Abstract 3825: Integrating CpG-level methylation and transcriptomics for high-resolution cancer epigenetics

Abstract Background: Promoter hypermethylation is a key mechanism for silencing tumor suppressor genes in cancer. While whole-transcriptome sequencing (WTS/RNA-seq) is widely used to study cancer biology, high-resolution integration of methylation and expression data remains challenging. Conventional differential methylation region (DMR) approaches aggregate signals across promoters, potentially obscuring fine-scale regulatory effects. Methods: We analyzed three pairs of parental and sotorasib-resistant NSCLC cell lines using PredicineEpic genome-wide DNA methylation profiling and PredicineWTS RNA-seq. Instead of promoter-level aggregation, we quantified fragment-level methylation changes at individual CpG sites within promoter regions. Correlations between CpG-specific methylation alterations and differential gene expression were evaluated across varying TPM log2 fold-change thresholds. In addition, more than 50 FFPE tissue biopsies from multiple cancer types were profiled with PredicineEpic and PredicineWTS to systematically assess the relationship between DNA methylation and gene expression. Results: CpG-level methylation changes showed stronger inverse correlations with gene expression than promoter-level averages. For genes with log2FC ≥ 4, CpG fragment beta differences correlated with expression changes at R = −0.92 (n = 7, p = 0.001). Similar trends were seen at lower thresholds: log2FC ≥ 3 yielded R = −0.63 (n = 22, p = 0.001), whereas promoter-level beta differences showed no significant correlation. Consistent patterns of stronger CpG-level correlations were also observed across all tissue biopsy datasets. Conclusions: High-resolution CpG-level methylation analysis provides greater sensitivity for linking epigenetic alterations to transcriptional changes than conventional promoter-level approaches. Fragment-level methylation profiling can reveal critical transcriptional regulatory events and may have important applications in liquid biopsy and biomarker discovery. Citation Format: Chao Dai, Guanglong Jiang, Ziqi Zhu, Giancarlo Bonora, Yong Huang, Kemin Zhou, Pan Du. Integrating CpG-level methylation and transcriptomics for high-resolution cancer epigenetics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 3825.

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Publication Details

Journal
Cancer Research
Published
2026-04-03
DOI
https://doi.org/10.1158/1538-7445.am2026-3825
Primary Topic
Epigenetics and DNA Methylation
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article
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article

Abstract 3825: Integrating CpG-level methylation and transcriptomics for high-resolution cancer epigenetics

Guanglong Jiang, Kemin Zhou, Giancarlo Bonora, Yong Huang et al.
Cancer Research
Epigenetics and DNA Methylation
article

Abstract 3825: Integrating CpG-level methylation and transcriptomics for high-resolution cancer epigenetics

Guanglong Jiang, Kemin Zhou, Giancarlo Bonora, Yong Huang, Ziqi Zhu, Pan Du., Chao Dai
article en

Abstract

Abstract Background: Promoter hypermethylation is a key mechanism for silencing tumor suppressor genes in cancer. While whole-transcriptome sequencing (WTS/RNA-seq) is widely used to study cancer biology, high-resolution integration of methylation and expression data remains challenging. Conventional differential methylation region (DMR) approaches aggregate signals across promoters, potentially obscuring fine-scale regulatory effects. Methods: We analyzed three pairs of parental and sotorasib-resistant NSCLC cell lines using PredicineEpic genome-wide DNA methylation profiling and PredicineWTS RNA-seq. Instead of promoter-level aggregation, we quantified fragment-level methylation changes at individual CpG sites within promoter regions. Correlations between CpG-specific methylation alterations and differential gene expression were evaluated across varying TPM log2 fold-change thresholds. In addition, more than 50 FFPE tissue biopsies from multiple cancer types were profiled with PredicineEpic and PredicineWTS to systematically assess the relationship between DNA methylation and gene expression. Results: CpG-level methylation changes showed stronger inverse correlations with gene expression than promoter-level averages. For genes with log2FC ≥ 4, CpG fragment beta differences correlated with expression changes at R = −0.92 (n = 7, p = 0.001). Similar trends were seen at lower thresholds: log2FC ≥ 3 yielded R = −0.63 (n = 22, p = 0.001), whereas promoter-level beta differences showed no significant correlation. Consistent patterns of stronger CpG-level correlations were also observed across all tissue biopsy datasets. Conclusions: High-resolution CpG-level methylation analysis provides greater sensitivity for linking epigenetic alterations to transcriptional changes than conventional promoter-level approaches. Fragment-level methylation profiling can reveal critical transcriptional regulatory events and may have important applications in liquid biopsy and biomarker discovery. Citation Format: Chao Dai, Guanglong Jiang, Ziqi Zhu, Giancarlo Bonora, Yong Huang, Kemin Zhou, Pan Du. Integrating CpG-level methylation and transcriptomics for high-resolution cancer epigenetics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 3825.

Cancer ResearchVol. 86(7_Supplement)
Openalex Percentile: Top 31%
Epigenetics and DNA Methylation
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