Identification of Prognostic Genes and Establishment of a Risk Score Model Related to Pancreatic Adenocarcinoma and Brown Adipose Tissue Based on Transcriptomics and Experimental Validation.

Background: Pancreatic adenocarcinoma (PAAD), often referred to as the "king of cancers," remains poorly understood in terms of the regulatory mechanisms involving brown adipocytes (BAs). Methods: Bioinformatics approaches were employed to explore the role of BAs in PAAD progression, utilizing transcriptomic data from public databases. Prognostic genes were identified through differential expression analysis, univariate Cox regression, and machine learning. A risk model categorizing patients into high- and low-risk groups was developed, accompanied by a nomogram. Functional analysis, immune microenvironment profiling, somatic mutation analysis, and drug sensitivity testing were performed, with further validation via gene localization, immunohistochemistry, and clinical sample analysis. Results: Six prognostic genes (SERPINB5, CALU, TFRC, LY6D, SFRP1, and GBP2) were identified, with the model and nomogram exhibiting robust predictive performance. Notable differences between the high- and low-risk groups were found in immune pathways, cell infiltration, tumor mutational burden, and drug sensitivity (e.g., axitinib). Conclusions: SERPINB5, SFRP1, and TFRC were highly expressed in PAAD samples, providing new insights into potential therapeutic strategies in PAAD treatment.