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Perturbation-driven sensitive gene discovery in colorectal cancer
Lifang Huang1, Xiaoyu Liao1, Haohua Wang2
1School of Statistics and Data Science, Guangdong University of Finance & Economics, Guangzhou 510275, PR China.
Computational Biology and Chemistry
|August 10, 2026
Summary
This study introduces a novel computational method to identify network-sensitive genes in colorectal cancer (CRC) by analyzing gene regulatory network dynamics. The approach reveals new candidate genes crucial for understanding cancer progression beyond traditional methods.
Area of Science:
- Computational Biology
- Systems Biology
- Genomics
Background:
- Colorectal cancer (CRC) progression is driven by complex gene regulatory interactions.
- Conventional analyses often miss non-canonical genes impacting network stability.
Purpose of the Study:
- To develop a dynamic computational framework for quantifying gene regulatory stability changes.
- To identify network-sensitive genes in CRC using perturbation-induced network entropy.
Main Methods:
- Integration of time-series co-expression networks with an autoregressive neural network and local network entropy (ARNN-LNE).
- In silico perturbation analysis to evaluate network entropy changes and identify sensitive genes.
- Cross-species analysis to validate framework robustness.
Main Results:
- Identification of candidate sensitive genes (e.g., MATCAP1, FAM107B, SNX24, SLC26A2 in human; mt-Co1 in mouse) not found by differential expression analysis.
- Demonstration of partial consistency in identified genes across human and mouse datasets.
- Classification analysis showed moderate discriminative ability for disease states (ROC-AUC ≈ 0.86; PR-AUC ≈ 0.76).
Conclusions:
- Perturbation-entropy profiling offers a novel framework for identifying network-sensitive genes in cancer.
- This approach provides complementary insights to conventional methods for exploring gene regulatory dynamics.
- The identified genes serve as potential biomarkers and warrant further investigation in CRC systems biology.