Molecular subtypes of colorectal cancer (CRC) are currently identified via the snapshot transcriptional profiles, largely ignoring the dynamic changes of gene expressions. Conversely, biological networks remain relatively stable irrespective of time and condition. Here, we introduce an individual-specific gene interaction perturbation network-based (GIN) approach and identify six GIN subtypes with distinguishing features.
In this paper, the investigators investigate CRC tumor heterogeneity by using a clustering method to understand perturbation of gene networks. The approach is robust and resulted in identification of significant differences in gene expression signals. The investigators identified six distinct gene interaction networks (GINs) that characterize tumor landscapes with varying degrees of oncogenic driver mutations, immune infiltration, and drug susceptibilities. These results provide a solid contribution to the field that, if validated, could be utilized as a useful predictive and prognostic correlative biomarkers in future clinical trials.
Contributions and Distinctions from Previous Works
TL;DR
Molecular subtypes of colorectal cancer (CRC) are currently identified via the snapshot transcriptional profiles, largely ignoring the dynamic changes of gene expressions. Conversely, biological networks remain relatively stable irrespective of time and condition. Here, we introduce an individual-specific gene interaction perturbation network-based (GIN) approach and identify six GIN subtypes with distinguishing features.
Paper Link
https://elifesciences.org/articles/81114
Author/Institution
Zaoqu Liu (University, Zhengzhou, China)
Overview
In this paper, the investigators investigate CRC tumor heterogeneity by using a clustering method to understand perturbation of gene networks. The approach is robust and resulted in identification of significant differences in gene expression signals. The investigators identified six distinct gene interaction networks (GINs) that characterize tumor landscapes with varying degrees of oncogenic driver mutations, immune infiltration, and drug susceptibilities. These results provide a solid contribution to the field that, if validated, could be utilized as a useful predictive and prognostic correlative biomarkers in future clinical trials.
Contributions and Distinctions from Previous Works
Methods
colorectal cancer. survival analysis. network analysis.
Results
Cite
Zaoqu Liu Siyuan Weng Qin Dang Hui Xu Yuqing Ren Chunguang Guo Zhe Xing Zhenqiang Sun Xinwei Han (2022) Gene interaction perturbation network deciphers a high-resolution taxonomy in colorectal cancer eLife 11:e81114. https://doi.org/10.7554/eLife.81114
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