Background Urothelial bladder cancer (BLCA) is one of the most common internal malignancies worldwide with poor prognosis. This study aims to explore effective prognostic biomarkers and construct a prognostic risk score model for patients with BLCA. Methods Weighted gene co-expression network analysis (WGCNA) was used for identifying the co-expression module related to the pathological stage of BLCA based on the RNA-Seq data retrieved from The Cancer Genome Atlas database. Prognostic biomarkers screened by Cox proportional hazard regression model and random forest were used to construct a risk score model that can predict the prognosis of patients with BLCA. The GSE13507 dataset was used as the independent testing dataset to test the performance of the risk score model in predicting the prognosis of patients with BLCA. Results WGCNA identified seven co-expression modules, in which the brown module consisted of 77 genes was most significantly correlated with the pathological stage of BLCA. Cox proportional hazard regression model and random forest identified TPST1 and P3H4 as prognostic biomarkers. Elevated TPST1 and P3H4 expressions were associated with the high pathological stage and worse survival. The risk score model based on the expression level of TPST1 and P3H4 outperformed pathological stage indicators and previously proposed prognostic models. Conclusion The gene co-expression network-based study could provide additional insight into the tumorigenesis and progression of BLCA, and our proposed risk score model may aid physicians in the assessment of the prognosis of patients with BLCA. Electronic supplementary material The online version of this article (10.1186/s41065-019-0100-1) contains supplementary material, which is available to authorized users.

Introduction: Primary immunodeficiencies (PID) are a group of rare genetic disorders with a multitude of clinical symptoms. Characterization of epidemiological and clinical data via national registries has proven to be a valuable tool of studying these diseases. Materials and Methods: The Russian PID registry was set up in 2017, by the National Association of Experts in PID (NAEPID). It is a secure, internet-based database that includes detailed clinical, laboratory, and therapeutic data on PID patients of all ages. Results: The registry contained information on 2,728 patients (60% males, 40% females), from all Federal Districts of the Russian Federation. 1,851/2,728 (68%) were alive, 1,426/1,851 (77%) were children and 425/1,851 (23%) were adults. PID was diagnosed before the age of 18 in 2,192 patients (88%). Antibody defects (699; 26%) and syndromic PID (591; 22%) were the most common groups of PID. The minimum overall PID prevalence in the Russian population was 1.3:100,000 people; the estimated PID birth rate is 5.7 per 100,000 live births. The number of newly diagnosed patients per year increased dramatically, reaching the maximum of 331 patients in 2018. The overall mortality rate was 9.8%. Genetic testing has been performed in 1,740 patients and genetic defects were identified in 1,344 of them (77.2%). The median diagnostic delay was 2 years; this varied from 4 months to 11 years, depending on the PID category. The shortest time to diagnosis was noted in the combined PIDs-in WAS, DGS, and CGD. The longest delay was observed in AT, NBS, and in the most prevalent adult PID: HAE and CVID. Of the patients, 1,622 had symptomatic treatment information: 843 (52%) received IG treatment, mainly IVIG (96%), and 414 (25%) patients were treated with biological drugs. HSCT has been performed in 342/2,728 (16%) patients, of whom 67% are currently alive, 17% deceased, and 16% lost to follow-up. Three patients underwent gene therapy for WAS; all are currently alive. Conclusions: Here, we describe our first analysis of the epidemiological features of PID in Russia, allowing us to highlight the main challenges around PID diagnosis and treatment.

Basal and luminal subtypes of muscle-invasive bladder cancer (MIBC) have distinct molecular profiles and heterogeneous clinical behaviors. The interactions between mRNAs and lncRNAs, which might be regulated by miRNAs, have crucial roles in many cancers. However, the miRNA-dependent crosstalk between lncRNA and mRNA in specific MIBC subtypes still remains unclear. In this study, we first classified MIBC into two conservative subtypes using miRNA, mRNA and lncRNA expression data derived from The Cancer Genome Atlas. Then we investigated subtype-related biological pathways and evaluated the subtype classification performance using Decision Trees, Random Forest and eXtreme Gradient Boosting (XGBoost). At last, we explored potential miRNA-mediated lncRNA-mRNA crosstalks based on co-expression analysis. Our results show that: (1) the luminal subtype is primarily characterized by upregulation of metabolism-related pathways while the basal subtype is predominantly characterized by upregulation of epithelial-mesenchymal transition, metastasis, and immune system process-related pathways; (2) the XGBoost prediction model is consistently robust for classification of the molecular subtypes of MIBC across four datasets (The area under the ROC curve > 0.9); (3) the expression levels of the molecules in the miR-200c and miR141-mediated lncRNA-mRNA crosstalks differ considerably between the two subtypes and have close relationships with the prognosis of MIBC. The miR-200c and miR-141-dependent mRNA-lncRNA crosstalks might be of great significance in tumorigenesis and tumor progression and may serve as the novel prognostic predictors and classification markers of MIBC subtypes.

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