ArticleFrontiers in oncology2023
Establishment and validation of a ferroptosis-related signature predicting prognosis and immunotherapy effect in colon cancer.
Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 3 citations in OpenAlex.
- A mitochondrial ferroptosis-related gene signature predicts prognosis and immune landscape in colon cancer.Frontiers in medicine · 2025Article
- Integration of single-cell and bulk RNA-seq via machine learning to reveal ferroptosis- and lipid metabolism-driven immune landscape heterogeneity and predict immunotherapy response in colon cancer.Frontiers in immunology · 2025Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Ferroptosis, a novel form of regulating cell death, is related to various cancers. However, the role of ferroptosis-related genes (FRGs) on the occurrence and development of colon cancer (CC) needs to be further elucidated. Method: CC transcriptomic and clinical data were downloaded from TCGA and GEO databases. The FRGs were obtained from the FerrDb database. The consensus clustering was performed to identify the best clusters. Then, the entire cohort was randomly divided into the training and testing cohorts. Univariate Cox, LASSO regression and multivariate Cox analyses were used to construct a novel risk model in training cohort. The testing and the merged cohorts were performed to validate the model. Moreover, CIBERSORT algorithm analyze TIME between high- and low- risk groups. The immunotherapy effect was evaluated by analyzing the TIDE score and IPS between high- and low- risk groups. Lastly, RT-qPCR were performed to analyze the expression of the three prognostic genes, and the 2-years OS and DFS between the high- and low- risk groups of 43 clinical CC samples to further validate the value of the risk model. Results: SLC2A3, CDKN2A, and FABP4 were identified to construct a prognostic signature. Kaplan-Meier survival curves showed that OS between the high- and low-risk groups were statistically significant (p Conclusion: This study established a novel prognostic signature and provided more insight into the immunotherapy effect of CC.
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