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<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 ), <i>p</i> &lt; 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 ), <i>p</i> &lt; 0.001]. The nomogram, presenting the clinical prognosis model for GC patients, demonstrated accuracy through KM analysis [HR = 3.97 (2.56–6.16), <i>p</i> &lt; 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>

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Identification of an extracellular matrix signature for predicting prognosis and sensitivity to therapy of patients with gastric cancer

  • 期刊论文

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  • 发表日期：

    2025年3月3日

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  • Nan Xu,
  • Taojing Zhang,
  • Weiwei Sun,
  • Chenxiao Ye,
  • Huamiao Zhou

摘要

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.