UNC93B1 promotes pancreatic cancer progression through modulation of cGAS–STING signaling - Yang, Hao; Li, Yukun; Su, Jing; Zhang, Haiyan; Zhu, Wenxin; Shen, Kanger; Xu, Wei (Frontiers in Immunology, 2026)
BackgroundPancreatic 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 in vitro functional validation focusing on the role of UNC93B1 in malignant phenotypes. The ultimate goal of this research is to inform the development of precise therapeutic strategies to enhance patient survival and quality of life.MethodsWe 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). In vitro validation was performed using four PDAC cell lines (PANC-1, BxPC-3, Capan-1, SW1990). Following qPCR confirmation of high UNC93B1 expression, a stable knockdown model (sh-UNC93B1) was generated in Capan-1 cells. Functional consequences were assessed using CCK-8, wound healing, transwell and colony formation assays. A subcutaneous xenograft model was established to evaluate tumor growth in vivo. Mechanistic insights were gained through flow cytometry for cell cycle analysis and molecular profiling of the cGAS-STING pathway, senescence markers (e.g., p16^INK4a), and epithelial-mesenchymal transition (EMT)-related genes.ResultsSingle-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. In vitro, UNC93B1 knockdown in Capan-1 cells significantly attenuated proliferative capacity (22.3% reduction in OD450 at 72h, P < 0.05), migratory ability (29.6% reduction in wound closure, P < 0.05), and clonogenic survival (342 fewer colonies, P < 0.01). Mechanistically, sh-UNC93B1 cells exhibited G1/S phase arrest (8.9% increase, P < 0.05), activation of the STING/IFN-β/CXCL10 cascade, elevated p16^INK4a expression, and a reversal of EMT, evidenced by downregulation of VIM and upregulation of CDH1. Consistently, in vivo xenograft experiments demonstrated that UNC93B1 silencing markedly impeded tumor growth, concomitant with reduced UNC93B1 protein and enhanced STING pathway activation. Critically, the tumor-suppressive phenotypes induced by UNC93B1 knockdown, including the inhibition of proliferation, migration, and clonogenicity, were largely reversed upon treatment with the selective STING inhibitor H-151, confirming that the observed functional consequences are causally mediated through the activation of the cGAS-STING pathway.ConclusionBy 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.