Risk of systemic lupus erythematosus in patients with idiopathic thrombocytopenic purpura: a population-based cohort study.

BACKGROUND: Idiopathic thrombocytopenic purpura (ITP) may play a role in early-stage systemic lupus erythematosus (SLE). The incidence of SLE in patients with ITP and the potential relationship between them is still unclear. This study was performed to provide epidemiological evidence regarding the relationship between ITP and SLE occurrence. METHODS: In this population-based retrospective cohort study, the risk of SLE was analysed in a cohort of patients newly diagnosed with ITP between 2000 and 2013. Controls were selected at a 1:2 ratio through propensity score matching (PSM) using the greedy algorithm. The Cox proportional hazard model was used to analyse the association between ITP and SLE incidence. There were four different Cox regression models, and the sensitivity analyses were implemented to evaluate the HR of SLE after exposure with ITP. RESULTS: In the age-matched and sex-matched ITP and non-ITP cohort, the average follow-up time was about 80 months in this study. There were 34 (4.70%) and 27 (0.19%) incident cases of SLE in ITP and non-ITP group. The incidence rates were 62.0 (95% CI 44.3 to 86.8) and 2.10 (95% CI 1.44 to 3.06), respectively. The adjusted HR of incidental SLE in the ITP group was 25.1 (95% CI 13.7 to 46.0). The other risk factors for SLE were female sex and Sjogren's syndrome. After PSM, the incidence rate and Kaplan-Meir curves of SLE were consistent with the results for the age-matched and sex-matched population, the HR 17.4 (95% CI 5.28 to 57.4) was estimated by conditional Cox model. CONCLUSION: This cohort study demonstrated that patients with ITP have a higher risk of SLE. Clinically, patients with ITP should be monitored for incidental lupus.

Using Prognosis-Related Gene Expression Signature and Connectivity Map for Personalized Drug Repositioning in Multiple Myeloma.

BACKGROUND Multiple myeloma (MM) is the second most common hematologic cancer with poor prognosis. Novel therapeutic strategies are needed to decrease the high mortality rate. The aim of this study was to identify prospective agents for MM. MATERIAL AND METHODS A microarray dataset was mined, which contains the transcriptome profiles of 588 MM patients. Univariate Cox analysis was performed to analyze the relationships between genes and clinical outcome. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were determined. Protective and risky genes were uploaded to Connectivity Map (CMAP) database to identify the potentially unknown effects of existing drugs. An example was selected to be docked on the known molecules. RESULTS A total of 1445 genes significantly correlated with the event free survival (EFS) of MM patients were identified and included 676 protective and 769 risky indicators. KEGG pathway analysis revealed that these prognosis-associated genes were enriched in the "cell cycle," "DNA replication," and "P53 signaling pathway". The top t3 most significant potential molecules were vorinostat, trifluoperazine, and thioridazine. CDK1 (cyclin-dependent kinase-1) ranked as the core in the class of prognosis-related genes in MM based on protein-protein interaction (PPI) network analysis. With Sybyl-X 2.0, the majority of the top 10 molecules aforementioned displayed high binding forces with CDK1. Among these molecules, trichostatin A had the greatest ability in combining with CDK1. CONCLUSIONS Genes that mainly accumulate in the cell cycle pathway play an essential role in the prognosis of MM, and these prognosis-related genes also have great value in drug development.

[Effect of parthenolide on serum expressions of interleukin-1beta and tumor necrosis factor-alpha in rabbits with knee osteoarthritis].

OBJECTIVE: To observe the therapeutic effect of parthenolide (PTL) on rabbit knee arthritis (KOA) and its effects on serum expression of interleukin-1beta (IL-1beta) and contents of tumor necrosis factor-alpha (TNF-alpha). METHODS: Eight rabbits were randomly selected from 40 healthy pure-bred New Zealand rabbits as the normal control group. The KOA model was established in the rest 32 rabbits by plaster cast fixation of the right hind limb extension position. After modeling they were randomly divided into 4 groups, i.e., the model control group, the high dose PTL group, the middle dose PTL group, and the low dose PTL group, 8 in each group. Serum contents of IL-1beta and TNF-alpha were detected using enzyme-linked immunosorbent assay. RESULTS: Compared with the model group, IL-1beta and TNF-alpha concentration decreased in the 3 PTL groups (P < 0.01). The decrement was positively correlated with PTL concentrations (IL-1beta: r = 0.55, P < 0.01; TNF-alpha: r = 0.56, P < 0.01). The inhibition reached the peak when the PTL concentration arrived at 20 micromol/L. CONCLUSIONS: PTL could down-regulate the blood IL-1beta and TNF-alpha concentrations of KOA rabbits. Besides, the decrement was positively correlated with the PTL concentration.

Construction of a prognosis­associated long noncoding RNA­mRNA network for multiple myeloma based on microarray and bioinformatics analysis.

At present, the association between prognosis­associated long noncoding RNAs (lncRNAs) and mRNAs is yet to be reported in multiple myeloma (MM). The aim of the present study was to construct prognostic models with lncRNAs and mRNAs, and to map the interactions between these lncRNAs and mRNAs in MM. LncRNA and mRNA data from 559 patients with MM were acquired from the Genome Expression Omnibus (dataset GSE24080), and their prognostic values were calculated using the survival package in R. Multivariate Cox analysis was used on the top 20 most significant prognosis­associated mRNAs and lncRNAs to develop prognostic signatures. The performances of these prognostic signatures were tested using the survivalROC package in R, which allows for time­dependent receiver operator characteristic (ROC) curve estimation. Weighted correlation network analysis (WGCNA) was conducted to investigate the associations between lncRNAs and mRNAs, and a lncRNA­mRNA network was constructed using Cytoscape software. Univariate Cox regression analysis identified 39 lncRNAs and 1,445 mRNAs that were significantly associated with event­free survival of MM patients. The top 20 most significant survival­associated lncRNAs and mRNAs were selected as candidates for analyzing independent MM prognostic factors. Both signatures could be used to separate patients into two groups with distinct outcomes. The areas under the ROC curves were 0.739 for the lncRNA signature and 0.732 for the mRNA signature. In the lncRNA­mRNA network, a total of 143 mRNAs were positively or negatively associated with 23 prognosis­associated lncRNAs. NCRNA00201, LOC115110 and RP5­968J1.1 were the most dominant drivers. The present study constructed a model that predicted prognosis in MM and formed a network with the corresponding prognosis­associated mRNAs, providing a novel perspective for the clinical diagnosis and treatment of MM, and suggesting novel directions for interpreting the mechanisms underlying the development of MM.

A predicted risk score based on the expression of 16 autophagy-related genes for multiple myeloma survival.

Autophagy has an important role in the pathogenesis of plasma cell development and multiple myeloma (MM); however, the prognostic role of autophagy-related genes (ARGs) in MM remains undefined. In the present study, the expression profiles of 234 ARGs were obtained from a Gene Expression Omnibus dataset (accession GSE24080), which contains 559 samples of patients with MM analyzed with 54,675 probes. Univariate Cox regression analysis identified 55 ARGs that were significantly associated with event-free survival of MM. Furthermore, a risk score with 16 survival-associated ARGs was developed using multivariate Cox regression analysis, including ATIC, BNIP3L, CALCOCO2, DNAJB1, DNAJB9, EIF4EBP1, EVA1A, FKBP1B, FOXO1, FOXO3, GABARAP, HIF1A, NCKAP1, PRKAR1A and SUPT20H, was constructed. Using this prognostic signature, patients with MM could be separated into high- and low-risk groups with distinct clinical outcomes. The area under the curve values for the receiver operating characteristic curves were 0.740, 0.741 and 0.712 for 3, 5 and 10 years prognosis predictions, respectively. Notably, the prognostic role of this risk score could be validated with another four independent cohorts (accessions: GSE57317, GSE4581, GSE4452 and GSE4204). In conclusion, ARGs may serve vital roles in the progression of MM, and the ARGs-based prognostic model may provide novel ideas for clinical applications in MM.