Microrna - based drug repurposing analysis in glioblastoma multiforme via machine learning approaches
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Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Adana Alparslan Türkeş Bilim ve Teknoloji Üniversitesi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Glioblastoma (GBM) tüm beyin tümörleri arasında en yaygın kanserdir ve en kötü prognoza sahiptir. Bu tezde, GBM'nin biyomoleküler mekanizmalarını göstermek ve daha iyi anlamak için GBM verilerine çeşitli biyoinformatik analizler ve makine öğrenimi sınıflandırma teknikleri uyguladık. İlk olarak, analiz edilecek GBM veri kümeleri seçilmiştir. Ardından, transkriptom ve miRNA verilerinin ekspresyon analizleri gerçekleştirilmiştir. Değişmiş ekspresyona sahip genler ve miRNA'lar tanımlanmış, ortak genlerle birlikte eksprese edilen gen kümeleri ve miRNA'lar tarafından hedeflenen genler belirlenmiş ve ko-ekspresyon ağı çizilmiştir. Bu gen kümelerinin prognostik özellikleri incelenmiş ve Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB) ve The K-Nearest Neighbours (KNN) gibi makine öğrenmesi sınıflandırma algoritmaları ile performans analizleri gerçekleştirilmiştir. Performans analizleri sonucunda RF, SVM ve KNN algoritmalarının %80'in üzerinde doğruluk skorları ile mevcut gen grupları ile çalışan güvenilir ve doğru sınıflandırma algoritmaları olduğu görülmüştür. Ayrıca yukarı ve aşağı doğru özellik seçimi ile elde edilen sonuçlara göre SEPT4, VAMP1, MAP1A, KIF5C, NPTX1 ve ATP8A1 genlerinin yüksek etkileşim verdiği ve GBM hastalığında önemli biyobelirteç adayları olabileceği tespit edilmiştir. Ayrıca elde edilen gen kümeleri ilaç yeniden konumlandırma analizleri için kullanılmıştır. Bu testler sonucunda gen ko-ekspresyon ağ modülü ve miRNA bazlı ko-ekspresyon ağ modülü verilerine ait gen kümelerinin içinde yer alan makine öğrenmesi analizlerinde en yüksek etkileşimlere sahip olan gen grupları ile ortak ilaçları belirlenmiştir. GBM tedavisinde kullanılabilecek, Emetine Dihydrochloride Hydrate (74), 16beta Bromoandrosterone, AS605240, 480743.cdx, BRD K00627859, HDAC6 inhibitörü ISOX, BRD-K12184916 ve 16-HYDROXYTRIPTOLIDE gibi çok sayıda ilaç adayı bulundu. Bu ilaçların ve küçük moleküllerin gelecekteki klinik çalışmalarda test edilebileceğini ve GBM tedavisi için yeni ilaç adayları olabileceği belirlenmiştir.
Glioblastoma (GBM) is the most common cancer among all brain tumours and has the worst prognosis. In this thesis, we applied various bioinformatic analyses and machine learning classification techniques to GBM data to illustrate and better understand the biomolecular mechanisms of GBM. First, GBM datasets to be analysed were selected. Then, expression analyses of transcriptome and miRNA data were performed. Differentially expressed genes and miRNAs were identified, gene clusters co-expressed with common genes and genes targeted by miRNAs were identified, and the co-expression network was drawn. The prognostic properties of these gene clusters were examined and performance analyses were performed with machine learning classification algorithms such as Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB) and The K-Nearest Neighbours (KNN). As a result of the performance analyses, RF, SVM and KNN algorithms were found to be reliable and accurate classification algorithms that work with existing gene groups with accuracy scores above 80%. In addition, according to the results obtained by upward and downward feature selection, it was determined that SEPT4, VAMP1, MAP1A, KIF5C, NPTX1 and ATP8A1 genes give high interaction and may be important biomarker candidates in GBM disease. In addition, the gene clusters obtained were used for drug repositioning analyses. As a result of these tests, the gene groups with the highest interactions in the gene clusters belonging to the gene co-expression network module and miRNA-based co-expression network module data in machine learning analyses and their common drugs were determined. A large number of drug candidates such as Emetine Dihydrochloride Hydrate (74), 16beta Bromoandrosterone, AS605240, 480743.cdx, BRD K00627859, HDAC6 inhibitor ISOX, BRD-K12184916 and 16-HYDROXYTRIPTOLIDE were found for the treatment of GBM. It was determined that these drugs and small molecules can be tested in future clinical trials and may be new drug candidates for the treatment of GBM.
Glioblastoma (GBM) is the most common cancer among all brain tumours and has the worst prognosis. In this thesis, we applied various bioinformatic analyses and machine learning classification techniques to GBM data to illustrate and better understand the biomolecular mechanisms of GBM. First, GBM datasets to be analysed were selected. Then, expression analyses of transcriptome and miRNA data were performed. Differentially expressed genes and miRNAs were identified, gene clusters co-expressed with common genes and genes targeted by miRNAs were identified, and the co-expression network was drawn. The prognostic properties of these gene clusters were examined and performance analyses were performed with machine learning classification algorithms such as Random Forest (RF), Support Vector Machine (SVM), Naive Bayes (NB) and The K-Nearest Neighbours (KNN). As a result of the performance analyses, RF, SVM and KNN algorithms were found to be reliable and accurate classification algorithms that work with existing gene groups with accuracy scores above 80%. In addition, according to the results obtained by upward and downward feature selection, it was determined that SEPT4, VAMP1, MAP1A, KIF5C, NPTX1 and ATP8A1 genes give high interaction and may be important biomarker candidates in GBM disease. In addition, the gene clusters obtained were used for drug repositioning analyses. As a result of these tests, the gene groups with the highest interactions in the gene clusters belonging to the gene co-expression network module and miRNA-based co-expression network module data in machine learning analyses and their common drugs were determined. A large number of drug candidates such as Emetine Dihydrochloride Hydrate (74), 16beta Bromoandrosterone, AS605240, 480743.cdx, BRD K00627859, HDAC6 inhibitor ISOX, BRD-K12184916 and 16-HYDROXYTRIPTOLIDE were found for the treatment of GBM. It was determined that these drugs and small molecules can be tested in future clinical trials and may be new drug candidates for the treatment of GBM.
Açıklama
Lisansüstü Eğitim Enstitüsü, Biyomühendislik Ana Bilim Dalı, Biyomühendislik Bilim Dalı
Anahtar Kelimeler
Biyomühendislik, Bioengineering
