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Updated: Jun 24, 2025
Gene Regulation and Targeted Therapy in Gastric Cancer Peritoneal Metastasis: Radiological Findings from Dual Energy CT and PET/CT
Published on: January 22, 2018
Potential therapeutic targets of gastric cancer explored under endogenous network modeling of clinical data
Xile Zhang1, Yong-Cong Chen2, Mengchao Yao1
1Center for Quantitative Life Sciences and Physics Department, Shanghai University, Shanghai, 200444, China.
Abstract:
Improvement in the survival rate of gastric cancer, a prevalent global malignancy and the leading cause of cancer-related mortality calls for more avenues in molecular therapy. This work aims to comprehend drug resistance and explore multiple-drug combinations for enhanced therapeutic treatment. An endogenous network modeling clinic data with core gastric cancer molecules, functional modules, and pathways is constructed, which is then transformed into dynamics equations for in-silicon studies. Principal component analysis, hierarchical clustering, and K-means clustering are utilized to map the attractor domains of the stochastic model to the normal and pathological phenotypes identified from the clinical data. The analyses demonstrate gastric cancer as a cluster of stable states emerging within the stochastic dynamics and elucidate the cause of resistance to anti-VEGF monotherapy in cancer treatment as the limitation of the single pathway in preventing cancer progression. The feasibility of multiple objectives of therapy targeting specified molecules and/or pathways is explored. This study verifies the rationality of the platform of endogenous network modeling, which contributes to the development of cross-functional multi-target combinations in clinical trials.
Insights
Understanding gastric cancer (GC) drug resistance is key. This study uses network modeling to reveal why anti-VEGF therapy fails and suggests multi-target drug combinations for better GC treatment outcomes.
Area of Science:
- Oncology
- Systems Biology
- Computational Biology
Background:
- Gastric cancer (GC) remains a leading cause of cancer mortality worldwide.
- Effective molecular therapies are crucial for improving GC survival rates.
- Drug resistance, particularly to anti-VEGF therapy, presents a significant challenge in GC treatment.
Purpose of the Study:
- To comprehend the mechanisms underlying drug resistance in gastric cancer.
- To explore the potential of multi-drug combinations for enhanced therapeutic efficacy.
- To investigate novel therapeutic strategies for overcoming resistance in gastric cancer.
Main Methods:
- Construction of an endogenous network model using clinical data, core GC molecules, and pathways.
- Transformation of the network model into dynamic equations for in-silico analysis.
- Application of Principal Component Analysis (PCA), hierarchical clustering, and K-means clustering to map phenotypic states.
Main Results:
- Gastric cancer was identified as a cluster of stable states within stochastic dynamics.
- Resistance to anti-VEGF monotherapy was attributed to the limitations of targeting single pathways.
- The study elucidated the cause of resistance to anti-VEGF monotherapy in cancer treatment.
Conclusions:
- Endogenous network modeling provides a rational platform for understanding complex diseases like GC.
- Multi-target drug combinations show feasibility for improved therapeutic outcomes.
- This approach can guide the development of cross-functional, multi-target therapies for clinical trials.
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