Cancer informatics · 2025
Comprehensive Computational Assessment of SNAI1 and SNAI2 in Gastric Cancer: Linking EMT, Tumor Microenvironment, and Survival Outcomes.
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Kalantari-Dehaghi Maryam, Rahimi-Tamandegani Hasan, Emadi-Baygi Modjtaba
Аннотация
BACKGROUND: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.
METHODS: 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.
RESULTS: 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.
CONCLUSION: 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.