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Abstract：
Lung cancer is a leading cause of cancer-related mortality globally, with a dismal 5-year survival rate, particularly for Lung Adenocarcinoma (LUAD). Mechanical changes within the tumor microenvironment, such as extracellular matrix (ECM) remodeling and fibroblast activity, play pivotal roles in cancer progression and metastasis. However, the specific impact of the basement membrane (BM) on the mechanical characteristics of LUAD remains unclear. This study aims to identify BM genes influencing internal mechanical stress in tumors, elucidating their effects on LUAD metastasis and therapy resistance, and exploring strategies to counteract these effects. Using Matrigel overlay and Transwell assays, we found that mechanical stress, mimicked by matrix application, augmented LUAD cell migration and invasion, correlating with ECM alterations and activation of the epithelial-mesenchymal transition (EMT) pathway. Employing machine learning, we developed the SVM_Score model based on relevant BM genes, which accurately predicted LUAD patient prognosis and EMT propensity across multiple datasets. Lower SVM_Scores were associated with worse survival outcomes, elevated cancer-related pathways, increased Tumor Mutation Burden, and higher internal mechanical stress in LUAD tissues. Notably, the SVM_Score was closely linked to COL5A1 expression in myofibroblasts, a key marker of mechanical stress. High COL5A1 expression from myofibroblasts promoted tumor invasiveness and EMT pathway activation in LUAD cells. Additionally, treatment with Sorafenib, which targets COL5A1 secretion, attenuated the tumor-promoting effects of myofibroblast-derived COL5A1, inhibiting LUAD cell proliferation, migration, and enhancing chemosensitivity. In conclusion, this study elucidates the complex interplay between mechanical stress, ECM alterations, and LUAD progression. The SVM_Score emerges as a robust prognostic tool reflecting tumor mechanical characteristics, while Sorafenib intervention targeting COL5A1 secretion presents a promising therapeutic strategy to mitigate LUAD aggressiveness. These findings deepen our understanding of the biomechanical aspects of LUAD and offer insights for future research and clinical applications.
摘要：
肺癌是全球癌症相关死亡的主要原因。令人沮丧的5年生存率，特别是肺腺癌（LUAD）。肿瘤微环境内的力学变化，如细胞外基质（ECM）重塑和成纤维细胞活性，在癌症进展和转移中起关键作用。然而,基底膜(BM)对LUAD力学特性的具体影响尚不清楚.本研究旨在确定影响肿瘤内部机械应力的BM基因，阐明它们对LUAD转移和治疗抵抗的影响，并探索抵消这些影响的策略。使用Matrigel覆盖和Transwell分析，我们发现机械应力，通过矩阵应用程序模仿，增强LUAD细胞迁移和侵袭，与ECM改变和上皮-间质转化（EMT）途径的激活有关。采用机器学习，我们开发了基于相关BM基因的SVM_Score模型，在多个数据集中准确预测LUAD患者预后和EMT倾向。较低的SVM_分数与较差的生存结果相关，升高的癌症相关途径，肿瘤突变负担增加，LUAD组织的内部机械应力较高。值得注意的是,SVM_Score与肌成纤维细胞中的COL5A1表达密切相关，机械应力的关键标志。肌成纤维细胞的高COL5A1表达促进LUAD细胞的肿瘤侵袭和EMT通路激活。此外,用索拉非尼治疗，以COL5A1分泌为目标，减弱肌成纤维细胞来源的COL5A1的促肿瘤作用，抑制LUAD细胞增殖，迁移,增强化学敏感性。总之,这项研究阐明了机械应力之间复杂的相互作用，ECM变更，和LUAD进步。SVM_Score作为反映肿瘤机械特征的强大预后工具，而索拉非尼针对COL5A1分泌的干预为减轻LUAD侵袭性提供了一种有希望的治疗策略.这些发现加深了我们对LUAD生物力学方面的理解，并为未来的研究和临床应用提供了见解。
