The efficacy and safety of aspirin in preventing venous thrombosis in major orthopedic surgery: An updated meta-analysis of randomized controlled trials.

BACKGROUND: Major orthopedic surgery, including hip and knee replacement and lower extremity trauma fractures surgery, is associated with a high risk of venous thromboembolism (VTE), especially proximal deep vein thrombosis (DVT), and pulmonary embolism (PE), and is linked with high morbidity and mortality rates. Chemical anticoagulation is routinely used to prevent VTE, with previous meta-analyses reporting on the efficacy and safety of aspirin and other anticoagulants, however, opinions are divided. In the past 2 years, several large randomized controlled trials have been published, therefore, we reanalyzed aspirin efficacy and safety when compared with other anticoagulants in preventing VTE in major orthopedic surgery. METHODS: Using PubMed, The Cochrane Library, Embase, and Web of Science databases, we conducted a RCT search in August 2023. The main outcomes included VTE, proximal DVT or PE. Additional outcomes included bleeding events, wound complications, wound infections, blood transfusions, and death events. RESULTS: In total, 17 eligible articles, involving 29,522 patients (15,253 aspirin vs 14,269 other anticoagulant cases), were included. Primary outcomes showed that VTE incidence was more high in the aspirin group when compared with other anticoagulants (risk ratio [RR] = 1.45, 95% confidence interval [CI] = 1.18-1.77, P = .0004) and proximal in the aspirin group the DVT and/or PE incidence was significantly higher in the aspirin group when compared with other anticoagulants (RR = 1.19, 95% CI = 1.02-1.39, P = .03). No significant secondary outcome differences were identified in the aspirin group when compared with other anticoagulants (bleeding events [RR] = 0.83, 95% CI = 0.63-1.10, P = .20); wound complications (RR = 0.45, 95% CI = 0.20-1.04, P = .06); wound infection (RR = 1.08, 95% CI = 0.85-1.38, P = .53); blood transfusion events (RR = 1.00, 95% CI = 0.84-1.19, P = 1.00) and death events (RR = 1.11, 95% CI = 0.78-1.57, P = .55). CONCLUSIONS: Our updated meta-analysis showed that aspirin was inferior to when compared with other anticoagulants in VTE-related orthopedic major surgery, including proximal DVT and/or PE, and was more likely to form VTE. No differences between groups were identified for bleeding, wound complications, wound infections, transfusion, or death events.

circ_ARF3 regulates the pathogenesis of osteosarcoma by sponging miR-1299 to maintain CDK6 expression.

Increasing evidence suggests that circular RNAs are emerging biomarkers or targets for early cancer diagnosis and treatment. However, the studies of circular RNA in osteosarcoma (OS) are limited. In this study we found that circ_ARF3 were highly expressed in osteosarcoma cell lines and tumor tissues. Knocking down circ_ARF3 greatly ceased OS cell growth, impaired cell colony formation and halted cell cycle transition from G1 to S phase. Bioinformatic analysis suggested that miR-1299 is the target of circ_ARF3. Luciferase assay and biotin labeled circ_ARF3 pull down assay confirmed their interactions in OS cells. The regulatory roles of circ_ARF3 on miR-1299 was also investigated. Further bioinformatic analysis showed that CDK6 is the target of miR-1299. Overexpressing miR-1299 in OS cells decreased CDK6 expression and arrested OS cell growth and cell cycle progression. However, the roles of miR-1299 in regulating CDK6 expression, OS cell growth and cell cycle progression were greatly impaired in the presence of circ_ARF3. In general, our study demonstrated that in the OS, highly expressed circ_ARF3 acts as a sponge of miR-1299 to inhibit miR-1299 mediated CDK6 downregulation which further promoted OS pathogenesis. circ_ARF3 could be a potential target for OS treatment in the future.

DUSP19 regulates IL-1ß-induced apoptosis and MMPs expression in rat chondrocytes through JAK2/STAT3 signaling pathway.

Osteoarthritis (OA) is a disease with degeneration of articular cartilage and its development and progression is characterized by chondrocyte apoptosis. To examine whether DUSP19 and inhibitor of the JAK2/STAT3 will influence the response of rat chondrocytes cultured with IL-1ß. Dose-response studies were conducted under IL-1ß conditions. In separate experiments, chondrocytes were treated with an appropriate concentration of IL-1ß with either DUSP19-expressing constructs or AG490, whereas chondrocytes were also treated with DUSP19-RNA interference constructs with or without AG490. The expression of DUSP19, apoptosis markers, JAK2/STAT3 and phosphorylation of JAK2/STAT3 was measured by Real-time PCR and/or western blot assay. CCK-8 assay and Annexin V/propidium iodide staining was used to detect chondrocyte viability and apoptosis, respectively. IL-1ß dose-dependently decreased the expression of DUSP19 and the viability of chondrocytes. Chondrocytes with DUSP19 up-regulation inhibited IL-1ß-induced increases in the ratio of p-JAK2/JAK2 and p-STAT3/STAT3 expression as well as cell apoptosis. However, DUSP19 down-regulation mimicked the effect of IL-1ß on JAK2/STAT3 activity and chondrocyte apoptosis. AG490 inhibited JAK2/STAT3 activation as well as apoptosis in chondrocytes induced by IL-1ß or DUSP19 down-regulation, evidenced by decreased expression of Bax, Caspase-3 and increased Bcl-2 expression as well as MMP-3, -9 and -13 expressions. Taken together, our results demonstrate that DUSP19 up-regulation inhibited IL-1ß-induced chondrocytes apoptosis and MMPs expression through inactivating JAK2/STAT3 pathway.

Potential prognostic long non-coding RNA identification and their validation in predicting survival of patients with multiple myeloma.

Multiple myeloma, a typical hematological malignancy, is characterized by malignant proliferation of plasma cells. This study was to identify differently expressed long non-coding RNAs to predict the survival of patients with multiple myeloma efficiently. Gene expressing profiles of diagnosed patients with multiple myeloma, GSE24080 (559 samples) and GSE57317 (55 samples), were downloaded from Gene Expression Omnibus database. After processing, survival-related long non-coding RNAs were identified by Cox regression analysis. The prognosis of multiple myeloma patients with differently expressed long non-coding RNAs was predicted by Kaplan-Meier analysis. Meanwhile, stratified analysis was performed based on the concentrations of serum beta 2-microglobulin (S-beta 2m), albumin, and lactate dehydrogenase of multiple myeloma patients. Gene set enrichment analysis was performed to further explore the functions of identified long non-coding RNAs. A total of 176 long non-coding RNAs significantly related to the survival of multiple myeloma patients (p < 0.05) were identified. In dataset GSE24080 and GSE57317, there were 558 and 55 patients being clustered into two groups with significant differences, respectively. Stratified analysis indicated that prediction of the prognoses with these long non-coding RNAs was independent from other clinical phenotype of multiple myeloma. Gene set enrichment analysis-identified pathways of cell cycle, focal adhesion, and G2-M checkpoint were associated with these long non-coding RNAs. A total of 176 long non-coding RNAs, especially RP1-286D6.1, AC008875.2, MTMR9L, AC069360.2, and AL512791.1, were potential biomarkers to evaluate the prognosis of multiple myeloma patients. These long non-coding RNAs participated indispensably in many pathways associated to the development of multiple myeloma; however, the molecular mechanisms need to be further studied.

Identification of Key Gene Modules in Human Osteosarcoma by Co-Expression Analysis Weighted Gene Co-Expression Network Analysis (WGCNA).

Osteosarcoma is the eighth-most common form of childhood cancer, comprising about 20% of all primary bone cancers. To date, systemic co-expression analysis for this cancer is still insufficient to explain the pathogenesis of poorly understood OC. The objective of this study was to construct a gene co-expression network to predict clusters of candidate genes involved in the pathogenesis of osteosarcoma. First, we contributed co-expression modules via weighted gene co-expression network analysis (WGCNA) and investigated the functional enrichment analysis of co-expression genes in terms of GO and KEGG. In result, seven co-expression modules were identified, containing 2,228 differentially expressed genes identified from the 22 human osteosarcoma samples. Subsequently, correlation study showed that the hub-genes between pair-wise modules displayed significant differences. Lastly, functional enrichment analysis of the co-expression modules showed that the module 5 enriched in progresses of immune response, antigen processing, and presentation. In conclusion, we identified essential genes in module 5 which were associated to human osteosarcoma. The key genes in our findings might provide the framework of co-expression gene modules of human osteosarcoma. Further, the functional analysis of these associated genes provides references to understand the mechanism of Osteosarcoma. J. Cell. Biochem. 118: 3953-3959, 2017. © 2017 Wiley Periodicals, Inc.

Identification of four potential predicting miRNA biomarkers for multiple myeloma from published datasets.

BACKGROUND: Multiple myeloma is a cancer which has a high occurrence rate and causes great injury to people worldwide. In recent years, many studies reported the effects of miRNA on the appearance of multiple myeloma. However, due to the differences of samples and sequencing platforms, a large number of inconsistent results have been generated among these studies, which limited the cure of multiple myeloma at the miRNA level. METHODS: We performed meta-analyses to identify the key miRNA biomarkers which could be applied on the treatment of multiple myeloma. The key miRNAs were determined by overlap comparisons of seven datasets in multiple myeloma. Then, the target genes for key miRNAs were predicted by the software TargetScan. Additionally, functional enrichments and binding TFs were investigated by DAVID database and Tfacts database, respectively. RESULTS: Firstly, comparing the normal tissues, 13 miRNAs were differently expressed miRNAs (DEMs) for at least three datasets. They were considered as key miRNAs, with 12 up-regulated (hsa-miR-106b, hsa-miR-125b, hsa-miR-130b, hsa-miR-138, hsa-miR-15b, hsa-miR-181a, hsa-miR-183, hsa-miR-191, hsa-miR-19a, hsa-miR-20a, hsa-miR-221 and hsa-miR-25) and one down-regulated (hsa-miR-223). Secondly, functional enrichment analyses indicated that target genes of the upregulated miRNAs were mainly transcript factors and enriched in transcription regulation. Besides, these genes were enriched in multiple pathways: the cancer signal pathway, insulin signal metabolic pathway, cell binding molecules, melanin generation, long-term regression and P53 signaling pathway. However, no significant enrichment was found for target genes of the down-regulated genes. Due to the distinct regulation function, four miRNAs (hsa-miR-19a has-miR-221 has-miR25 and has-miR223) were ascertained as the potential prognostic and diagnostic markers in MM. Thirdly, transcript factors analysis unveiled that there were 148 TFs and 60 TFs which bind target genes of the up-regulated miRNAs and target genes of the down-regulated miRNAs, respectively. They respectively generated 652 and 139 reactions of TFs and target genes. Additionally, 50 (31.6%) TFs were shared, while higher specificity was found in TFs of target genes for the upregulated miRNAs. DISCUSSIONS: Together, our findings provided the key miRNAs which affected occurrence of multiple myeloma and regulation function of these miRNAs. It is valuable for the prognosis and diagnosis of multiple myeloma.

A new ten-gene risk fraction model serving as prognostic indicator for clinical outcome of multiple myeloma.

Multiple myeloma (MM) is a kind of aggressive tumor prevalent with high heterogeneity. Abnormal expression of certain genes may lead to the occurrence and development of MM. Nowadays, it is not commonly seen in clinical research to predict the prognostic circumstances of patients with MM by multiple gene expression profiling method. Identification of potential genes in prognostic process could be beneficial for clinical management of MM. Therefore, we aimed to build a risk fraction model to screen out the prognostic indicator for clinical outcome of MM. Microarray data were downloaded from the Genome Expression Omnibus (GEO) datasets with accession numbers GSE24080 and GSE57317. A total of 279 samples were selected out randomly. Besides, a risk formula was constructed and verified in the dataset. Time-dependent receiver operating characteristic (ROC) curve was applied in evaluating the accurate prognostic conditions of patients. Finally, a ten genes model in the training dataset was found to be closely related to the survival condition of MM patients. Patients with MM were divided into two groups, high-risk and low-risk, by the expression of these ten genes, and significant statistical difference was found between the two groups. Furthermore, the result of multivariate cox regression and stratified analysis indicated that this model was independent of other clinical phenotypes. ROC curves also showed its feasibility to predict the survival status of MM patients. Our results demonstrated that the fraction risk model constructed by the selected ten genes could be used to predict survival status of multiple myeloma patients, which could also help in improvement of prognostic and therapeutic tool of MM.