ArticleBMC bioinformatics2019
Detecting the stable point of therapeutic effect of chronic myeloid leukemia based on dynamic network biomarkers.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- Unravelling cancer subtype-specific driver genes in single-cell transcriptomics data with CSDGI.PLoS computational biology · 2023Article
- Trehalose metabolism coordinates transcriptional regulatory control and metabolic requirements to trigger the onset of cassava storage root initiation.Scientific reports · 2023Article
- Network Control Models With Personalized Genomics Data for Understanding Tumor Heterogeneity in Cancer.Frontiers in oncology · 2022Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMost researches of chronic myeloid leukemia (CML) are currently focused on the treatment methods, while there are relatively few researches on the progress of patients' condition after drug treatment. Traditional biomarkers of disease can only distinguish normal state from disease state, and cannot recognize the pre-stable state after drug treatment.
resultsA therapeutic effect recognition strategy based on dynamic network biomarkers (DNB) is provided for CML patients' gene expression data. With the DNB criteria, the DNB with 250 genes is selected and the therapeutic effect index (TEI) is constructed for the detection of individual disease. The pre-stable state before the disease condition becomes stable is 1 month. Through functional analysis for the DNB, some genes are confirmed as key genes to affect the progress of CML patients' condition.
conclusionsThe results provide a certain theoretical direction and theoretical basis for medical personnel in the treatment of CML patients, and find new therapeutic targets in the future. The biomarkers of CML can help patients to be treated promptly and minimize drug resistance, treatment failure and relapse, which reduce the mortality of CML significantly.
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