Hepatosellüler karsinom prognozunun belirlenmesinde epigenetik faktörlerin ileri biyoinformatik yöntemlerle analizi
Küçük Resim Yok
Tarih
2023
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
İnönü Üniversitesi
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Amaç: Bu çalışmanın amacı HCC'nin biyolojik davranışında belirleyici olduğu düşünülen moleküler genetik mekanizmaların transkriptomik ve epigenetik analizler sonucu elde edilen veriler kullanılarak tespit edilmesidir. Materyal ve Metot: HCC ile ilişkili transkriptomik veriler NCBI GEO veri tabanından indirilmiştir. GSE46444 ve GSE63898 erişim numaralı veri setlerinde bulunan gruplar arası ekspresyon farklılıkları GEO2R ile analiz edilmiştir. Ekspresyonda farklılık gösteren genler GO ve KEGG metabolik yolak analizleri ile zenginleştirilmiştir. WGBS ve MeDIP-Seq veri setleri NCBI SRA veri tabanından indirilmiştir. WGBS ve MeDIP-Seq verileri sırasıyla Bismark ve QSEA yazılımı ile analiz edilmiştir. Gruplar arası metilasyona uğrayan bölgeler ile işlevsel zenginleştirme analizi gerçekleştirilmiştir. Bulgular: GSE46444 veri setinde sirozlu dokuyla karşılaştırıldığında HCC'li dokuda protein kodlayan 80 genin up- ve 315 genin down-regule olduğu görülmüştür. GSE63898 veri setinde HCC'li grup ile karşılaştırıldığında sirozlu grupta 1.261 genin up- ve 458 genin down-regule olduğu görüldü. WGBS sonucunda sağlıklı dokuyla karşılaştırıldığında HCC'li dokuda hipermetile olan ilk 20 genomik lokasyonda protein-kodlayan genlerin olduğu ve hipometile bölgelerin çoğunun da kodlayan genomik bölge olduğu görülmüştür. Sirozlu dokuyla karşılaştırıldığında HCC'li dokuda metile olan lokasyonlar da sağlıklı dokudaki karşılaştırmalar ile benzerdir. MeDIP-Seq ile HCC'li ve HCC'siz dokular karşılaştırılmıştır. Hiper- ve hipometile olan bölgelerde protein-kodlayan genler belirlenmiştir. Bu bölgelerin işlev zenginleştirme analizinde peroksizom, fokal adhezyon, mTOR, RAP1, Fosfolipaz D, Ras ve PI3K/AKT sinyal yolağında rol oynadığı belirlenmiştir. Sonuç: Transkriptomik ve epigenetik analizlerle HCC'nin biyolojik davranışında etkili moleküler düzeyde veriler elde edilmiştir. Bunların ileride geliştirilecek hedeflenmiş tedavi için potansiyel aday olabileceği düşünülmektedir. Anahtar Kelimeler: Biyoinformatik analiz, Epigenetik, Genetik, Hepatoselüler karsinom, Transkriptomik
Aim: The aim of the present study is to evaluate the molecular genetic mechanisms that play a role in the biologic behavior of HCC by analyzing of the transcriptomic and epigenetic signatures of the tumors. Material and Method: Transcriptomic data were downloaded from the NCBI GEO database. The expression differences between the GSE46444 and GSE63898 data sets were analyzed using the GEO2R. The genes that have shown and expression difference were further evaluated using GO and KEGG metabolic pathway analysis websites. WGBS and MeDIP-Seq data sets were downloaded from the NCBI SRA database. WGBS and MeDIP-Seq data were analyzed by using Bismark and QSEA, respectively. The methylation differences between the groups were evaluated by using the functional enrichment analysis. Results: In the GSE46444 data set, 80 genes were upregulated, and 315 genes were down-regulated in the HCC tissue when compared to the non-tumorous cirrhotic tissue. In the GSE63898 data set, 1261 genes were upregulated, and 458 genes were down-regulated in the cirrhotic tissue when compared to the HCC tissues. WGBS showed that 20 protein coding loci were hypermethylated and majority of the hypomethylated regions were non-protein coding/protein coding. The methylated residues of the HCC, cirrhotic and healthy tissues were statistically comparable. The MeDIP-Seq was comparatively performed on the HCC ad non-HCC tissues and hypermethylated or hypomethylated areas were determined to be protein coding regions. The functional enrichment analysis showed that these genes were related with peroxisome, focal adhesion, mTOR, RAP1, Phospholipase D, Ras and PI3K/AKT signal transduction pathways. Conclusions: The results of the present study using the transcriptomic and epigenetic methods identified various genes that were effective in the biologic behavior of HCC. These can be potential candidates for developing targeted therapy. Keywords: Bioinformatic analysis, Epigenetics, Genetics, Hepatocellular carcinoma, Transcriptomics,
Aim: The aim of the present study is to evaluate the molecular genetic mechanisms that play a role in the biologic behavior of HCC by analyzing of the transcriptomic and epigenetic signatures of the tumors. Material and Method: Transcriptomic data were downloaded from the NCBI GEO database. The expression differences between the GSE46444 and GSE63898 data sets were analyzed using the GEO2R. The genes that have shown and expression difference were further evaluated using GO and KEGG metabolic pathway analysis websites. WGBS and MeDIP-Seq data sets were downloaded from the NCBI SRA database. WGBS and MeDIP-Seq data were analyzed by using Bismark and QSEA, respectively. The methylation differences between the groups were evaluated by using the functional enrichment analysis. Results: In the GSE46444 data set, 80 genes were upregulated, and 315 genes were down-regulated in the HCC tissue when compared to the non-tumorous cirrhotic tissue. In the GSE63898 data set, 1261 genes were upregulated, and 458 genes were down-regulated in the cirrhotic tissue when compared to the HCC tissues. WGBS showed that 20 protein coding loci were hypermethylated and majority of the hypomethylated regions were non-protein coding/protein coding. The methylated residues of the HCC, cirrhotic and healthy tissues were statistically comparable. The MeDIP-Seq was comparatively performed on the HCC ad non-HCC tissues and hypermethylated or hypomethylated areas were determined to be protein coding regions. The functional enrichment analysis showed that these genes were related with peroxisome, focal adhesion, mTOR, RAP1, Phospholipase D, Ras and PI3K/AKT signal transduction pathways. Conclusions: The results of the present study using the transcriptomic and epigenetic methods identified various genes that were effective in the biologic behavior of HCC. These can be potential candidates for developing targeted therapy. Keywords: Bioinformatic analysis, Epigenetics, Genetics, Hepatocellular carcinoma, Transcriptomics,
Açıklama
Anahtar Kelimeler
Biyoistatistik, Biostatistics, Genel Cerrahi
