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• W4366338873 abstract "Abstract Background Skin cutaneous melanoma (SKCM) remains the deadliest form of skin cancer. However, mechanism of prognosis of SKCM is not yet clear, and accurate prediction of the prognosis remains difficult. Here, we developed and validated three prognostic risk models based on immune cell infiltration, DNA methylation features, and immune-related genes for SKCM patients. Methods We downloaded the datasets from high-dimensional public databases. Univariate and multivariable Cox regression analysis was performed to develop models based on prognostic tumor-infiltrating immune cells, methylation related DEGs (mrDEGs), and immune-related genes (IRGs). Functional enrichment analysis was performed using clusterProfiler and GSVA R package. Prediction of sensitivity to drugs was conducted using pRRophetic R package. The prognostic value of these models was assessed using survival analysis and receiver operating characteristic curve analysis. Results The immune cell model was constructed based on 5 immune cell subtypes. Nine mrDEGs were identified to develop the methylation model. Furthermore, the prognostic IS model based on 5 IRGs was established. These models showed good performance to predict the prognosis of SKCM patients. The IS and methylation score were demonstrated to be associated with immune cell infiltration, microsatellite instability, and tumor mutational burden in SKCM. Moreover, the IS could predict the sensitivity to drugs and the responsiveness to immunotherapy. Finally, IHC staining of the clinical samples confirmed the increased expression of the prognostic IRGs. Conclusions These risk models based on immune cell infiltration, DNA methylation, and IS could be used to predict the survival of SKCM patients and further to guide treatment decisions." @default.
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• W4366338873 date "2023-04-18" @default.
• W4366338873 modified "2023-09-25" @default.
• W4366338873 title "Identification and validation of three risk models in skin melanoma based on bioinformatics analysis" @default.
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• W4366338873 doi "https://doi.org/10.21203/rs.3.rs-2772317/v1" @default.
• W4366338873 hasPublicationYear "2023" @default.
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