Identifying Cancer Type-Specific Transcriptional Programs through Network Analysis

Identifying cancer type-specific genes that define cell states is important to develop effective therapies for patients and methods detection, early diagnosis, prevention. While molecular mechanisms drive malignancy have been identified various cancers, the identification of cell-type defining transcription factors (TFs) distinguish normal cells from has not fully elucidated. Here, we utilized a network biology framework, which assesses fidelity fate conversions, identify gene regulatory networks (GRN) 17 types cancer. Through an integrative analysis compendium expression data, elucidated core TFs GRNs multiple types. Moreover, by comparing tissues GRNs, found key network-influencing can be as survival prognostic indicator diverse cohort patients. These findings offer valuable resource exploring across broad range