Lipid Nanoparticle Technology for Delivering Biologically Active Fatty Acids and Monoglycerides

There is enormous interest in utilizing biologically active fatty acids and monoglycerides to treat phospholipid membrane-related medical diseases, especially with the global health importance of membrane-enveloped viruses and bacteria. However, it is difficult to practically deliver lipophilic fatty acids and monoglycerides for therapeutic applications, which has led to the emergence of lipid nanoparticle platforms that support molecular encapsulation and functional presentation. Herein, we introduce various classes of lipid nanoparticle technology and critically examine the latest progress in utilizing lipid nanoparticles to deliver fatty acids and monoglycerides in order to treat medical diseases related to infectious pathogens, cancer, and inflammation. Particular emphasis is placed on understanding how nanoparticle structure is related to biological function in terms of mechanism, potency, selectivity, and targeting. We also discuss translational opportunities and regulatory needs for utilizing lipid nanoparticles to deliver fatty acids and monoglycerides, including unmet clinical opportunities.

Integration analysis for novel lncRNA markers predicting tumor recurrence in human colon adenocarcinoma

BACKGROUND

Numerous evidence has suggested that long non-coding RNA (lncRNA) acts an important role in tumor biology. This study focuses on the identification of novel prognostic lncRNA biomarkers predicting tumor recurrence in human colon adenocarcinoma.

METHODS

We obtained the research data from The Cancer Genome Atlas (TCGA) database. The interaction among different expressed lncRNA, miRNA and mRNA markers between colon adenocarcinoma patients with and without tumor recurrence were verified with miRcode, starBase and miRTarBase databases. We established the lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) network based on the verified association between the selected markers. We performed the functional enrichment analysis to obtain better understanding of the selected lncRNAs. Then we use multivariate logistic regression to identify the prognostic lncRNA markers with covariates. We also generated a nomogram predicting tumor recurrence risk based on the identified lncRNA biomarkers and clinical covariates.

RESULTS

We included 12,727 lncRNA, 1881 miRNA and 47,761 mRNA profiling and clinical features for 113 colon adenocarcinoma patients obtained from the TCGA database. After filtration, we used 37 specific lncRNAs, 60 miRNAs and 148 mRNAs in the ceRNA network analysis. We identified five lncRNAs as prognostic lncRNA markers predicting tumor recurrence in colon adenocarcinoma, in which four of them were identified for the first time. Finally, we generated a nomogram illustrating the association between the identified lncRNAs and the tumor recurrence risk in colon adenocarcinoma.

CONCLUSIONS

The four newly identified lncRNA biomarkers might be potential prognostic biomarkers predicting tumor recurrence in colon adenocarcinoma. We recommend that further clinical and fundamental researches be conducted on the identified lncRNA markers.