Hesaplamalı yöntemler kullanılarak beyin neoplazmasında prognoz ve teşhis işaretlerinin belirlenmesi

Low-grade gliomas (LGG) are central nervous system Grade I tumors, and as they progress become one of the deadliest brain tumors. Today, there is still great need for timely and accurate diagnosis and prognosis. The aim of this study, was to identify diagnostic and prognostic biomarkers associated with LGG, by employing using diverse computational approaches. For this purpose, differential gene expression analysis between LGG tissue and corresponding normal tissue was performed by using three methodologies. A total of four thousand four hundred and ninety six common differentially expressed genes (DEGs) between LGG and healthy brain tissue were detected. Next, those DEGs were used to conduct Weighted Gene Co-Expression Network Analysis (WGCNA) in order to identify clusters of co-expressed genes. In this way, two modules significantly correlated with clinical traits were detected. The DEGs comprising the modules were further used to construct gene co-expression and protein-protein interaction networks. Based on this analysis, we derived a consensus of eighteen hub genes, namely, CD74, CD86, CDC25A, CYBB, HLA-DMA, ITGB2, KIF11, KIFC1, LAPTM5, LMNB1, MKI67, NCKAP1L, NUSAP1, SLC7A7, TBXAS1, TOP2A, TYROBP, and WDFY4. All detected hub genes were up-regulated in LGG, and were also associated with unfavorable prognosis in LGG patients. These findings could be applicable in the clinical setting for diagnosing and monitoring LGG.