Fig. 8

Impact of C1QL1 expression on tumor progression and prognosis in ccRCC: Insights from RAS score analysis and multi-omics data. (A) illustrates the differential gene analysis between patients with high and low RAS scores. (B) explores the relationship between C1QL1 expression and tumor progression. (C) highlights the differential expression of C1QL1 across single-cell subsets of urinary tumors. (D–G) detail expression variations of C1QL1 in different subsets of the KIRC-GSE159115 dataset. (H) shows the correlation between gene expression and pathway levels quantified by TCPA-RPPA sequencing of functional proteins. (I) presents the Pearson correlation of GSVA scores between z-scores of C1QL1 expression level and 14 tumor states.(J) discusses GSEA analysis on hallmark gene sets. (K) Heatmap displaying the differential expression of genes involved in glucose and lipid metabolism between the C1QL1 high and low expression groups. (L) C1QL1 high and low expression and glycogen staining. (M) C1QL1 high and low expression and oil red staining. (N) GSVA relationship analysis C1QL1 expression and metabolism. (O) identifies the maximum values of each spot cell component post-deconvolution of the spatial transcriptome. (P) maps the single-gene spatial transcriptome . (Q) delineates the differential expression of specific genes in malignant, mixed, and normal regions. (R) analyzes the Spearman correlation between gene expression and microenvironmental components at single-cell resolution. (S–U) Sequentially analyze, respectively, the synergistic effects of C1QL1 expression and the infiltration of plasma cells, NK cells, and cancer-associated fibroblasts (CAFs) on survival outcomes. (V) Multiple immunofluorescence analysis showing low expression of C1QL1 with infiltration of plasma cells, cancer-associated fibroblasts (CAFs), and NK cells.( Fig. 8W) Multiple immunofluorescence analysis showing high expression of C1QL1 with infiltration of plasma cells, CAFs, and NK cells.