Mesenchymal stem cells (MSCs) are non-hematopoietic progenitor cells with self-renewal ability and multipotency of osteogenic, chondrogenic, and adipogenic differentiation. MSCs have appeared as a promising approach for tissue regeneration and immune therapies, which are attributable not only to their differentiation into the desired cells but also to their paracrine secretion. MSC-sourced secretome consists of soluble components including growth factors, chemokines, cytokines, and encapsulated extracellular vesicles (EVs). Apoptotic bodies (ABs) are large EVs (diameter 500𠀓2000 nm) harboring a variety of cellular components including microRNA, mRNA, DNA, protein, and lipids related to the characteristics of the originating cell, which are generated during apoptosis. The released ABs as well as the genetic information they carry are engulfed by target cells such as macrophages, dendritic cells, epithelial cells, and fibroblasts, and subsequently internalized and degraded in the lysosomes, suggesting their ability to facilitate intercellular communication. In this review, we discuss the current understanding of the biological functions and therapeutic potential of MSC-derived ABs, including immunomodulation, tissue regeneration, regulation of inflammatory response, and drug delivery system.

Lung cancer predominates in cancer-related deaths worldwide, with lung adenocarcinoma (LUAD) being a common histological subtype of lung cancer. The aim at this study was to search for biomarkers associated with the progression and prognosis of LUAD. We have integrated the expression profiles of 1174 lung cancer patients from five GEO datasets (GSE18842, GSE19804, GSE30219, GSE40791 and GSE68465) and identified a set of differentially expressed genes. Functional enrichment analysis showed that these genes are closely related to the progression of LUAD, such as cell cycle, mitosis and adhesion. Cytoscape software was used to establish a protein-protein interaction (PPI) network to analyze important modules using Molecular Complex Detection (MCODE), and finally CCNB1, BUB1B and TTK were selected for further study. The study found that compared with non-tumor lung tissue, CCNB1, BUB1B and TTK are highly expressed in LUAD. Kaplan-Meier analysis showed that CCNB1, BUB1B and TTK were negatively correlated with the overall survival and disease-free survival of patients. Gene set enrichment analysis (GSEA) demonstrated that for the samples of any hub gene highly expressed, most of the functional gene sets enriched in cell cycle. In summary, CCNB1, BUB1B and TTK can be used as biomarkers of poor prognosis of LUAD. The high expression of CCNB1, BUB1B and TTK can accelerate the progression of LUAD and lead to shorter survival, suggesting that they may be potential targets for treatment in LUAD.

The non-small cell lung cancer (NSCLC), including lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD), accounts for a large proportion of lung cancer cases. However, the mechanisms of LUSC and LUAD are very different, especially the pathogenesis of LUSC remains unclear. At present, the research on the targeted therapeutic sites of LUAD has approached maturity and these targets are of clinical significance. However, effective therapeutic targets have not been identified in LUSC, and at present, the same targeted therapeutic strategy for LUAD is also applied in LUSC treatment. We used the data from The Cancer Genome Atlas program to analyze the driver genes of LUAD and LUSC by two types of algorithms, namely, the OncodriveCLUST and Multi-Dendrix. Our results showed that the driver genes of LUAD concentrates in the KRAS/epidermal growth factor receptor/TP53 pathways, while LUSC involves multiple pathways, including PIK3CA, NFE2L2, and TP53. The results showed that different carcinogenic mechanisms exist between these two types of NSCLC, implying that different therapeutic targets for LUSC deserve our attention. At the same time, the results of survival analysis proved that the driver genes identified using the two algorithms in combination may be more valid and reliable than those identified by solely using MutsigCV.