NLM DIR Seminar Schedule
UPCOMING SEMINARS
RECENT SEMINARS
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May 2, 2025 Pascal Mutz
Characterization of covalently closed cirular RNAs detected in (meta)transcriptomic data -
May 2, 2025 Dr. Lang Wu
Integration of multi-omics data in epidemiologic research -
April 22, 2025 Stanley Liang, PhD
Large Vision Model for medical knowledge adaptation -
April 18, 2025 Valentina Boeva, Department of Computer Science, ETH Zurich
Decoding tumor heterogeneity: computational methods for scRNA-seq and spatial omics -
April 8, 2025 Jaya Srivastava
Leveraging a deep learning model to assess the impact of regulatory variants on traits and diseases
Scheduled Seminars on May 30, 2023
Contact NLMDIRSeminarScheduling@mail.nih.gov with questions about this seminar.
Abstract:
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 markers (potential regulators) of this transition. To our knowledge, TranNet is the first computational method to infer such regulation.
We applied TranNet to the data of several cancer types and their matched control samples from The Cancer Genome Atlas (TCGA).
Many markers 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 markers are enriched in pathways related to tissue remodelling (including the epithelial-mesenchymal Transition (EMT)), immune response, and cell proliferation.
This implies that the markers are markers and potential stromal facilitators of tumor progression. Our results provide new insights for the relationships between tumor adjacent control sample, tumor and the tumor environment. Moreover, the set of markers identified by TranNet will provide a valuable resource for future investigations.