Prognostic role of the preoperative neutrophil-to-lymphocyte ratio and albumin for 30-day mortality in patients with postoperative acute pulmonary embolism.

BACKGROUND: This retrospective study aimed to investigate the prognostic value of the neutrophil-to-lymphocyte ratio (NLR) and albumin for 30-day mortality in patients with postoperative acute pulmonary embolism (PAPE). METHODS: We retrospectively reviewed the medical records of 101 patients with PAPE admitted from September 1, 2012, to March 31, 2019. The characteristics, surgical information, admission examination data and mortality within 30 days after PAPE were obtained from our electronic medical recording system and follow-up. The associations between the NLR, PLR, and other predictors and 30-day mortality were analyzed with univariate and multivariate analyses. Then, the nomogram including the independent predictors was established and evaluated. RESULTS: Twenty-four patients died within 30 days, corresponding to a 30-day mortality rate of 23.8%. The results of the multivariate analysis indicated that both the NLR and albumin were independent predictors for 30-day mortality in patients with PAPE. The probability of death increased by approximately 17.1% (OR = 1.171, 95% CI: 1.073-1.277, P = 0.000) with a one-unit increase in the NLR, and the probability of death decreased by approximately 15.4% (OR = 0.846, 95% CI: 0.762c-0.939, P = 0.002) with a one-unit increase in albumin. The area under the curve of the nomogram was 0.888 (95% CI: 0.812-0.964). CONCLUSION: Our findings showed that an elevated NLR and decreased albumin were related to poor prognosis in patients with PAPE. The NLR and albumin were independent prognostic factors for PAPE.

Co-expression network analysis identifies a gene signature as a predictive biomarker for energy metabolism in osteosarcoma.

BACKGROUND: Osteosarcoma (OS) is a common malignant bone tumor originating in the interstitial tissues and occurring mostly in adolescents and young adults. Energy metabolism is a prerequisite for cancer cell growth, proliferation, invasion, and metastasis. However, the gene signatures associated with energy metabolism and their underlying molecular mechanisms that drive them are unknown. METHODS: Energy metabolism-related genes were obtained from the TARGET database. We applied the "NFM" algorithm to classify putative signature gene into subtypes based on energy metabolism. Key genes related to progression were identified by weighted co-expression network analysis (WGCNA). Based on least absolute shrinkage and selection operator (LASSO) Cox proportional regression hazards model analyses, a gene signature for the predication of OS progression and prognosis was established. Robustness and estimation evaluations and comparison against other models were used to evaluate the prognostic performance of our model. RESULTS: Two subtypes associated with energy metabolism was determined using the "NFM" algorithm, and significant modules related to energy metabolism were identified by WGCNA. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) suggested that the genes in the significant modules were enriched in kinase, immune metabolism processes, and metabolism-related pathways. We constructed a seven-gene signature consisting of SLC18B1, RBMXL1, DOK3, HS3ST2, ATP6V0D1, CCAR1, and C1QTNF1 to be used for OS progression and prognosis. Upregulation of CCAR1, and C1QTNF1 was associated with augmented OS risk, whereas, increases in the expression SCL18B1, RBMXL1, DOK3, HS3ST2, and ATP6VOD1 was correlated with a diminished risk of OS. We confirmed that the seven-gene signature was robust, and was superior to the earlier models evaluated; therefore, it may be used for timely OS diagnosis, treatment, and prognosis. CONCLUSIONS: The seven-gene signature related to OS energy metabolism developed here could be used in the early diagnosis, treatment, and prognosis of OS.

Network Pharmacology Identifies the Mechanisms of Action of Shaoyao Gancao Decoction in the Treatment of Osteoarthritis.

BACKGROUND Osteoarthritis (OA) affects the health and wellbeing of the elderly. Shaoyao Gancao decoction (SGD) is used in traditional Chinese medicine (TCM) for the treatment of OA and has two active components, shaoyao (SY) and gancao (GC). This study aimed to undertake a network pharmacology analysis of the mechanism of the effects of SGD in OA. MATERIAL AND METHODS The active compounds and candidates of SGD were obtained from the Traditional Chinese Medicine (TCM) Databases@Taiwan, the Traditional Chinese Medicine Systems Pharmacology (TCMSP) database, the STITCH database, the ChEMBL database, and PubChem. The network pharmacology approach involved network construction, target prediction, and module analysis. Significant signaling pathways of the cluster networks for SGD and OA were identified using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. RESULTS Twenty-three bioactive compounds were identified, corresponding to 226 targets for SGD. Also, 187 genes were closely associated with OA, of which 161 overlapped with the targets of SGD and were considered to be therapeutically relevant. Functional enrichment analysis suggested that SGD exerted its pharmacological effects in OA by modulating multiple pathways, including cell cycle, cell apoptosis, drug metabolism, inflammation, and immune modulation. CONCLUSIONS A novel approach was developed to systematically identify the mechanisms of the TCM, SGD in OA using network pharmacology analysis.

Integrated analysis of a competing endogenous RNA network reveals key lncRNAs as potential prognostic biomarkers for human bladder cancer.

Human bladder cancer (BCa) is one of the most commonly diagnosed malignancies worldwide. It has high recurrence rates and low-grade malignancy, thus representing an important public health concern. An increasing number of studies suggest that long-noncoding RNAs (lncRNAs) play important roles in various biological processes and disease pathologies, including cancer.We analyzed the expression profiles of lncRNA, miRNA, and mRNA, along with the clinical information of BCa patients collected from the Cancer Genome Atlas database to identify lncRNA biomarkers for prognosis. We also constructed an lncRNA-miRNA-mRNA global triple network (competitive endogenous RNA network) by bioinformational approach.This BCa lncRNA-miRNA-mRNA network consisted of 23 miRNA nodes, 52 mRNA nodes, 59 lncRNA nodes, and 365 edges. Subsequent gene ontology (GO) and pathway analyses were performed using BinGO for Cytoscape and Database for Annotation, Visualization, and Integration Discovery, respectively, highlighting important GO terms and pathways that were enriched in the network. Subnetworks were created using 3 key lncRNAs (MAGI2-AS3, ADAMTS9-AS2, and LINC00330), revealing associations with BCa-linked mRNAs and miRNAs. Finally, an analysis of significantly differentiating RNAs found 6 DElncRNAs (AC112721.1, ADAMTS9-AS1, ADAMTS9-AS2, HCG22, MYO16-AS1, and SACS-AS1), 1 DEmiRNA (miRNA-195), and 6 DEmRNAs (CCNB1, FAM129A, MAP1B, TMEM100, AIFM3, and HOXB5) that correlated with BCa patient survival.Our results provide a novel perspective from which to study the lncRNA-related ceRNA network in BCa, contributing to the development of future diagnostic biomarkers and therapeutic targets.

Identification of key genes in rheumatoid arthritis and osteoarthritis based on bioinformatics analysis.

Rheumatoid arthritis (RA) and osteoarthritis (OA) comprise the most common forms of arthritis. The aim of this study was to identify differentially expressed genes (DEGs) and associated biological processes between RA and OA using a bioinformatics approach to elucidate their potential pathogenesis.The gene expression profiles of the GSE55457 datasets, originally produced through use of the high-throughput Affymetrix Human Genome U133A Array, were downloaded from the Gene Expression Omnibus (GEO) database. The GSE55457 dataset contains information from 33 samples, including 10 normal control (NC) samples, 13 RA samples, and 10 OA samples. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed to identify functional categories and associated molecular and biochemical pathways, respectively, for the identified DEGs, and a protein-protein interaction (PPI) network of the DEGs was constructed using Cytoscape software.GO and KEGG results suggested that several biological pathways (ie, "immune response," "inflammation," and "osteoclast differentiation") are commonly involved in the development of both RA and OA, whereas several other pathways (eg, "MAPK signaling pathway," and "ECM-receptor interaction") presented significant differences between these disorders.This study provides further insights into the underlying pathogenesis of RA and OA, which may facilitate the diagnosis and treatment of these diseases.

Hepatocyte growth factor improves bone regeneration via the bone morphogenetic protein­2­mediated NF­κB signaling pathway.

Bone regeneration is an important process associated with the treatment of osteonecrosis, which is caused by various factors. Hepatocyte growth factor (HGF) is an active biological factor that has multifunctional roles in cell biology, life sciences and clinical medicine. It has previously been suggested that bone morphogenetic protein (BMP)­2 exerts beneficial roles in bone formation, repair and angiogenesis in the femoral head. The present study aimed to investigate the benefits and molecular mechanisms of HGF in bone regeneration. The viability of osteoblasts and osteoclasts were studied in vitro. In addition, the expression levels of tumor necrosis factor (TNF)­α, monocyte chemotactic protein (MCP)­1, interleukin (IL)­1 and IL­6 were detected in a mouse fracture model following treatment with HGF. The expression and activity of nuclear factor (NF)­κB were also analyzed in osteocytes post­treatment with HGF. Histological analysis was used to determine the therapeutic effects of HGF on mice with fractures. The migration and differentiation of osteoblasts and osteoclasts were investigated in HGF­incubated cells. Furthermore, angiogenesis and BMP­2 expression were analyzed in the mouse fracture model post­treatment with HGF. The results indicated that HGF regulates the cell viability of osteoblasts and osteoclasts, and also balanced the ratio between osteoblasts and osteoclasts. In addition, HGF decreased the serum expression levels of TNF­α, MCP­1, IL­1 and IL­6 in experimental mice. The results of a mechanistic analysis demonstrated that HGF upregulated p65, IκB kinase­ß and IκBα expression in osteoblasts from experimental mice. In addition, the expression levels of vascular endothelial growth factor, BMP­2 receptor, receptor activator of NF­κB ligand and macrophage colony­stimulating factor were upregulated by HGF, which may effectively promote blood vessel regeneration, and contribute to the formation and revascularization of tissue­engineered bone. Furthermore, HGF promoted BMP­2 expression and enhanced angiogenesis at the fracture location. These results suggested that HGF treatment may significantly promote bone regeneration in a mouse fracture model. In conclusion, these results indicated that HGF is involved in bone regeneration, angiogenesis and the balance between osteoblasts and osteoclasts, thus suggesting that HGF may be considered a potential agent for the treatment of fractures via the promotion of bone regeneration through regulation of the BMP­2­mediated NF­κB signaling pathway.