Pancreatic cancer is one of the most serious health issues in developed and developing countries, with a 5‐year overall survival rate currently <9%. Patients typically present with advanced disease due to vague symptoms or lack of screening for early cancer detection. Surgical resection represents the only chance for cure, but treatment options are limited for advanced diseases, such as distant metastatic or locally progressive tumors. Although adjuvant chemotherapy has improved long‐term outcomes in advanced cancer patients, its response rate is low. So, exploring other new treatments is urgent. In recent years, increasing evidence has shown that lipid metabolism can support tumorigenesis and disease progression as well as treatment resistance through enhanced lipid synthesis, storage, and catabolism. Therefore, a better understanding of lipid metabolism networks may provide novel and promising strategies for early diagnosis, prognosis estimation, and targeted therapy for pancreatic cancer patients. In this review, we first enumerate and discuss current knowledge about the advances made in understanding the regulation of lipid metabolism in pancreatic cancer. In addition, we summarize preclinical studies and clinical trials with drugs targeting lipid metabolic systems in pancreatic cancer. Finally, we highlight the challenges and opportunities for targeting lipid metabolism pathways through precision therapies in pancreatic cancer.

Pancreatic cancer (PC) is a highly lethal and aggressive disease with its incidence and mortality quite discouraging. A robust prognostic signature and novel biomarkers are urgently needed for accurate stratification of the patients and optimization of clinical decision-making. Since the critical role of immune microenvironment in the progression of PC, a prognostic signature based on seven immune-related genes was established, which was validated in The Cancer Genome Atlas (TCGA) training set, TCGA testing set, TCGA entire set and GSE71729 set. Furthermore, S100A14 (S100 Calcium Binding Protein A14) was identified as the gene occupying the most paramount position in risk signature. According to the GSEA, CIBERSORT and ESTIMATE algorithm, S100A14 was mainly associated with lower proportion of CD8 + T cells and higher proportion of M0 macrophages in PC tissue. Meanwhile, analysis of single-cell dataset CRA001160 revealed a significant negative correlation between S100A14 expression in PC cells and CD8 + T cell infiltration, which was further confirmed by tissue microenvironment landscape imaging and machine learning-based analysis in our own PUMCH cohort. Additionally, analysis of a pan-pancreatic cancer cell line illustrated that S100A14 might inhibit CD8 + T cell activation via the upregulation of PD-L1 expression in PC cells, which was also verified by the immunohistochemical results of PUMCH cohort. Finally, tumor mutation burden analysis and immunophenoscore algorithm revealed that patients with high S100A14 expression had a higher probability of responding to immunotherapy. In conclusion, our study established an efficient immune-related prediction model and identified the potential role of S100A14 in regulating the immune microenvironment and serving as a biomarker for immunotherapy efficacy prediction.

Pancreatic steatosis is closely associated with metabolic disorders, pancreatitis, and pancreatic cancer, yet the underlying mechanisms remain incompletely understood and ideal animal models are lacking. We established and compared four mouse models based on distinct pathogenic mechanisms. In the high-fat diet (HFD)-fed model, pancreatic steatosis occurred later than hepatic steatosis. In ob/ob mice, despite severe obesity and hepatic steatosis, no significant intrapancreatic fat deposition was observed. In the caerulein combined with HFD model, caerulein markedly accelerated HFD-induced intrapancreatic fat accumulation, indicating a synergistic effect of inflammation and metabolic stress. Orthotopic injection of adipogenically differentiated 3T3-L1 cells established a focal model without altering systemic metabolism, enabling investigation of localized fat deposition. Collectively, these four models recapitulate metabolic, genetic, inflammatory, and localized mechanisms, exhibit divergent histological features, and provide a complementary platform for mechanistic studies and drug screening in pancreatic steatosis.