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

Yilmaz H., Toy H. I., Marquardt S., KARAKÜLAH G., KÜÇÜK C., Kontou P., ...More

International journal of molecular sciences, vol.22, no.17, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 22 Issue: 17
  • Publication Date: 2021
  • Doi Number: 10.3390/ijms22179601
  • Journal Name: International journal of molecular sciences
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, CAB Abstracts, EMBASE, Food Science & Technology Abstracts, MEDLINE, Veterinary Science Database, Directory of Open Access Journals
  • Keywords: acute myeloid leukemia, transcriptomics, clinical traits, bioinformatics, long-term survivors, minimal residual disease, diagnostics, prognostics, LONG-TERM SURVIVORS, MOLECULAR CHARACTERIZATION, OLDER PATIENTS, BONE-MARROW, CANCER, YOUNGER, IMMUNE, CLASSIFICATION, TRANSCRIPTOME, ADULTS
  • Dokuz Eylül University Affiliated: Yes


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.