Acute myeloid leukemia (AML) is a highly heterogeneous hematological malignancy, and drug resistance and relapse are key factors in the failure of leukemia treatment. Studies have increasingly shown that circRNA and LncRNA play important roles in the development of tumors, but their roles remain unclear in the mechanism of AML resistance. Materials and Methods: Resistant AML cell line HL-60/ADM (adriamycin, ADM) was constructed and circRNA, LncRNA, and mRNA expression profiles were screened followed by high-throughput sequencing. Bioinformatics analysis was then carried out, and the circRNA-miRNA ceRNA network was constructed and confirmed using qRT-PCR analysis. Results: A total of 1824 circRNAs, 2414 LncRNAs, and 5346 mRNAs were screened for differentially expressed genes. Enrichment analysis was performed utilizing Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes, which mainly involved protein domain specific binding, transforming growth factor-β (TGF-β) receptor, and cellular metabolism. The mTOR signaling pathway, MAPK signaling pathway, RAP1 signaling pathway, and Akt signaling pathway were closely related to drug resistance. Conclusion: Our study provides a systematic outlook on the potential function of ncRNA in the molecular mechanisms of resistant AML cells. Hsa-circ-0000978 and hsa-circ-0000483 might serve as potential prognostic biomarkers and therapeutic targets of AML resistance.
Quantitative system pharmacology (QSP) is a discipline that combines computational models of systems biology and systems pharmacology. With the development of high-throughput genomics techniques (genomics, transcriptomics, proteomics, and metabolomics) as well as computer and bioinformatics methods, systems biology and systems pharmacology modeling are widely used to comprehend human biology and disease progression, predict the effectiveness and safety of drug candidates. Due to the advancement of big data and high-quality database, the application of QSP, especially the pre-clinical stage that guides early drug discovery, is increasingly widespread. The traditional drug discovery process takes a long time yet has a low success rate. The early intervention and full participation of QSP in the development of new drugs discovery can form a model-led drug development model to improve the efficiency of drug discovery and scientific appraise, reduce the cost of research and development, and shorten the time to market for new drugs. This article reviews the differences between QSP and other quantitative pharmacology, the problems faced by traditional Chinese medicine research, and the value of QSP in traditional Chinese medicine research, with a view to providing reference and support for the research and development of new traditional Chinese medicine.
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