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Updated: Aug 16, 2025

Modeling Oral-Esophageal Squamous Cell Carcinoma in 3D Organoids
Published on: December 23, 2022
Ferroptosis patterns and tumor microenvironment infiltration characterization in esophageal squamous cell cancer
Lu-Lu Zhang1, Wei-Jie Zhu1, Xin-Xin Zhang2
1Department of Molecular Diagnostics, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, Guangdong, China.
Abstract:
Background: Esophageal Squamous Cell Cancer (ESCC) is an aggressive disease associated with a poor prognosis. As a newly defined form of regulated cell death, ferroptosis plays a crucial role in cancer development and treatment and might be a promising therapeutic target. However, the expression patterns of ferroptosis-related genes (FRGs) in ESCC remain to be systematically analyzed. Methods: First, we retrieved the transcriptional profile of ESCC from TCGA and GEO datasets (GSE47404, GSE23400, and GSE53625) and performed unsupervised clustering to identify different ferroptosis patterns. Then, we used the ssGSEA algorithm to estimate the immune cell infiltration of these patterns and explored the differences in immune cell abundance. Common genes among patterns were finally identified as signature genes of ferroptosis patterns. Results: Herein, we depicted the multi-omics landscape of FRGs through integrated bioinformatics analysis and identified three ESCC subtypes with distinct immune characteristics: clusters A-C. Cluster C was abundant in CD8+ T cells and other immune cell infiltration, while cluster A was immune-barren. By comparing the differently expressed genes between clusters of diverse datasets, we defined a gene signature for each cluster and successfully validated it in the TCGA-ESCC dataset. Conclusion: We provided a comprehensive insight into the expression pattern of ferroptosis genes and their interaction with immune cell infiltration. Additionally, we established a gene signature to define the ferroptosis patterns, which might be used to predict the response to immunotherapy.
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