Multi-omics and artificial intelligence nominate PCLAF as a prognostic and druggable target for hepatitis B virus-associated hepatocellular carcinoma.

BACKGROUND: PCLAF (PCNA clamp-associated factor) is a protein involved in DNA replication and DNA repair. Aberrant PCLAF expression has been reported in multiple malignancies and is associated with tumor progression and poor clinical outcomes. However, the biological role of PCLAF in hepatocellular carcinoma (HCC) remains incompletely understood, particularly with respect to its relationship with the tumor immune microenvironment. Therefore, this study aimed to systematically investigate the clinical significance, biological functions, and therapeutic potential of PCLAF in HCC through integrated multi-omics analyses, experimental validation, and drug screening approaches. METHODS: In this study, an integrative multi-omics framework was employed to systematically investigate the molecular characteristics and biological functions of PCLAF in hepatocellular carcinoma (HCC). Transcriptomic datasets from the GEO database (GSE83148 and GSE121248) and the TCGA-LIHC cohort were analyzed to identify differentially expressed genes, followed by protein-protein interaction network construction and machine learning algorithms to screen and validate key hub genes. Pan-cancer analysis, clinicopathological correlation analysis, survival analysis, and receiver operating characteristic (ROC) curve analysis were subsequently performed to evaluate the clinical significance of PCLAF. Single-cell RNA sequencing data (GSE202642) were analyzed to characterize the cellular heterogeneity of hepatitis B virus-associated HCC and identify PCLAF-associated cell populations. Functional module scoring and gene set enrichment analysis were performed to investigate the biological features of Cycling T cells. Spatial transcriptomic data (GSE245908) were integrated with deconvolution, cell-cell communication, and spatial regulatory analyses to explore the spatial distribution patterns and intercellular interactions associated with PCLAF. To identify potential therapeutic agents targeting PCLAF, virtual drug screening, molecular docking, and molecular dynamics simulations were conducted. Finally, clinical specimens and HCC cell lines were used for experimental validation. Immunohistochemistry, RT-qPCR, western blotting, colony formation, wound-healing, Transwell migration, and CCK-8 assays were performed to evaluate the effects of PCLAF on HCC cell proliferation and migration. RESULTS: Through integrated transcriptomic analysis, protein-protein interaction network construction, and machine learning approaches, PCLAF was identified as a key candidate gene associated with HCC. Pan-cancer and clinical analyses demonstrated that elevated PCLAF expression was associated with advanced tumor stage, higher pathological grade, and poor prognosis across multiple cancer types. Single-cell transcriptomic analysis revealed that PCLAF was predominantly enriched in Cycling T cells. Functional characterization showed that Cycling T cells exhibited a hyperproliferative but functionally restricted phenotype, characterized by enhanced proliferation accompanied by reduced activation and cytotoxicity. Spatial transcriptomic and cell-cell communication analyses further identified a potential epithelial cell-Cycling T cell interaction network, in which the MIF-CD74-CXCR4 signaling axis represented a major communication pathway. To explore therapeutic opportunities, AI-assisted drug screening, molecular docking, and molecular dynamics simulations were performed, leading to the identification of BRD-K12189280 as a promising candidate compound targeting PCLAF. Experimental validation confirmed that PCLAF was significantly overexpressed in HCC tissues, and its knockdown markedly inhibited the proliferation, migration, and viability of HCC cells in vitro. CONCLUSION: Our findings demonstrate that PCLAF is significantly upregulated in HBV-associated hepatocellular carcinoma and promotes malignant cellular phenotypes. Multi-omics analyses revealed a close association between PCLAF expression and a hyperproliferative but functionally restricted Cycling T-cell state, highlighting a potential link between tumor progression and immune microenvironment remodeling. These results identify PCLAF as a promising prognostic biomarker and therapeutic target in HCC, while BRD-K12189280 emerges as a potential candidate compound for future drug development.