Exosomal MicroRNA-23-5p Derived from Bone Marrow Mesenchymal Stem Cells Relieves Inflammatory Response in Rheumatoid Arthritis

We aimed to explore the mechanism underlying microRNA-23-5p from exosomes (exo-miR-23-5p) of BMSCs in rheumatoid arthritis (RA). The candidate related genes of miR-23-5p were screened in RA by bioinformatics analysis through gain- and loss-function method along with analysis of histopathological changes in mice and RAC2 expression as well as the level of pro-inflammatory factors. In vivo RA model was established to detect miR-23-5p’s effect on RA. miR-23-5p level was significantly reduced in RA cells and RAC2 was highly expressed. Expression of RAC2 was inhibited and targeted by miR-23-5p in RA. Exo-miR-23-5p treatment effectively alleviated joint destruction, reduced inflammatory factor secretion in tissues and serum, as well as decreased RAC2 expression in RA model. In conclusion, the miR-23-5p in the BMSC-exo delays the inflammatory response in RA, indicating that it might be a new target for treating RA.

Identification of Hub Genes Associated with Immune Infiltration in Cardioembolic Stroke by Whole Blood Transcriptome Analysis

Cardioembolic stroke (CS) is the most common type of ischemic stroke in the clinic, leading to high morbidity and mortality worldwide. Although many studies have been conducted, the molecular mechanism underlying CS has not been fully grasped. This study was aimed at exploring the molecular mechanism of CS using comprehensive bioinformatics analysis and providing new insights into the pathophysiology of CS. We downloaded the public datasets GSE58294 and GSE16561. Differentially expressed genes (DEGs) were screened via the limma package using R software. CIBERSORT was used to estimate the proportions of 22 immune cells based on the gene expression profiling of CS patients. Using weighted gene correlation network analysis (WGCNA) to cluster the genes into different modules and detect relationships between modules and immune cell types, hub genes were identified based on the intersection of the protein-protein interaction (PPI) network analysis and WGCNA, and their clinical significance was then verified using another independent dataset GSE16561. Totally, 319 genes were identified as DEGs and 5413 genes were clustered into nine modules using WGCNA. The blue module, with the highest correlation coefficient, was identified as the key module associated with stroke, neutrophils, and B cells naïve. Based on the PPI analysis and WGCNA, five genes (MCEMP1, CLEC4D, GPR97, TSPAN14, and FPR2) were identified as hub genes. Correlation analysis indicated that hub genes had general association with infiltration-related immune cells. ROC analysis also showed they had potential clinical significance. The results were verified using another dataset, which were consistent with our analysis. Five crucial genes determined using integrative bioinformatics analysis might play significant roles in the pathophysiological mechanism in CS and be potential targets for pharmaceutic therapies.

Identification of Key Genes and Pathways in Osteosarcoma by Bioinformatics Analysis

Purpose. Osteosarcoma (OS) is the most primary bone malignant tumor in adolescents. Although the treatment of OS has made great progress, patients’ prognosis remains poor due to tumor invasion and metastasis. Materials and Methods. We downloaded the expression profile GSE12865 from the Gene Expression Omnibus database. We screened differential expressed genes (DEGs) by making use of the R limma software package. Based on Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis, we performed the function and pathway enrichment analyses. Then, we constructed a Protein-Protein Interaction network and screened hub genes through the Search Tool for the Retrieval of Interacting Genes. Result. By analyzing the gene expression profile GSE12865, we obtained 703 OS-related DEGs, which contained 166 genes upregulated and 537 genes downregulated. The DEGs were primarily abundant in ribosome, cell adhesion molecules, ubiquitin-ubiquitin ligase activity, and p53 signaling pathway. The hub genes of OS were KDR, CDH5, CD34, CDC42, RBX1, POLR2C, PPP2CA, and RPS2 through PPI network analysis. Finally, GSEA analysis showed that cell adhesion molecules, chemokine signal pathway, transendothelial migration, and focal adhesion were associated with OS. Conclusion. In this study, through analyzing microarray technology and bioinformatics analysis, the hub genes and pathways about OS are identified, and the new molecular mechanism of OS is clarified.

Identification of AHSA1 as a Potential Therapeutic Target for Breast Cancer: Bioinformatics Analysis and In Vitro Studies

Background: Shenling Baizhu Powder (SBP), a famous Traditional Chinese Medicine (TCM) formulation, has been widely used in the adjuvant treatment of cancers, including breast cancer. This study aims to identify potential new targets for breast cancer treatment based on the network pharmacology of SBP. Methods: By analyzing the relationship between herbs and target proteins, potential targets of multiple herbs in SBP were identified by network pharmacology analysis. Besides, by comparing the data of breast cancer tissue with normal tissue, upregulated genes in two breast cancer expression profiles were found. Thereafter, the expression level and prognosis of activator of heat shock protein 90 (HSP90) ATPase activity 1 (AHSA1) were further analyzed in breast cancer by bioinformatics analysis, and the network module of AHSA1 binding protein was constructed. Furthermore, the effect of knocking down AHSA1 on the proliferation, migration, and invasion of breast cancer cells was verified by MTT, clone formation assay, and transwell assay. Results: Vascular endothelial growth factor A (VEGFA), intercellular adhesion molecule 1 (ICAM1), chemokine (C-X-C motif) ligand 8 (CXCL8), AHSA1, and serpin family E member 1 (SERPINE1) were associated with multiple herbs in SBP. AHSA1 was remarkably upregulated in breast cancer tissues and positively correlated with poor overall survival and disease metastasis-free survival. Furthermore, knockdown of AHSA1 significantly inhibited the migration and invasion in MCF-7 and MDA-MB-231 breast cancer cells but had no obvious effect on proliferation. In addition, among the proteins that bind to AHSAl, the network composed of proteasome, chaperonin, and heat shock proteins is closely connected, and these proteins are associated with poor prognosis in a variety of cancers. Conclusion: AHSA1 is positively correlated with breast cancer progression and might act as a novel therapeutic target for breast cancer.

Weighted Gene Correlation Network Analysis Applied to Identify the Immune Cell-related Hub Genes in ANCA Nephritis

Background: Antineutrophil cytoplasmic antibody (ANCA) associated vasculitis (AAV) is the most common reason caused rapidly progressive glomerulonephritis worldwide. But the molecular mechanisms of ANCA - associated nephritis (AAN) have not been thoroughly expounded. So that，we aim to seek the potential molecular pathogenesis of AAN by bioinformatic.Result: Finally, four hub genes, PBK, CEP55, CCNB1 and BUB1B, were identified. These four hub geneswas verified higher in AAN than normal.Conclusion: Those four genes identified by integrated bioinformatics analysis may play a critical role in AAN. May offering a new insights and potential therapeutic to the AAN

TMC5 is Highly Expressed in Human Cancers and Corelates to Prognosis and Immune Cell Infiltration: A Comprehensive Bioinformatics Analysis

Background: The oncological role of TMC5 in human cancers has only been revealed partially. We performed integrated bioinformatics analysis to provide a thorough and detailed insight of associations between TMC5 and tumorigenesis, cancer progression, and prognosis.Methods: With reference to the accessible online databases, the TMC5 expressions in tumor tissues and corresponding normal tissues, different pathological stages, and various cancer cells were analyzed, while the protein levels of TMC5 in different cancers were also inspected. Meanwhile, the prognostic value of TMC5 expression in multiple cancers as well as in advanced-stage patients was investigated. Furthermore, the mutational data of TMC5 and its correlation with cancer prognosis were assessed. Moreover, the association between the TMC5 level and immune cell infiltration was evaluated. Next, TMC5-related pathway alterations and drug responses were summarized. Finally, the TMC5 based protein network was generated, and relevant enrichment was performed.Results: In our study, the expression level of TMC5 was significantly higher in the tumor tissue than that of the normal tissues in most cancer types. Fluctuations of TMC5 levels were also observed among different pathological stages. In the meantime, the protein level elevated in the tumor tissue in the cancers enrolled. Moreover, the expression of TMC5 was not only prognostic for overall survival (OS) or recurrence free survival (RFS) in various types of cancers but also correlated to OS in patients with more advanced cancers. Additionally, the mutational status of TMC5 is also associated with prognosis in cancer patients. It is worth noting that the TMC5 level was closely related to immune cell infiltrations, especially in ESCA, TGCT, and USC. The TMC5 expression was also identified as an activator for pathways including PI3K/AKT, RAS/MAPK, and TSC/mTOR, proved to be associated with multiple drug responses and assessed to be interactive with the TMEM family.Conclusion: TMC5 might function as a potential marker for cancer survival and immune responses.

RNA Sequencing Analysis Between Ruptured and Un-Ruptured Brain AVM

BackgroundBrain arteriovenous malformation (BAVM) arises as congenital vascular abnormalities with a significant risk for intracerebral hemorrhage (ICH). RNA sequencing technology has been recently used to investigate the mechanism of diseases owing to its ability to identify the gene changes on a transcriptome-wide level. In this study, we aimed to gain insights into the potential mechanism involved in BAVM rupture. MethodsSixty-five BAVM nidus samples were collected among which 28 were ruptured and 37 were un-ruptured. Then next-generation RNA sequencing were performed on all of them to obtain differential expressed genes (DEGs) between these two groups. In addition, bioinformatics analysis was performed to evaluate the involved biological processes and pathways by GO and KEGG analysis. Finally, univariate Cox regression analysis was utilized to obtain the early rupture-prone DEGs. Results: A total of 951 genes were differentially expressed between ruptured and un-ruptured BAVM group, of which 740 genes were up-regulated and 211 genes were down-regulated in ruptured BAVMs. Then bioinformatics analysis showed the biological processes and pathways related to the inflammatory processes and extracellular matrix organization were significantly enriched. Meanwhile, some of down-regulated genes are involved in cell adhesion and genes participating in response to muscle activity, as well as the terms about nervous system development. Finally, one hundred and twenty-five genes, a large number of which were involved in inflammation, were correlated with the early rupture of BAVMs. Conclusions: The up-regulated genes in ruptured BAVM group were involved in inflammatory processes and extracellular matrix organization while some of the down-regulated genes were participating in cell adhesion and myofibril assembly, indicating the role of enhanced inflammation and reduced vessel strength in BAVMs rupture.

Pre-transplant Transcriptional Signature in Peripheral Blood Mononuclear Cells of Acute Renal Allograft Rejection

Acute rejection (AR) is closely associated with renal allograft dysfunction. Here, we utilised RNA sequencing (RNA-Seq) and bioinformatic methods to characterise the peripheral blood mononuclear cells (PBMCs) of patients with acute renal allograft rejection. Pretransplant blood samples were collected from 32 kidney allograft donors and 42 corresponding recipients with biopsies classified as T cell-mediated rejection (TCMR, n = 18), antibody-mediated rejection (ABMR, n = 5), and normal/non-specific changes (non-AR, n = 19). The patients with TCMR and ABMR were assigned to the AR group, and the patients with normal/non-specific changes (n = 19) were assigned to the non-AR group. We analysed RNA-Seq data for identifying differentially expressed genes (DEGs), and then gene ontology (GO) analysis, Reactome, and ingenuity pathway analysis (IPA), protein—protein interaction (PPI) network, and cell-type enrichment analysis were utilised for bioinformatics analysis. We identified DEGs in the PBMCs of the non-AR group when compared with the AR, ABMR, and TCMR groups. Pathway and GO analysis showed significant inflammatory responses, complement activation, interleukin-10 (IL-10) signalling pathways, classical antibody-mediated complement activation pathways, etc., which were significantly enriched in the DEGs. PPI analysis showed that IL-10, VEGFA, CXCL8, MMP9, and several histone-related genes were the hub genes with the highest degree scores. Moreover, IPA analysis showed that several proinflammatory pathways were upregulated, whereas antiinflammatory pathways were downregulated. The combination of NFSF14+TANK+ANKRD 33 B +HSPA1B was able to discriminate between AR and non-AR with an AUC of 92.3% (95% CI 82.8–100). Characterisation of PBMCs by RNA-Seq and bioinformatics analysis demonstrated gene signatures and biological pathways associated with AR. Our study may provide the foundation for the discovery of biomarkers and an in-depth understanding of acute renal allograft rejection.