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Multi-omics Analysis Identifies Glioblastoma Dependency on H3K9me3 Methyltransferase Activity [ChIP-seq]

ABSTRACT: Multi-omics Analysis Identifies Glioblastoma Dependency on H3K9me3 Methyltransferase Activity [ChIP-seq]

PROVIDER: PRJNA1142523 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR30059149_1.fastq.gz	Fastqsanger.gz
	SRR30059149_2.fastq.gz	Fastqsanger.gz
	SRR30059150_1.fastq.gz	Fastqsanger.gz
	SRR30059150_2.fastq.gz	Fastqsanger.gz
	SRR30059151_1.fastq.gz	Fastqsanger.gz

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Project description:Histone H3 lysine 9 dimethylation or trimethylation (H3K9me2 or H3K9me3) marks more than half of the human genome, particularly in heterochromatin regions and specific genes within euchromatic regions. Enzymes catalyzing the methylation of H3K9 have individually been associated with the modulation of gene expression patterns involved in cancer progression, including suppressor of variegation 3–9 homologue 1 (SUV39H1), SUV39H2, SET domain bifurcated 1 (SETDB1), SETDB2, euchromatic histone-lysine N-methyltransferase 1 and 2 (EHMT1/2). However, a comprehensive comparison and understanding of the characteristics and mechanisms of these chromatin-modifying enzymes in cancers remains incompletely understood. In this study, we demonstrated that these six H3K9 methyltransferases differentially expressed in tumors and correlated expression with somatic copy number variations (CNVs) and DNA methylation patterns. Through integrative multi-omics analyses, we identified SUV39H1, SUV39H2, and SETDB1 as the key players among the six H3K9 methyltransferases that exhibited the most significant associations with cancer phenotypes. By incorporating SUV39H1, SUV39H2, and SETDB1, we developed a novel signature termed “H3K9me3 MtSig” (H3K9me3 methyltransferases signature). H3K9me3 MtSig was unique for various tumor types, had prognostic implications and was linked to significant signaling pathways, particularly in glioblastoma (GBM). Furthermore, elevated H3K9me3 MtSig was confirmed in GBM patient-derived cells and tissues. In addition, single-cell expression analysis of H3K9me3 MtSig in GBM tissues demonstrated a pattern related to the G2/M cell cycle and was negatively correlated with immune responses. H3K9me3-mediated repetitive sequence silencing by H3K9me3 MtSig, determined using ChIP-sequencing, contributed to these phenotypes, and inhibiting H3K9me3 MtSig in patient-derived GBM cells suppressed proliferation and increased immune responses. Translationally, H3K9me3 MtSig performed as an independent prognostic factor in a clinical prediction model, and drug susceptibility screening integrating H3K9me3 MtSig identified potential biomarkers and therapeutics for GBM. In summary, H3K9me3 MtSig has the potential to elucidate novel prognostic markers, therapeutic targets, and predictors of treatment response in GBM and other cancer types for clinical intervention.

Project description:Purpose: Enzymes catalyzing histone H3 lysine 9 (H3K9) methylation, such as SUV39H1, SUV39H2, SETDB1, SETDB2, EHMT1, and EHMT2, are individually linked to cancer progression. However, a comprehensive comparison and understanding of their characteristics and mechanisms in cancers is lacking. This study aims to fill this knowledge gap and explore its translational implications. Experimental Design: We conducted multi-omics analyses, including gene expression, mutations, copy number variations, and DNA methylation, to assess the expression patterns, regulation, and prognostic relevance of H3K9 methyltransferases across tumor types. This led to the development of "H3K9me3 MtSig", incorporating SUV39H1, SUV39H2, and SETDB1, with a focus on GBM. Correlations with signaling pathways were explored using gene set enrichment analysis (GSEA) and single-cell expression analyses. ChIP-sequencing was employed to detect H3K9me3 genomic binding sites in GBM cells. We validated the dependency of H3K9me3 MtSig in GBM using patient-derived samples through western blot, immunohistochemistry, immunofluorescence, cell growth, and quantitative PCR. Clinical prediction modeling and drug susceptibility screening were utilized to identify potential therapeutic avenues. Results: H3K9 methyltransferases display diverse expression patterns across cancers, influenced by mutations, CNVs, and DNA methylation associated with the genes. The unique H3K9me3 MtSig signature shows prognostic implications and associations with signaling pathways, particularly in GBM. Elevated H3K9me3 MtSig levels in GBM cells correlate with the G2/M cell cycle positively and immune responses negatively. H3K9me3-mediated repetitive sequence silencing contributes to these phenotypes, with inhibiting it suppressing proliferation and enhancing immune responses. Clinically, H3K9me3 MtSig could act as an independent prognostic factor, and drug susceptibility screening identifies potential GBM biomarkers and therapeutics. Conclusions: H3K9me3 MtSig has the potential to elucidate novel prognostic markers, therapeutic targets, and predictors of treatment response in GBM and other cancer types for clinical intervention.

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Multi-omics Analysis Identifies Glioblastoma Dependency on H3K9me3 Methyltransferase Activity [ChIP-seq]

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