gene classification of pancreatic cancer.

Pancreatic adenocarcinoma (PAAD) remains one of the most lethal cancers, largely due to the lack of reliable diagnostic biomarkers. Through integrative transcriptomic and proteomic analyses, we evaluated a four-gene signature comprising CLDN18, CEACAM5, FUT3, and FUT6, capable of distinguishing tumor from normal pancreatic tissue with high accuracy (AUC > 0.80). Among these genes, CLDN18 emerged as a structurally distinct and underappreciated biomarker, showing strong transcriptional association with CEACAM5 and the glycosyltransferases FUT3/FUT6, which are involved in the biosynthesis of CA19.9, the current clinical standard for PAAD diagnosis. Functional enrichment analyses indicated that CLDN18 is involved in pathways related to epithelial polarity, tight junction organization, and extracellular vesicle (EVs)-mediated transport. Machine learning models demonstrated robust classification performance, with CLDN18 consistently ranking among the top predictive features. In vitro validation confirmed the overexpression of CLDN18 in pancreatic cancer cells compared with non-tumoral pancreatic epithelial cells and its selective enrichment in tumor-derived EVs. Furthermore, by immunohistochemical evaluation of primary pancreatic adenocarcinomas, we demonstrated that CLDN18 was indeed overexpressed in tumor samples compared to corresponding normal tissues. Overall, these findings underscore the value of integrating junctional, glycosylation-related, and antigenic markers into multi-marker classifiers to improve diagnostic accuracy. Given its consistent overexpression, prognostic relevance, and compatibility with emerging immunotherapeutic strategies, CLDN18 represents a promising addition to current biomarker panels and a potential target for molecular imaging and drug development in PAAD.