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.

Background: Pancreatic cancer’s poor prognosis is caused by distal metastasis, which is associated with epigenetic changes. However, the role of the 3D epigenome in pancreatic cancer biology, especially its metastasis, remains unclear. Methods: Here, we developed high-resolution 3D epigenomic maps of cells derived from normal pancreatic epithelium, primary and metastatic pancreatic cancer by in situ Hi-C, ChIP-seq, ATAC-seq, and RNA-seq to identify key genes involved in pancreatic cancer metastasis Results: We found that A/B compartments, contact domains, and chromatin loops changed significantly in metastatic pancreatic cancer cells, which are associated with epigenetic state alterations. Moreover, we found that upregulated genes, which were located in switched compartments, changed contact domains, and metastasis-specific enhancer-promoter loops, were related to cancer metastasis and poor prognosis of patients with pancreatic cancer. We also found that transcription factors in specific enhancer-promoter loop formation were also associated with metastasis. Finally we demonstrated that LIPC, looped to metastasis-specific enhancers, could promote pancreatic cancer metastasis. Conclusions: These results highlight the multiscale 3D epigenome reprogramming during pancreatic cancer metastasis and expand our knowledge of mechanisms of gene regulation during pancreatic cancer metastasis.

Background: Pancreatic cancer (PC) is a highly fatal and aggressive disease with its incidence and mortality quite discouraging. An effective prediction model is urgently needed for the accurate assessment of patients’ prognosis to assist clinical decision-making.Methods: Gene expression data and clinicopathological data of the samples were acquired from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) databases. Differential expressed genes (DEGs) analysis, univariate Cox regression analysis, least absolute shrinkage and selection operator (LASSO) regression analysis, random forest screening and multivariate Cox regression analysis were applied to construct the risk signature. The effectiveness and independence of the model were validated by time-dependent receiver operating characteristic (ROC) curve, Kaplan–Meier (KM) survival analysis and survival point graph in training set, test set, TCGA entire set and GSE57495 set. The validity of the core gene was verified by immunohistochemistry and our own independent cohort. Meanwhile, functional enrichment analysis of DEGs between the high and low risk groups revealed the potential biological pathways. Finally, CMap database and drug sensitivity assay were utilized to identify potential small molecular drugs as the risk model-related treatments for PC patients.Results: Four histone modification-related genes were identified to establish the risk signature, including CBX8, CENPT, DPY30 and PADI1. The predictive performance of risk signature was validated in training set, test set, TCGA entire set and GSE57495 set, with the areas under ROC curve (AUCs) for 3-year survival were 0.773, 0.729, 0.775 and 0.770 respectively. Furthermore, KM survival analysis, univariate and multivariate Cox regression analysis proved it as an independent prognostic factor. Mechanically, functional enrichment analysis showed that the poor prognosis of high-risk population was related to the metabolic disorders caused by inadequate insulin secretion, which was fueled by neuroendocrine aberration. Lastly, a cluster of small molecule drugs were identified with significant potentiality in treating PC patients.Conclusions: Based on a histone modification-related gene signature, our model can serve as a reliable prognosis assessment tool and help to optimize the treatment for PC patients. Meanwhile, a cluster of small molecule drugs were also identified with significant potentiality in treating PC patients.

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.

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