Tang Xiaohuan, Wu Xiaolong, Guo Ting, Jia Fangzhou, Hu Ying, Xing Xiaofang, Gao Xiangyu, Li Ziyu
Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Gastrointestinal Cancer Center, Peking University Cancer Hospital & Institute, Beijing, China.
Biological Sample Bank, Peking University Cancer Hospital & Institute, Beijing, China.
Front Oncol. 2022 May 4;12:808817. doi: 10.3389/fonc.2022.808817. eCollection 2022.
The current tumor-node-metastasis (TNM) staging system is insufficient for predicting the efficacy of chemotherapy in patients with gastric cancer (GC). This study aimed to analyze the association between the focal adhesion pathway and therapeutic efficacy of chemotherapy in patients with GC.
RNA sequencing was performed on 33 clinical samples from patients who responded or did not respond to treatment prior to neoadjuvant chemotherapy. The validation sets containing 696 GC patients with RNA data from three cohorts (PKUCH, TCGA, and GSE14210) were analyzed. A series of machine learning and bioinformatics approaches was combined to build a focal adhesion-related signature model to predict the treatment efficacy and prognosis of patients with GC.
Among the various signaling pathways associated with cancer, focal adhesion was identified as a risk factor related to the treatment efficacy of chemotherapy and prognosis in patients with GC. The focal adhesion-related gene model (FAscore) discriminated patients with a high FAscore who are insensitive to neoadjuvant chemotherapy in our training cohort, and the predicted value was further verified in the GSE14210 cohort. Survival analysis also demonstrated that patients with high FAscores had a relatively shorter survival compared to those with low FAscores. In addition, we found that the levels of tumor mutation burden (TMB) and microsatellite instability (MSI) increased with an increase in FAscore, and the tumor microenvironment (TME) also shifted to a pro-tumor immune microenvironment.
The FAscore model can be used to predict the treatment efficacy of chemotherapy and select appropriate treatment strategies for patients with GC.
目前的肿瘤-淋巴结-转移（TNM）分期系统不足以预测胃癌（GC）患者化疗的疗效。本研究旨在分析粘着斑通路与GC患者化疗疗效之间的关联。
对新辅助化疗前治疗有反应或无反应的患者的33份临床样本进行RNA测序。分析了包含来自三个队列（PKUCH、TCGA和GSE14210）的696例有RNA数据的GC患者的验证集。结合一系列机器学习和生物信息学方法构建粘着斑相关特征模型，以预测GC患者的治疗疗效和预后。
在与癌症相关的各种信号通路中，粘着斑被确定为与GC患者化疗治疗疗效和预后相关的危险因素。粘着斑相关基因模型（FAscore）在我们的训练队列中区分出对新辅助化疗不敏感的高FAscore患者，并且在GSE14210队列中进一步验证了预测值。生存分析还表明，高FAscore患者的生存期比低FAscore患者相对较短。此外，我们发现肿瘤突变负荷（TMB）和微卫星不稳定性（MSI）水平随着FAscore的增加而升高，并且肿瘤微环境（TME）也转变为促肿瘤免疫微环境。
FAscore模型可用于预测GC患者化疗的治疗疗效，并为其选择合适的治疗策略。
