In-Silico discovery of Pediatric Acute-Myeloid-Leukemia (pAML) causing druggable molecular signatures highlighting their pathogenetic processes and therapeutic agents through single-cell RNA-Seq profile analysis

Pediatric acute-myeloid-leukemia (pAML) is an aggressive malignancy and the second most common blood cancer in children. In spite of significant advances in the frontline therapeutic approaches, approximately 50% of pAML patients show poor prognosis and relapse. Though drugs show positive response against the cancer cells initially, however, it becomes resistant in the long run of treatment, requiring the use of alternative drugs. Therefore, this study aimed to discover pAML-causing druggable molecular signatures highlighting their pathogenetic processes and alternative therapeutic agents. To address these issues, at first, we performed an integrated single-cell RNA sequencing (scRNA-seq) profile analysis of two datasets with accession IDs GSE154109 and GSE235923, which revealed 6 pAML-related key cell types (Erythroid cells, GdT-cells, Naive B-cells, Naive CD4 T-cells, Non-Classical Monocytes, and T-regs) and 198 common differentially expressed genes (cDEGs) between pAML and healthy groups. The protein-protein interaction (PPI) analysis yielded top-ranked eight cDEGs (JUN, MDM2, FOS, SOD2, FBXW7, CHD3, MCL1, and MAP2K1) as common key genes (cKGs) across the key cell types. Disease-cKGs enrichment analysis further confirmed the relevance of these genes to pAML and other leukemic diseases. Regulatory network analysis identified top four transcription factors (FOXC1, GATA2, RELA, and TP53) and three microRNAs (hsa-let-7a-5p, hsa-let-7e-5p, hsa-miR-15a-5p) that regulate these cKGs. Gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis results reflected their potential roles in pAML pathogenesis. Pathway perturbation analysis through gene-set enrichment analysis (GSEA) tool identified significantly perturbed pathways, highlighting how they are altered in pAML environment and how the cKGs are linked in the process. Subsequently, three potential therapeutic candidates (IRINOTECAN HYDROCHLORIDE, IMATINIB and IBRUTINIB) were disclosed through an integrative strategy combining molecular docking, drug-likeness, ADME/T, and DFT analyses. Molecular dynamics (MD) simulation studies for the top three drug-target complexes indicated the stability of complexes. Thus, the findings potentially offer valuable insights for pAML pathogenesis and effective therapeutic candidates for pAML patients.

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PLoS ONE
Published
2025-10-31
DOI
https://doi.org/10.1371/journal.pone.0335410
Citations
2
Primary Topic
Acute Myeloid Leukemia Research
Type
article
Field-Weighted Citation Impact
3.54
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article

In-Silico discovery of Pediatric Acute-Myeloid-Leukemia (pAML) causing druggable molecular signatures highlighting their pathogenetic processes and therapeutic agents through single-cell RNA-Seq profile analysis

M. Jamaluddin Ahmed, Md. Nurul Haque Mollah, Md. Foysal Ahmed, Md. Fahim Faysal et al.
2 citations
PLoS ONE
Acute Myeloid Leukemia Research
3.54
article

In-Silico discovery of Pediatric Acute-Myeloid-Leukemia (pAML) causing druggable molecular signatures highlighting their pathogenetic processes and therapeutic agents through single-cell RNA-Seq profile analysis

M. Jamaluddin Ahmed, Md. Nurul Haque Mollah, Md. Foysal Ahmed, Md. Fahim Faysal, Md Mehedi Hasan, Md. Noman, Md. Bayazid Hossen, Md. Shariful Islam, Abdul Latif, Md. Sanoar Hossain, Md. Al Amin Pappu
article en
2 citations

Abstract

Pediatric acute-myeloid-leukemia (pAML) is an aggressive malignancy and the second most common blood cancer in children. In spite of significant advances in the frontline therapeutic approaches, approximately 50% of pAML patients show poor prognosis and relapse. Though drugs show positive response against the cancer cells initially, however, it becomes resistant in the long run of treatment, requiring the use of alternative drugs. Therefore, this study aimed to discover pAML-causing druggable molecular signatures highlighting their pathogenetic processes and alternative therapeutic agents. To address these issues, at first, we performed an integrated single-cell RNA sequencing (scRNA-seq) profile analysis of two datasets with accession IDs GSE154109 and GSE235923, which revealed 6 pAML-related key cell types (Erythroid cells, GdT-cells, Naive B-cells, Naive CD4 T-cells, Non-Classical Monocytes, and T-regs) and 198 common differentially expressed genes (cDEGs) between pAML and healthy groups. The protein-protein interaction (PPI) analysis yielded top-ranked eight cDEGs (JUN, MDM2, FOS, SOD2, FBXW7, CHD3, MCL1, and MAP2K1) as common key genes (cKGs) across the key cell types. Disease-cKGs enrichment analysis further confirmed the relevance of these genes to pAML and other leukemic diseases. Regulatory network analysis identified top four transcription factors (FOXC1, GATA2, RELA, and TP53) and three microRNAs (hsa-let-7a-5p, hsa-let-7e-5p, hsa-miR-15a-5p) that regulate these cKGs. Gene ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis results reflected their potential roles in pAML pathogenesis. Pathway perturbation analysis through gene-set enrichment analysis (GSEA) tool identified significantly perturbed pathways, highlighting how they are altered in pAML environment and how the cKGs are linked in the process. Subsequently, three potential therapeutic candidates (IRINOTECAN HYDROCHLORIDE, IMATINIB and IBRUTINIB) were disclosed through an integrative strategy combining molecular docking, drug-likeness, ADME/T, and DFT analyses. Molecular dynamics (MD) simulation studies for the top three drug-target complexes indicated the stability of complexes. Thus, the findings potentially offer valuable insights for pAML pathogenesis and effective therapeutic candidates for pAML patients.

PLoS ONEVol. 20(10)
Louisiana State University (US), Rajshahi University of Engineering and Technology (BD), Bangladesh Agricultural University (BD), University of Rajshahi (BD)
Openalex Percentile: Top 6%
Acute Myeloid Leukemia Research
3.54
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