NLM IRP Seminar Schedule
UPCOMING SEMINARS
-
April 30, 2024 Wenya Rowe
The conformal central charge of the spin-1/2 XX model derived from long-chain asymptotics -
May 2, 2024 OPEN
TBD -
May 7, 2024 OPEN
TBD -
May 9, 2024 Pascal Mutz
TBD -
May 14, 2024 Stanley Liang
TBD
RECENT SEMINARS
-
April 25, 2024 Ermin Hodzic
Condition-Aware Cell Type Deconvolution of Bulk Tissues -
April 23, 2024 OPEN
TBD -
April 16, 2024 Jaya Srivastava
Regulatory plasticity of the human genome -
April 11, 2024 Sergey Shmakov
Comprehensive survey of the TnpB RNA-guided nucleases -
April 2, 2024 Yifan Yang
Fairness and Bias in Biomedical AI
Scheduled Seminars on May 30, 2023
Contact NLM_IRP_Seminar_Scheduling@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.