title: Integrative Genomic Analyses Yield Cell-Cycle Regulatory Programs with Prognostic Value.
abstract: Liposarcoma is the second most common form of sarcoma, which has been categorized into four molecular subtypes, which are associated with differential prognosis of patients. However, the transcriptional regulatory programs associated with distinct histologic and molecular subtypes of liposarcoma have not been investigated. This study uses integrative analyses to systematically define the transcriptional regulatory programs associated with liposarcoma. Likewise, computational methods are used to identify regulatory programs associated with different liposarcoma subtypes, as well as programs that are predictive of prognosis. Further analysis of curated gene sets was used to identify prognostic gene signatures. The integration of data from a variety of sources, including gene expression profiles, transcription factor-binding data from ChIP-Seq experiments, curated gene sets, and clinical information of patients, indicated discrete regulatory programs (e.g., controlled by E2F1 and E2F4), with significantly different regulatory activity in one or multiple subtypes of liposarcoma with respect to normal adipose tissue. These programs were also shown to be prognostic, wherein liposarcoma patients with higher E2F4 or E2F1 activity associated with unfavorable prognosis. A total of 259 gene sets were significantly associated with patient survival in liposarcoma, among which > 50% are involved in cell cycle and proliferation.
  
  
  CONCLUSIONS
  These integrative analyses provide a general framework that can be applied to investigate the mechanism and predict prognosis of different cancer types.
authors: Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire. Institute for Quantitative Biomedical Sciences, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire. Norris Cotton Cancer Center, Geisel School of Medicine at Dartmouth, Lebanon, New Hampshire. Chao.Cheng@dartmouth.edu.
authors: Chao
authors: C
authors: Cheng
authors: Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire.
authors: Shaoke
authors: S
authors: Lou
authors: Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire.
authors: Erik H
authors: EH
authors: Andrews
authors: Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire.
authors: Matthew H
authors: MH
authors: Ung
authors: Department of Genetics, Geisel School of Medicine at Dartmouth, Hanover, New Hampshire.
authors: Frederick S
authors: FS
authors: Varn
mesh_terms: D000465:Algorithms; D059447:Cell Cycle Checkpoints; D045744:Cell Line, Tumor; D019295:Computational Biology / Q000379:methods*; D050687:E2F1 Transcription Factor / Q000235:genetics*; D050704:E2F4 Transcription Factor / Q000235:genetics*; D020869:Gene Expression Profiling / Q000379:methods; D015972:Gene Expression Regulation, Neoplastic; D053263:Gene Regulatory Networks; D006801:Humans; D008080:Liposarcoma / Q000235:genetics / Q000473:pathology*; D011379:Prognosis; D016019:Survival Analysis
chemical_list: D050687:E2F1 Transcription Factor; C493753:E2F1 protein, human; D050704:E2F4 Transcription Factor; C493762:E2F4 protein, human