Additional file 1 of Analyzing molecular typing and clinical application of immunogenic cell death-related genes in hepatocellular carcinoma

Additional file 1: Figure S1. A. Frequenciesof CNV gain, loss, and non-CNV among ICDs in ICD-low clusters. B. Frequenciesof CNV gain, loss, and non-CNV among ICDs in ICD-high clusters. Figure S2. A. mRNAlevels of BAX in THLE-2 and HCC cells. B. mRNA levels of BAX in HepG2 and Huh7HCC cells after BAX was knocked down. C-D. A colony formation assay was used toexplore the function of BAX in HCC cells. Their representative images are shownin C. E-F. Knockdown of BAX inhibits HCC cell migration. Wound healing assayswere used to assess the migration of HepG2 and Huh7 cells after the BAXknockdown. Representative images are shown in E (* P<0.05, ** P<0.01, ***P<0.001).All experiments were repeated at least three times. Figure S3. A. Differencesin the expression of RNA modification genes between ICD-low and ICD-highclusters. B. Differences in the expression of chemokine genes between ICD-lowand ICD-high clusters. C. Differences in the expression of receptor genesbetween ICD-low and ICD-high clusters. D. Differences in the expression of HLAgenes between ICD-low and ICD-high clusters. Figure S4. A. Frequencies of CNVgain, loss, and non-CNV among ICDs in Risk-high clusters. B. Frequencies of CNVgain, loss, and non-CNV among ICDs in Risk-low clusters. Figure S5. A.Prognostic differences according to high or low TMB scores in TCGA. B.Comparison of ICDRM and TMB in predicting prognosis. C. Heatmap of immuneinfiltration differences between ICDRM subpopulations and ICD clusters in TCGA.Figure S6. A. Differences in the expression of RNA modification genes between ICDRMRsk-low and Risk-high subpopulations. B. Differences in the expression ofchemokine genes between ICDRM Risk-low and Risk-high subpopulations. C.Differences in the expression of receptor genes between ICDRM Risk-low and Risk-highsubpopulations. D. Differences in the expression of HLA genes between ICDRM Risk-lowand Risk-high subpopulations. Figure S7. Analysis of drug sensitivity between ICDRMRisk-low and Risk-high subpopulations about clinically used Chemotherapeuticand targeted drugs through R package "pRRophetic". Figure S8. A. Thecorrelation of ICDRM with immune cells. B. Scatter plots show the correlationof ICDRM with the infiltration of activated CD4+ T memory, CD8+ T, T follicularhelper, and gd T cells. Figure S9. A. Expression differences of PRF1 between normaland tumor samples in TCGA. B. ROC curves of PRF1 in predicting the diagnosticvalue in TCGA. Figure S10. A. Expression differences of PRF1 betweenPre-treatment and On-treatment samples in each immunotherapy dataset. B. Expressiondifferences of PRF1 between Response and Non-response based on Pre-treatmentsamples in all immunotherapy datasets. Figure S11. A. The cell types and theirdistribution in HCC GSE125449 10x dataset. B. Relationship between PRF1expression and immune cells in HCC GSE125449 10x dataset. C. The distributionof PRF1 in NK/NKT cells was analyzed using single-cell resolution. SupplementaryTable S1. The sequences of qRT-PCR primers and shRNA sequences of BAX and HSP90AA1.Supplementary Table S2. DEGs between ICD-low and ICD-high clusters. SupplementaryTable S3. GSEA enrichment analysis of the DEGs between ICD-low and ICD-highclusters. Supplementary Table S4. DEGs between high-risk and low-risksubpopulations. Supplementary Table S5. GSEA enrichment analysis of the DEGs betweenhigh-risk and low-risk subpopulations. Supplementary Table S6. The intersectionof the drugs was predicted using the R package "pRRophetic" and the cMAPwebsite. Supplementary Table S7. 1136 Co-expressed genes that weresignificantly related to PRF1. Supplementary Table S8. 1813 DEGs of the PRF1high and PRF1 low expression subpopulations. Supplementary Table S9. 581 overlappinggenes between 1813 DEGs and 1136 co-expressed genes.