14 research outputs found
Role of CD44 in clear cell renal cell carcinoma invasiveness after antiangiogenic treatment
Treballs Finals de Grau de FarmĂ cia, Facultat de FarmĂ cia, Universitat de Barcelona, 2017. Tutor/a: Joan Carles RodrĂguez Rubio.[eng] During last century, big effort to understand the biochemical basis of cancer was carried
out. One of the principal branches of these cancer investigations used drugs to prevent
the formation of new blood vessels –process called angiogenesis– responsible for the
nutrients supply of the tumour. These drugs are generally called antiangiogenics.
It was discovered that some types of tumour have or develop resistance to these drugs
when treatment was long enough. For that reason, mechanisms of resistance,
aggressiveness, invasion and/or metastasis after the treatment are nowadays relevant
to study. Recently, a protein that could be involved in the increased invasiveness of
tumour cells after the antiangiogenic treatment appeared.
This project collects some evidence that indicates that this protein, called CD44, might
play a role in the increased invasion after antiangiogenic treatment in mouse models of
renal carcinoma.[cat] Durant l’últim segle, s’ha fet un gran esforç per aprofundir en la basant bioquĂmica de la
investigació contra el cà ncer. Una de les branques principals d’aquesta investigació
utilitza fà rmacs que prevenen la formació de nous vasos sanguinis –procés anomenat
angiogènesis- encarregats de nodrir el tumor. Aquests fà rmacs es diuen generalment
antiangiogènics. S’ha descobert que alguns tipus de tumor tenen o desenvolupen
resistència a aquests fà rmacs quan el tractament és prou llarg. Per aquesta raó,
actualment s’està investigant profundament quins són els mecanismes pels quals
apareix aquesta resistència, aixà com també perquè els tumors es tornen més agressius,
invasius i/o metastà tics després del tractament. Recentment s’ha descobert una
proteïna que podria estar involucrada en l’augment de la invasivitat de les cèl·lules
tumorals després del tractament antiangiogènic.
Aquest treball recull algunes de les evidències que apunten cap al paper de la proteïna
CD44 en l’increment de la invasió tumoral post-tractament amb fà rmacs antiangiogènics
en models ratolins de cĂ ncer renal
Additional file 1: Table S1. of 2-amino-1-methyl-6-phenylimidazo(4,5-b) pyridine (PhIP) induces gene expression changes in JAK/STAT and MAPK pathways related to inflammation, diabetes and cancer
Global gene expression changes in h-MSC-derived adipocytes exposed to PhIP. (DOCX 24Ă‚Â kb
Gene Expression Variability in Human Hepatic Drug Metabolizing Enzymes and Transporters
<div><p></p><p>Interindividual variability in the expression of drug-metabolizing enzymes and transporters (DMETs) in human liver may contribute to interindividual differences in drug efficacy and adverse reactions. Published studies that analyzed variability in the expression of DMET genes were limited by sample sizes and the number of genes profiled. We systematically analyzed the expression of 374 DMETs from a microarray data set consisting of gene expression profiles derived from 427 human liver samples. The standard deviation of interindividual expression for DMET genes was much higher than that for non-DMET genes. The 20 DMET genes with the largest variability in the expression provided examples of the interindividual variation. Gene expression data were also analyzed using network analysis methods, which delineates the similarities of biological functionalities and regulation mechanisms for these highly variable DMET genes. Expression variability of human hepatic DMET genes may affect drug-gene interactions and disease susceptibility, with concomitant clinical implications.</p> </div
Interindividual Variability in the Expression of Nuclear Receptor Genes among 427 Subjects.
<p>Interindividual Variability in the Expression of Nuclear Receptor Genes among 427 Subjects.</p
Interindividual Variability of the 20 Most Variably Expressed DMET Genes among 427 Subjects.
a<p>The number of the related drugs was derived from PharmGKB database.</p>b<p>O.E.R stands for Over the Evaluation Range.</p
Drug-gene interaction network.
<p>The figure indicates the relationship among the ten most influential DMETs and the top 100 prescribed medications. A line between a gene and a drug suggest that the DMET is involved in the metabolism or transporting of the drug. A drug is labeled as a circle and a gene is labeled.</p
The standard deviation (SD) for the expression of DMET and non-DMET genes among 427 individuals.
<p>The DMET genes appear to have a higher likelihood of having high SD compared to non-DMET genes. The X-axis shows the SD interval and Y-axis represents the probability of 427 individuals with an indicated SD interval value.</p
Expression Variability of Top 10 Most Important DMETs and Their Biological Significances.
<p>Expression Variability of Top 10 Most Important DMETs and Their Biological Significances.</p
Regulation pathways for DMET expression by GeneGo analysis.
<p>The figure indicates the relationship among the ten most influential DMETs and drugs (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0060368#pone-0060368-t002" target="_blank">Table 2</a>), and the most common nuclear receptors (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0060368#pone-0060368-t003" target="_blank">Table 3</a>). The Panel A indicats the CAR/RXR mediated pathways in the regulation of DMET gene expression, and the Panel B indicates the PXR/RXR mediated pathways in the regulation of DMET gene expression. Panel C lists the legends to visualize the GeneGo pathway maps.</p
The visualization of the coexpression network for DMET genes.
<p>The graph highlights that genes in a liver coexpression network fall into 10 distinct modules, where genes within a module are more highly interconnected with each other than with genes outside the module.</p