Kalantari-Dehaghi Maryam, Rahimi-Tamandegani Hasan, Emadi-Baygi Modjtaba
Department of Genetics, Faculty of Basic Sciences, Shahrekord University, Iran.
Drug Design and Bioinformatics Unit, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran, Iran.
Cancer Inform. 2025 Jun 30;24:11769351251352892. doi: 10.1177/11769351251352892. eCollection 2025.
Gastric cancer is aggressive with poor prognosis due to high invasion and metastasis rates, a hallmark of cancer. The Snail family (SNAI1 and SNAI2) drives EMT, enabling epithelial cells to gain migratory and invasive traits.
We used "limma" package to identify genes with differential expression between high and low levels of SNAI1/SNAI2 in TCGA stomach adenocarcinoma dataset, intersecting these with cancer invasion and metastasis genes obtained from 5 databases. Using Cox regression analysis, we developed a risk score model and created a nomogram incorporating clinical data. The model's prognostic accuracy was validated with survival and ROC analyses in both TCGA and GEO datasets. Additionally, we performed WGCNA and constructed a ceRNA network to investigate gene interactions, and used CIBERSORT analysis to evaluate immune cell composition in the tumor microenvironment.
We developed 5 and 9 risk signatures and nomograms incorporating clinical data. Survival analysis showed high-risk patients had worse overall survival than low-risk patients. WGCNA identified a lightyellow module associated with SNAI1 and SNAI2 expressions, emphasizing extracellular matrix organization. CeRNA network analyses found 6 common hub genes linked to SNAI1 and SNAI2. Immune profiling showed that SNAI1 expression was related to 8 types of immune cells, while SNAI2 was connected to 6, indicating their roles in influencing the tumor microenvironment.
This study highlights the significant prognostic impact of SNAI1 and SNAI2 in stomach adenocarcinoma, linking their high expression to poorer survival and aggressive tumor behavior, while also identifying potential therapeutic targets through comprehensive computational analysis.
胃癌具有侵袭性，由于其高侵袭和转移率，预后较差，这是癌症的一个标志。Snail家族（SNAI1和SNAI2）驱动上皮-间质转化（EMT），使上皮细胞获得迁移和侵袭特性。
我们使用“limma”软件包在TCGA胃腺癌数据集中鉴定SNAI1/SNAI2高表达和低表达之间差异表达的基因，并将这些基因与从5个数据库获得的癌症侵袭和转移基因进行交叉分析。使用Cox回归分析，我们开发了一个风险评分模型，并创建了一个纳入临床数据的列线图。在TCGA和GEO数据集中通过生存分析和ROC分析验证了该模型的预后准确性。此外，我们进行了加权基因共表达网络分析（WGCNA）并构建了一个竞争性内源性RNA（ceRNA）网络来研究基因相互作用，并使用CIBERSORT分析来评估肿瘤微环境中的免疫细胞组成。
我们开发了5个和9个风险特征以及纳入临床数据的列线图。生存分析显示，高危患者的总生存期比低危患者差。WGCNA鉴定出一个与SNAI1和SNAI2表达相关的浅黄模块，强调细胞外基质组织。ceRNA网络分析发现6个与SNAI1和SNAI2相关的常见枢纽基因。免疫图谱显示，SNAI1表达与8种免疫细胞相关，而SNAI2与6种免疫细胞相关，表明它们在影响肿瘤微环境中的作用。
本研究强调了SNAI1和SNAI2在胃腺癌中的显著预后影响，将它们的高表达与较差的生存率和侵袭性肿瘤行为联系起来，同时还通过综合计算分析确定了潜在的治疗靶点。
