基于蛋白质的胰腺导管腺癌预后模型：构建与验证

Probing relevant proteomic biomarkers may facilitate effective pancreatic adenocarcinoma (PDAC) diagnosis, treatment and prevention. Here, we developed a protein-based prognostic model for PDAC by using relevant proteomic biomarkers data from The Cancer Genome Atlas (TCGA).

We obtained PDAC's proteomic and clinical data from TCGA and used various analytical tools to identify differentially expressed proteins between normal and cancer tissues. We constructed our protein-based prognostic model and confirmed its accuracy using receiver operating characteristic curve and Kaplan-Meier survival analyses. We elucidated clinical factor-signature protein correlations by clinical correlation assessments and protein coexpression networks. We also used immunohistochemistry (protein expression assessment), Gene Set Enrichment Analysis (protein role identification) and CIBERSORT (infiltrating immune cell distribution assessment).

CIITA, BRAF_pS445, AR, YTHDF2, IGFBP2 and CDK1_pT14 were identified as PDAC-associated prognostic proteins. All risk scores calculated using our model provided 1-, 3-, 5-year survival probability at 70 % accuracy. The reliability of our model was validated by the GEO as well. In high- and low-risk groups, age, sex, T- and N- stage disparities were significant, and prognostic and coexpressed proteins correlated. PDAC tissues demonstrated significant CDK1_pT14 overexpression but significant BRAF_pS445, YTHDF2, and IGFBP2 underexpression. Downstream proteins of BRAF were validated by IHC. Low-risk tissues demonstrated more naïve B cells, eosinophils, activated NK cells and regulatory T cells, whereas high-risk tissues demonstrated more activated memory T cells, monocytes, neutrophils, dendritic cells and resting NK cells.

Our protein-based prognostic model for PDAC, along with six signature proteins, might aid in predicting PDAC prognosis and therapeutic targets.