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Article

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

1
Izmir Biomedicine and Genome Center, Balcova, 35340 Izmir, Turkey
2
Izmir International Biomedicine and Genome Institute, Dokuz Eylül University, Balcova, 35340 Izmir, Turkey
3
Institute of Experimental Gene Therapy and Cancer Research, Rostock University Medical Center, 18057 Rostock, Germany
4
Department of Medical Biology, Faculty of Medicine, Dokuz Eylül University, Balcova, 35340 Izmir, Turkey
5
Department of Computer Science and Biomedical Informatics, University of Thessaly, 35131 Lamia, Greece
*
Authors to whom correspondence should be addressed.
These authors have contributed equally to this work and share first authorship.
Academic Editors: Ronald Moura and Sergio Crovella
Int. J. Mol. Sci. 2021, 22(17), 9601; https://doi.org/10.3390/ijms22179601
Received: 16 July 2021 / Revised: 13 August 2021 / Accepted: 20 August 2021 / Published: 5 September 2021
(This article belongs to the Special Issue In Silico Analyses: Translating and Making Sense of Omics Data 2.0)
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. View Full-Text
Keywords: acute myeloid leukemia; transcriptomics; clinical traits; bioinformatics; long-term survivors; minimal residual disease; diagnostics; prognostics acute myeloid leukemia; transcriptomics; clinical traits; bioinformatics; long-term survivors; minimal residual disease; diagnostics; prognostics
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MDPI and ACS Style

Yılmaz, H.; Toy, H.I.; Marquardt, S.; Karakülah, G.; Küçük, C.; Kontou, P.I.; Logotheti, S.; Pavlopoulou, A. In Silico Methods for the Identification of Diagnostic and Favorable Prognostic Markers in Acute Myeloid Leukemia. Int. J. Mol. Sci. 2021, 22, 9601. https://doi.org/10.3390/ijms22179601

AMA Style

Yılmaz H, Toy HI, Marquardt S, Karakülah G, Küçük C, Kontou PI, Logotheti S, Pavlopoulou A. In Silico Methods for the Identification of Diagnostic and Favorable Prognostic Markers in Acute Myeloid Leukemia. International Journal of Molecular Sciences. 2021; 22(17):9601. https://doi.org/10.3390/ijms22179601

Chicago/Turabian Style

Yılmaz, Hande, Halil Ibrahim Toy, Stephan Marquardt, Gökhan Karakülah, Can Küçük, Panagiota I. Kontou, Stella Logotheti, and Athanasia Pavlopoulou. 2021. "In Silico Methods for the Identification of Diagnostic and Favorable Prognostic Markers in Acute Myeloid Leukemia" International Journal of Molecular Sciences 22, no. 17: 9601. https://doi.org/10.3390/ijms22179601

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