Recent advances of the function of sphingosine 1-phosphate (S1P) receptor S1P3.

Known as a variety of sphingolipid metabolites capable of performing various biological activities, sphingosine 1-phosphate (S1P) is commonly found in platelets, red blood cells, neutrophils, lymph fluid, and blood, as well as other cells and body fluids. S1P comprises five receptors, namely, S1P1-S1P5, with the distribution of S1P receptors exhibiting tissue selectivity to some degree. S1P1, S1P2, and S1P3 are extensively expressed in a wide variety of different tissues. The expression of S1P4 is restricted to lymphoid and hematopoietic tissues, while S1P5 is primarily expressed in the nervous system. S1P3 plays an essential role in the pathophysiological processes related to inflammation, cell proliferation, cell migration, tumor invasion and metastasis, ischemia-reperfusion, tissue fibrosis, and vascular tone. In this paper, the relevant mechanism in the role of S1P3 is summarized.

Integrated analysis of RNA-binding proteins in human colorectal cancer.

BACKGROUND: Although RNA-binding proteins play an essential role in a variety of different tumours, there are still limited efforts made to systematically analyse the role of RNA-binding proteins (RBPs) in the survival of colorectal cancer (CRC) patients. METHODS: Analysis of CRC transcriptome data collected from the TCGA database was conducted, and RBPs were extracted from CRC. R software was applied to analyse the differentially expressed genes (DEGs) of RBPs. To identify related pathways and perform functional annotation of RBP DEGs, Gene Ontology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out using the database for annotation, visualization and integrated discovery. Protein-protein interactions (PPIs) of these DEGs were analysed based on the Search Tool for the Retrieval of Interacting Genes (STRING) database and visualized by Cytoscape software. Based on the Cox regression analysis of the prognostic value of RBPs (from the PPI network) with survival time, the RBPs related to survival were identified, and a prognostic model was constructed. To verify the model, the data stored in the TCGA database were designated as the training set, while the chip data obtained from the GEO database were treated as the test set. Then, both survival analysis and ROC curve verification were conducted. Finally, the risk curves and nomograms of the two groups were generated to predict the survival period. RESULTS: Among RBP DEGs, 314 genes were upregulated while 155 were downregulated, of which twelve RBPs (NOP14, MRPS23, MAK16, TDRD6, POP1, TDRD5, TDRD7, PPARGC1A, LIN28B, CELF4, LRRFIP2, MSI2) with prognostic value were obtained. CONCLUSIONS: The twelve identified genes may be promising predictors of CRC and play an essential role in the pathogenesis of CRC. However, further investigation of the underlying mechanism is needed.