Prognostic value and immune infiltration of novel markers TNRC6C/AMPD1 in pancreatic cancer microenvironment.

BACKGROUND: Pancreatic cancer (PC) is a highly lethal malignancy with limited treatment options. Identifying novel prognostic biomarkers and therapeutic targets is crucial for improving patient outcomes. METHODS: A comprehensive bioinformatics analysis was conducted on the Gene Expression Omnibus (GEO, GSE79668, GSE183795) and The Cancer Genome Atlas- Pancreatic Adenocarcinoma (TCGA-PAAD) datasets to identify prognostic biomarkers. The prognostic value of these biomarkers was validated through survival analysis and a Cox proportional hazards model (Cox model). A clinical phenotypic prediction model was constructed using AMPD1 and TNRC6C expression levels, with logistic regression models being built for their combination. The nomogram was constructed to visually represent the model's predictive power. Additionally, immune infiltration and single-cell analyses were performed to explore the underlying mechanisms. Functional experiments were conducted to validate the effects of these biomarkers on PC cell behavior. RESULTS: Adenosine Monophosphate Deaminase 1 (AMPD1) and Trinucleotide Repeat Containing Adaptor 6C (TNRC6C) were identified as key prognostic biomarkers for PC. High expression of these genes was associated with improved patient survival. Furthermore, AMPD1 and TNRC6C were found to be positively correlated with various immune cells, suggesting their potential role in modulating the tumor immune microenvironment. Functional experiments confirmed that these genes inhibited cancer cell proliferation, migration, invasion, and promoted apoptosis. The prognostic model based on AMPD1 and TNRC6C expression showed significant predictive accuracy, suggesting its potential clinical utility. CONCLUSION: This study highlights the prognostic significance of AMPD1 and TNRC6C in PC. These findings provide potential new therapeutic targets for PC and warrant further investigation. The developed clinical prediction model further supports their potential utility as biomarkers for patient stratification and prognosis.