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My long-term goal is to uniJerstanid the role and mechanisms of microRNAs In regulating tumor metastasis, and to develop new candidate therapies for malignant diseases. It has become increasingly evident that cancer pathogenesis can Involve a superfamily of small non-coding RNAs named microRNAs. While the oncogenic or tumor-suppressing functions of a number of microRNAs have been characterized, the role played by microRNAs in mediating metastasis was addressed only recently by work from myself and several other groups. In my initial screening, I Identified three microRNAs that are most significantly upregulated in human breast cancer cell lines: mlR-155, miR-9, and miR-10b. Subsequent funcitonal experiments demonstrated that overexpression of miR-IOb induced tumor invasion and distant metastasis in two orthotopic models of breast cancer. In the K99 phase of this award, I discovered that therapeutic silendng of miR-1 Ob with 'antagomirs' suppressed metastasis in a mouse mammary tumor model. In addition, I Identified miR-9 as an E-cadherin-suppressIng and metastasis-promoting microRNA. In the ROO phase, I will extend these previous-studies by using molecular, genetic, pharmacological, and genomic approaches. Specifically, I will pursue the antagomlr-10b study by using multiple models and testing combination therapies In mice; I will establish a genetically engineered mouse model to determine the role and mechanisms of mtR-10b In normal development and In metastatic progression of spontaneous breast cancer; and I will explore the cell non-autonomous effects of mlR-lOb In metastasis formation. In parallel, I will perform pre-cllnical studies with the miR-9 antagomir; and I will also investigate E-cadherln-independent functions of miR-9 in tumor cells. Taken together, these studies will enable rnore precise eveluation of the role and mechanisms of these microRNAs In malignant progression, and will allow me to launch my independent research program and obtain preliminary data for R01 application.
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Targeting the LIFR-LCN2 pathway to improve liver cancer therapy
Statistical modeling of cross-sample variation and learning of latent structures in microbiome sequencing data
  • 批准号:
    10688000
  • 项目类别:
  • 资助金额:
    $34.57万
  • 财政年份:
    2020
  • 负责人:
    Li Ma
  • 依托单位:
Statistical modeling of cross-sample variation and learning of latent structures in microbiome sequencing data
  • 批准号:
    10263932
  • 项目类别:
  • 资助金额:
    $34.69万
  • 财政年份:
    2020
  • 负责人:
    Li Ma
  • 依托单位:
Statistical modeling of cross-sample variation and learning of latent structures in microbiome sequencing data
  • 批准号:
    10468838
  • 项目类别:
  • 资助金额:
    $34.63万
  • 财政年份:
    2020
  • 负责人:
    Li Ma
  • 依托单位:
海外基金