Five hub genes associated with pancreatic ductal adenocarcinoma (PDAC) demonstrated high diagnostic potential, achieving an AUC > 0.85.
The hub genes identified include CTSC, SMYD3, MFGE8, IGFBP7, and POC1B, which are causally linked to PDAC.
A diagnostic nomogram incorporating these genes achieved a C-index of 0.92, indicating strong predictive performance.
Functional analyses suggest that CTSC may play a role in regulating pathways related to tumor cell proliferation and survival.
The approach utilized a combination of transcriptomic data, genome-wide association studies, and to identify these biomarkers.
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BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy with a dismal 5-year survival rate, largely due to the absence of reliable biomarkers for early detection. The molecular mechanisms underpinning PDAC pathogenesis remain incompletely understood, highlighting the urgent need for novel diagnostic strategies.
OBJECTIVE: This study aimed to integrate -driven (MR) with transcriptomic and genome-wide association data to identify causal PDAC-associated genes and construct a diagnostic nomogram based on 5 hub genes (CTSC, SMYD3, MFGE8, IGFBP7, POC1B) for early detection of pancreatic ductal adenocarcinoma (PDAC).
METHODS: Transcriptomic data from GSE62165 and GSE25471 were retrieved from the Gene Expression Omnibus (GEO) and processed for differential expression using LIMMA and GEO2R, followed by batch correction and weighted gene co-expression network analysis (WGCNA). Summary-level eQTL statistics were obtained from OpenGWAS, and GWAS data included over 5000 PDAC cases. MR analysis was performed using inverse variance weighted (IVW) as the primary approach, supplemented with MR-Egger, weighted median, weighted mode, and MR-PRESSO. Instrument strength, pleiotropy, and heterogeneity were assessed via F-statistics, Egger intercept, and Cochran'stest. Candidate genes were filtered using a consensus approach combining random forest (RF), support vector machine-recursive feature elimination (SVM-RFE), and Lasso regression. Diagnostic performance was evaluated via ROC curves, C-index, calibration plots, and decision curve analysis. Mechanistic insights were derived from KEGG and GO enrichment analyses, as well as protein-protein interaction (PPI) network analyses. Q
RESULTS: Five eQTL-associated hub genes--were identified as causally linked to PDAC via robust MR analysis with minimal evidence of pleiotropy or heterogeneity. These genes demonstrated high diagnostic potential (AUC > 0.85, < .001). A diagnostic nomogram incorporating these genes achieved strong predictive performance (C-index = 0.92) with favorable clinical decision curve results. Functional enrichment and PPI analyses implicated these genes, particularly CTSC, in modulating the, contributing to PDAC cell cycle regulation and apoptosis resistance. CTSC, SMYD3, MFGE8, IGFBP7, and POC1B ITGAV/ITGB3-PI3K-Akt signaling axis P
CONCLUSIONS: This study presents a multi-omics, MR-informed framework for identifying eQTL-regulated biomarkers of PDAC. The identified hub genes offer promising avenues for early detection, while the mechanistic mapping of the PI3K-Akt pathway provides translational insights. These findings warrant further validation in clinical and experimental settings and hold potential to reshape PDAC diagnostic strategies.Pancreatic ductal adenocarcinoma (PDAC) remains a formidable clinical challenge due to its aggressive nature and lack of effective early diagnostic biomarkers. To address this, we integrated transcriptomic data, genome-wide association studies (GWAS), and expression quantitative trait loci (eQTL) information using Mendelian randomization (MR) to identify genes causally associated with PDAC risk. Differentially expressed genes were identified across 2 GEO datasets (GSE62165, GSE25471) and prioritized using weighted gene co-expression network analysis (WGCNA). MR analysis employing IVW, MR-Egger, weighted median, and MR-PRESSO identified 5 hub genes-CTSC, SMYD3, MFGE8, IGFBP7, and POC1B-as significant causal drivers of PDAC. These genes were incorporated into a diagnostic model constructed using machine learning approaches (random forest, SVM-RFE, Lasso), which achieved strong classification performance (AUC > 0.85) and excellent calibration (C-index = 0.92). Functional enrichment and protein-protein interaction analyses revealed that CTSC regulates the ECM-integrin-PI3K-Akt signaling pathway, contributing to tumor cell proliferation and survival. The findings establish a multi-omics-based biomarker panel with strong diagnostic utility and mechanistic relevance, suggesting a potential framework for future translational validation in clinical cohorts.
Key numbers
0.92
Diagnostic Model
Performance metric of the diagnostic nomogram based on hub genes.
5
Five Hub Genes Identified
Causal genes linked to through .
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