Xu Ruofan, Yang Le, Zhang Zhewen, Liao Yuxuan, Yu Yao, Zhou Dawei, Li Jiahao, Guan Haoyu, Xiao Wei
Department of Infectious Disease, Third Xiangya Hospital, Central South University, Changsha, Hunan, China.
Xiangya School of Medicine, Central South University, Changsha, Hunan, China.
Front Med (Lausanne). 2023 Jan 18;10:1079470. doi: 10.3389/fmed.2023.1079470. eCollection 2023.
Gastric cancer (GC) remains the major constituent of cancer-related deaths and a global public health challenge with a high incidence rate. Helicobacter pylori (HP) plays an essential role in promoting the occurrence and progression of GC. Cancer-associated fibroblasts (CAFs) are regarded as a significant component in the tumor microenvironment (TME), which is related to the metastasis of GC. However, the regulation mechanisms of CAFs in HP-related GC are not elucidated thoroughly.
HP-related genes (HRGs) were downloaded from the GSE84437 and TCGA-GC databases. The two databases were combined into one cohort for training. Furthermore, the consensus unsupervised clustering analysis was obtained to sort the training cohort into different groups for the identification of differential expression genes (DEGs). Weighted correlation network analysis (WGCNA) was performed to verify the correlation between the DEGs and cancer-associated fibroblasts which were key components in the tumor microenvironment. The least absolute shrinkage and selection operator (LASSO) was executed to find cancer-associated fibroblast-related differential expression genes (CDEGs) for the further establishment of a prognostic model.
In this study, 52 HP-related genes (HRGs) were screened out based on the GSE84437 and TCGA-GC databases. A total of 804 GC samples were analyzed, respectively, and clustered into two HP-related subtypes. The DEGs identified from the two subtypes were proved to have a relationship with TME. After WGCNA and LASSO, the CAFs-related module was identified, from which 21 gene signatures were confirmed. Then, a CDEGs-Score was constructed and its prediction efficiency in GC patients was conducted for validation. Overall, a highly precise nomogram was established for enhancing the adaptability of the CDEGs-Score. Furthermore, our findings revealed the applicability of CDEGs-Score in the sensitivity of chemotherapeutic drugs. In general, our research provided brand-new possibilities for comprehending HP-related GC, evaluating survival, and more efficient therapeutic strategies.
胃癌(GC)仍然是癌症相关死亡的主要构成因素,是一项发病率很高的全球公共卫生挑战。幽门螺杆菌(HP)在促进胃癌的发生和发展中起着至关重要的作用。癌症相关成纤维细胞(CAFs)被认为是肿瘤微环境(TME)的重要组成部分,这与胃癌的转移有关。然而,CAFs在HP相关胃癌中的调控机制尚未完全阐明。
从GSE84437和TCGA-GC数据库下载HP相关基因(HRGs)。将这两个数据库合并为一个队列进行训练。此外,进行共识无监督聚类分析,将训练队列分为不同组,以鉴定差异表达基因(DEGs)。进行加权基因共表达网络分析(WGCNA),以验证DEGs与肿瘤微环境中的关键成分癌症相关成纤维细胞之间的相关性。执行最小绝对收缩和选择算子(LASSO),以找到与癌症相关成纤维细胞相关的差异表达基因(CDEGs),用于进一步建立预后模型。
在本研究中,基于GSE84437和TCGA-GC数据库筛选出52个HP相关基因(HRGs)。分别对总共804个GC样本进行分析,并聚类为两种HP相关亚型。从这两种亚型中鉴定出的DEGs被证明与TME有关。经过WGCNA和LASSO分析后,确定了与CAFs相关的模块,从中确认了21个基因特征。然后,构建了CDEGs评分,并对其在GC患者中的预测效率进行了验证。总体而言,建立了一个高精度的列线图,以提高CDEGs评分的适应性。此外,我们的研究结果揭示了CDEGs评分在化疗药物敏感性方面的适用性。总的来说,我们的研究为理解HP相关胃癌、评估生存情况以及更有效的治疗策略提供了全新的可能性。