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Principal component analysis, hierarchical clustering, and K-means map stochastic attractor domains to normal and pathological phenotypes and clarify mechanisms behind resistance to anti-VEGF monotherapy.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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\nPotential therapeutic targets of gastric cancer explored under endogenous network modeling of clinical data  \nXile Zhang1, Yong‑Cong Chen1*, Mengchao Yao1, Ruiqi Xiong1, Bingya Liu2, Xiaomei Zhu3 & Ping Ao4  \nImprovement 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 ofthe 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.  \nGastric cancer (GC) is a leading cause of cancer-related deaths globally1,2. Its occurrence, development, and prognosis are influenced by various complex factors and regulatory/signaling pathways. Established risk factors for GC include Helicobacter pylori infection, family history, medication, obesity, alcohol consumption, smoking, radiation, and dietary factors3. Despite recent advancements in GC prevention, diagnosis, and treatment, early detection rates remain low, and the overall survival for advanced GC is only one year with a poor prognosis4–6. This can be attributed, in part, to the extensive heterogeneity of GC, both morphologically and molecularly, as evidenced by numerous histological and molecular classifications6–12. Importantly, this heterogeneity occurs not only between patients but also within the same tumor (inter-and intratumor heterogeneity)6.  \nThe study of combination molecular targeted therapies requires improved risk models that integrate genetic, lifestyle, and environmental factors13, 14, which require a comprehensive understanding at the molecular level. Although recent high-throughput studies have provided vast amounts of molecular data with clinical relevance, there is still a general need for prognostic, mechanistic, and predictive systems to identify novel therapeutic targets15–17. Additionally, some studies suggest that gastric cancer subtypes may be too robust to be disrupted18, 19, and experimental evidence supports the existence of high-dimensional attractors and functional robustness in gene expression patterns20,21. A collective model based on causal relationships is highly desirable, as it can serve as a “dry laboratory” to simulate carcinogenesis, metastasis, and cancer treatment efficacy. The concept of collective emergence in biology dates back to Waddington, Delbruck, Monod, Jacob, Hinshelwood, and Kauffman22–27. The above argument aligns with the core concept28 that cancer corresponds to an inherent state(s) formed by delicate molecular-cell interaction networks. The endogenous network, a causal, quantitative, and simplified system developed in a series of previous works28–30, emphasizes the molecular mechanisms behind the disease.  \n1Center for Quantitative Life Sciences and Physics Department, Shanghai University, Shanghai 200444, China. 2Department of General Surgery, Shanghai Institute of Digestive Surgery, Shanghai Key Laboratory of Gast","cbCaibHZ7Webppud","https://ap.wps.com/l/cbCaibHZ7Webppud","pdf",3405469,13,"English","# Introduction\n## Clinical challenge and heterogeneity\n## Need for mechanistic and predictive systems\n# Methods and modeling framework\n## Endogenous network and steady states\n# Results and implications","[{\"question\":\"What is the goal of modeling gastric cancer in this study?\",\"answer\":\"The work aims to understand drug resistance mechanisms and explore multiple-drug combinations to improve therapeutic outcomes.\"},{\"question\":\"How are normal and pathological phenotypes derived from the model?\",\"answer\":\"Stochastic dynamics attractor domains are mapped to phenotypes using principal component analysis, hierarchical clustering, and K-means clustering.\"},{\"question\":\"What explains resistance to anti-VEGF monotherapy according to the analysis?\",\"answer\":\"Resistance is attributed to limitations of a single pathway in preventing cancer progression within the modeled network dynamics.\"}]","Potential therapeutic targets of gastric cancer explored under endogenous network modeling of clinical data - Abstract | PDF",1790002402,33]