NLM DIR Seminar Schedule
UPCOMING SEMINARS
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March 11, 2025 Sofya Garushyants
Tmn – bacterial anti-phage defense system -
March 18, 2025 MG Hirsch
TBD -
March 25, 2025 Yifan Yang
TBD -
April 1, 2025 Roman Kogay
TBD -
April 8, 2025 Jaya Srivastava
TBD
RECENT SEMINARS
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March 4, 2025 Sanasar Babajanyan
Evolution of antivirus defense in prokaryotes depending on the environmental virus load -
Feb. 25, 2025 Zhizheng Wang
GeneAgent: Self-verification Language Agent for Gene Set Analysis using Domain Databases -
Feb. 18, 2025 Samuel Lee
Efficient predictions of alternative protein conformations by AlphaFold2-based sequence association -
Feb. 11, 2025 Po-Ting Lai
Enhancing Biomedical Relation Extraction with Directionality -
Feb. 4, 2025 Victor Tobiasson
On the dominance of Asgard contributions to Eukaryogenesis
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.