Fig 2: TIF1a/H3K14ac complex activation of RBM3 transcription is required for AFAP1-AS1–modulation of YAP stability. (A) Heatmaps of RNA-Seq data for RBM3 and its target genes in HNE-1 cells with KAT2B sgRNA or a control sgRNA. (B, C), KAT2B knockout inhibits RBM3 mRNA (B) and protein (C) expression. (D, E) Effects of KAT2B overexpression on AFAP1-AS1 knockdown-suppressed RBM3 protein expression (D) and RBM3 promoter activity (E). (F) ChIP-qPCR assay of KAT2B binding at different loci within the RBM3 promoter. IgG is used as a control. (G) RBM3 overexpression rescues AFAP1-AS1 knockdown-mediated suppression of YAP. (H) Effects of RBM3 overexpression on KAT2B knockout-mediated YAP repression. (I) TIF1a wild-type, but not the F979A/N980A mutant, restores AFAP1-AS1 knockdown-suppressed YAP expression in HNE-1 cells. (J, K) Effect of TIF1a wild-type and F979A/N980A mutant overexpression on AFAP1-AS1 knockdown-suppressed TIF1a binding (J), H3K14ac (K), and the RBM3 promoter. (L) A working model for AFAP1-AS1–mediated NPC tumorigenicity. Error bars represent the standard deviation. *P < 0.05. ***P < 0.001.The data represent three independent experiment.

Fig 3: Splicing machinery is drastically dysregulated in HGAs/glioblastomas. (A) Individual fold-change of the expression of all the splicing machinery components analysed in control brain tissues (CT) and HGA/glioblastoma samples. (B) Hierarchical heat map generated using the expression levels of all the spliceosome components and splicing factors determined in control brain tissues from different brain areas [Controls (Brocca, Wernicke, cingulate and medial); n = 4] and HGA/glioblastoma samples (grades AIII/AIV; n = 29). (C) Principal components analysis of the mRNA expression levels of the splicing machinery components in the same sample set. (D) VIP scores obtained from partial least squares discriminant analysis (PLS-DA) of all of the splicing machinery components studies. (E) Levels of mRNA from selected splicing machinery components (SRSF3, RBM22, PTBP1 and RBM3) in control and tumour tissues and ROC curves analysis. (F) Hierarchical heat map generated using the expression levels of selected splicing machinery components (SRSF3, RBM22, PTBP1 and RBM3) in control brain tissues from different brain areas (CTs) and HGAs/glioblastomas (grades AIII/AIV). (G) IHC analysis of nuclear levels of SRSF3, RBM22, PTBP1 and RBM3 in FFPE samples from control and HGA/glioblastoma tissues (representative images are depicted). (H) Protein expression levels of SRSF3, RBM22, PTBP1 and RBM3 in GBM compared to non-tumour samples (GTEx tissues) using the proteomic glioblastoma CPTAC dataset. Data represent median (interquartile range) or means ± SEM. *P, 0.05; **P, 0.01; ***P, 0.001, significantly different from control conditions. See also Supplementary Figs 1–4.

Fig 4: Silencing of SRSF3, RBM22, PTBP1 and RBM3 expression decreases functional parameters of aggressiveness in glioblastoma cell lines and primary patient-derived glioblastoma cells. (A) Diagram showing the in vitro generation of SRSF3, RBM22, PTBP1 and RBM3 knockdown glioblastoma cells by silencing with specific siRNAs. (B) Proliferation rate of SRSF3, RBM22, PTBP1 and RBM3-silenced cells compared to control scramble-transfected cells (U-87 MG and U-118 MG models and primary patient-derived glioblastoma cells; n = 3). (C) Migration rate of SRSF3, RBM22, PTBP1 and RBM3-silenced U-118 MG cells compared to control scramble-transfected cells (representative images of the migration capacity are also included; n = 3). (D) VEGF secretion of SRSF3, RBM22, PTBP1 and RBM3-silenced cells compared to control scramble-transfected cells (U-87 MG and U-118 MG models; n = 3). (E) Apoptosis levels in SRSF3, RBM22, PTBP1 and RBM3-silenced cells compared to control scramble-transfected cells (U-87 MG and U-118 MG models; n = 3). (F) Number of cells per tumorsphere and representative images of formation of tumorspheres in SRSF3, RBM22, PTBP1 and RBM3-silenced cells compared to control scramble-transfected cells on cell morphology (U-87 MG and U-118 MG models; n = 3). Data represent means ± SEM. *P, 0.05; **P, 0.01; ***P, 0.001, significantly different from control conditions. See also Supplementary Fig. 5.

Fig 5: Dysregulation in the splicing machinery is also confirmed in two mouse models with proneural-like and mesenchymal-like glioblastoma. (A) Diagram of generation of mouse models of glioblastoma by overexpression of PDGFB or PDGFA in a Cdkn2a null background in Nestin-expressing cells (adapted from Bejarano et al., 2017). (B) Hierarchical heat map generated using the mRNA expression levels of spliceosome components and splicing factors in non-tumour and tumour samples from PDGFA/B-induced mouse models. (C) Venn diagram of altered splicing machinery components in PDGFA/B-induced glioblastoma mouse models and human HGA/glioblastoma samples. (D) Expression levels of mRNA and ROC curve analysis of selected splicing machinery components in PDGFA/B-induced mouse model samples. (E) Hierarchical heat map generated using the mRNA expression levels of previously selected splicing machinery components in the human study (Srsf3, Rbm22, Ptbp1 and Rbm3) and the PCA in the same set of murine samples. (F) Analysis and western blot images showing the validation of SRSF3, RBM22, PTBP1 and RBM3 protein overexpression in PDGFA/B-induced mouse tumours. Data represent median (interquartile range) or means ± SEM. *P, 0.05; **P, 0.01; ***P, 0.001, significantly different from control conditions.