Yeni Bir İlaç Hedefi Olarak Protein Devir Hızının (Ribozom - Proteazom Yolakları) Genomik Veri Tabanlarında Sorgulanması ve In-Silico Validasyonu ile Kliniğe Translasyonu


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Başbınar Y. (Yürütücü), Ellidokuz E.

Yükseköğretim Kurumları Destekli Proje, 2021 - 2024

  • Proje Türü: Yükseköğretim Kurumları Destekli Proje
  • Başlama Tarihi: Eylül 2021
  • Bitiş Tarihi: Nisan 2024

Proje Özeti

Revealing the genes that have been shown to be effective in the protein

degradation process and the molecular mechanisms and pathways associated with

them will enable the provision of biomarkers and the elucidation of therapeutic targets.

Ubiquitin plays a role in many processes such as apoptosis, cell division and

proliferation, cell degeneration, DNA transcription and repair, immune response,

organelle biogenesis, antigen processes, receptor modulation, viral infections by

binding to proteins. E1 (activating enzyme), E2 (conjugating enzyme) and E3 (ligase)

enzymes that control binding as well as de-ubiquitination (DUB) enzymes that control

dissociation are involved in ubiquitin modification of proteins. Recently, an increasing

number of studies have been published on the association between altered expression

or germline/somatic mutations of DUB enzymes and many cancers. There are some

studies showing the association of DUB enzymes with EMT as well as many processes

related to tumor metastasis. The aim of the study is to interrogate the protein turnover

rate (ribosome-proteasome pathways) as a new drug target in colorectal cancer from

transcriptome gene expression data and to create a profile target by validating it with

in-silico bioinformatics methods. Genes related to the ubiquitin-proteasome pathway

(UPP) were compiled from previous studies and a ubiquitin-proteasome pathwayspecific

network was created with protein interactions. The network was analyzed and

drug targets were found for ADRB2, RXRA, CHRNA1, and FGFR2.

Key words: Novel drug targets, in-silico analysis, ubiquitin-proteasome

pathway genes, gene network construction, differential gene expression analysis, gene

set enrichment analysis.