Reverse the Resistance to PARP Inhibitors

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

Meta-Analysis of Esophageal Cancer Transcriptomes Using Independent Component Analysis

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

Understanding the Pathogenesis of Spondyloarthritis

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

Whole-transcriptomic sequencing data of esophageal squamous cell carcinoma patients in Kazakhstan, Table 3

Table 3</p

Whole-transcriptomic sequencing data of esophageal squamous cell carcinoma patients in Kazakhstan, Table 5

Table 5</p

Whole-transcriptomic sequencing data of esophageal squamous cell carcinoma patients in Kazakhstan, Table 2

Table 2</p

Whole-transcriptomic sequencing data of esophageal squamous cell carcinoma patients in Kazakhstan, Figure 2

Figure 2</p

Whole-transcriptomic sequencing data of esophageal squamous cell carcinoma patients in Kazakhstan, Table 4

Table 4</p

UNDERSTANDING THE PATHOGENESIS OF SPONDYLOARTHRITIS

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 individual

Meta-Analysis of Esophageal Cancer Transcriptomes Using Independent Component Analysis

International audienceIndependent 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