Identifying cancer type-specific genes that define cell states is important to develop effective therapies for patients and methods for detection, early diagnosis, and prevention. While molecular mechanisms that drive malignancy have been identified for various cancers, the identification of cell-type defining transcription factors (TFs) that distinguish normal cells from cancer cells has not been fully elucidated. Here, we utilized a network biology framework, which assesses the fidelity of cell fate conversions, to identify cancer type-specific gene regulatory networks (GRN) for 17 types of cancer. Through an integrative analysis of a compendium of expression data, we elucidated core TFs and GRNs for multiple cancer types. Moreover, by comparing normal tissues and cells to cancer type-specific GRNs, we found that the expression of key network-influencing TFs can be utilized as a survival prognostic indicator for a diverse cohort of cancer patients. These findings offer a valuable resource for exploring cancer type-specific networks across a broad range of cancer types.
识别癌症类型特异性基因以界定细胞状态，对于开发有效的患者治疗方法以及癌症检测、早期诊断和预防手段至关重要。尽管多种癌症的恶性驱动分子机制已被识别，但用于区分正常细胞与癌细胞的细胞类型决定转录因子（TFs）尚未得到充分阐明。本研究采用一种评估细胞命运转换保真度的网络生物学框架，识别了17种癌症类型的特异性基因调控网络（GRN）。通过对表达数据集的整合分析，我们阐明了多种癌症类型的核心转录因子及其调控网络。此外，通过比较正常组织细胞与癌症类型特异性基因调控网络，我们发现关键网络影响转录因子的表达可作为多种癌症患者队列的生存预后指标。这些发现为探索广泛癌症类型的特异性网络提供了宝贵资源。
Identifying Cancer Type-Specific Transcriptional Programs through Network Analysis
肿瘤(癌症)患者之家