RHOA in Gastric Cancer: Functional Roles and Therapeutic Potential

The well-known signal mediator and small GTPase family member, RHOA, has now been associated with the progression of specific malignancies. In this review, we appraise the biomedical literature regarding the role of this enzyme in gastric cancer (GC) signaling, suggesting potential clinical significance. To that end, we examined RHOA activity, with regard to second-generation hallmarks of cancer, finding particular association with the hallmark “activation of invasion and metastasis.” Moreover, an abundance of studies show RHOA association with Lauren classification diffuse subtype, in addition to poorly differentiated GC. With regard to therapeutic value, we found RHOA signaling to influence the activity of specific widely used chemotherapeutics, and its possible antagonism by various dietary constituents. We also review currently available targeted therapies for GC. The latter, however, showed a paucity of such agents, underscoring the urgent need for further investigation into treatments for this highly lethal malignancy

ChimerDB—a knowledgebase for fusion sequences

Chromosome translocation and gene fusion are frequent events in the human genome and are often the cause of many types of tumor. ChimerDB is the database of fusion sequences encompassing bioinformatics analysis of mRNA and expressed sequence tag (EST) sequences in the GenBank, manual collection of literature data and integration with other known database such as OMIM. Our bioinformatics analysis identifies the fusion transcripts that have non-overlapping alignments at multiple genomic loci. Fusion events at exon–exon borders are selected to filter out the cloning artifacts in cDNA library preparation. The result is classified into two groups—genuine chromosome translocation and fusion between neighboring genes owing to intergenic splicing. We also integrated manually collected literature and OMIM data for chromosome translocation as an aid to assess the validity of each fusion event. The database is available at for human, mouse and rat genomes

Mg2+ Effect on Argonaute and RNA Duplex by Molecular Dynamics and Bioinformatics Implications

RNA interference (RNAi), mediated by small non-coding RNAs (e.g., miRNAs, siRNAs), influences diverse cellular functions. Highly complementary miRNA-target RNA (or siRNA-target RNA) duplexes are recognized by an Argonaute family protein (Ago2), and recent observations indicate that the concentration of Mg2+ ions influences miRNA targeting of specific mRNAs, thereby modulating miRNA-mRNA networks. In the present report, we studied the thermodynamic effects of differential [Mg2+] on slicing (RNA silencing cycle) through molecular dynamics simulation analysis, and its subsequent statistical analysis. Those analyses revealed different structural conformations of the RNA duplex in Ago2, depending on Mg2+ concentration. We also demonstrate that cation effects on Ago2 structural flexibility are critical to its catalytic/functional activity, with low [Mg2+] favoring greater Ago2 flexibility (e.g., greater entropy) and less miRNA/mRNA duplex stability, thus favoring slicing. The latter finding was supported by a negative correlation between expression of an Mg2+ influx channel, TRPM7, and one miRNA’s (miR-378) ability to downregulate its mRNA target, TMEM245. These results imply that thermodynamics could be applied to siRNA-based therapeutic strategies, using highly complementary binding targets, because Ago2 is also involved in RNAi slicing by exogenous siRNAs. However, the efficacy of a siRNA-based approach will differ, to some extent, based on the Mg2+ concentration even within the same disease type; therefore, different siRNA-based approaches might be considered for patient-to-patient needs

ECgene: an alternative splicing database update

ECgene () was developed to provide functional annotation for alternatively spliced genes. The applications encompass the genome-based transcript modeling for alternative splicing (AS), domain analysis with Gene Ontology (GO) annotation and expression analysis based on the EST and SAGE data. We have expanded the ECgene's AS modeling and EST clustering to nine organisms for which sufficient EST data are available in the GenBank. As for the human genome, we have also introduced several new applications to analyze differential expression. ECprofiler is an ontology-based candidate gene search system that allows users to select an arbitrary combination of gene expression pattern and GO functional categories. DEGEST is a database of differentially expressed genes and isoforms based on the EST information. Importantly, gene expression is analyzed at three distinctive levels—gene, isoform and exon levels. The user interfaces for functional and expression analyses have been substantially improved. ASviewer is a dedicated java application that visualizes the transcript structure and functional features of alternatively spliced variants. The SAGE part of the expression module provides many additional features including SNP, differential expression and alternative tag positions

Detection of PIWI and piRNAs in the mitochondria of mammalian cancer cells

AbstractPiwi-interacting RNAs (piRNAs) are 26–31 nt small noncoding RNAs that are processed from their longer precursor transcripts by Piwi proteins. Localization of Piwi and piRNA has been reported mostly in nucleus and cytoplasm of higher eukaryotes germ-line cells, where it is believed that known piRNA sequences are located in repeat regions of nuclear genome in germ-line cells. However, localization of PIWI and piRNA in mammalian somatic cell mitochondria yet remains largely unknown. We identified 29 piRNA sequence alignments from various regions of the human mitochondrial genome. Twelve out 29 piRNA sequences matched stem-loop fragment sequences of seven distinct tRNAs. We observed their actual expression in mitochondria subcellular fractions by inspecting mitochondrial-specific small RNA-Seq datasets. Of interest, the majority of the 29 piRNAs overlapped with multiple longer transcripts (expressed sequence tags) that are unique to the human mitochondrial genome. The presence of mature piRNAs in mitochondria was detected by qRT-PCR of mitochondrial subcellular RNAs. Further validation showed detection of Piwi by colocalization using anti-Piwil1 and mitochondria organelle-specific protein antibodies

Improving gastric cancer preclinical studies using diverse in vitro and in vivo model systems

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.Abstract Background Biomarker-driven targeted therapy, the practice of tailoring patients treatment to the expression/activity levels of disease-specific genes/proteins, remains challenging. For example, while the anti-ERBB2 monoclonal antibody, trastuzumab, was first developed using well-characterized, diverse in vitro breast cancer models (and is now a standard adjuvant therapy for ERBB2-positive breast cancer patients), trastuzumab approval for ERBB2-positive gastric cancer was largely based on preclinical studies of a single cell line, NCI-N87. Ensuing clinical trials revealed only modest patient efficacy, and many ERBB2-positive gastric cancer (GC) patients failed to respond at all (i.e., were inherently recalcitrant), or succumbed to acquired resistance. Method To assess mechanisms underlying GC insensitivity to ERBB2 therapies, we established a diverse panel of GC cells, differing in ERBB2 expression levels, for comprehensive in vitro and in vivo characterization. For higher throughput assays of ERBB2 DNA and protein levels, we compared the concordance of various laboratory quantification methods, including those of in vitro and in vivo genetic anomalies (FISH and SISH) and xenograft protein expression (Western blot vs. IHC), of both cell and xenograft (tissue-sectioned) microarrays. Results The biomarker assessment methods strongly agreed, as did correlation between RNA and protein expression. However, although ERBB2 genomic anomalies showed good in vitro vs. in vivo correlation, we observed striking differences in protein expression between cultured cells and mouse xenografts (even within the same GC cell type). Via our unique pathway analysis, we delineated a signaling network, in addition to specific pathways/biological processes, emanating from the ERBB2 signaling cascade, as a potential useful target of clinical treatment. Integrated analysis of public data from gastric tumors revealed frequent (10 – 20 %) amplification of the genes NFKBIE, PTK2, and PIK3CA, each of which resides in an ERBB2-derived subpathway network. Conclusion Our comprehensive bioinformatics analyses of highly heterogeneous cancer cells, combined with tumor omics profiles, can optimally characterize the expression patterns and activity of specific tumor biomarkers. Subsequent in vitro and in vivo validation, of specific disease biomarkers (using multiple methodologies), can improve prediction of patient stratification according to drug response or nonresponse

Pathway-Based Evaluation in Early Onset Colorectal Cancer Suggests Focal Adhesion and Immunosuppression along with Epithelial-Mesenchymal Transition

Colorectal cancer (CRC) has one of the highest incidences among all cancers. The majority of CRCs are sporadic cancers that occur in individuals without family histories of CRC or inherited mutations. Unfortunately, whole-genome expression studies of sporadic CRCs are limited. A recent study used microarray techniques to identify a predictor gene set indicative of susceptibility to early-onset CRC. However, the molecular mechanisms of the predictor gene set were not fully investigated in the previous study. To understand the functional roles of the predictor gene set, in the present study we applied a subpathway-based statistical model to the microarray data from the previous study and identified mechanisms that are reasonably associated with the predictor gene set. Interestingly, significant subpathways belonging to 2 KEGG pathways (focal adhesion; natural killer cell-mediated cytotoxicity) were found to be involved in the early-onset CRC patients. We also showed that the 2 pathways were functionally involved in the predictor gene set using a text-mining technique. Entry of a single member of the predictor gene set triggered a focal adhesion pathway, which confers anti-apoptosis in the early-onset CRC patients. Furthermore, intensive inspection of the predictor gene set in terms of the 2 pathways suggested that some entries of the predictor gene set were implicated in immunosuppression along with epithelial-mesenchymal transition (EMT) in the early-onset CRC patients. In addition, we compared our subpathway-based statistical model with a gene set-based statistical model, MIT Gene Set Enrichment Analysis (GSEA). Our method showed better performance than GSEA in the sense that our method was more consistent with a well-known cancer-related pathway set. Thus, the biological suggestion generated by our subpathway-based approach seems quite reasonable and warrants a further experimental study on early-onset CRC in terms of dedifferentiation or differentiation, which is underscored in EMT and immunosuppression

miRDM-rfGA: Genetic algorithm-based identification of a miRNA set for detecting type 2 diabetes

Abstract Background Type 2 diabetes mellitus (T2DM) affects approximately 451 million adults globally. In this study, we identified the optimal combination of marker candidates for detecting T2DM using miRNA-Seq data from 95 samples including T2DM and healthy individuals. Methods We utilized the genetic algorithm (GA) in the discovery of an optimal miRNA biomarker set. We discovered miRNA subsets consisting of three miRNAs for detecting T2DM by random forest-based GA (miRDM-rfGA) as a feature selection algorithm and created six GA parameter settings and three settings using traditional feature selection methods (F-test and Lasso). We then evaluated the prediction performance to detect T2DM in the miRNA subsets derived from each setting. Results The miRNA subset in setting 5 using miRDM-rfGA performed the best in detecting T2DM (mean AUROC = 0.92). Target mRNA identification and functional enrichment analysis of the best miRNA subset (hsa-miR-125b-5p, hsa-miR-7-5p, and hsa-let-7b-5p) validated that this combination was involved in T2DM. We also confirmed that the targeted genes were negatively correlated with the clinical variables related to T2DM in the BxD mouse genetic reference population database. Conclusions Using GA in miRNA-Seq data, we identified the optimal miRNA biomarker set for T2DM detection. GA can be a useful tool for biomarker discovery and drug-target identification