Comprehensive analysis for cellular senescence-related immunogenic characteristics and immunotherapy prediction of acute myeloid leukemia

Mao, Yan Ping; Xu, Jianming; Xu, Xuejiao; Qiu, Jiayun; Hu, Zhengyun; Jiang, Feng; Zhou, Guoping

doi:10.3389/fphar.2022.987398

Cited by 6 publications

(7 citation statements)

References 82 publications

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“…Also, AML enables the myeloid cells to proceed uncontrolled and limitless number of cell cycles 43 . Cellular senescence promotes the evasion of tumor cells from immunosurveillance 44 . The coupling of JAK-STAT signaling pathway and cell cycle suggests increased cell-cell communication and expedited cell growth, which is shown in recent in vitro experiments 42 .…”

Section: Resultsmentioning

confidence: 99%

Data-driven modeling of core gene regulatory network underlying leukemogenesis in IDH mutant AML

Katebi,

Chen,

Ramirez

et al. 2024

npj Syst Biol Appl

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Acute myeloid leukemia (AML) is characterized by uncontrolled proliferation of poorly differentiated myeloid cells, with a heterogenous mutational landscape. Mutations in IDH1 and IDH2 are found in 20% of the AML cases. Although much effort has been made to identify genes associated with leukemogenesis, the regulatory mechanism of AML state transition is still not fully understood. To alleviate this issue, here we develop a new computational approach that integrates genomic data from diverse sources, including gene expression and ATAC-seq datasets, curated gene regulatory interaction databases, and mathematical modeling to establish models of context-specific core gene regulatory networks (GRNs) for a mechanistic understanding of tumorigenesis of AML with IDH mutations. The approach adopts a new optimization procedure to identify the top network according to its accuracy in capturing gene expression states and its flexibility to allow sufficient control of state transitions. From GRN modeling, we identify key regulators associated with the function of IDH mutations, such as DNA methyltransferase DNMT1, and network destabilizers, such as E2F1. The constructed core regulatory network and outcomes of in-silico network perturbations are supported by survival data from AML patients. We expect that the combined bioinformatics and systems-biology modeling approach will be generally applicable to elucidate the gene regulation of disease progression.

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Section: Resultsmentioning

confidence: 99%

Data-driven modeling of core gene regulatory network underlying leukemogenesis in IDH mutant AML

Katebi,

Chen,

Ramirez

et al. 2024

npj Syst Biol Appl

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“…First, we used the PubMed database [ 75 ] and characterized each of the 39 novel PAG-related DEGs of the tame and aggressive rats ( Table 2 ) by answering the question as to how under- or overexpression of their human orthologs can aggravate or alleviate ARDs [ 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 98 , 99 , 100 , 101 , 102 , 103 , 104 , 105 , 106 , 107 , 108 , 109 , 110 , 111 , 112 , 113 , 114 , 115 , 116 , 117 , 118 , 119 , 120 , 121 , 122 , 123 , 124 , 125 , 126 , 127 , 128 , 129 , 130 , 131 , 132 , 133 , 134 , 135 , 136 , ...…”

Section: Resultsmentioning

confidence: 99%

“…By looking at the entries in the left half of Table 9 , it becomes clear why underexpression of the human FCGR2B gene, which is orthologous to the rat Fcgr2b gene (this being the only gene we have identified as a theranostic molecular marker for ARD; Table 9 ), alleviates such diseases [ 117 , 118 ], while its overexpression aggravates them [ 119 , 120 ].…”

Section: Resultsmentioning

confidence: 99%

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Differentially Expressed Genes and Molecular Susceptibility to Human Age-Related Diseases

Shikhevich

Chadaeva

Khandaev

et al. 2023

IJMS

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Mainstream transcriptome profiling of susceptibility versus resistance to age-related diseases (ARDs) is focused on differentially expressed genes (DEGs) specific to gender, age, and pathogeneses. This approach fits in well with predictive, preventive, personalized, participatory medicine and helps understand how, why, when, and what ARDs one can develop depending on their genetic background. Within this mainstream paradigm, we wanted to find out whether the known ARD-linked DEGs available in PubMed can reveal a molecular marker that will serve the purpose in anyone’s any tissue at any time. We sequenced the periaqueductal gray (PAG) transcriptome of tame versus aggressive rats, identified rat-behavior-related DEGs, and compared them with their known homologous animal ARD-linked DEGs. This analysis yielded statistically significant correlations between behavior-related and ARD-susceptibility-related fold changes (log2 values) in the expression of these DEG homologs. We found principal components, PC1 and PC2, corresponding to the half-sum and the half-difference of these log2 values, respectively. With the DEGs linked to ARD susceptibility and ARD resistance in humans used as controls, we verified these principal components. This yielded only one statistically significant common molecular marker for ARDs: an excess of Fcγ receptor IIb suppressing immune cell hyperactivation.

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Section: Grn Modeling Identifies the Presence And Coupling Of Key Bio...mentioning

confidence: 99%

Data-driven modeling of core gene regulatory network underlying leukemogenesis in IDH mutant AML

Katebi,

Chen,

et al. 2023

Preprint

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Acute myeloid leukemia (AML) is characterized by uncontrolled proliferation of poorly differentiated myeloid cells, with a heterogenous mutational landscape. Mutations in IDH1 and IDH2 are found in 20% of the AML cases. Although much effort has been made to identify genes associated with leukemogenesis, the regulatory mechanism of AML state transition is still not fully understood. To alleviate this issue, here we develop a new computational approach that integrates genomic data from diverse sources, including gene expression and ATAC-seq datasets, curated gene regulatory interaction databases, and mathematical modeling to establish models of context-specific core gene regulatory networks (GRNs) for a mechanistic understanding of tumorigenesis of AML with IDH mutations. The approach adopts a novel optimization procedure to identify the optimal network according to its accuracy in capturing gene expression states and its flexibility to allow sufficient control of state transitions. From GRN modeling, we identify key regulators associated with the function of IDH mutations, such as DNA methyltransferase DNMT1, and network destabilizers, such as E2F1. The constructed core regulatory network and outcomes of in-silico network perturbations are supported by survival data from AML patients. We expect that the combined bioinformatics and systems-biology modeling approach will be generally applicable to elucidate the gene regulation of disease progression.

show abstract

Comprehensive analysis for cellular senescence-related immunogenic characteristics and immunotherapy prediction of acute myeloid leukemia

Cited by 6 publications

References 82 publications

Data-driven modeling of core gene regulatory network underlying leukemogenesis in IDH mutant AML

Data-driven modeling of core gene regulatory network underlying leukemogenesis in IDH mutant AML

Differentially Expressed Genes and Molecular Susceptibility to Human Age-Related Diseases

Data-driven modeling of core gene regulatory network underlying leukemogenesis in IDH mutant AML

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