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Screening of key genes related to ferroptosis and a molecular interaction network analysis in colorectal cancer using machine learning and bioinformatics
BACKGROUND: This study sought to identify the key genes and molecular interactions related to ferroptosis in colorectal cancer (CRC) using machine-learning and bioinformatics analyses. METHODS: The Gene Expression Omnibus (National Institutes of Health, US) datasets for CRC were downloaded from the...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10331738/ https://www.ncbi.nlm.nih.gov/pubmed/37435214 http://dx.doi.org/10.21037/jgo-23-405 |
Sumario: | BACKGROUND: This study sought to identify the key genes and molecular interactions related to ferroptosis in colorectal cancer (CRC) using machine-learning and bioinformatics analyses. METHODS: The Gene Expression Omnibus (National Institutes of Health, US) datasets for CRC were downloaded from the National Center for Biotechnology Information (NCBI) (https://www.ncbi.nlm.nih.gov/). The 291 ferroptosis genes were downloaded and screened from the FerrDb (http://www.zhounan.org/ferrdb) and GeneCards (https://www.genecards.org/) databases. The least absolute shrinkage and selection operator regression model and support vector machine model were constructed to identify the different ferroptosis-related hub genes. The immune infiltrates were identified and a survival curve analysis was conducted. RESULTS: We identified 11 ferroptosis-related differentially expressed genes (DEGs) from the COADREAD (Colon and Rectal Cancer) dataset. We found that angiopoietin-related protein 7 (ANGPTL7) gene expression was positively correlated to both the neuroglobin (NGB) (r=0.678) and ceruloplasmin (CP) (r=0.454) genes but was negatively correlated with transferrin receptor 2 (TFR2) expression (r=–0.426). In addition, TFR2 gene expression was positively correlated with the arachidonate lipoxygenase 3 (ALOXE3) (r=0.452) and carbonic anhydrase 9 (CA9) (r=0.411) genes. A total of 4 hub genes were identified by the machine-learning analysis [i.e., NADPH oxidase 4 (NOX4), TFR2, ALOXE3, and CA9]. The expression of the NOX4 gene was significantly positively correlated with neutrophil (r=0.543) and M0 macrophage (r=0.422) infiltration. In addition, a positive correlation between ALOXE3 and activated natural-killer cells (r=0.356) was found. Conversely, the NOX4, TFR2, and CA9 genes were negatively correlated with the resting mast cells. A strong negative correlation was observed between NOX4 and CD160 antigen (CD160) expression; however, a significant positive correlation was observed between NOX4 and transforming growth factor beta receptor 1 (TGFBR1) expression (r=0.397). The patients had a more favorable prognosis when the NOX4 expression levels were relatively low. CONCLUSIONS: Our study identified 4 ferroptosis-related DEGs in CRC (i.e., NOX4, TFR2, ALOXE3, and CA9), and further validated their relationship with immune cell infiltration and the associated immune checkpoints. Our findings confirm the influence of the immune microenvironment on CRC. Low NOX4 levels were more favorable to patient outcomes. Our findings may facilitate future clinical diagnoses and outcome assessments of CRC. |
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