Associations between genetically predicted blood protein biomarkers and pancreatic cancer risk
Menée notamment à partir de données d'études d'association sur le génome entier portant sur 8 280 patients atteints d'un cancer du pancréas et sur 6 728 témoins, cette étude identifie 38 biomarqueurs sanguins susceptibles de prédire le risque d'adénocarcinome canalaire du pancréas
Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal malignancies with few known risk factors and biomarkers. Several blood protein biomarkers have been linked to PDAC in previous studies, but these studies have assessed only a limited number of biomarkers usually in small samples. In this study, we evaluated associations of circulating protein levels and PDAC risk using genetic instruments.
Methods: To identify novel circulating protein biomarkers of PDAC, we studied 8,280 cases and 6,728 controls of European descent from the Pancreatic Cancer Cohort Consortium and the Pancreatic Cancer Case-Control Consortium, using genetic instruments of protein quantitative trait loci (pQTL).
Results: We observed associations between predicted concentrations of 38 proteins and PDAC risk at a false discovery rate of < 0.05, including 23 of those proteins that showed an association even after Bonferroni correction. These include the protein encoded by ABO, which has been implicated as a potential target gene of PDAC risk variant. Eight of the identified proteins (LMA2L, TM11D, IP-10, ADH1B, STOM, TENC1, DOCK9, and CRBB2) were associated with PDAC risk after adjusting for previously reported PDAC risk variants (odds ratio ranged from 0.79 to 1.52). Pathway enrichment analysis showed that the encoding genes for implicated proteins were significantly enriched in cancer-related pathways, such as STAT3 and IL-15 production.
Conclusions: We identified 38 candidates of protein biomarkers for PDAC risk.
Impact: This study identifies novel protein biomarker candidates for PDAC, which if validated by additional studies, may contribute to the etiological understanding of PDAC development.
Cancer Epidemiology Biomarkers & Prevention , résumé, 2019