日益认识到，癌症研究中作为对照样本的肿瘤邻近正常组织并不能充分代表健康组织。相反，它们介于健康组织和肿瘤之间。导致这种对照样本与健康状态的偏离的因素包括暴露于促肿瘤因子、肿瘤相关免疫反应以及肿瘤微环境的其他方面。表征肿瘤邻近对照样本基因表达与肿瘤之间的关系对于理解肿瘤起始和进展中微环境的作用，以及癌症的诊断和预后标志物的鉴定至关重要。为了满足这一需求，我们开发并验证了TranNet，这是一种利用匹配的对照和肿瘤样本的基因表达来研究它们基因表达剖面之间关系的计算方法。TranNet从基因表达剖面中推断出一个稀疏加权的二分图，用以展示对照样本与肿瘤之间的关联。结果使我们能够确定这种转换的预测因子（潜在的调控因子）。据我们所知，TranNet是第一个推断此类依赖关系的计算方法。我们将TranNet应用于多种癌症类型以及它们从The Cancer Genome Atlas (TCGA)获取的对照样本数据。TranNet所确定的许多预测因子是与肿瘤微环境调控相关的基因，因为它们在G蛋白耦联受体信号通路、细胞间通讯、免疫过程和细胞黏附中富集。相应地，推断出的预测因子的靶点在与组织重塑（包括上皮间质转变(EMT)）、免疫反应和细胞增殖相关的途径中富集。这意味着这些预测因子是肿瘤进展的标志物和潜在的基质促进因子。我们的结果对于肿瘤邻近对照样本、肿瘤和肿瘤环境之间的关系提供了新的见解。此外，TranNet确定的预测因子集将为未来的研究提供宝贵的资源。 版权声明：本文章是一个开放获取的文章，没有任何版权限制，任何人都可以自由地复制、分发、传播、修改、建立衍生作品或者以任何合法的方式使用。本作品以知识共享CC0公共领域捐献方式提供。

There is a growing awareness that tumor-adjacent normal tissues used as control samples in cancer studies do not represent fully healthy tissues. Instead, they are intermediates between healthy tissues and tumors. The factors that contribute to the deviation of such control samples from healthy state include exposure to the tumor-promoting factors, tumor-related immune response, and other aspects of tumor microenvironment. Characterizing the relation between gene expression of tumor-adjacent control samples and tumors is fundamental for understanding roles of microenvironment in tumor initiation and progression, as well as for identification of diagnostic and prognostic biomarkers for cancers. To address the demand, we developed and validated TranNet, a computational approach that utilizes gene expression in matched control and tumor samples to study the relation between their gene expression profiles. TranNet infers a sparse weighted bipartite graph from gene expression profiles of matched control samples to tumors. The results allow us to identify predictors (potential regulators) of this transition. To our knowledge, TranNet is the first computational method to infer such dependencies. We applied TranNet to the data of several cancer types and their matched control samples from The Cancer Genome Atlas (TCGA). Many predictors identified by TranNet are genes associated with regulation by the tumor microenvironment as they are enriched in G-protein coupled receptor signaling, cell-to-cell communication, immune processes, and cell adhesion. Correspondingly, targets of inferred predictors are enriched in pathways related to tissue remodelling (including the epithelial-mesenchymal Transition (EMT)), immune response, and cell proliferation. This implies that the predictors are markers and potential stromal facilitators of tumor progression. Our results provide new insights into the relationships between tumor adjacent control sample, tumor and the tumor environment. Moreover, the set of predictors identified by TranNet will provide a valuable resource for future investigations.Copyright: This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.