Additional file 1 of A novel CAF-cancer cell crosstalk-related gene prognostic index based on machine learning: prognostic significance and prediction of therapeutic response in head and neck squamous cell carcinoma
Creators: name: Xu, Yuming
name: Li, Junda
name: Wang, Jinming
name: Deng, Feilong
Publisher: name: figshare
Year: 2024
Subjects: subject: Immunology
subject: FOS: Clinical medicine

description: Additional file 1: Figure S1. Analysis of the scRNA-seq data. (A) Quality control of the scRNA-seq data. (B) Integration of the scRNA-seq data. (C) Expression of marker genes of CAFs in three CAFs subgroups. (D) Highly expressed genes in single-cell subgroups. CAFs, cancer-associated fibroblasts. Figure S2. PairLRs involved in CCCT. CCCT, cancer-associated fibroblast (CAF)-cancer cell crosstalk; pairLRs, paired ligands and receptors. Figure S3. Construction and Validation of the CCRGPI. (A) PairLRs selected from LASSO regression analysis. (B) Proportion of MatCAF, MyoCAF, SCC1, and SCC2 cells in the TCGA cohort (ns: not significant, *p &lt; 0.05). (C) Proportion of patients with various clinicopathologic factors in different CCRGPI subgroups. CCRGPI, cancer-associated fibroblast (CAF)-cancer cell crosstalk-related gene prognostic index; LASSO, least absolute shrinkage and selection operator; pairLRs, paired ligands and receptors; MatCAF, matrix CAF; MyoCAF, myofibroblast; SCC2, squamous cell carcinoma 2; TCGA, The Cancer Genome Atlas. Figure S4. Expression of pairLRs contained in the CCRGPI in scRNA-seq data. CCRGPI, cancer-associated fibroblast (CAF)-cancer cell crosstalk-related gene prognostic index; pairLRs, paired ligands and receptors. Figure S5. Differential expression analysis in different CCRGPI subgroups. (A) Volcano map showing differentially expressed genes. (B) Activated and inhibited HALLMARK pathways in the high-CCRGPI group. (C) The top 10 genes with the highest mutation frequencies in different CCRGPI subgroups. (D) Forest plot showing genes with mutational differences in different CCRGPI subgroups (**p &lt; 0.01, ***p &lt; 0.001). (E) Interaction effect of genes with significant mutation differences. (F) Interaction effect of genes contained in the CCRGPI (*p &lt; 0.05,.p &lt; 0.01). (G) TMB in different CCRGPI subgroups. CCRGPI, cancer-associated fibroblast (CAF)-cancer cell crosstalk-related gene prognostic index; TMB, tumor mutation burden. Figure S6. Immune characteristics in different HPV subgroups. (A) Correlation between the immunescore and CCRGPI in different HPV subgroups (ns: not significant, *p &lt; 0.05). (B) Correlation between the ES of immune-related gene sets and CCRGPI in different HPV subgroups (*p &lt; 0.05, **p &lt; 0.01, ***p &lt; 0.001). (C) Immune checkpoints differentially expressed among CCRGPI subgroups in different HPV subgroups (*p &lt; 0.05, **p &lt; 0.01, ***p &lt; 0.001, ****p &lt; 0.0001). (D) Proportion of immune cells based on the XCELL, TIMER, MCPCOUNTER, EPIC, and CIBERSORT algorithms in different HPV subgroups. CCRGPI, cancer-associated fibroblast (CAF)-cancer cell crosstalk-related gene prognostic index; ES, enrichment score; HPV, human papillomavirus. Figure S7. Screening of small-molecule drugs and molecular docking. (A) Schematic diagram of the CMap analysis. (B) PPI network of genes contained in the CCRGPI. (C) The three-dimensional interaction diagrams of Ganetespib with IGF1R. CCRGPI, cancer-associated fibroblast (CAF)-cancer cell crosstalk-related gene prognostic index; CMap, connectivity map; PPI, protein–protein interaction.