In Silico Methods for the Identification of Diagnostic and Favorable Prognostic Markers in Acute Myeloid Leukemia

International Journal of Molecular Sciences

Panagiota I. Kontou¹  Stephan Marquardt²  Stella Logotheti²  Halil Ibrahim Toy³  Gökhan Karakülah³  Athanasia Pavlopoulou³  Hande Yılmaz³  Can Küçük³ 

[1] Department of Computer Science and Biomedical Informatics, University of Thessaly, 35131 Lamia, Greece;Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, 18057 Rostock, Germany;Izmir Biomedicine and Genome Center, Balcova, 35340 Izmir, Turkey;

关键词: acute myeloid leukemia;    transcriptomics;    clinical traits;    bioinformatics;    long-term survivors;    minimal residual disease;   

DOI  :  10.3390/ijms22179601

来源: DOAJ

【 摘 要 】

Acute myeloid leukemia (AML), the most common type of acute leukemia in adults, is mainly asymptomatic at early stages and progresses/recurs rapidly and frequently. These attributes necessitate the identification of biomarkers for timely diagnosis and accurate prognosis. In this study, differential gene expression analysis was performed on large-scale transcriptomics data of AML patients versus corresponding normal tissue. Weighted gene co-expression network analysis was conducted to construct networks of co-expressed genes, and detect gene modules. Finally, hub genes were identified from selected modules by applying network-based methods. This robust and integrative bioinformatics approach revealed a set of twenty-four genes, mainly related to cell cycle and immune response, the diagnostic significance of which was subsequently compared against two independent gene expression datasets. Furthermore, based on a recent notion suggesting that molecular characteristics of a few, unusual patients with exceptionally favorable survival can provide insights for improving the outcome of individuals with more typical disease trajectories, we defined groups of long-term survivors in AML patient cohorts and compared their transcriptomes versus the general population to infer favorable prognostic signatures. These findings could have potential applications in the clinical setting, in particular, in diagnosis and prognosis of AML.

【 授权许可】

Unknown