شناسایی ویژگی های مرتبط با دگرگون آنکوژن با یادگیری ماشین Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation

INTRODUCTION Stemness, defined as the potential for self-renewal and differentiation from the cell of origin, was originally attributed to normal stem cells that possess the ability to give rise to all cell types in the adult organism. Cancer progression involves gradual loss of a differentiated phenotype and acquisition of progenitor-like, stem-cell-like features. Undifferentiated primary tumors are more likely to result in cancer cell spread to distant organs, causing disease progression and poor prognosis, particularly because metastases are usually resistant to available therapies (Friedmann-Morvinski and Verma, 2014; Ge et al., 2017; Shibue and Weinberg, 2017; Visvader and Lindeman, 2012). An increasing number of genomic, epigenomic, transcriptomic, and proteomic signatures have been associated with cancer stemness. Those molecular features are causally connected to particular oncogenic signaling pathways that regulate transcriptional networks that sustain the growth and proliferation of cancer cells (Ben-Porath et al., 2008; Eppert et al., 2011; Kim et al., 2010). Transcriptional and epigenetic dysregulation of cancer cells frequently leads to oncogenic dedifferentiation and acquisition of stemness features by altering core signaling pathways that regulate the phenotypes of normal stem cells (Bradner et al., 2017; Young, 2011). Cell-extrinsic mechanisms can also affect maintenance of the undifferentiated state, largely through epigenetic mechanisms. Tumors comprise a complex, diverse, integrated ecosystem of relatively differentiated cancer cells, cancer stem cells, endothelial cells, tumor-associated fibroblasts, and infiltrating immune cells, among other cell types. The microenvironment of a tumor, considered as a pathologically formed ‘‘organ,’’ is frequently characterized by hypoxia, as well as by abnormal levels of various cytokines, growth factors, and metabolites (Lyssiotis and Kimmelman, 2017). It provides numerous opportunities for cell-cell signals to modulate the epigenome and expression of stem-cell-like programs in cancer cells, frequently independent of their genetic backgrounds (Gingold et al., 2016).