SUV39H1 maintains cancer stem cell chromatin state and properties in glioblastoma
Chunying Li, Qiqi Xie, Sugata Ghosh, Bihui Cao, Yuanning Du, Giau V. Vo, Timothy Y. Huang, Charles Spruck, Richard L. Carpenter, Y. Alan Wang, Q. Richard Lu, Kenneth P. Nephew, Jia Shen
Chunying Li, Qiqi Xie, Sugata Ghosh, Bihui Cao, Yuanning Du, Giau V. Vo, Timothy Y. Huang, Charles Spruck, Richard L. Carpenter, Y. Alan Wang, Q. Richard Lu, Kenneth P. Nephew, Jia Shen
View: Text | PDF
Research Article Cell biology Oncology Stem cells

SUV39H1 maintains cancer stem cell chromatin state and properties in glioblastoma

Abstract

Glioblastoma (GBM) is the most lethal brain cancer, with GBM stem cells (GSCs) driving therapeutic resistance and recurrence. Targeting GSCs offers a promising strategy for preventing tumor relapse and improving outcomes. We identify SUV39H1, a histone-3, lysine-9 methyltransferase, as critical for GSC maintenance and GBM progression. SUV39H1 is upregulated in GBM compared with normal brain tissues, with single-cell RNA-seq showing its expression predominantly in GSCs due to super-enhancer–mediated activation. Knockdown of SUV39H1 in GSCs impaired their proliferation and stemness. Whole-cell RNA-seq analysis revealed that SUV39H1 regulates G2/M cell cycle progression, stem cell maintenance, and cell death pathways in GSCs. By integrating the RNA-seq data with ATAC-seq data, we further demonstrated that knockdown of SUV39H1 altered chromatin accessibility in key genes associated with these pathways. Chaetocin, an SUV39H1 inhibitor, mimics the effects of SUV39H1 knockdown, reducing GSC stemness and sensitizing cells to temozolomide, a standard GBM chemotherapy. In a patient-derived xenograft model, targeting SUV39H1 inhibits GSC-driven tumor growth. Clinically, high SUV39H1 expression correlates with poor glioma prognosis, supporting its relevance as a therapeutic target. This study identifies SUV39H1 as a crucial regulator of GSC maintenance and a promising therapeutic target to improve GBM treatment and patient outcomes.

Authors

Chunying Li, Qiqi Xie, Sugata Ghosh, Bihui Cao, Yuanning Du, Giau V. Vo, Timothy Y. Huang, Charles Spruck, Richard L. Carpenter, Y. Alan Wang, Q. Richard Lu, Kenneth P. Nephew, Jia Shen

×

Figure 9

SUV39H1 serves as a therapeutic target and prognostic indicator in GBM.

Options: View larger image (or click on image) Download as PowerPoint
SUV39H1 serves as a therapeutic target and prognostic indicator in GBM. ...
(A) RNA-seq, whole-exome-seq, and clinical phenotype data from TCGA GBM and low-grade glioma (LGG) datasets were integrated to visualize expression patterns of SUV39H1, NES, EGFR, and CD44 across glioma types. (B) SUV39H1 expression across glioma grades. A 2-sample Wilcoxon’s rank-sum test was used to compare SUV39H1 expression levels between grades II+III and grade IV. (C) SUV39H1 expression across glioma subtypes, including astrocytoma (AS), oligoastrocytoma (OA), oligodendroglioma (OD), and GBM. A 2-sample Wilcoxon’s rank-sum test was conducted to determine significant differences in expression levels, comparing the non-GBM group (AS, OA, OD) to the GBM group. The displayed P value reflects this comparison. (D and E) SUV39H1 expression in GBM subtypes in the TCGA (D) and Gravendeel (E) datasets. In the box-and-whisker plots, the boxes represent the interquartile range (IQR) from the first quartile (Q1) to the third quartile (Q3), with the horizontal line inside indicating the median. The whiskers extend to the smallest and largest values within 1.5 × IQR from Q1 and Q3, respectively, while outliers beyond this range are shown as individual dots. CL, classical; MES, mesenchymal; PN, proneural. Statistical analysis was conducted using the Kruskal-Wallis test, with P values indicating the significance of differences among the 3 groups. (F–H) Survival curves of patients with higher and lower SUV39H1 expression in TCGA glioma (F), CGGA glioma (G), and Gravendeel GBM (H) datasets. Kaplan-Meier survival analysis was used, with patients divided into high and low SUV39H1 expression groups based on the median expression level as a cutoff. Statistical significance between survival curves was determined using the log-rank test.