Fig. 2 | Scientific Reports

Fig. 2

From: Developing angiogenesis-related prognostic biomarkers and therapeutic strategies in bladder cancer using deep learning and machine learning

Fig. 2

Functional characterization, screening, and modeling of DEGs in BLCA for ARGS model construction. A, B Heatmaps illustrating differential expression levels of DEGs between BLCA samples and normal bladder tissues; Volcano plots depicting expression fold-changes of DEGs. C, D Functional enrichment analysis of DEGs using GO and KEGG. E, F Unicox and LASSO regression were used to screen prognostic risk genes for BLCA, which were then utilized to construct the ARGS model. G Stratification of BLCA patients into high-risk and low-risk subgroups based on prognostic genes, with comparative analysis of gene expression patterns between subgroups.

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