ArticleApplied biochemistry and biotechnology2024
Construction and Evaluation of an M2 Macrophage-Related Prognostic Model for Colon Cancer.
Article in Applied biochemistry and biotechnology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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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1 citing paper in PubMed.
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6 authors.
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Abstract
Colon cancer (CC) is a primary human malignancy. Recently, the mechanism of the tumor microenvironment (TME) in CC has been a hot topic of research. However, there is uncertainty regarding the contribution of M2 macrophages and related genes to the prognosis for CC. M2 macrophage-related genes (M2RGs) were obtained from The Cancer Genome Atlas (TCGA) database. Immune cell infiltration in CC tissue was assessed by Cibersort. Based on the TCGA-COAD training set, a Least Absolute Shrinkage and Selection Operator (LASSO) Cox risk model was constructed and its efficiency was evaluated by analyzing risk profiles and survival profiles. Using gene set enrichment analysis (GSEA), the functional distinctions between high-risk and low-risk categories were further investigated. Finally, potential immune checkpoints, immunotherapy efficiency, and clinical treatment of high-risk patients were evaluated. A total of 1063 M2RGs were identified in TCGA-COAD, 32 of these were confirmed to be strongly related to overall survival (OS), and 14 of these were picked to construct an OS-oriented prognostic model in CC patients. The M2RG signature had a positive correlation with unfavorable prognosis according to the survival analysis. Correlation analysis revealed that the risk model was positively associated with clinicopathological characteristics, immune cell infiltration, immune checkpoint inhibitor targets, the risk of immune escape, and the efficiency of anti-cancer medications. The risk model created using M2RGs may be useful in predicting the prognosis of CC.
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