Farklı taksonomik gruplarda kanserin moleküler mekanizmalarının hesaplamalı biyoloji uygulamalarıyla araştırılması

Investigating the fundamental mechanisms of tumorigenesis is considered crucial in developing alternative therapies and new diagnostic strategies. These mechanisms, which were investigated in this study, could provide an indispensable guide to identifying alternative treatment methods in cancer research. Data mining methods allow the investigation of large-scale biological data. To this end, cross-disciplinary in silico systems biology methods, by combining comparative genomics, differentially expressed gene profiles, biological networks and biological text mining, were employed to investigate the molecular mechanisms of carcinogenesis in diverse taxonomic groups. For this purpose, the following steps were carried out: (1) Identification of novel genes/proteins involved in different aspects of cancer through mining publicly accessible primary genomic databases, (2) Conduct of comprehensive in silico phylogenetic analyses of genes/proteins, covering the major taxonomic divisions, (3) Identification of patterns of differentially expressed genes in different types of cancers through processing high-throughput data derived from public repositories, (4) Construction of functional networks and subsequent identification of gene/protein modules by employing biological network inference techniques, (5) Natural language processing using artificial intelligence techniques for the detection of semantic relations. The findings of this study could have potential application in the clinical setting. In this regard, the corresponding genes can be exploited as diagnostic or prognostic tumor markers, and they could also be utilized in cancer therapeutic decision-making or in future experimental studies for the rational design of targeted cancer drugs.