Extracellular vesicles (EVs) are membrane-delineated particles secreted by most types of cells under both normal and pathophysiological conditions. EVs are believed to mediate intercellular communication by serving as carriers of different bioactive ingredients, including proteins, nucleic acids and lipids. Glycoconjugates are complex molecules consisting of covalently linked carbohydrate with proteins or lipids. These glycoconjugates play essential roles in the sorting of vesicular protein and the uptake of small extracellular vesicles (30–100 nm, sEVs) into recipient cells. Glycosphingolipids (GSLs), one subtype of glycolipids, which are ubiquitous membrane components in almost all living organisms, are also commonly distributed on sEVs. However, the study of functional roles of GSLs on sEVs are far behind than other functional cargos. The purpose of this review is to highlight the importance of GSLs on sEVs. Initially, we described classification and structure of GSLs. Then, we briefly introduced the essential functions of GSLs, which are able to interact with functional membrane proteins, such as growth factor receptors, integrins and tetraspanins, to modulate cell growth, adhesion and cell motility. In addition, we discussed analytical methods for studying GSLs on sEVs. Finally, we focused on the function of GSLs on sEVs, including regulating the aggregation of extracellular α-synuclein (α-syn) or extracellular amyloid-β (Aβ) and influencing tumor cell malignancy.

Background: Lung cancer is the most common cancer worldwide, and metastasis is the leading cause of lung cancer related death. However, the molecular network involved in lung cancer metastasis remains incompletely described. Here, we aimed to construct a metastasis-associated ceRNA network and identify a lncRNA prognostic signature in lung cancer.Methods: RNA expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses and gene set enrichment analysis (GSEA) were performed to investigate the function of these genes. Using Cox regression analysis, we found that a 6 lncRNA signature may serve as a candidate prognostic factor in lung cancer. Finally, we used Transwell assays with lung cancer cell lines to verify that LINC01010 acts as a tumor suppressor.Results: We identified 1249 differentially expressed (DE) mRNAs, 440 DE lncRNAs and 26 DE miRNAs between nonmetastatic and metastatic lung cancer tissues. GO and KEGG analyses confirmed that the identified DE mRNAs are involved in lung cancer metastasis. Using bioinformatics tools, we constructed a metastasis-associated ceRNA network for lung cancer that includes 117 mRNAs, 23 lncRNAs and 22 miRNAs. We then identified a 6 lncRNA signature (LINC01287, SNAP25-AS1, LINC00470, AC104809.2, LINC00645 and LINC01010) that had the greatest prognostic value for lung cancer. Furthermore, we found that suppression of LINC01010 promoted lung cancer cell migration and invasion.Conclusions: This study might provide insight into the identification of potential lncRNA biomarkers for diagnosis and prognosis in lung cancer.

Background: Lung cancer is the most common cancer worldwide, and metastasis is the leading cause of lung cancer related death. However, the molecular network involved in lung cancer metastasis remains incompletely described. Here, we aimed to construct a metastasis-associated ceRNA network and identify a lncRNA prognostic signature in lung cancer.Methods: RNA expression profiles were downloaded from The Cancer Genome Atlas (TCGA) database. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses and gene set enrichment analysis (GSEA) were performed to investigate the function of these genes. Using Cox regression analysis, we found that a 6 lncRNA signature may serve as a candidate prognostic factor in lung cancer. Finally, we used Transwell assays with lung cancer cell lines to verify that LINC01010 acts as a tumor suppressor.Results: We identified 1249 differentially expressed (DE) mRNAs, 440 DE lncRNAs and 26 DE miRNAs between nonmetastatic and metastatic lung cancer tissues. GO and KEGG analyses confirmed that the identified DE mRNAs are involved in lung cancer metastasis. Using bioinformatics tools, we constructed a metastasis-associated ceRNA network for lung cancer that includes 117 mRNAs, 23 lncRNAs and 22 miRNAs. We then identified a 6 lncRNA signature (LINC01287, SNAP25-AS1, LINC00470, AC104809.2, LINC00645 and LINC01010) that had the greatest prognostic value for lung cancer. Furthermore, we found that suppression of LINC01010 promoted lung cancer cell migration and invasion.Conclusions: This study might provide insight into the identification of potential lncRNA biomarkers for diagnosis and prognosis in lung cancer.

Decitabine (5‐aza‐2‐deoxycytidine, DAC), a DNA‐hypomethylating agent, has been one of the frontline therapies for clonal hematopoietic stem cell disorders, such as myelodysplastic syndrome and acute myeloid leukemia, but DAC‐resistance often occurs and leads to treatment failure. Therefore, elucidating the mechanisms of DAC resistance is important for improving its therapeutic efficacy. The extracellular vesicles and particles (EVPs) have been reported to be involved in mediating drug resistance by transporting diverse bioactive components. In this study, we established the DAC‐resistant cell line (KG1a‐DAC) from its parental human leukemia‐derived cell line KG1a and observed that EVPs released from KG1a‐DAC can promote DAC‐resistant in KG1a cells. Moreover, treatment with KG1a‐DAC EVPs reduced the expression of cyclin‐dependent kinase inhibitor 2B (CDKN2B) in KG1a cells. miRNA‐Seq analysis revealed that miR‐4755‐5p is overexpressed in EVPs from KG1a‐DAC. Dual‐luciferase reporter assay and flow cytometry analysis confirmed that miR‐4755‐5p rendered KG1a cells resistant to the DAC by targeting CDKN2B gene. Taken together, miR‐4755‐5p in EVPs released from the DAC‐resistant cells plays an essential role in inducing DAC‐resistance, and is a potential therapeutic target for suppression of DAC resistance.