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Analiza celičnih komunikacijskih omrežij proteinov β-aktin, CD9, FTL, S100A9 in TRIM28 kot potencialnih kandidatov za označevalce glioblastomskih matičnih celic
Vidak, Marko (Avtor), Komel, Radovan (Mentor) Več o mentorju... Povezava se odpre v novem oknu

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Izvleček
Glioblastom (GBM) je najbolj agresivna maligna bolezen na možganih. Čeprav so že identificirali nekatere kandidate za označevalce glioblastoma, trenutno še ne poznamo dovolj kandidatov za označevalce, ki bi se izražali na celični površini ter bi bili sposobni ločevati med nemalignim možganskim tkivom in glioblastomskimi tumorskimi celicami. Znotraj glioblastomskega tumorja se med običajnimi tumorskimi celicami nahajajo glioblastomske matične celice (GMC), ki so vpletene v proces ponovnega nastanka tumorja po kirurški odstranitvi. V doktorski nalogi smo želeli najti nove kandidate za označevalce glioblastoma ali glioblastomskih matičnih celic. Za osnovo smo vzeli izhodiščne kandidate (IK) CD9, FTL, S100A9, beta-aktin in TRIM28, ki so jih pred kratkih predlagali kot kandidate za označevalce GMC s povišanim izražanjem v GMC. V literaturi smo poiskali maligne bolezni, v katerih so IK prav tako povišano izraženi kot v GBM, in interakcijske partnerje, s katerimi se povezujejo v patologiji teh bolezni. Za vsak par IK-interakcijski partner smo naredili model interakcijskega omrežja na genski in proteinski ravni s pomočjo podatkovnih zbirk Biomine Explorer (genska raven) in String (proteinska raven). V teh omrežjih smo poiskali skupna vozlišča med IK in interakcijskim partnerjem, ki so predstavljala morebitne nove kandidate za označevalce GBM/GMC. Za kandidate iz skupnih vozlišč smo preverili, če so jih že povezali s karcinogenezo GBM. Tiste kandidate, ki jih še niso povezali z GBM, smo bioinformatsko validirali s primerjavo njihovega izražanja v različnih celičnih/tkivnih tipih z izražanjem uveljavljenih kandidatov za označevalce GBM/GMC. S tem namenom smo naredili meta-analizo podatkov o izražanju mRNA na ravni celotnega genoma, ki smo jih našli v treh podatkovnih zbirkah (GEO, ArrayExpress in GLIOMASdb). V omenjenih podatkovnih zbirkah smo našli deset podatkovnih nizov, ki smo jih uporabili za validacijo kandidatov iz skupnih vozlišč. Našli smo 16 interakcijskih partnerjev, ki so se povezovali z našimi IK pri 10 različnih malignih patologijah. Z analizo vseh možnih omrežij IK-interakcijski partner smo prišli do 82 skupnih vozlišč, med katerimi je bilo 23 še nepovezanih s karcinogenezo GBM. Te kandidate smo bioinformatsko validirali in prišli do treh morebitnih novih kandidatov za označevalce GBM/GMC s povišanim izražanjem v malignih celicah (CCT2, RUVBL1, BST1). Podrobnejša analiza rezultatov je pokazala, da se vsi trije kandidati neenakomerno izražajo v različnih za GBM/GMC relevantnih celičnih/tkivnih tipih (povišano izražanje v tkivnih vzorcih GBM, ne pa tudi v matičnih celičnih linijah in nevrosferah). Zato smo poiskali kandidate, ki so v bioinformatskih testih dosegli najboljše rezultate in so se torej najbolj konsistentno izražali v vseh relevantnih celičnih/tkivnih tipih. V tej fazi smo se osredotočili na kandidate, ki se na proteinski ravni povišano izražajo na celični površini tumorskih celic. Za izbiro najboljših kandidatov smo uporabili podatkovne nize GSE4290/GDS1962, GSE23806/GDS3885 in GLIOMASdb, preostalih sedem nizov, ki smo jih našli v podatkovnih zbirkah (GSE4412/GDS1975, GSE4412/GDS1976, E-GEOD-52009, E-GEOD-68848, E-GEOD-16011, E-GEOD-4536 in E-GEOD-74571), pa smo uporabili za validacijo izbranih kandidatov. V fazi selekcije najboljših kandidatov smo identificirali štiri gene, ki kodirajo površinske proteine (CD276, FREM2, SPRY1 in SLC47A1), in z bioinformatsko validacijo potrdili njihovo povišano izraženost v GBM/GMC. Pregled literature je razkril, da so CD276 že povezali s karcinogenezo glioblastoma, medtem ko je SLC47A1 v bioinformatski validaciji dosegel najslabši rezultat med štirimi novimi kandidati, zato ga nismo vključili v fazo eksperimentalne validacije. V tej fazi – v katero smo vključili kandidata FREM2 in SPRY1 – smo ugotovili, da je izražanje FREM2 – ne pa tudi SPRY1 – višje v glioblastomskih celičnih linijah kot v nemalignih astrocitih. Poleg tega se je FREM2 tako na genski kot na proteinski ravni višje izražal v glioblastomskim matičnim celicam podobnih celičnih linijah kot v konvencionalnih glioblastomskih celičnih linijah. Zato predlagamo FREM2 kot novega kandidata za označevalca glioblastomskih celic in tudi kot potencialnega kandidata za označevalca GMC. Tako FREM2 kot SPRY1 sta izražena na površini glioblastomskih celic, medtem ko smo povišano izraženost v citosolu nemalignih astrocitov opazili samo pri SPRY1. Tudi SPRY1 bi lahko bil zanimiva terapevtska tarča, saj se v malignih glioblastomskih celicah izraža na površini, v nemalignih astrocitih pa v citosolu.

Jezik:Slovenski jezik
Ključne besede:glioblastom, glioblastomske matične celice, označevalci, podatkovne zbirke, omrežja, meta-analiza, celična površina, eksperimentalna validacija, FREM2, SPRY1
Vrsta gradiva:Doktorsko delo/naloga (mb31)
Organizacija:MF - Medicinska fakulteta
Leto izida:2018
Število ogledov:115
Število prenosov:154
Metapodatki:XML RDF-CHPDL DC-XML DC-RDF
 
Skupna ocena:(0 glasov)
Vaša ocena:Ocenjevanje je dovoljeno samo prijavljenim uporabnikom.
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Sekundarni jezik

Jezik:Angleški jezik
Naslov:Analysis of cell communication networks of β-actin, CD9, FTL, S100A9 and TRIM28 proteins as potential candidates for glioblastoma stem cell markers
Izvleček:
Glioblastoma (GBM) is the most aggressive brain malignancy. Although some potential glioblastoma biomarkers have already been identified, there is a lack of cell membrane-bound biomarkers capable of distinguishing brain tissue from glioblastoma and/or glioblastoma stem cells (GSC), which are responsible for the rapid post-operative tumor reoccurrence. Our aim was to find new candidates for glioblastoma or glioblastoma stem cell markers. Our startig point were the candidates CD9, FTL, S100A9, beta-actin and TRIM28 (starting point candidates – SPC). These candidates have ben recenty proposed as novel GSC biomarkers with elevated expression in GSC. We conducted a literature search in order to find all malignant pathologies in which SPC likewise have elevated expression – as in GBM. In each such pathology, we identified interaction partners for our SPC. For each pair SPC-interaction partner an interaction network was created at the gene and protein levels of expression, using the databases Biomine Explorer (gene level) and String (protein level). In each of these networks we identified common nodes between SPC and its interaction partner, and these nodes represented potential novel GBM/GSC marker candidates. For each candidate represented in the common nodes, a check was made whether it had already been linked to GBM carcinogenesis. Only candidates with no such links were bioinformatically validated by comparing their expression in various GBM/GSC-relevant cell/tissue types with that of established GBM/GSC marker candidates. This validation consisted of meta-analysis of genome-scale mRNA expression data from three data repositories (GEO, ArrayExpress and GLIOMASdb). The search yielded ten appropriate datasets, which were used for validation of the candidates from the common nodes. 16 interaction partners that were linked to our SPC in 10 different malignant pathologies were identified. Analysis of all possible networks SPC-interaction partner yielded 82 common nodes, of which only 23 had not yet been linked to GBM carcinogenesis. Bioinformatic validation of these candidates revealed three potential new GBM/GSC marker candidates with elevated expression in malignant cells (CCT2, RUVBL1, BST1). However, detailed results analysis highlighted that all these three candidates have uneven expression in various GBM/GSC-relevant cell/tissue types (e.g., elevated expression in GBM tissue samples, but not in stem-like cell lines and neurospheres). Thus, we decided to identify candidates that achieved the best results in the bioinformatic validation tests and had the most consistent expression pattern in all relevant cell/tissue types. In this stage the search was focused on candidates with elevated expression at the protein level on the tumor cells surface. Three datasets (GSE4290/GDS1962, GSE23806/GDS3885, and GLIOMASdb) were used for selection of new GBM/GSC marker candidates, while the other seven (GSE4412/GDS1975, GSE4412/GDS1976, E-GEOD-52009, E-GEOD-68848, E-GEOD-16011, E-GEOD-4536, and E-GEOD-74571) were used for bioinformatic validation of the selected candidates. The selection identified four new CSP-encoding candidate genes—CD276, FREM2, SPRY1, and SLC47A1—and the bioinformatic validation confirmed their elevated expression in GBM/GSC. A review of the literature revealed that CD276 is not a novel candidate, while SLC47A1 had lower validation test scores than the other new candidates and was therefore not considered for experimental validation. This validation revealed that the expression of FREM2—but not SPRY1—is higher in glioblastoma cell lines when compared to non-malignant astrocytes. In addition, FREM2 gene and protein expression levels are higher in glioblastoma stem-like cell lines than in conventional glioblastoma cell lines. FREM2 is thus proposed as a novel GBM marker candidate, as well as a putative candidate for GSC biomarker. Both FREM2 and SPRY1 are expressed on the surface of the GBM cells, while SPRY1 alone was found over-expressed in the cytosol of non-malignant astrocytes. SPRY could also be useful as a therapeutic target since it is expressed on the surface of malignant GBM cells but in the cytosol of non-malignant astrocytes.

Ključne besede:glioblastoma, glioblastoma stem cells, biomarkers, data repositories, networks, meta-analysis, cell surface, experimental validation, FREM2, SPRY1

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