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Plakophilin 3 (PKP3) affects cell signal transduction and cell adhesion and performs a crucial function in tumorigenesis. The current investigation evaluated the predictive significance and underlying processes of PKP3 within pancreatic cancer (PC) tissues. The assessment of differences in PKP3 expression was conducted through an analysis of RNA-seq data acquired from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Additionally, clinical samples were collected to validate the findings. The predictive significance of PKP3 was investigated by analyzing survival data derived from TCGA and clinical specimens. PKP3′s biological function was assessed via phenotypic experiments after the suppression of PKP3 expression within PC cells. Functional enrichment analysis, encompassing KEGG, GO, and GSEA, was employed to assess the underlying mechanism of PKP3. Immune infiltration analysis was conducted in the present investigation to determine the association between PKP3 and tumor-infiltrating immune cells (TICs). In PC tissues, PKP3 expression was abnormally upregulated and correlated with a negative prognosis in individuals with PC. PKP3 can promote the progression, migration, and invasive capacity of PC cells and is relevant to the regulation of the PI3K–Akt and MAPK signaling pathways. Immune infiltration analysis demonstrated that PKP3 impeded CD8+ T-cell infiltration and immune cytokine expression within the tumor microenvironment. The PKP3 protein was identified as a prospective independent predictive indicator and represents a viable approach for immunotherapy in the context of PC. PKP3 may impact prognosis by broadly inhibiting immune cell infiltration and promoting the activation of tumor-associated signaling pathways. [ABSTRACT FROM AUTHOR]

Tumor-associated macrophages M2 (TAM2), which are highly prevalent infiltrating immune cells in the stroma of pancreatic cancer (PC), have been found to induce an immunosuppressive tumor microenvironment, thus enhancing tumor initiation and progression. However, the immune therapy response and prognostic significance of regulatory genes associated with TAM2 in PC are currently unknown. Based on TCGA transcriptomic data and single-cell sequencing data from the GEO database, we identified TAM2-driven genes using the WGCNA algorithm. Molecular subtypes based on TAM2-driven genes were clustered using the ConsensusClusterPlus algorithm. The study constructed a prognostic model based on TAM2-driven genes through Lasso-COX regression analysis. A total of 178 samples obtained by accessing TCGA were accurately categorized into two molecular subtypes, including the high-TAM2 infiltration (HMI) cluster and the low-TAM2 infiltration (LMI) cluster. The HMI cluster exhibits a poor prognosis, a malignant tumor phenotype, immune-suppressive immune cell infiltration, resistance to immunotherapy, and a high number of genetic mutations, while the LMI cluster is the opposite. The prognostic model composed of six hub genes from TAM2-driven genes exhibits a high degree of accuracy in predicting the prognosis of patients with PC and serves as an independent risk factor. The induction of TAM2 was employed as a means of verifying these six gene expressions, revealing the significant up-regulation of BCAT1, BST2, and MERTK in TAM2 cells. In summary, the immunophenotype and prognostic model based on TAM2-driven genes offers a foundation for the clinical management of PC. The core TAM2-driven genes, including BCAT1, BST2, and MERTK, are involved in regulating tumor progression and TAM2 polarization, which are potential targets for PC therapy. [ABSTRACT FROM AUTHOR]