One of the DNA repair machineries is activated by Poly (ADP-ribose) Polymerase (PARP) enzyme. Particularly, this enzyme is involved in repair of damages to single-strand DNA, thus decreasing the chances of generating double-strand breaks in the genome. Therefore, the concept to block PARP enzymes by PARP inhibitor (PARPi) was appreciated in cancer treatment. PARPi has been designed and tested for many years and became a potential supplement for the conventional chemotherapy. However, increasing evidence indicates the appearance of the resistance to this treatment. Specifically, cancer cells may acquire new mutations or events that overcome the positive effect of these drugs. This paper describes several molecular mechanisms of PARPi resistance which were reported most recently, and summarizes some strategies to reverse this type of drug resistance.

Spondyloarthritis comprises a group of inflammatory diseases of the joints and spine, with various clinical manifestations. The group includes ankylosing spondylitis, reactive arthritis, psoriatic arthritis, arthritis associated with inflammatory bowel disease, and undifferentiated spondyloarthritis. The exact etiology and pathogenesis of spondyloarthritis are still unknown, but five hypotheses explaining the pathogenesis exist. These hypotheses suggest that spondyloarthritis is caused by arthritogenic peptides, an unfolded protein response, HLA-B*27 homodimer formation, malfunctioning endoplasmic reticulum aminopeptidases, and, last but not least, gut inflammation and dysbiosis. Here we discuss the five hypotheses and the evidence supporting each. In all of these hypotheses, HLA-B*27 plays a central role. It is likely that a combination of these hypotheses, with HLA-B*27 taking center stage, will eventually explain the development of spondyloarthritis in predisposed individuals.

Receptor tyrosine kinase EGFR usually is localized on plasma membrane to induce progression of many cancers including cancers in children (Bodey et al. In Vivo. 2005, 19:931-41), but it contains a nuclear localization signal (NLS) that mediates EGFR nuclear translocation (Lin et al. Nat Cell Biol. 2001, 3:802-8). Here we report that NLS of EGFR has its old evolutionary origin. Protein-protein interaction maps suggests that nEGFR pathways are different from membrane EGFR and EGF is not found in nEGFR network while androgen receptor (AR) is found, which suggests the evolution of prostate cancer, a well-known AR driven cancer, through changes in androgen- or EGF-dependence. Database analysis suggests that nEGFR correlates with the tumor grades especially in prostate cancer patients. Structural predication analysis suggests that NLS can compromise the differential protein binding to EGFR through stretch linkers with evolutionary mutation from N to V. In experiment, elevation of nEGFR but not membrane EGFR was found in castration resistant prostate cancer cells. Finally, systems analysis of NLS and transmembrane domain (TM) suggests that NLS has old origin while NLS neighboring domain of TM has been undergone accelerated evolution. Thus nEGFR has an old origin resembling the cancer evolution but TM may interfere with NLS driven signaling for natural selection of survival to evade NLS induced aggressive cancers. Our data suggest NLS is a dynamic inducer of EGFR oncogenesis during evolution for advanced cancers. Our model provides novel insights into the evolutionary role of NLS of oncogenic kinases in cancers.

Independent Component Analysis is a matrix factorization method for data dimension reduction. ICA has been widely applied for the analysis of transcriptomic data for blind separation of biological, environmental, and technical factors affecting gene expression. The study aimed to analyze the publicly available esophageal cancer data using the ICA for identification and comprehensive analysis of reproducible signaling pathways and molecular signatures involved in this cancer type. In this study, four independent esophageal cancer transcriptomic datasets from GEO databases were used. A bioinformatics tool « BiODICA—Independent Component Analysis of Big Omics Data» was applied to compute independent components (ICs). Gene Set Enrichment Analysis (GSEA) and ToppGene uncovered the most significantly enriched pathways. Construction and visualization of gene networks and graphs were performed using the Cytoscape, and HPRD database. The correlation graph between decompositions into 30 ICs was built with absolute correlation values exceeding 0.3. Clusters of components—pseudocliques were observed in the structure of the correlation graph. The top 1,000 most contributing genes of each ICs in the pseudocliques were mapped to the PPI network to construct associated signaling pathways. Some cliques were composed of densely interconnected nodes and included components common to most cancer types (such as cell cycle and extracellular matrix signals), while others were specific to EC. The results of this investigation may reveal potential biomarkers of esophageal carcinogenesis, functional subsystems dysregulated in the tumor cells, and be helpful in predicting the early development of a tumor.

Objectives Kazakhstan is a Central Asian crossroad of European and Asian populations situated along the way of the Great Silk Way. The territory of Kazakhstan has historically been inhabited by nomadic tribes and today is the multi-ethnic country with the dominant Kazakh ethnic group. We sequenced and analyzed the whole-genomes of five ethnic healthy Kazakh individuals with high coverage using next-generation sequencing platform. This whole-genome sequence data of healthy Kazakh individuals can be a valuable reference for biomedical studies investigating disease associations and population-wide genomic studies of ethnically diverse Central Asian region. Data description Blood samples have been collected from five ethnic healthy Kazakh individuals living in Kazakhstan. The genomic DNA was extracted from blood and sequenced. Sequencing was performed on Illumina HiSeq2000 next-generation sequencing platform. We sequenced and analyzed the whole-genomes of ethnic Kazakh individuals with the coverage ranging from 26 to 32X. Ranging from 98.85 to 99.58% base pairs were totally mapped and aligned on the human reference genome GRCh37 hg19. Het/Hom and Ts/Tv ratios for each whole genome ranged from 1.35 to 1.49 and from 2.07 to 2.08, respectively. Sequencing data are available in the National Center for Biotechnology Information SRA database under the accession number PRJNA374772.