Multi-omics analysis reveals the role of the autophagy-related gene AGT in chemotherapy resistance in colorectal cancer and the therapeutic potential of its inhibitors

Figures

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  Fig. 1. A Differential expression of AGT between tumor and normal groups (TCGA combined with GTEx analysis); B Immunohistochemical validation of AGT expression in normal and CRC tissues from the HPA database (Left: normal tissue, Right: tumor tissue); C Meta-analysis of univariate Cox survival analysis across multiple datasets; D KEGG gene set enrichment analysis

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  Fig. 2. A Analysis of immune cell infiltration proportions between high and low AGT expression groups (CIBERSORT); B Correlation analysis of AGT expression with ESTIMATE scores, immune scores, and stromal scores in the TCGA colorectal cancer cohort; C Comparison of tumor mutational burden, neoantigen load, and MANTIS scores between high and low AGT expression groups; D Comparison of somatic mutation frequencies between high and low AGT expression groups; E Spearman correlation between AGT expression and IC50 values from the CTRP database; F Spearman correlation between AGT expression and drug sensitivity IC50 values

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  Fig. 3. A UMAP plot showing 21 distinct cell subpopulations categorized into 13 major cell types; B Heatmap displaying the top five marker genes for each cell subpopulation; C Bar plot showing the number and proportion of each cell type across normal and tumor groups; D AGT expression levels in normal vs. tumor samples, indicating higher expression in tumor tissues; E AGT expression across different cell types

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  Fig. 4. A AGT expression across 10 fibroblast subpopulations; B UMAP plot showing 10 fibroblast subpopulations (C1–C10) identified in CRC tissues; C Pseudotime trajectory analysis of fibroblast subpopulations; E Heatmap displaying the activity of hallmark pathways across the fibroblast subpopulations; F Random forest analysis indicating the importance of WNT signaling in the C6 subpopulation; G Correlation plot showing the relationship between AGT expression and WNT pathway activation

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  Fig. 5. A Tissue imaging; (B) Post-deconvolution cell composition for each spot, showing the maximum value; C Spatial resolution of malignant (Malignant), mixed malignant (Mixed), and normal (Normal) regions; D Differential AGT expression across malignant, mixed, and normal regions; E Spearman correlation between gene expression and microenvironment components at spatial resolution; F Results and forest plot of Mendelian randomization analysis of the causal relationship between AGT inhibition and CRC

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  Fig. 6. A–B Funnel plots demonstrating the symmetry of the causal effect of AGT inhibitor on heart failure and colorectal cancer; The black dots in the figure represent single nucleotide polymorphisms, with the horizontal axis displaying the β values of these polymorphisms, and the vertical axis showing their standard errors. C–D Scatter plots of the five MR analysis methods; The vertical axis denotes the influence of single nucleotide polymorphisms on exposure variables, the horizontal axis delineates their impact on outcome variables. E–F Leave-one-out analysis of statistically significant variations in the causal effects of AGT inhibitor on heart failure and colorectal cancer; The Y-axis corresponds to each excluded rsid number and the ‘all’ condition not excluded by the IVW method. The X-axis corresponds to specific IVW values. G–H Forest plot of each SNP’s causal relationship with heart failure and colorectal cancer

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  Fig. 7. PheWAS analysis showing associations of AGT with both binary and continuous traits