Fig 1: Role of MRGs in the tumor microenvironment. (A) The heatmap showing the relationship between MRGs and immune cells on the basis of the Cibersort algorithm. (B) Differences in infiltration of immune cells between MRGs-high and -low groups. (C) Chord plot demonstrating the correlation of MRGs with four immunosuppressive checkpoints. (D) Differences in immune subtypes of HCC patients between MRGs-high and -low groups. C1: wound healing; C2: IFN-gamma dominant; C3: inflammatory; C4: lymphocyte depleted; C6: TGF-β dominant. (E,F) Single-cell analysis demonstrated the expression abundance of DTYMK, ABCB6, GOT2, and TOMM40L on immune cells using the GSE98638 cohort. (G) Volcano diagram showing differentially expressed genes on Tprolif and Tex cells. (H) Functional enrichment of the differentially expressed genes. (I,J) Heatmap showing the correlation of MRGs members with Tprolif-upregulated and Tex-downregulated genes. *P < 0.05, **P < 0.01, ***P < 0.001.
Fig 2: Knockdown of ABCB6 significantly inhibited HCC cell proliferation, migration, and invasion in vitro. (A) Time-dependent ROC curves were utilized to screen core genes for MRGs. (B–D) Differential expression of ABCB6 in different T-stages, tumor status and vascular invasion. (E) WB and qPCR assays were performed to validate the knockdown efficiency of ABCB6 in HepG-2 and Huh-7 cells. (F,G) The CCK-8 and colony formation assays demonstrated the effect of knockdown of ABCB6 expression on the proliferation of HCC cells. (H) Flow cytometry showing the effect of ABCB6 abrogation on the HCC cell cycle. (I,J) Wound healing and transwell assays exhibited the effects of ABCB6 knockdown on HCC cell migration and invasion. *P < 0.05, **P < 0.01, ***P < 0.001.
Fig 3: MRGs with genetic mutations and immunotherapy. (A) The oncoplot depicted gene mutation differences in HCC patients between MRGs-high and -low groups. (B) Differential expression of DTYMK, ABCB6, GOT2 and TOMM40L between TP53-mut and -wt groups. (C) The scatterplot demonstrated the correlation of MRGs with tumor stemness indicators, DNAss and RNAss. (D–F) Differences in TMB, MSI, and TIDE scores of HCC patients between MRGs-high and -low groups. (G,H) Kaplan–Meier curves demonstrated the difference in progression-free survival (PFS) of patients treated with nivolumab or ipilimumab between the MRGs-high and -low groups. (I,J) Kaplan–Meier curves demonstrated the difference in PFS of melanoma patients treated with anti-PD-1 or ACT between the MRGs-high and -low groups using the GSE91061 and GSE100797 cohort, respectively. **P < 0.01, ***P < 0.001.
Fig 4: Validation of MRGs expression levels and prognostic value using multiple independent cohorts. (A–C) ICGC-JP, GSE14520, and GSE112790 cohorts were used to validate the expression levels of DTYMK, ABCB6, GOT2, TOMM40L, and MRGs in HCC and normal tissues, respectively. (D–F) ROC curves demonstrating the accuracy of MRGs in diagnosing HCC tissue. The scatterplot of MRGs, ICGC-JP (G) and GSE14520 (J) cohorts were the validation subset 1 and validation subset 2, respectively. (H,K) Kaplan–Meier survival analyses were performed to demonstrate the difference in overall survival of HCC patients between the MRGs-high and -low groups. (I,L) Time-dependent ROC curves demonstrating the accuracy of MRGs in predicting 1- and 3-year survival in HCC patients. *P < 0.05, **P < 0.01, ***P < 0.001.
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