Novel robust biomarkers for human bladder cancer based on activation of intracellular signaling pathways

Sherpa Romeo blue journal. Open access article. Creative Commons Attribution 3.0 License (CC BY 3.0) applies.We recently proposed a new bioinformatic algorithm called OncoFinder for quantifying the activation of intracellular signaling pathways. It was proved advantageous for minimizing errors of high-throughput gene expression analyses and showed strong potential for identifying new biomarkers. Here, for the first time, we applied OncoFinder for normal and cancerous tissues of the human bladder to identify biomarkers of bladder cancer. Using Illumina HT12v4 microarrays, we profiled gene expression in 17 cancer and seven non-cancerous bladder tissue samples. These experiments were done in two independent laboratories located in Russia and Canada. We calculated pathway activation strength values for the investigated transcriptomes and identified signaling pathways that were regulated differently in bladder cancer (BC) tissues compared with normal controls. We found, for both experimental datasets, 44 signaling pathways that serve as excellent new biomarkers of BC, supported by high area under the curve (AUC) values. We conclude that the OncoFinder approach is highly efficient in finding new biomarkers for cancer. These markers are mathematical functions involving multiple gene products, which distinguishes them from “traditional” expression biomarkers that only assess concentrations of single genes.Ye

Efficiency of Transport Infrastructure in Asian Russia, China, Mongolia, and Kazakhstan in the Context of Creating New Trans-Eurasian Transport Corridors

This article discusses the efficiency of transport infrastructure and cooperation of neighboring regions located in Asian Russia, China, Mongolia, and Kazakhstan in the context of creating new international economic corridors from the Silk Road and trans-Eurasian transport corridors. This study aims to highlight the possible ways of strengthening cross-border cooperation in the field of transport infrastructure. We evaluated the current state of the transport infrastructure, the dynamics of its development, and its influence on the territorial–production complex. Using quantitative data and the unified indicator for the efficiency of transport infrastructure, we also characterized the territorial differentiation, its causes, and prerequisites for further economic and trade cooperation between these countries. The main results are as follows: (1) The lowest levels of the efficiency of transport infrastructure are typical for the northeast of Asian Russia, as well as for the border regions of China, Mongolia, and Kazakhstan. (2) For Asian Russia, Kazakhstan, and Mongolia, the highest levels of the unified indicator are typical for regions located along the main transport routes and for regions with a developed mining industry. This is due to the strong unevenness of the socio-economic development of the territories. (3) The largest industrial and economic centers have been developing along the main transport corridors primarily due to the accumulated potential of equivalent freight turnover and export potential. This study can be useful for authorities and business, as well as for other users of transport infrastructure to improve its regulation and efficiency

<i>ALDH3A2</i>, <i>ODF2</i>, <i>QSOX2</i>, and MicroRNA-503-5p Expression to Forecast Recurrence in <i>TMPRSS2-ERG</i>-Positive Prostate Cancer

Following radical surgery, patients may suffer a relapse. It is important to identify such patients so that therapy tactics can be modified appropriately. Existing stratification schemes do not display the probability of recurrence with enough precision since locally advanced prostate cancer (PCa) is classified as high-risk but is not ranked in greater detail. Between 40 and 50% of PCa cases belong to the TMPRSS2-ERG subtype that is a sufficiently homogeneous group for high-precision prognostic marker search to be possible. This study includes two independent cohorts and is based on high throughput sequencing and qPCR data. As a result, we have been able to suggest a perspective-trained model involving a deep neural network based on both qPCR data for mRNA and miRNA and clinicopathological criteria that can be used for recurrence risk forecasts in patients with TMPRSS2-ERG-positive, locally advanced PCa (the model uses ALDH3A2 + ODF2 + QSOX2 + hsa-miR-503-5p + ISUP + pT, with an AUC = 0.944). In addition to the prognostic model’s use of identified differentially expressed genes and miRNAs, miRNA–target pairs were found that correlate with the prognosis and can be presented as an interactome network