This study was designed to investigate the mechanism by which miR-129-5p affects the biological function of liver cancer cells. The expression levels of miR-129–5p in liver cancer tissues and cells were, respectively, determined. Crystal violet staining and flow cytometry were used to detect cell proliferation and apoptosis. Wound healing assay and transwell assay were performed to test cell migration and invasion. The target gene of miR-129–5p was analyzed and verified by bioinformatics analysis and luciferase reporter assay. Tumorigenicity assays in nude mice were used to test the antitumor ability of calcium calmodulin-dependent protein kinase IV (CAMK4). miR-129–5p was found to be underexpressed in hepatocellular cancer tissues and cells and also to inhibit liver cells proliferation, migration, and invasion and promote apoptosis. CAMK4 was a direct target for miR-129–5p and was lowly expressed in liver cancer tissues and cells. CAMK4 was also found to inhibit liver cells proliferation, migration and invasion, and promote apoptosis. CAMK4 might exert an antitumor effect by inhibiting the activation of mitogen-activated protein kinase (MAPK). MiR-129–5p was a tumor suppressor with low expression in liver cancer tissues and cells. CAMK4, which is a direct target gene of miR-129–5p, could inhibit tumor by inhibiting the activation of MAPK signaling pathway.
Background: In recent years, sepsis-induced acute respiratory distress syndrome (ARDS) has remained a major clinical challenge for patients in intensive care units. While some progress has been reported over the years, the pathogenesis of ARDS still needs to be further expounded. Methods: In the present study, gene set enrichment analysis, differentially expressed genes analysis, short time-series expression miner, protein–protein interaction (PPI) networks, module analysis, hypergeometric test, and functional enrichment analysis were performed in whole blood gene expression profiles of sepsis and induced-sepsis ARDS to explore the molecular mechanism of sepsis-induced ARDS. Results: Further dysregulated genes in the process evolving from healthy control through sepsis to sepsis-induced ARDS were identified and organized into 10 functional modules based on their PPI networks. These functional modules were significantly involved in cell cycle, ubiquitin mediated proteolysis, spliceosome, and other pathways. MYC, STAT3, LEF1, and BRCA1 were potential transcription factors (TFs) regulating these modules. A TF-module-pathway global regulation network was constructed. In particular, our findings suggest that MYC and STAT3 may be the key regulatory genes in the underlying dysfunction of sepsis-induced ARDS. Receiver operating characteristic curve analysis showed the core genes in the global regulation network may be biomarkers for sepsis or sepsis-induced ARDS. Conclusions: We found that MYC and STAT3 may be the key regulatory genes in the underlying dysfunction of sepsis-induced ARDS. The reviews of this paper are available via the supplementary material section.
Purpose: The aim of this study was to generate a novel miRNA expression signature to effectively assess nodal metastasis, distant metastasis and predict prognosis for patients with kidney renal clear cell carcinoma (KIRC) and explore its potential mechanism of affecting the prognosis.Method: Using expression profiles downloaded from the Cancer Genome Atlas database, we identified multiple miRNAs with differential expression between KIRC and paired normal tissues. The diagnostic values of the differentially expressed miRNAs for nodal metastasis and distant metastasis were evaluated by Receiver Operating Characteristic (ROC) curve analysis. Then, we evaluated the impact of miRNAs on overall survival (OS) by univariate and multivariate COX regression analyzes. This analysis was ultimately used to construct a miRNA signature that effectively assessed nodal metastasis, distant metastasis and predicted prognosis. The functional enrichment analysis of the miRNAs included in the signatures was used to explore its potential molecular mechanism in KIRC.Results: Based on our cutoff criteria (P < 0.05 and |log2FC| > 1.0), we identified 104 differentially expressed microRNAs (miRNAs), including 43 that were up-regulated in KIRC tissues and 61 that were down-regulated. We found 12 miRNAs were potentially diagnostic biomarkers of nodal metastasis and distant metastasis by ROC curve analysis. Two miRNAs (miRNA-21 and miRNA-223) were significant miRNAs independently associated with OS based on Cox univariate and multivariate analysis. We generated a signature index based on expression of these two miRNAs, and the two-miRNA signature is promising as a biomarker for diagnosing nodal metastasis, distant metastasis and predicting 5-year survival rate of KIRC with areas under the curve (AUC)=0.738, 0.659 and 0.731, respectively. Patients were stratified into high-risk and low-risk groups, according to median of the signature prognosis indexes. Patients in the high-risk group had significantly shorter survival times than those in the low-risk group (P = 0.000). The functional enrichment analysis suggested that the target genes of two miRNAs may be involved in various pathways related to cancer, p53 signaling pathway, apoptosis, and MAPK signaling pathway.Conclusion: The two-miRNA signature could assess nodal metastasis, distant metastasis and predict survival of KIRC. As a promising prediction tool, the mechanism of the two miRNAs in KIRC deserves further study.
Sepsis is a disease associated with high mortality. We performed bioinformatic analysis to identify key biomarkers associated with sepsis and septic shock. Methods: The top 20% of genes showing the greatest variance between sepsis and controls in the GSE13904 dataset (children) were screened by co-expression network analysis. The differentially expressed genes (DEGs) were identified through analyzing differential gene expression between sepsis patients and control in the GSE13904 (children) and GSE154918 (adult) data sets. Intersection analysis of module genes and DEGs was performed to identify common DEGs for enrichment analysis, protein-protein interaction network (PPI network) analysis, and Short Time-series Expression Miner (STEM) analysis. The PPI network genes were ranked by degree of connectivity, and the top 100 sepsis-associated genes were identified based on the area under the receiver operating characteristic curve (AUC). In addition, we evaluated differences in immune cell infiltration between sepsis patients and controls in children (GSE13904, GSE25504) and adults (GSE9960, GSE154918). Finally, we analyzed differences in DNA methylation levels between sepsis patients and controls in GSE138074 (adults). Results: The common genes were associated mainly with up-regulated inflammatory and metabolic responses, as well as down-regulated immune responses. Sepsis patients showed lower infiltration by most types of immune cells. Genes in the PPI network with AUC values greater than 0.9 in both GSE13904 (children) and GSE154918 (adults) were screened as key genes for diagnosis. These key genes (MAPK14, FGR, RHOG, LAT, PRKACB, UBE2Q2, ITK, IL2RB, and CD247) were also identified in STEM analysis to be progressively dysregulated across controls, sepsis patients and patients with septic shock. In addition, the expression of MAPK14, FGR, and CD247 was modified by methylation. Conclusion: This study identified several potential diagnostic genes and inflammatory and metabolic responses mechanisms associated with the development of sepsis.
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