Buiar Pedro G, Junior José Danilo Szezech, Sales Matheus Rolim, Favero Giovani Marino
Medical Oncology Department, Instituto Sul Paranaense de Oncologia, Ponta Grossa, Brazil.
https://orcid.org/0000-0001-5144-1197.
Ecancermedicalscience. 2025 Feb 18;19:1850. doi: 10.3332/ecancer.2025.1850. eCollection 2025.
Breast cancer is the most common cancer in women worldwide, with significant advances in understanding its multifactorial nature in recent years. The complex structure of molecular and cellular interactions in cancer pathophysiology presents challenges for developing effective treatments. One theoretical model used to study these interactions is the Graph model or Complex Networks, which uses mathematical methods to create graphical figures by connecting vertices (factors) through edges (interactions). This study uses the graph model to determine the complex interactions within the tumour microenvironment of HER2-positive breast cancer. Through a narrative review, 37 factors involved in the pathophysiology of HER2-positive breast cancer were identified and incorporated into a complex network design, starting with the HER2 vertex. The impact of each vertex was determined by calculating the relative error, and a knockout (KO) analysis of vertices was performed to identify their influences within the network. The Wilcoxon test was used to analyze the statistical significance of each KO. Significant alterations in the network structure were observed with the KOs of matrix metalloproteinases (MMPMMP2, MMP9, cyclin-dependent kinases 4/6, TWIST, vascular endothelial growth factor and transforming growth factor-beta. Notably, the KOs of (MMPs) MMP2 and MMP9 significantly impacted the network structure and downregulated the HER2 vertex. This raises questions about the potential applicability of targeting MMPs, including the option of HER2-directed antibody-drug conjugates. Could a metalloprotease inhibitor be a good choice for conjugation? Despite the theoretical nature of this model, the results suggest potential avenues for therapeutic intervention.
乳腺癌是全球女性中最常见的癌症，近年来在理解其多因素性质方面取得了重大进展。癌症病理生理学中分子和细胞相互作用的复杂结构给开发有效治疗方法带来了挑战。用于研究这些相互作用的一种理论模型是图模型或复杂网络，它使用数学方法通过边（相互作用）连接顶点（因素）来创建图形。本研究使用图模型来确定HER2阳性乳腺癌肿瘤微环境内的复杂相互作用。通过叙述性综述，确定了37个参与HER2阳性乳腺癌病理生理学的因素，并将其纳入一个复杂网络设计中，从HER2顶点开始。通过计算相对误差来确定每个顶点的影响，并对顶点进行敲除（KO）分析以确定它们在网络中的影响。使用Wilcoxon检验来分析每个KO的统计学意义。观察到基质金属蛋白酶（MMP）MMP2、MMP9、细胞周期蛋白依赖性激酶4/6、TWIST、血管内皮生长因子和转化生长因子-β的敲除导致网络结构发生显著改变。值得注意的是，MMP2和MMP9的敲除显著影响了网络结构并下调了HER2顶点。这引发了关于靶向MMPs的潜在适用性的问题，包括HER2导向抗体药物偶联物的选择。金属蛋白酶抑制剂是否是偶联的好选择？尽管该模型具有理论性质，但结果提示了治疗干预的潜在途径。
