To identify miRNAs that are involved in cell migration in human umbilical vein endothelial cells (HUVECs), we employed RNA sequencing under high glucose incubation and text mining within the databases miRWalk and TargetScanHuman using 83 genes that regulate HUVECs migration. From both databases, 307 predicted miRNAs were retrieved. Differentially expressed miRNAs were determined by exposing HUVECs to high glucose stimulation, which significantly inhibited the migratory ability of HUVECs as compared to cells cultured in normal glucose. A total of 35 miRNAs were found as differently expressed miRNAs in miRNA sequencing, and 4 miRNAs, namely miR-21-3p, miR-107, miR-143-3p, and miR-106b-5p, were identified as overlapping hits. These were subjected to hub gene analysis and pathway analysis using the Kyoto Encyclopedia of Genes and Genomes (KEGG), identifing 71 pathways which were influenced by all four miRNAs. The influence of all four miRNAs on HUVEC migration was phenomorphologically confirmed. miR21 and miR107 promoted migration in HUVECs while miR106b and miR143 inhibited migration. Pathway analysis also revealed eight shared pathways between the four miRNAs. Protein–protein interaction (PPI) network analysis was then performed to predict the functionality of interacting genes or proteins. This revealed six hub genes which could firstly be predicted to be related to HUVEC migration.

Cancer immunotherapy plays a crucial role in bladder cancer (BC) progression. Increasing evidence has elucidated the clinicopathologic significance of the tumor microenvironment (TME) in predicting outcomes and therapeutic efficacy. This study sought to establish a comprehensive analysis of the immune-gene signature combined with TME to assist in BC prognosis. We selected sixteen immune-related genes (IRGs) after a weighted gene co-expression network and survival analysis. Enrichment analysis revealed that these IRGs were actively involved in Mitophagy and Renin secretion pathways. After multivariable COX analysis, the IRGPI comprising NCAM1, CNTN1, PTGIS, ADRB3, and ANLN was established to predict the overall survival of BC, which was validated in both TCGA and GSE13507 cohorts. In addition, a TME gene signature was developed for molecular and prognosis subtyping with unsupervised clustering, followed by a panoramic landscape characterization of BC. In summary, the IRGPI model developed in our study provided a valuable tool with an improved prognosis for BC.