Immune-related gene methylation prognostic instrument for stratification and targeted treatment of ovarian cancer patients toward advanced 3PM approach

Abstract

Background: DNA methylation is an important mechanism in epigenetics, which can change the transcription ability of genes and is closely related to the pathogenesis of ovarian cancer (OC). We hypothesize that DNA methylation is significantly different in OCs compared to controls. Specific DNA methylation status can be used as a biomarker of OC, and targeted drugs targeting these methylation patterns and DNA methyltransferase may have better therapeutic effects. Studying the key DNA methylation sites of immune-related genes (IRGs) in OC patients and studying the effects of these methylation sites on the immune microenvironment may provide a new method for further exploring the pathogenesis of OC, realizing early detection and effective monitoring of OC, identifying effective biomarkers of DNA methylation subtypes and drug targets, improving the efficacy of targeted drugs or overcoming drug resistance, and better applying it to predictive diagnosis, prevention, and personalized medicine (PPPM; 3PM) of OC.

Method: Hypermethylated subtypes (cluster 1) and hypomethylated subtypes (cluster 2) were established in OCs based on the abundance of different methylation sites in IRGs. The differences in immune score, immune checkpoints, immune cells, and overall survival were analyzed between different methylation subtypes in OC samples. The significant pathways, gene ontology (GO), and protein-protein interaction (PPI) network of the identified methylation sites in IRGs were enriched. In addition, the immune-related methylation signature was constructed with multiple regression analysis. A methylation site model based on IRGs was constructed and verified.

Results: A total of 120 IRGs with 142 differentially methylated sites (DMSs) were identified. The DMSs were clustered into a high-level methylation group (cluster 1) and a low-level methylation group (cluster 2). The significant pathways and GO analysis showed many immune-related and cancer-associated enrichments. A methylation site signature based on IRGs was constructed, including RORC|cg25112191, S100A13|cg14467840, TNF|cg04425624, RLN2|cg03679581, and IL1RL2|cg22797169. The methylation sites of all five genes showed hypomethylation in OC, and there were statistically significant differences among RORC|cg25112191, S100A13|cg14467840, and TNF|cg04425624 (p < 0.05). This prognostic model based on low-level methylation and high-level methylation groups was significantly linked to the immune microenvironment as well as overall survival in OC.

Conclusions: This study provided different methylation subtypes for OC patients according to the methylation sites of IRGs. In addition, it helps establish a relationship between methylation and the immune microenvironment, which showed specific differences in biological signaling pathways, genomic changes, and immune mechanisms within the two subgroups. These data provide ones to deeply understand the mechanism of immune-related methylation genes on the occurrence and development of OC. The methylation-site signature is also to establish new possibilities for OC therapy. These data are a precious resource for stratification and targeted treatment of OC patients toward an advanced 3PM approach.

Supplementary information: The online version contains supplementary material available at 10.1007/s13167-024-00359-3.

Keywords: Biomarker; DNA methylation; Differentially methylated sites; Immune-related genes; Methylation subtypes; Methylation-site signature; Ovarian cancer; Overall survival; Patient stratification; Risk score; Targeted treatment; Tumor immune microenvironment; Predictive preventive personalized medicine (PPPM; 3PM).

Fig. 1

Experiment flow-chart to identify immune-related gene methylation signature in ovarian cancers. IRGs: immune-related genes. DMSs: differentially methylated sites. DMGs: differentially methylated genes

Fig. 2

The DNA methylation subtypes of ovarian cancer based on hierarchical cluster analysis of differentially methylated sites of immune-related genes. A The volcano plots of differentially methylated sites in ovarian cancer tissues compared to normal controls. B Clustering heat map of samples at consensus k = 2. Different colors reflect different cluster numbers; the color gradient is from white to blue, indicating the consensus of progression. C Clustering analysis of 142 differentially methylated sites divided into ovarian cancer samples into hypermethylation (cluster 1) and hypomethylation (cluster 2) groups. Red represents hypermethylation level, and green represents hypomethylation level. D Total methylation level between hypermethylation and hypomethylation subtypes. E The overall survival analysis between hypermethylation and hypomethylation subtypes in ovarian cancer

Fig. 3

Immune status between hyper- and hypomethylation subtypes of ovarian cancers. A Immune cell infiltration status between hyper- and hypomethylation subtypes of ovarian cancers. B Immune-related events based on ssGSEA between hyper- and hypomethylation subtypes of ovarian cancers. C Immune score between hyper- and hypomethylation subtypes of ovarian cancers. D Innate immune cells and adaptive immune cells between hyper- and hypomethylation subtypes of ovarian cancers. E Immune checkpoint genes between hyper- and hypomethylation subtypes of ovarian cancers. *p < 0.05, **p < 0.01, and ***p < 0.001

Fig. 4

DNA methylation–mediated signaling pathway changes identified with DMSs of immune-related genes in ovarian cancers. A DNA methylation–mediated cytokine-cytokine receptor interaction pathway changes in ovarian cancer. B DNA methylation–mediated PD-L1 expression and PD-1 checkpoint pathway changes in ovarian cancers

Fig. 5

DNA methylation–mediated biology processes and protein–protein interaction (PPI) network identified with DMSs of immune-related genes in ovarian cancers. A The significant biology processes of DMSs of immune-related genes in ovarian cancers. B The PPI network of DMSs of immune-related genes in ovarian cancers

Fig. 6

The correlation coefficient between methylation and mRNA expression of immune-related genes in ovarian cancers

Fig. 7

The drug sensitivity of the identified methylation-driven sites in ovarian cancers

Fig. 8

Construction of risk score model based on five methylation-driven sites in ovarian cancer. A Cox regression identified the prognostic model in ovarian cancer. B Overall survival analysis between high- and low-risk score groups in train cohort. C Overall survival analysis between high- and low-risk score groups in test cohort. D The heatmap of clinical features between high- and low-risk score groups. E The univariate Cox regression analysis of risk factors in ovarian cancer. F The different immune cells between high- and low-risk score groups. G The risk score assessment nomogram to evaluate prognosis in ovarian cancer (1-, 3-, and 5-year survival rates). *p < 0.05, **p < 0.01, and ***p < 0.001

Fig. 9

Experimental verification of methylation status of five immune-related gene CpG sites in human ovarian cancer tissue. A MSP qualitative analysis of methylation at CpG sites of five immune-related genes in ovarian cancer cells vs. control cells. B MSP qualitative analysis of methylation at CpG sites of five immune-related genes in ovarian cancer tissues vs. control tissues. C BSP quantitative analysis of methylation at RORC CpG sites in ovarian cancer tissues vs. control tissues. D BSP quantitative analysis of methylation at S100A13 CpG sites in ovarian cancer tissues vs. control tissues. E BSP quantitative analysis of methylation at TNF CpG sites in ovarian cancer tissues vs. control tissues. F BSP quantitative analysis of methylation at IL1RL2 CpG sites in ovarian cancer tissues vs. control tissues. G BSP quantitative analysis of methylation at RLN2 CpG site in ovarian cancer tissues vs. controls