| Naslov: | Decreased gene expression of antiangiogenic factors in endometrial cancer : qPCR analysis and machine learning modelling |
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| Avtorji: | ID Roškar, Luka (Avtor)
ID Kokol, Marko (Avtor)
ID Pavlič, Renata (Avtor)
ID Roškar, Irena (Avtor)
ID Smrkolj, Špela (Avtor)
ID Lanišnik-Rižner, Tea (Korespondenčni avtor) |
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| Datoteke: |  Decreased_Gene_Expression_of_Antia-Roskar-2023.pdf (4,90 MB)
MD5: BF71537E092DE3D0301B51CE7D611E1D
 https://www.mdpi.com/2072-6694/15/14/3661
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| Jezik: | Angleški jezik |
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| Vrsta gradiva: | Znanstveno delo |
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| Tipologija: | 1.01 - Izvirni znanstveni članek |
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| Organizacija: | FERI - Fakulteta za elektrotehniko, računalništvo in informatiko
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| Opis: | Endometrial cancer (EC) is an increasing health concern, with its growth driven by an angiogenic switch that occurs early in cancer development. Our study used publicly available datasets to examine the expression of angiogenesis-related genes and proteins in EC tissues, and compared them with adjacent control tissues. We identified nine genes with significant differential expression and selected six additional antiangiogenic genes from prior research for validation on EC tissue in a cohort of 36 EC patients. Using machine learning, we built a prognostic model for EC, combining our data with The Cancer Genome Atlas (TCGA). Our results revealed a significant up-regulation of IL8 and LEP and down-regulation of eleven other genes in EC tissues. These genes showed differential expression in the early stages and lower grades of EC, and in patients without deep myometrial or lymphovascular invasion. Gene co-expressions were stronger in EC tissues, particularly those with lymphovascular invasion. We also found more extensive angiogenesis-related gene involvement in postmenopausal women. In conclusion, our findings suggest that angiogenesis in EC is predominantly driven by decreased antiangiogenic factor expression, particularly in EC with less favourable prognostic features. Our machine learning model effectively stratified EC based on gene expression, distinguishing between low and high-grade cases. |
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| Ključne besede: | endometrial cancer, angiogenic factor, tumour-adjacent tissue, machine learning, TCGA, LEP |
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| Status publikacije: | Objavljeno |
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| Verzija publikacije: | Objavljena publikacija |
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| Poslano v recenzijo: | 15.03.2023 |
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| Datum sprejetja članka: | 14.07.2023 |
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| Datum objave: | 18.07.2023 |
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| Leto izida: | 2023 |
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| Št. strani: | str. 1-25 |
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| Številčenje: | Letn. 15, št. 14, št. članka 3661 |
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| PID: | 20.500.12556/DKUM-99227  |
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| UDK: | 616-006 |
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| COBISS.SI-ID: | 159457795 |
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| DOI: | 10.3390/cancers15143661 |
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| ISSN pri članku: | 2072-6694 |
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| NUK URN: | URN:SI:UM:DK:JLX56V5T |
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| Datum objave v DKUM: | 17.08.2026 |
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| Število ogledov: | 166 |
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| Število prenosov: | 6 |
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| Metapodatki: |    |
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| Področja: | Ostalo
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