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. 2024 Jan 18;16(2):1733-1749.
doi: 10.18632/aging.205454. Epub 2024 Jan 18.

Identification and validation of methylation-CpG prognostic signature for prognosis of hepatocellular carcinoma

Chunmei He  1   2 , Zehao Guo  1   3 , Hao Zhang  1   3 , Ganqing Yang  1 , Jintao Gao  1   3 , Zhijing Mo  1   3
Affiliations

Identification and validation of methylation-CpG prognostic signature for prognosis of hepatocellular carcinoma

Chunmei He et al. Aging (Albany NY). .

Abstract

Epigenetic biomarkers help predict the prognosis of cancer patients and evaluating the clinical outcome of immunization therapy. In this study, we present a personalized gene methylation-CpG signature to enhance the accuracy of survival prediction for individuals with hepatocellular carcinoma (HCC). Utilizing RNA sequencing and methylation datasets from GEO as well as TCGA, we conducted single sample GSEA (ssGSEA), WGCNA, as well as Cox regression. Through these analyses, we identified 175 oxidative stress and immune-related genes along with 4 CpG loci that are associated with the prognosis of HCC. Subsequently, we constructed a prognostic signature for HCC utilizing these 4 CpG sites, referred to as the HCC Prognostic Signature of Methylation-CpG sites (HPSM). Further investigation revealed an enrichment of immune-related signal pathways in the HPSM-low group, which demonstrated a positive correlation with better survival among HCC patients. Moreover, the methylation of the CpG sites in HPSM was found to be closely linked to drug sensitivity. In vitro experiments tentatively confirmed that promoter methylation regulated the expression of BMPER, one of the CpG sites within HPSM. The expression of BMPER was significantly correlated with cell death in the oxidative stress pathway, and overexpression of BMPER effectively inhibited HCC cell proliferation. Consequently, our findings suggest that HPSM is an independent predictive factor and holds promise for accurately predicting the prognosis of HCC patients.

Keywords: hepatocellular carcinoma; immune checkpoint; methylation; oxidative stress; prognostic.

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Conflict of interest statement

CONFLICTS OF INTEREST: The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
WGCNA of DMGs and DEGs in HCC. (A) Identification of overlapping genes between DMGs and DEGs. (B) Analysis of the scale-free fit index for different soft threshold powers (β). (C) Clustering dendrogram revealing the presence of 12 modules. (D) Correlation matrix showing the relationships between these modules in normal and cancer tissues: red indicates positive correlations, while green represents negative correlations. (E) GO enrichment analysis of 175 genes associated with oxidative stress and immunity.
Figure 2
Figure 2
Prognostic analysis of the HPSM subgroups. (A) Forest plot displaying the hazard ratios (HRs) for 4 CpGs and OS. (B) Kaplan-Meier survival curves for OS comparing HPSM-high group with HPSM-low group within the TCGA training dataset. (C) Kaplan-Meier survival curves for OS comparing HPSM-high group with HPSM-low group within the TCGA testing dataset. (D) Kaplan-Meier survival curves for OS comparing HPSM-high group with HPSM-low groups within the GSE52018 dataset. (E) ROC curves to predict the OS between HPSM groups within the TCGA training dataset. (F) ROC curves for OS prediction between HPSM groups within the TCGA testing dataset. (G) ROC curves for OS prediction between HPSM groups within the GSE52018 dataset.
Figure 3
Figure 3
Molecular and immune function analysis among HPSM groups. (A) Enriched gene sets among the HPSM-high group. (B) Enriched gene sets among the HPSM-low group. (C, D) GSEA of HPSM subgroups in survival-related gene sets. (E) GSEA enrichment analysis of HPSM subgroups in immune-related gene sets. (F) Scores of 29 tumor-infiltrating immune cell subsets and immune-related functions. (G) TMB analysis in HPSM-low groups. (H) TMB analysis in HPSM-high groups.
Figure 4
Figure 4
Prognostic analysis of HPSM and nomogram development. (A) Univariate and multivariate Cox regression analyses of HPSM risk score as well as prognostic parameters. (B) ROC curves of HPSM risk score and prognostic parameters. (C) Prognostic nomogram predicting 1-, 2-, and 3-year OS of HCC individuals. (D) ROC curves of the prognostic nomogram.
Figure 5
Figure 5
Relationship between methylation levels of CpG sites in the HPSM and FDA drug sensitivity.
Figure 6
Figure 6
Regulation of BMPER expression by promoter methylation. (A) Methylation level of cg17561435 in different HPSM subgroups. (B) Negative correlation between BMPER mRNA expression and cg17561453 methylation levels. (C) Prediction of CpG islands in the BMPER promoter using the MethPrimer website, and detection of BMPER promoter methylation status using bisulfite sequencing PCR (BSP). (D) Quantification of BMPER mRNA expression levels after 5-aza-dC treatment using qPCR (***p < 0.01). (E) Detection of BMPER protein expression levels after 5-aza-dC treatment using western blotting.
Figure 7
Figure 7
Inhibition of HCC cell proliferation by BMPER overexpression. (A) GO biological process pathway enrichment analysis using GSEA. (B) Confirmation of BMPER overexpression in Hep3B cell line using western blotting. (C) Effects of BMPER overexpression on cell proliferation assessed by MTT assays. (D) Clonal formation assays comparing the LV-NC and LV-BMPER groups.
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