Comprehensive analysis of the role of cuproptosis-related genes in the prognosis and immune infiltration of adrenocortical Carcinoma

Figures

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  Fig. 1. Differential expression and mutations of the CRGs in ACC. (A) The different expression levels of the 11 CRGs in ACC and normal tissue samples. The central line represents the median value, and the upper and lower lines represent the quartile values. (B, C) The correlation between the transcription of the CRGs, with a negative correlation indicated in blue and a positive correlation indicated in red. (D) The genetic changes in the 10 CRGs. *P < 0.01, **P < 0.005, ***P < 0.001. CRG: cuproptosis-related gene; ACC: adrenocortical carcinoma, TPM: transcripts per kilobase of exon model per million mapped reads. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

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  Fig. 2. Differential expression and validation of CRGs in the GEO datasets. CRG expression levels in (A) GSE10927 and (B) GSE33371. *P < 0.01, **P < 0.005, ***P < 0.001, ns: no significant differences. CRG: cuproptosis-related gene; ACC: adrenocortical carcinoma.

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  Fig. 3. Pathway enrichment and gene network analysis of the CRGs. (A) The enriched pathways in the Gene Ontology analysis: (A1) biological process (BP), (A2) molecular function (MF), and (A3) cell component (CC). (B) The enriched pathways in the Kyoto Encyclopedia of Genes and Genomes analysis. (C) CRG network analysis. FDX1, CDKN2A, DLAT, and ATP7B are the core genes. GO: Gene Ontology, KEGG: Kyoto Encyclopedia of Genes and Genomes, CRG: cuproptosis-related gene.

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  Fig. 4. Prognostic model for CRGs in ACC. OS results: (A) distribution diagram of the risk scores, expression levels of the CRGs, and survival statuses; (B) K‒M plots of OS and CRRS, indicating that higher risk scores predict worse prognostic results; and (C) survival predictions at 3, 5, and 10 years, showing good predictive powers. The areas under the time-dependent ROC curves (AUCs) were 0.884, 0.798, and 0.741, respectively. The K‒M plots for the expression levels of (D) CYP2D6 and (E) CDKN2A and OS results; (G) distribution diagram of risk scores, expression levels of the CRGs and survival statuses; (H) K‒M plot of PFI and CRRS, and (I) survival status prediction at 3, 5, and 8 years. The AUCs were 0.801, 0.834, and 0.935, respectively. The K‒M plots for the expression levels of (J) CYP2D6 and (K) CDKN2A and PFI results. OS: overall survival, PFI: progression-free interval, K–M: Kaplan‒Meier, CRG: cuproptosis-related gene, ACC: adrenocortical carcinoma, ROC: receiver-operating characteristic.

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  Fig. 5. Development of the nomogram model and independent cohort validation. The nomogram model predicts the 3-, 5-, and 8-year OS (A), including the differential expression levels of FDX1 and CDKN2A, pathological stage, and age of onset. The time-dependent ROC curves (B) for OS and calibration curves (C) show that our model has superior prognosis predictive results in patients with ACC. In the independent cohorts GSE33371 and GSE10927, the time-dependent ROC curves (D) and calibration curves (E) demonstrated the reliable generalization of our constructed prognostic nomogram. OS: overall survival, ACC: adrenocortical carcinoma, AUC: area under the time-dependent ROC curve, TPR: true positive rate, FPR: false positive rate.

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  Fig. 6. Correlation between the expression of CDKN2A (A), SLC31A1 (B), DLAT (C), and FDX1 (D) and immune infiltration levels. Cor: correlation.

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  Fig. 7. Correlation between the expression of (A) CDKN2A, (B) FDX1, and (C) SLC31A1 and the immune checkpoints HAVCR2, CTLA-4, CD274, and PDCD1 in patients with ACC. ACC: adrenocortical carcinoma, Exp: expression.