Dynamic perturbation analysis: a modular rewiring framework for decoding cancer stage progression and identifying therapeutic vulnerabilities.
- 1Biomedical Basic Research Center (BBRC) of Jiangsu, MOE Key Laboratory of Geriatric Diseases and Immunology, School of Life Sciences, Soochow University, No. 199, Ren'ai Road, Suzhou Industrial Park, Suzhou, Jiangsu, 215123, China.
- 2College of Pharmacy, Gansu University of Chinese Medicine, No. 35, Dingxi East Road, Chengguan District, Lanzhou, Gansu, 730000, China.
- 3Institute of Health Data Science at Soochow University, No. 199, Ren'ai Road, Suzhou Industrial Park, Suzhou, Jiangsu, 215123, China.
Abstract
Disease progression is driven not only by transcriptional alterations but also by dynamic reorganization of molecular interaction networks. However, most existing approaches focus on differential gene expression and fail to capture modular-level reorganization. Here, we present dynamic perturbation analysis (DPA), a modular rewiring framework that quantifies dynamic perturbations across disease stages using a novel metric termed the degree of perturbation. Using gastric cancer (GC) progression as a model system, DPA revealed that extensive modular rewiring persists despite limited stage-specific transcriptional changes after tumor initiation. DPA identified progression-associated perturbation programs characterized by extracellular matrix remodeling, oncogenic signaling activation, stemness acquisition, and late-stage microenvironmental adaptation. Perturbation analysis revealed two major disease-driving programs and prioritized previously unrecognized regulators, including protein tyrosine phosphatase non-receptor type 12 (PTPN12) and leucine rich repeat and Ig domain containing 1 (LINGO1), whose functional roles in GC progression were experimentally validated. Furthermore, modular rewiring-guided drug repurposing uncovered PJ-34 as a promising therapeutic candidate targeting late-stage disease-associated perturbations. Collectively, DPA provides a generalizable framework for decoding disease progression through dynamic modular rewiring, enabling the identification of driver programs, candidate regulators, and therapeutic vulnerabilities. This framework offers a scalable strategy for network-guided mechanistic discovery and therapeutic prioritization in complex diseases.
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BibTeX
@article{yang2026dynamic,
title = {{Dynamic perturbation analysis: a modular rewiring framework for decoding cancer stage progression and identifying therapeutic vulnerabilities.}},
author = {Bin Yang and Mi Li and Xingyi Liu and Xiaojie Jin and Guang Hu},
journal = {Briefings in bioinformatics},
year = {2026},
month = sep,
doi = {10.1093/bib/bbag572},
url = {https://europepmc.org/article/MED/42894390},
note = {CC BY},
}
RIS
TY - JOUR
TI - Dynamic perturbation analysis: a modular rewiring framework for decoding cancer stage progression and identifying therapeutic vulnerabilities.
AU - Bin Yang
AU - Mi Li
AU - Xingyi Liu
AU - Xiaojie Jin
AU - Guang Hu
PY - 2026
DA - 2026/09/01
JO - Briefings in bioinformatics
DO - 10.1093/bib/bbag572
UR - https://europepmc.org/article/MED/42894390
AB - Disease progression is driven not only by transcriptional alterations but also by dynamic reorganization of molecular interaction networks. However, most existing approaches focus on differential gene expression and fail to capture modular-level reorganization. Here, we present dynamic perturbation analysis (DPA), a modular rewiring framework that quantifies dynamic perturbations across disease stages using a novel metric termed the degree of perturbation. Using gastric cancer (GC) progression as a model system, DPA revealed that extensive modular rewiring persists despite limited stage-specific transcriptional changes after tumor initiation. DPA identified progression-associated perturbation programs characterized by extracellular matrix remodeling, oncogenic signaling activation, stemness acquisition, and late-stage microenvironmental adaptation. Perturbation analysis revealed two major disease-driving programs and prioritized previously unrecognized regulators, including protein tyrosine phosphatase non-receptor type 12 (PTPN12) and leucine rich repeat and Ig domain containing 1 (LINGO1), whose functional roles in GC progression were experimentally validated. Furthermore, modular rewiring-guided drug repurposing uncovered PJ-34 as a promising therapeutic candidate targeting late-stage disease-associated perturbations. Collectively, DPA provides a generalizable framework for decoding disease progression through dynamic modular rewiring, enabling the identification of driver programs, candidate regulators, and therapeutic vulnerabilities. This framework offers a scalable strategy for network-guided mechanistic discovery and therapeutic prioritization in complex diseases.
SN - 1467-5463
SN - 1477-4054
ER -