UNC93B1 promotes pancreatic cancer progression through modulation of cGAS-STING signaling.

Background: Pancreatic ductal adenocarcinoma (PDAC) remains among the most lethal solid tumors, largely due to its intricate and immunosuppressive tumor microenvironment (TME). While single-cell sequencing technologies have begun to unravel the cellular heterogeneity of PDAC, a comprehensive understanding of how genetic determinants influence and are influenced by the TME is still lacking. To bridge this knowledge gap, our study employs an integrated multi-omics approach, incorporating single-cell transcriptomics, genomics, and proteomics, complemented by computational biology and machine learning. We aimed to delineate the core molecular drivers of PDAC pathogenesis, with subsequent Methods: We assembled a comprehensive multi-omics dataset, including single-cell RNA-seq data from 22 PDAC samples (GSE154778, GSE212966), bulk transcriptomic cohorts (GSE28735, GSE62452), survival data from the TCGA-PAAD project (n=172), spatial transcriptomics, and genome-wide association study data (bbj-a-140, n=196,187). The single-cell data were processed using Seurat v5, which involved rigorous quality control, batch effect correction with Harmony, unsupervised clustering, and cell type annotation to characterize TME heterogeneity. Genetic susceptibility was mapped onto single-cell data using scPagwas to calculate trait-regulated scores (TRS) and identify trait-associated genes. Co-expression networks were constructed via high-diversity WGCNA (hdWGCNA), and key candidate genes were refined through survival analysis and a machine learning framework integrating LASSO regression, Random Forest, and Support Vector Machine algorithms. The functional role of the pivotal gene, UNC93B1, was systematically investigated through Gene Set Variation Analysis (GSVA), pseudotime trajectory inference (Monocle2), and cell-cell communication analysis (CellChat). Results: Single-cell transcriptomic profiling delineated nine distinct cell populations within the PDAC TME. hdWGCNA identified three gene modules (8, 11, 16) positively associated with tumorigenesis. The intersection of these modules with differentially expressed genes yielded 320 candidates, which were subsequently filtered to 61 genes significantly linked to patient prognosis (P < 0.05) via Cox regression. Cross-validation across machine learning models and scPagwas analysis converged on UNC93B1 as the sole overlapping gene with consistent diagnostic and prognostic relevance. UNC93B1 was robustly upregulated in tumor tissues across independent datasets (TCGAxGTEx, bulk RNA-seq), a finding corroborated at the protein level by HPA and CPTAC data (P < 0.01). Its expression positively correlated with higher pathological grade and was spatially enriched within tumor regions. Functional enrichment analysis (GSVA) suggested that UNC93B1 is involved in the suppression of the cGAS-STING signaling axis. Pseudotime analysis indicated that UNC93B1 expression escalates along tumor progression trajectories. CellChat suggested strengthened intercellular communication networks in UNC93B1-high cells, particularly modulated by the cGAS-STING pathway. Conclusion: By integrating multi-omics data, including GWAS and spatial transcriptomics, this study systematically defines a pivotal role for UNC93B1 in PDAC progression. Our findings demonstrate that UNC93B1 is associated with an immunosuppressive TME and facilitates metastatic spread, potentially through inhibiting the cGAS-STING-mediated innate immunity pathway. The strong correlation between UNC93B1 overexpression and adverse clinical outcomes underscores its potential as a dual diagnostic biomarker and therapeutic target. This work not only provides a mechanistic foundation for novel precision immunotherapies in PDAC but also establishes a robust methodological paradigm for multi-omics-driven discovery in oncology.