20.500.12556/RUL-171844 Modeliranje metabolizma duktalnega adenokarcinoma na ravni genoma Genome-scale metabolic modelling of pancreatic ductal adenocarcinoma Rak trebušne slinavke je ena najbolj smrtonosnih vrst raka, s stopnjo umrljivosti, ki dosega skoraj 95 %. V pomembnem deležu primerov se tumorji razvijejo v eksokrinih izvodilih trebušne slinavke, kar bolezen klasificira kot duktalni adenokarcinom trebušne slinavke (PDAC, angl. pancreatic ductal adenocarcinoma). Zaradi asimptomatske narave te bolezni v zgodnjih fazah in pomanjkanja učinkovitih metod odkrivanja, je PDAC pogosto diagnosticiran šele v naprednih stadijih, kar omejuje možnosti zdravljenja in prispeva k slabši prognozi bolnikov. Globlje razumevanje presnovnih sprememb, povezanih z rakom trebušne slinavke, je ključno za razjasnitev napredovanja in zgodnjega odkrivanja bolezni. V sklopu diplomskega dela smo uporabili transkriptomske podatke bolnikov s PDAC za postavitev kontekstnospecifičnih metabolnih modelov na nivoju genoma (GEMs, angl. genome-scale metabolic models) s pomočjo algoritma ftINIT (angl. fast task-driven integrative network inference for tissues). Algoritem ftINIT smo sistematično uporabili na različnih kombinacijah vhodnih podatkov, da bi identificirali optimalne parametre za generiranje biološko relevantnih presnovnih modelov. Poleg tega smo raziskali presnovni vpliv prekomernega izražanja melanomskega antigena A3 (MAGE-A3) v pankreatičnih rakastih celicah. MAGE-A3 sodeluje pri tumorigenezi, njegovo prekomerno izražanje v tumorskih celicah je povezana s slabšimi kliničnimi izidi, vendar njegova vloga v metabolizmu rakastih celic ostaja slabo raziskana. S karakterizacijo presnovnih posledic povečanega izražanja MAGE-A3 smo želeli pridobiti nova spoznanja o njegovem možnem funkcionalnem vplivu na metabolizem pankreatičnih rakastih celic, kar bi lahko odprlo pot nadaljnjim raziskavam njegove vloge v napredovanju bolezni. Pancreatic cancer is one of the most lethal malignancies, with a mortality rate nearing 95 %. In a significant proportion of cases, tumors arise in the pancreatic ducts, classifying the disease as pancreatic ductal adenocarcinoma (PDAC). Due to the asymptomatic nature of early-stage PDAC and the lack of effective screening methods, diagnosis often occurs at advanced stages, limiting treatment options and contributing to poor patient outcomes. A deeper understanding of the metabolic alterations associated with pancreatic cancer is crucial for elucidating disease progression. In this study, we utilized RNA transcriptomic data from PDAC patients to construct context-specific genome-scale metabolic models (GEMs) using the fast task-driven integrative network inference for tissues (ftINIT) algorithm. We systematically applied ftINIT to multiple input combinations to identify the optimal parameters for generating biologically relevant metabolic models. Furthermore, we investigated the metabolic impact of melanoma associated antigen A3 (MAGE-A3) overexpression in pancreatic cancer cells. MAGE-A3 is known to contribute to tumorigenesis and is associated with poor clinical outcomes; however, its role in cancer cell metabolism remains largely unexplored. By characterizing the metabolic consequences of MAGE-A3 upregulation, we aimed to provide novel insights into its potential functional impact on pancreatic cancer metabolism, paving the way for further research into its role in disease progression. Transkriptomika kontekstnospecifični metabolni modeli na nivoju genoma ftINIT protein MAGE-A3 Transcriptomics context-specific genome-scale metabolic models fast task-driven integrative network inference for tissues algorithm MAGE-A3 protein true false false Slovenski jezik Angleški jezik Diplomsko delo/naloga 2025-09-03 11:35:03 2025-09-03 11:35:20 2025-10-09 14:50:25 0000-00-00 00:00:00 2025 0 0 0000-00-00 NiDoloceno NiDoloceno NiDoloceno 0000-00-00 0000-00-00 0000-00-00 25359 252557571 Petrovic_Filip_-_Modeliranje_metabolizma_duktalnega_adenokarcinoma_na_ravni_genoma.pdf Petrovic_Filip_-_Modeliranje_metabolizma_duktalnega_adenokarcinoma_na_ravni_genoma.pdf 1 23E17984A9EE04DC69CE079A31B6DEFF 3f45239f12452dae5a8c7cc67047343cb6940bd7d7b81b1f120cf2c2a4ee4f61 2d2eb5ba-88a9-11f0-9328-0050569b8976 https://repozitorij.uni-lj.si/Dokument.php?lang=slv&id=215360 Fakulteta za kemijo in kemijsko tehnologijo 0 0 0