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Bioinformatics Analysis Reveals Genes Involved in PI3K-Akt Pathway as Novel Potential Biomarkers and Drug Targets for Acute Myeloid Leukemia (AML)

Cancer Genetics and Therapeutics
  • Primary Categories:
    • Cancer
  • Secondary Categories:
    • Cancer
Introduction:


Acute myeloid leukemia (AML) is an aggressive form of blood cancer associated with a high mortality rate. The standard method for detecting AML is DNA sequencing, which identifies mutations in genomic DNA sequences. However, this approach is insufficient for predicting disease progression. In this study, we employed bioinformatics analyses to identify novel biomarkers based on gene expression differences and explore opportunities for drug repurposing.

Methods:
The dataset SRP518774 was obtained from the NCBI's Sequence Read Archive (SRA) database and underwent preprocessing and quality control. Subsequent analyses included quantifying gene expression, exploratory data analysis, normalization, and differential expression analysis using DESeq2. Functional annotation of differentially expressed genes (DEGs) was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analysis, along with gene-drug interaction analysis using DGIdb.

Results:


The result analysis identified 8112 differentially expressed genes (DEGs). Among these, 951 genes were significantly upregulated (AUC > 0.8) in AML patients, with 185 mapped to pathways in the KEGG database. Notably, 18 biomarkers were associated with the PI3K-Akt signaling pathway, which is implicated in leukemia pathogenesis. Gene-drug interaction analysis revealed 17 potential interactions involving BDNF, EGF, recombinant hepatocyte growth factor drugs, and the 18 identified genes.

Conclusion:


These findings provide valuable insights for future studies on AML biomarkers and drug target discoveries to advance diagnostics and therapeutic strategies for AML.

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