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Pancreatic cancer is a lethal malignancy with a dismal prognosis. Gemcitabine is currently used to treat pancreatic cancer, but it is limited by significant toxicity. Clinical trials on the combination of gemcitabine and erlotinib reported unsatisfactory outcomes along with concerns of toxicity. The encapsulation of chemotherapy drugs in polylactic-co-glycolic acid (PLGA) nanoparticles (NPs) can alleviate toxicity through targeted delivery and sustained release. In addition, camouflaging the NPs with a macrophage membrane can evade the immune system and further improve tumor homing. We designed gemcitabine-loaded PLGA NPs with a macrophage membrane coating (MPGNPs) to reduce drug toxicity and increase the accumulation in the tumor. The combination of MPGNPs and erlotinib synergistically inhibited pancreatic cancer cell proliferation in vitro and in vivo by targeting the PI3K/AKT/mTOR and Ras/Raf/MEK/ERK signaling pathways. The MPGNPs were also able to evade phagocytosis and achieve passive targeting to the pancreatic tumors. The combination of MPGNPs and erlotinib showed synergistic anti-tumor efficacy in vitro and in vivo. This study provides a proof-of-concept for treating pancreatic cancer with a combination of MPGNPs and erlotinib.

Background and Aims: Emerging studies indicate that long noncoding RNAs (lncRNAs) play a role as prognostic markers in many cancers, including liver cancer. Here, we focused on the lncRNA lung cancer-associated transcript 1 (LUCAT1) for liver cancer prognosis. Methods: RNA-seq and phenotype data were downloaded from the Cancer Genome Atlas (TCGA). Chisquare tests were used to evaluate the correlations between LUCAT1 expression and clinical features. Survival analysis and Cox regression analysis were used to compare different LUCAT1 expression groups (optimal cutoff value determined by ROC). The log-rank test was used to calculate the p-value of the Kaplan-Meier curves. A ROC curve was used to evaluate the diagnostic value. Gene Set Enrichment Analysis (GSEA) was performed, and competing endogenous RNA (ceRNA) networks were constructed to explore the potential mechanism. Results: Data mining of the TCGA -Liver Hepatocellular Carcinoma (LIHC) RNA-seq data of 371 patients showed the overexpression of LUCAT1 in cancerous tissue. High LUCAT1 expression was associated with age (p=0.007), histologic grade (p=0.009), T classification (p=0.022), and survival status (p=0.002). High LUCAT1 patients had a poorer overall survival and relapse-free survival than low LUCAT1 patients. Multivariate analysis identified LUCAT1 as an independent risk factor for poor survival. The ROC curve indicated modest diagnostic performance. GSEA revealed the related signaling pathways, and the ceRNA network uncovered the underlying mechanism. Conclusion: High LUCAT1 expression is an independent prognostic factor for liver cancer.

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