부산대학교 도서관

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. Author summary: In oncological studies, control samples are usually biopsied from tumor-adjacent normal tissue. However, there is an increasing understanding that such samples represent a state that is intermediate between tumor and normal, and is influenced by environmental factors common to tumor and normal tissues, and by tumor microenvironment. Therefore, uncovering the relation between gene expressions across control and tumors samples can inform us about the roles of microenvironment in tumor initiation and progression. Here we present a predictive model, TranNet, to study the functional relationship between matched control and tumor samples. TranNet infers a transition function from gene expression in a control sample to that in the matched tumor sample. Simultaneously, the method identifies a set of genes that are predictors of this transition. To our knowledge, TranNet is the first computational method to infer such dependencies. Our results demonstrated that TranNet efficiently captured the relation between tumors and their microenvironment, generating important implications for the detection, diagnosis, and prognosis of cancers. [ABSTRACT FROM AUTHOR]