Comparative Analysis of High-Throughput Data in AML Detection

Acute myeloid leukemia (AML) is a heterogeneous hematological melanoma disease, which faces significant challenges in diagnosis and treatment due to its genetic complexity and variable clinical outcomes.NF342 by juxtaposition with MPO promoter/enhan In the current work, the potential of microarrays and next-generation RNA sequencing (RNA-seq) is assessed for the study of cancer and specifically to separate differences in expression that distinguish samples with AML from control ones. By using datasets derived from microarrays and RNA-seq, the study observes that there are differences in gene expression between AML samples and control. The selected probes were used as a foundation in the development of a classifier that can distinguish the AML samples. The probes then underwent cross-mapping from the microarrays platform to the RNA-seq one and vice versa. This ensured the adaptability and the reliability of the classifier on different platforms. The classifier that was trained (using probes from the same platform) showed greater reliability in the dataset from next-generation RNA-seq platform, with an accuracy of 98.9%, 98.7% sensitivity, and 100% specificity. However, when opposite platform probes were used, that underwent cross-mapping, the reliability of the classifier in the dataset from microarrays platform significantly increased. In particular, it reached an accuracy of 99.3%, 99.4% specificity, and a sensitivity of 96.4%. Lastly, the selection methods were used again with a higher number of genes and then gene set enrichment analysis was performed to find the pathways where the genes are connected. This showed the significance of multiple pathways including "Protein processing in endoplasmic reticulum Homo sapiens hsa04141" and "Proteoglycans in cancer Homo sapiens hsa05205."