Nan Xu, Taojing Zhang, Weiwei Sun, Chenxiao Ye, Huamiao Zhou
{"title":"预测胃癌患者预后和治疗敏感性的细胞外基质特征的鉴定。","authors":"Nan Xu, Taojing Zhang, Weiwei Sun, Chenxiao Ye, Huamiao Zhou","doi":"10.1038/s41598-025-88376-8","DOIUrl":null,"url":null,"abstract":"<p><p>Extracellular matrix (ECM) is a vital component of the tumor microenvironment and plays a crucial role in the development and progression of gastric cancer (GC). Co-expression networks were established by means of the \"WGCNA\" package, the optimal model for extracellular matrix scores (ECMs) was developed and validated, with its accuracy in predicting the prognosis and treatment sensitivity of GC patients assessed. We performed univariate cox regression analysis [HR = 6.8 ( 3.3-14 ), p < 0.001] which demonstrated that ECMs was an independent risk character and perceptibly superior to other factors with further analysis of multivariate Cox regression [HR = 8.68 ( 4.16-18.08 ), p < 0.001]. The nomogram, presenting the clinical prognosis model for GC patients, demonstrated accuracy through KM analysis [HR = 3.97 (2.56-6.16), p < 0.001] and ROC curves with AUC values of 0.70, 0.72, and 0.72 at 1, 3, and 5 years, respectively. Using the ECMs model, we stratified GC patients into high- and low-risk groups, enabling precise predictions of prognosis and drug sensitivity. This stratification provides a new strategic direction for the personalized treatment of GC.</p>","PeriodicalId":21811,"journal":{"name":"Scientific Reports","volume":"15 1","pages":"7464"},"PeriodicalIF":3.9000,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11876314/pdf/","citationCount":"0","resultStr":"{\"title\":\"Identification of an extracellular matrix signature for predicting prognosis and sensitivity to therapy of patients with gastric cancer.\",\"authors\":\"Nan Xu, Taojing Zhang, Weiwei Sun, Chenxiao Ye, Huamiao Zhou\",\"doi\":\"10.1038/s41598-025-88376-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Extracellular matrix (ECM) is a vital component of the tumor microenvironment and plays a crucial role in the development and progression of gastric cancer (GC). Co-expression networks were established by means of the \\\"WGCNA\\\" package, the optimal model for extracellular matrix scores (ECMs) was developed and validated, with its accuracy in predicting the prognosis and treatment sensitivity of GC patients assessed. We performed univariate cox regression analysis [HR = 6.8 ( 3.3-14 ), p < 0.001] which demonstrated that ECMs was an independent risk character and perceptibly superior to other factors with further analysis of multivariate Cox regression [HR = 8.68 ( 4.16-18.08 ), p < 0.001]. The nomogram, presenting the clinical prognosis model for GC patients, demonstrated accuracy through KM analysis [HR = 3.97 (2.56-6.16), p < 0.001] and ROC curves with AUC values of 0.70, 0.72, and 0.72 at 1, 3, and 5 years, respectively. Using the ECMs model, we stratified GC patients into high- and low-risk groups, enabling precise predictions of prognosis and drug sensitivity. This stratification provides a new strategic direction for the personalized treatment of GC.</p>\",\"PeriodicalId\":21811,\"journal\":{\"name\":\"Scientific Reports\",\"volume\":\"15 1\",\"pages\":\"7464\"},\"PeriodicalIF\":3.9000,\"publicationDate\":\"2025-03-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11876314/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Scientific Reports\",\"FirstCategoryId\":\"103\",\"ListUrlMain\":\"https://doi.org/10.1038/s41598-025-88376-8\",\"RegionNum\":2,\"RegionCategory\":\"综合性期刊\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"MULTIDISCIPLINARY SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Scientific Reports","FirstCategoryId":"103","ListUrlMain":"https://doi.org/10.1038/s41598-025-88376-8","RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"MULTIDISCIPLINARY SCIENCES","Score":null,"Total":0}
引用次数: 0
摘要
细胞外基质(Extracellular matrix, ECM)是肿瘤微环境的重要组成部分,在胃癌的发生发展中起着至关重要的作用。通过“WGCNA”包构建共表达网络,建立并验证细胞外基质评分(extracellular matrix scores, ECMs)的最优模型,评估其预测胃癌患者预后的准确性和治疗敏感性。我们进行了单因素cox回归分析[HR = 6.8 (3.3-14), p
Identification of an extracellular matrix signature for predicting prognosis and sensitivity to therapy of patients with gastric cancer.
Extracellular matrix (ECM) is a vital component of the tumor microenvironment and plays a crucial role in the development and progression of gastric cancer (GC). Co-expression networks were established by means of the "WGCNA" package, the optimal model for extracellular matrix scores (ECMs) was developed and validated, with its accuracy in predicting the prognosis and treatment sensitivity of GC patients assessed. We performed univariate cox regression analysis [HR = 6.8 ( 3.3-14 ), p < 0.001] which demonstrated that ECMs was an independent risk character and perceptibly superior to other factors with further analysis of multivariate Cox regression [HR = 8.68 ( 4.16-18.08 ), p < 0.001]. The nomogram, presenting the clinical prognosis model for GC patients, demonstrated accuracy through KM analysis [HR = 3.97 (2.56-6.16), p < 0.001] and ROC curves with AUC values of 0.70, 0.72, and 0.72 at 1, 3, and 5 years, respectively. Using the ECMs model, we stratified GC patients into high- and low-risk groups, enabling precise predictions of prognosis and drug sensitivity. This stratification provides a new strategic direction for the personalized treatment of GC.
期刊介绍:
We publish original research from all areas of the natural sciences, psychology, medicine and engineering. You can learn more about what we publish by browsing our specific scientific subject areas below or explore Scientific Reports by browsing all articles and collections.
Scientific Reports has a 2-year impact factor: 4.380 (2021), and is the 6th most-cited journal in the world, with more than 540,000 citations in 2020 (Clarivate Analytics, 2021).
•Engineering
Engineering covers all aspects of engineering, technology, and applied science. It plays a crucial role in the development of technologies to address some of the world''s biggest challenges, helping to save lives and improve the way we live.
•Physical sciences
Physical sciences are those academic disciplines that aim to uncover the underlying laws of nature — often written in the language of mathematics. It is a collective term for areas of study including astronomy, chemistry, materials science and physics.
•Earth and environmental sciences
Earth and environmental sciences cover all aspects of Earth and planetary science and broadly encompass solid Earth processes, surface and atmospheric dynamics, Earth system history, climate and climate change, marine and freshwater systems, and ecology. It also considers the interactions between humans and these systems.
•Biological sciences
Biological sciences encompass all the divisions of natural sciences examining various aspects of vital processes. The concept includes anatomy, physiology, cell biology, biochemistry and biophysics, and covers all organisms from microorganisms, animals to plants.
•Health sciences
The health sciences study health, disease and healthcare. This field of study aims to develop knowledge, interventions and technology for use in healthcare to improve the treatment of patients.