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An in silico approach to the identification of diagnostic and prognostic markers in low-grade gliomas
Ist Teil von
PeerJ (San Francisco, CA), 2023-03, Vol.11, p.e15096-e15096, Article e15096
Ort / Verlag
United States: PeerJ. Ltd
Erscheinungsjahr
2023
Link zum Volltext
Quelle
Free E-Journal (出版社公開部分のみ)
Beschreibungen/Notizen
Low-grade gliomas (LGG) are central nervous system Grade I tumors, and as they progress they are becoming one of the deadliest brain tumors. There is still great need for timely and accurate diagnosis and prognosis of LGG. Herein, we aimed to identify diagnostic and prognostic biomarkers associated with LGG, by employing diverse computational approaches. For this purpose, differential gene expression analysis on high-throughput transcriptomics data of LGG
corresponding healthy brain tissue, derived from TCGA and GTEx, respectively, was performed. Weighted gene co-expression network analysis of the detected differentially expressed genes was carried out in order to identify modules of co-expressed genes significantly correlated with LGG clinical traits. The genes comprising these modules were further used to construct gene co-expression and protein-protein interaction networks. Based on the network analyses, we derived a consensus of eighteen hub genes, namely,
, and
. All detected hub genes were up-regulated in LGG, and were also associated with unfavorable prognosis in LGG patients. The findings of this study could be applicable in the clinical setting for diagnosing and monitoring LGG.