myc pvt1 copy number neutral cell lines  (ATCC)

Journal: Frontiers in Medicine

Article Title: Validation of ITPR2 , DPF3 , EPAS1 , and PVT1 -associated SNPs as biomarkers for RCC in an independent case-control cohort

doi: 10.3389/fmed.2026.1734511

Figure Lengend Snippet: Five-year overall survival analysis according to SNPs genotypes and metastatic status in RCC patients. (A) Forest plot from the Cox proportional hazards regression model (dominant model). (B) Adjusted survival curves derived from the Cox proportional hazards model comparing five-years overall survival between ITPR2 rs1049380 and PVT1 rs35252396 genotypes. (C) Kaplan–Meier survival curves based on observed data for ITPR2 rs1049380 and PVT1 rs35252396 stratified by metastasis status, showing survival probability, cumulative events, and cumulative hazard curves.

Article Snippet: Quantitative polymerase chain reaction (qPCR) was performed with TaqMan TM gene expression assays (Thermo Fisher Scientific, Waltham, MA, United States) for: ITPR2 (Assay ID: Hs00181916_m1), ZEB2 (Assay ID: Hs00207691_m1), MYC (Assay ID: Hs00153408_m1) and PVT1 (Assay ID: Hs00413039_m1). qPCR reactions were performed as follows: 95 °C during 10 min for enzyme activation; followed by 45 cycles of 15 s at 95 °C and 1 min at 60 °C for denaturing and annealing/extension.

Techniques: Derivative Assay

Journal: Frontiers in Medicine

Article Title: Validation of ITPR2 , DPF3 , EPAS1 , and PVT1 -associated SNPs as biomarkers for RCC in an independent case-control cohort

doi: 10.3389/fmed.2026.1734511

Figure Lengend Snippet: Expression levels of ITPR2 , PVT1 , and MYC genes by genetic model in tumor and adjacent healthy FFPE tissues.

Article Snippet: Quantitative polymerase chain reaction (qPCR) was performed with TaqMan TM gene expression assays (Thermo Fisher Scientific, Waltham, MA, United States) for: ITPR2 (Assay ID: Hs00181916_m1), ZEB2 (Assay ID: Hs00207691_m1), MYC (Assay ID: Hs00153408_m1) and PVT1 (Assay ID: Hs00413039_m1). qPCR reactions were performed as follows: 95 °C during 10 min for enzyme activation; followed by 45 cycles of 15 s at 95 °C and 1 min at 60 °C for denaturing and annealing/extension.

Techniques: Expressing

Journal: Cell reports

Article Title: Long noncoding RNA-dependent control of Myc transcriptional bursting

doi: 10.1016/j.celrep.2025.116439

Figure Lengend Snippet: (A) smRNA-FISH visualization of nascent Pvt1 ( Pvt1i , green) and Myc ( Myci , red) ( left ) and mature ( Myce , green) and nascent ( Myci , red) Myc ( right ) using indicated probes in wild-type (WT) MEFs. DNA stained with DAPI, blue. Scale bar is 5 μM. Enlarged images highlight signal pattern and co-localization at sites of transcription. (B) Quantification of the fraction of cells ( n ≥ 50) with indicated number of Pvt1i and Myci foci per cell from experiment in (A). Foci were counted independent of size in two biological replicates. (C) Schematic of the Pvt1/Myc locus, highlighting the insertion of the polyadenylation signal (PAS) in exon 1a of Pvt1 . (D) qRT-PCR detection of Pvt1a , Pvt1b , and total Pvt1 levels in RNA isolated from littermate WT (+/+) and homozygous PAS mutant ( P/P ) MEFs. (E and F) smRNA-FISH visualization of nascent Pvt1 ( Pvt1i , green) and Myc ( Myci , red) (E) and mature ( Myce , green) and nascent ( Myci , red) Myc (F) using indicated probes in +/+ and P/P MEFs. DNA stained with DAPI, blue. Scale bar is 5 μM. Enlarged images highlight signal pattern and co-localization at sites of transcription. (G) Violin plot showing the distribution of the number of Myci foci per cell in +/+ and P/P MEFs in three biological replicates (Rep). n indicates the number of cells scored in each experiment. Red lines indicate median. (H) Average Myci foci per cell from experiments in (G). (I) Distribution of Myci foci per cell from experiments in (G), showing fraction of cells with indicated number of Myci foci. (J) RT-qPCR detection of nascent and spliced Myc in RNA isolated from +/+ and P/P MEFs. (K) Representative immunoblot and quantification of Myc protein levels in whole-cell extracts (WCEs) from +/+ and P/P MEFs. Hsp90, loading control. Ladder markers (kilodalton [kDa]) indicated. (L) Cumulative frequency distribution plot showing the upregulation of genes from GSEA Hallmark geneset MYC_TARGETS_V1 relative to an expression-matched control set in P/P relative to +/+ MEFs in RNA-seq analysis of 4 biological replicates. KS test used to determine p value. In this figure, bar graphs show individual values and mean ± SEM of indicated number of biological replicates. Paired t test (D and H–K) and unpaired t test (G), * p < 0.05, ** p < 0.01, and *** p < 0.01; ns, not significant. See also , , , and .

Article Snippet: Pvt1 PAS (P) mice were generated using CRISPR/Cas9-mediated engineering in C57BL/6J blastocysts at Jackson Laboratory.

Techniques: Staining, Quantitative RT-PCR, Isolation, Mutagenesis, Western Blot, Control, Expressing, RNA Sequencing

Journal: Cell reports

Article Title: Long noncoding RNA-dependent control of Myc transcriptional bursting

doi: 10.1016/j.celrep.2025.116439

Figure Lengend Snippet: (A) Schematic of the two alternative transcription start sites of Pvt1 , highlighting the p53 response element (p53RE, pink star) and visualizing Pvt1 isoform transcriptional states (black [ Pvt1a ] or orange [ Pvt1b ] solid [high expression] or dashed [low/no expression] lines) in wild-type ( Pvt1 + / + ; p53 + / + ), PAS mutant only ( Pvt1 P/P ; p53 + / + ), and combined Pvt1 mutant and p53 knockout ( Pvt1 P/P ; p53 Δ/Δ ) MEFs. Percentages indicate the observed transcriptional output of total Pvt1 in each cell line normalized to wild-type cells. (B) qRT-PCR detection of Pvt1a , Pvt1b , and total Pvt1 levels in RNA isolated from littermate Pvt1 P/P ; p53 + / + and Pvt1 P/P ; p53 Δ/Δ MEFs. (C) Representative examples of smRNA-FISH visualization of nascent Pvt1 ( Pvt1i , green) and Myc ( Myci , red) ( left ) and mature ( Myce , green) and nascent ( Myci , red) Myc ( right) using indicated probes in Pvt1 P/P ; p53 Δ/Δ MEFs. DNA stained with DAPI, blue. Scale bar is 5 μM. Enlarged images highlight signal pattern. (D) Violin plot showing the distribution of the number of Myci foci per cell in littermate Pvt1 + / + ; p53 + / + and three independent Pvt1 P/P ; p53 Δ/Δ MEFs lines. n indicates the number of cells scored in each experiment. Red lines indicate median. (E) Average Myci foci per cell from experiments in (D). Paired t test compares the three independent Pvt1 P/P ; p53 Δ/Δ MEF lines to the littermate Pvt1 + / + ; p53 + / + control. (F) Distribution of Myci foci per cell from experiments in (D), showing fraction of cells with indicated number of Myci foci. Statistical analysis performed as in (E). (G) qRT-PCR detection of nascent and spliced Myc in RNA isolated from indicated MEFs. In this figure, bar graphs show individual values and mean ± SEM of indicated number of biological replicates. Paired t test (B and G) and unpaired t test (D), * p < 0.05, ** p < 0.01, and *** p < 0.01; ns, not significant.

Article Snippet: Pvt1 PAS (P) mice were generated using CRISPR/Cas9-mediated engineering in C57BL/6J blastocysts at Jackson Laboratory.

Techniques: Expressing, Mutagenesis, Knock-Out, Quantitative RT-PCR, Isolation, Staining, Control

Journal: Cell reports

Article Title: Long noncoding RNA-dependent control of Myc transcriptional bursting

doi: 10.1016/j.celrep.2025.116439

Figure Lengend Snippet: (A) Growth curve analyses showing cumulative cell number over passaging of three independent +/+ and P/P MEF littermate pairs. (B) Fraction of BrdU-positive cells in (A). (C) Representative brightfield images of cells from (A) visualized at passage 14. +/+ MEFs show features of cellular senescence, while P/P MEFs maintain high mitotic index (arrowheads). (D) Representative examples and quantification of colony formation assay in +/+ and P/P cells expressing empty vector (EV) or E1A oncogene. (E) Representative H&E images of lungs from 5-month-old Kras-LA1; Pvt1 + / + ( n = 18) and Kras-LA1; Pvt1 P/P ( n = 18) mice. Enlarged images highlight grade 1 and mixed grade 1–2 tumors in Kras-LA1; Pvt1 + / + and Kras-LA1; Pvt1 P/P lungs, respectively. (F) Quantification of tumor burden in mice from (E). (G) Quantification of the fraction of tumors assigned to AAH-1, 2–3, or 4+ grade categories in mice from (E). Numbers above bars indicate the number of tumors scored in each condition. (H) Quantification of the number of pHH3-positive cells per tumor area in mice from (E). (I) Schematic of experiment and representative H&E images of lungs from KP; Pvt1 + / + ( n = 8) and KP; Pvt1 P/P ( n = 6) mice at 12 weeks post-tumor initiation (pti) by intratracheal infection with adenoviral Cre (AdCre). (J) Quantification of tumor burden in mice from (E). (K) Quantification of the fraction of tumors assigned to AAH-1, 2–3, or 4+ grade categories in mice from (I). Numbers above bars indicate the number of tumors scored in each condition. In this figure, bar graphs show individual values and mean ± SEM of indicated number of biological replicates. Unpaired t test, * p < 0.05, ** p < 0.01, and *** p < 0.01.

Article Snippet: Pvt1 PAS (P) mice were generated using CRISPR/Cas9-mediated engineering in C57BL/6J blastocysts at Jackson Laboratory.

Techniques: Passaging, Colony Assay, Expressing, Plasmid Preparation, Infection

Journal: Cell reports

Article Title: Long noncoding RNA-dependent control of Myc transcriptional bursting

doi: 10.1016/j.celrep.2025.116439

Figure Lengend Snippet: (A) ATAC-seq read coverage of left, Myc , and right Pvt1 gene bodies, showing comparable patterns in +/+ and P/P MEFs in 4 biological replicates, with the exception of one significantly reduced peak in the promoter of Pvt1a (FDR < 0.01), indicated by a purple star. (B) Volcano plot showing log2FC (fold change) of peaks in Pvt1 mutant (PAS) compared to wild-type (WT) MEFs from ATAC-seq experiment in (A). Dot colors represent peaks with significantly (FDR < 0.01) reduced (blue), increased (red), or unaltered (black) chromatin accessibility. Green arrow highlights the peak in the Pvt1a promoter. (C) Top : ATAC-seq and RNA-seq profiles of the Myc/Pvt1 locus in +/+ and P/P samples included for reference. Middle : HiC arc plot visualization of loops in the Myc/Pvt1 locus detected from merged reads of two biological replicates of +/+ and P/P MEFs. Differential enrichment of loops between +/+ and P/P MEFs is not statistically significant (black) and can be attributed to increased sequencing depth in P/P compared to +/+ MEFs . Bottom : arc plot visualization of significantly (FDR < 0.001) depleted (blue) or enriched (red) intrachromosomal contacts (ICs) in the Myc/Pvt1 locus in P/P compared to +/+ MEFs. (D) Volcano plot showing log2FC (fold change) of chromosome 15 ICs in Pvt1 mutant (PAS) compared to WT MEFs from HiC experiment in (C). Dot colors represent ICs that are significantly (FDR < 0.001) depleted (blue), enriched (red), or unaltered (black). Green halos highlight differential contacts in the Myc/Pvt1 locus, illustrated in (C). See also and , , and .

Article Snippet: Pvt1 PAS (P) mice were generated using CRISPR/Cas9-mediated engineering in C57BL/6J blastocysts at Jackson Laboratory.

Techniques: Mutagenesis, RNA Sequencing, Sequencing

Journal: Cell reports

Article Title: Long noncoding RNA-dependent control of Myc transcriptional bursting

doi: 10.1016/j.celrep.2025.116439

Figure Lengend Snippet: (A) Schematic of the Pvt1/Myc locus and Pvt1 promoter eletion strategy. (B) qRT-PCR detection of Pvt1a , Pvt1b , and total Pvt1 levels in RNA isolated from p53-deficient control gRNA (Con) and Pvt1 promoter deletion (ΔPro) MEF lines. (C) qRT-PCR detection of nascent and spliced Myc in RNA isolated from Con and ΔPro MEFs. (D) Representative immunoblot and quantification of Myc protein levels in whole-cell extracts (WCEs) from Con and ΔPro MEFs. Hsp90, loading control. Ladder markers (kilodalton [kDa]) indicated. (E) Growth curve analysis of Con and ΔPro MEFs. (F) smRNA-FISH visualization of nascent Pvt1 ( Pvt1i , green) and Myc ( Myci , red) using indicated probes in Con and ΔPro MEFs. DNA stained with DAPI, blue. Scale bar is 5 μM. Enlarged images highlight signal pattern and co-localization at sites of transcription. Note p53-deficient cells are aneuploid (3N). (G) Violin plot showing the distribution of the number of Myci foci per cell in Con and ΔPro MEFs in three biological replicates (Rep). n indicates the number of cells scored in each experiment. Red lines indicate median. (H) Average Myci foci per cell from experiments in (G). (I) Distribution of Myci foci per cell from experiments in (G). (J) Left : representative loci illustrating inactive, active, and bursting Myc transcriptional states in smRNA-FISH co-localization of indicated probes; Pvt1 is constitutively transcribed. Bursting Myc loci are defined as nuclear regions containing ≥4 Myci independent foci within a 20 μm 2 area. Right : quantification of the fraction of cells with bursting Myc loci in Con and ΔPro MEFs. (K) Combined IF-smRNA-FISH visualization of nascent Pvt1 ( Pvt1i , green) and Myc ( Myci , red) ( left ) and Brd4 (green) and Myc ( Myci , red) ( right ) in Con and ΔPro MEFs. Scale bar is 5 μM. Enlarged images illustrate signal and co-localization patterns. (L) Quantification of the fraction of Myci foci that were found to co-localize (Brd4+) or not co-localize (Brd4−) with Brd4 in experiment in (K) in 4 biological replicates. In this figure, bar graphs show individual values and mean ± SEM of indicated number of biological replicates. Paired t test (B–D, H–J, and L) and unpaired t test (G), * p < 0.05, ** p < 0.01, and *** p < 0.01; ns, not significant. See also .

Article Snippet: Pvt1 PAS (P) mice were generated using CRISPR/Cas9-mediated engineering in C57BL/6J blastocysts at Jackson Laboratory.

Techniques: Quantitative RT-PCR, Isolation, Control, Western Blot, Staining

Journal: Cell reports

Article Title: Long noncoding RNA-dependent control of Myc transcriptional bursting

doi: 10.1016/j.celrep.2025.116439

Figure Lengend Snippet: (A) Representative smRNA-FISH visualization of nascent Pvt1 ( Pvt1i , green) and Myc ( Myci , red) using indicated probes in triploid p53-deficient MEFs with heterozygous Pvt1 promoter deletion (ΔPro/ΔPro/+, Het). DNA stained with DAPI, blue. Scale bar is 5 μM. Enlarged images highlight signal pattern at Pvt1 -proficient wild-type (WT) and Pvt1 -deficient mutant (ΔPro) loci. (B) Quantification of the fraction of WT and ΔPro alleles in Het MEFs with bursting Myc loci, as defined in . (C) Violin plot showing the distribution of the mean intensity (MI) of Myci foci at WT and ΔPro alleles in Het MEFs in two biological replicates (Rep). MI is represented as arbitrary units (a.u.). n indicates the number of alleles scored in each experiment. Red lines indicate median. Unpaired t test (G), * p < 0.05 and *** p < 0.01. (D) Average Myci MI per allele from experiments in (C). (E) Distribution of Myci MI per allele from experiments in (C). In this figure, bar graphs show individual values and mean ± SEM of indicated number of biological replicates. Color of individual data points indicates paired samples.

Article Snippet: Pvt1 PAS (P) mice were generated using CRISPR/Cas9-mediated engineering in C57BL/6J blastocysts at Jackson Laboratory.

Techniques: Staining, Mutagenesis

Journal: Cell reports

Article Title: Long noncoding RNA-dependent control of Myc transcriptional bursting

doi: 10.1016/j.celrep.2025.116439

Figure Lengend Snippet: Top : constitutively transcribed Pvt1 (green) maintains elevated levels of local RNA abundance in the vicinity of Myc promoter. This limits the number of Myc molecules (red) that are transcribed during each burst prior to reaching the local RNA concentration threshold that triggers dissociation from transcriptional condensates. Middle : reduced baseline Pvt1 abundance in loss-of-function studies permits the production of a greater number of Myc molecules in a longer burst before the same threshold is reached. Bottom : increased baseline Pvt1 abundance during the cellular response to stress limits the production of Myc molecules in a shorter burst before the same threshold is reached.

Article Snippet: Pvt1 PAS (P) mice were generated using CRISPR/Cas9-mediated engineering in C57BL/6J blastocysts at Jackson Laboratory.

Techniques: Concentration Assay

Journal: bioRxiv

Article Title: PVT1 splicing activity predicts genome-wide gene expression with miRNA regulatory signatures

doi: 10.1101/2025.07.25.666741

Figure Lengend Snippet: Schematic representation of the workflow used in the study. (A) The initial cohort was built on hundreds of de-identified breast cancer (BRCA) samples for which RNA-seq data was downloaded from The Cancer Genome Atlas (TCGA) and used to extract i) splicing efficiency values at the PVT1 locus and ii) genome-wide gene expression using exon level read counts. Splicing efficiency was calculated as the fraction of split reads to the sum of split plus non-split reads at the 3’ splice sites of PVT1 (top schematic). (B) A total of 34 unique PVT1 3’ splice sites were used in classification analysis of TCGA-BRCA tumor samples. Samples were subtyped with the PAM50 signature (LumA, LumB, Her2, Basal) and Random Forest with 10 x cross-validation was applied using the extracted splicing efficiencies as features in binary classifications (specific subtype versus rest samples). (C) To assess whether splicing activity at the PVT1 locus can be predictive of gene expression, the same set of 34 3’ splice site splicing efficiencies were employed as predictor variables in linear regression models with 10 x cross-validation (linear regression, lm and regularized linear regression with elastic net, glmnet). Genome-wide screening using elastic net models (30,887 genes) revealed sets of genes predicted at high-confidence (based on specific significance cutoffs) across all-tumor samples and within PAM50 subtyped cohorts. (D) In order to uncover regulatory relationships between splicing activity at PVT1 3’ splice sites and their predicted target genes, k-means clustering of high-confidence predicted genes was performed using the regression coefficients from the splicing-based elastic net models. Clustering at both all-tumor and PAM50 subtype levels identified distinct sets of splice sites predicting different sets of genes. (E) Cross-tissue generalization of BRCA-trained PVT1 splicing-based gene expression models was tested on unseen data from TCGA Prostate (PRAD), Ovary (OV), Uterus (UCEC), Testis (TGCT) and Adrenal gland (ACC). (F) To uncover causal relationships between splicing activity of PVT1 and regulation of gene expression in trans , we leveraged BRCA whole genome (WGS) and exon (WXS) SNV data. Tumor-specific somatic SNVs near PVT1 splice sites were assigned to their closest splice site. Upon identification of samples with SNV-associated perturbation in splice-site splicing efficiency, causal inference analysis was performed to identify gene expression changes best explained by the causal model (SNV → Splicing → Expression). Posterior probabilities were calculated for eight alternative Bayesian network models.

Article Snippet: Then, nested PCR reactions were performed with “PVT1 Com Fw” and “PVT1 Mid Exon Rv (R4)” or “PVT1 Last Exon Rv (R10)” (NEB, M0273) and got analysed by agarose gel electrophoresis (1%).

Techniques: RNA Sequencing, Genome Wide, Gene Expression, Biomarker Discovery, Activity Assay, Expressing

Journal: bioRxiv

Article Title: PVT1 splicing activity predicts genome-wide gene expression with miRNA regulatory signatures

doi: 10.1101/2025.07.25.666741

Figure Lengend Snippet: UCSC screenshot of the PVT1 locus (hg38). Tracks include coordinate positions of the 3’ splice sites analyzed, their splicing efficiency extracted from TCGA BRCA RNA-seq. miR-200 seed sites, perfect matches to the 7-mer CAGTRUU, are found exclusively within PVT1 intronic intervals. PVT1 expression is shown with plus-strand-specific bigwig (reads per million of uniquely mapped read at nucleotide position) in MCF-7 whole-cell total RNA-seq (from ), in nascent RNA from the nuclear chromatin-released RNA fraction upon 20 min 4-SU pulse-chase , in nascent chromatin-associated RNA (0 min pulse-chase from ), and in steady-state nuclear chromatin-associated polyA+ enriched RNA (this study, GEO-submitted). Lower track shows alternative PVT1 transcript isoforms identified and quantified with Salmon using TCGA BRCA RNA-seq; only the top 19 transcripts out of 190 identified are shown, the most variable in expression across all BRCA tumor samples analyzed (see also Suppl. Figure S7B-C).

Article Snippet: Then, nested PCR reactions were performed with “PVT1 Com Fw” and “PVT1 Mid Exon Rv (R4)” or “PVT1 Last Exon Rv (R10)” (NEB, M0273) and got analysed by agarose gel electrophoresis (1%).

Techniques: RNA Sequencing, Expressing, Pulse Chase

Journal: bioRxiv

Article Title: PVT1 splicing activity predicts genome-wide gene expression with miRNA regulatory signatures

doi: 10.1101/2025.07.25.666741

Figure Lengend Snippet: PVT1 splicing activity predicts gene expression in elastic net models. ( A ) Left panel, example of elastic net fit plot of observed values of gene expression (for SEC61B, ENSG00000106803.10) to elastic net-predicted values. Elastic net models for all 30,887 genes were trained in 10 x cross-validation using the splicing efficiency values at 34 PVT1 3’ splice sites as variables. Right panel, boxplot distributions of the glmnet coefficients across 10 folds (from 10 x cross-validation) for the 34 PVT1 3’ splice sites for this specific gene expression model. Positive glmnet coefficients at certain PVT1 3’ splice sites means that splicing efficiency at these sites contributes positively to target gene expression. Negative glmnet coefficients indicate negative contribution of PVT1 splicing efficiency to target gene expression. The bigger the absolute value of a glmnet coefficient, the greater the impact (either positive or negative) of PVT1 3’ splice site splicing efficiency on target gene expression. ( B ) Empirical cumulative distribution function (ECDF) of elastic net (glmnet) performance metrics, model trained using the 34 PVT1 3’ splice site splicing efficiencies, for all the 30,887 assayed genes. Left panel: ECDF of root-mean-square error (RMSE) values, blue line: mean (average) RMSE from 10 x cross-validation, and red line: minimum RMSE (designating best fold from 10 x cross-validation) per gene expression model. Right panel: ECDF of the coefficient of determination (R 2 ) values, blue line: mean R 2 across the 10 folds, and red line: R 2 from the best fold (defined as the fold with the minimum RMSE). Genes that satisfied i) best-fold R 2 ≥ 0.2, ii) a negative correlation between R 2 and RMSE, and iii) 10-fold mean RMSE < Q₃ + 1.5 × IQR were considered as significant, yielding a total of 365 genes predicted as significant when the elastic net models were trained across all tumor samples. ( C ) miR-200 target genes are significantly enriched among the 365 elastic net-predicted genes (Fisher’s exact test p-value 1.51e-14, odds ratio 3.12). Right panels, GO terms enrichment analysis using clusterProfiler , terms related to translation are enriched among the 365 elastic net-predicted genes. ( D ) K-means clustering of the glmnet coefficients from the PVT1-splicing based models. The 365 elastic-net predicted genes were clustered based on the elastic net glmnet coefficients into 6 clusters. miR-200 target genes are significantly enriched in clusters 1 and 3 (Fisher’s exact test p-value 8.14e-07, odds ratio 2.28), and translation-related terms are enriched in clusters 4 and 6 (defined by negative contribution of splice sites ss10, ss13, ss14, ss15, and positive contribution of splice sites ss2, ss4, ss6, ss11, ss12, ss29). ( E ) K-means clustering of 471 genes predicted in Basal subtype, using the PVT1 splicing-based elastic net model (glmnet) coefficients of the 34 splice sites as clustering parameters. These 471 genes were predicted as significant (passing the pre-defined cutoffs) by the PVT1 splicing-based elastic net models trained across 155 Basal subtype samples. Translation-related terms were enriched in clusters 1, 3 and 5, defined by positive contribution of splice sites ss4, ss6, ss7, ss33. miR-200 target genes were significantly enriched in cluster 4 (Fisher’s exact test p-value 2.6e-05, odds ratio 3.63) and 6 (Fisher’s exact test p-value 0.0001556, odds ratio 2.57). Clusters 4 and 6 share positive contributions of splice sites ss13, ss14, ss15. No other specific terms were enriched in clusters 2,4,6.

Article Snippet: Then, nested PCR reactions were performed with “PVT1 Com Fw” and “PVT1 Mid Exon Rv (R4)” or “PVT1 Last Exon Rv (R10)” (NEB, M0273) and got analysed by agarose gel electrophoresis (1%).

Techniques: Activity Assay, Gene Expression, Biomarker Discovery, Targeted Gene Expression

Journal: bioRxiv

Article Title: PVT1 splicing activity predicts genome-wide gene expression with miRNA regulatory signatures

doi: 10.1101/2025.07.25.666741

Figure Lengend Snippet: PVT1 splicing activity distinguishes BRCA subtypes (and reflects tumor aggressiveness). ( A ) Distribution of classification accuracy values from 10-fold cross-validation of Random Forest models trained on PVT1 3’ splice site splicing efficiencies to distinguish each subtype from the rest. Highest performance was obtained for Her2, followed by Basal and LumB, while LumA showed mean classification accuracy < 0.65. ( B ) Feature mean accuracy and mean importance plots of Random Forest subtype classification models. Top-ranked splice sites contributing to subtype classification (feature mean importance and mean accuracy scores are plotted; mean Gini scores are shown in Suppl. Figure S5B), uncovering subtype-specific splicing signatures. ( C ) Comparison of PVT1 3’ splice site splicing efficiency values across binary classifications. Top-ranked features from the respective Random Forest classification models are marked in red font.

Article Snippet: Then, nested PCR reactions were performed with “PVT1 Com Fw” and “PVT1 Mid Exon Rv (R4)” or “PVT1 Last Exon Rv (R10)” (NEB, M0273) and got analysed by agarose gel electrophoresis (1%).

Techniques: Activity Assay, Biomarker Discovery, Comparison

Journal: bioRxiv

Article Title: PVT1 splicing activity predicts genome-wide gene expression with miRNA regulatory signatures

doi: 10.1101/2025.07.25.666741

Figure Lengend Snippet: Causal inference analysis identifies specific PVT1 splice sites in affecting distal gene expression. ( A ) Schematic of Bayesian network models tested to infer causal relationships between SNVs, splicing efficiency at PVT1 3’ splice sites, and gene expression. For each of the 30,887 genes and per splice site we tested 8 models: Causal (SNV → splicing → gene expression), Reactive (SNV → gene expression → splicing), Independent (SNV → splicing, SNV → gene expression), Model 4 (SNV → splicing, gene expression varies independently), Model 5 (SNV → gene expression, splicing varies independently), Model 6 – Null (no relationship between the nodes), Model 7 (gene expression → splicing, SNV independent), Model 8 (splicing → gene expression, SNV independent). ( B ) Overview of SNV-to-splice site assignment: Tumor-specific SNVs identified from WXS/WGS data (n = 592 TCGA BRCA tumor samples) were assigned to the closest PVT1 3’ splice site (out of 34, with bedtools closest ), resulting in a binary SNV matrix per splice site, per sample. (Note that not all samples carried all depicted SNVs; 80 % of the samples carried less than 5 SNVs, either WGS or WXS, at the PVT1 locus). For each SNV–splice site pair, the presence or absence of an SNV was tested for association with changes in splicing efficiency (See also Suppl. Figure S10A). ( C ) Boxplots illustrating splice site-specific splicing efficiency (SE) changes (reduction) associated with nearby SNVs. Samples carrying SNVs at PVT1 3’ splice sites showing reduction in splicing efficiency (SE < median of all SE values) were assigned ‘1’. Splice sites carrying no SNVs or carrying SNVs but not showing reduction in splicing efficiency were assigned ‘0’. The respective results for SNV-splice sites associated with an increase in splicing efficiency are shown in Supplementary Figure S10A. ( D-E ) Causal inference analysis results for the splice sites ss13 and ss15. (D) From the 592 BRCA samples genotyped, 22 samples had an SNV assigned to ss13 associated with a reduction in splicing efficiency. Across all 30,887 SNV-splicing-gene expression triplets tested, variance in the expression of 3165 genes was best explained by the causal model at a posterior probability > 0.9. Gene ontology terms related to translation and RNA processing are significantly enriched among those genes best explained by the causal model. Similar for ss15 SNV-splicing-gene expression triplets (E), translation and RNA processing terms are significantly enriched among the genes best explained by the causal model, whereas no specific terms were enriched among 7159 genes best explained by the independent model (see also Suppl. Figure S10B). As shown in the cumulative plots for ss13 (D) and ss15 (E), for all assayed splice site (SNV – splicing – gene expression) triplets, most genes were best explained by the independent model (SNV → PVT1 splicing, SNV → gene expression), followed by the causal model, while a smaller fraction of genes were best explained by model 4 (SNV → splicing, gene expression varies independently). See also Supplementary Figure S10.

Article Snippet: Then, nested PCR reactions were performed with “PVT1 Com Fw” and “PVT1 Mid Exon Rv (R4)” or “PVT1 Last Exon Rv (R10)” (NEB, M0273) and got analysed by agarose gel electrophoresis (1%).

Techniques: Gene Expression, Expressing

Journal: bioRxiv

Article Title: PVT1 splicing activity predicts genome-wide gene expression with miRNA regulatory signatures

doi: 10.1101/2025.07.25.666741

Figure Lengend Snippet: Generalizability of PVT1 splicing-based gene expression models trained in BRCA on unseen data of different cancer types. ( A ) Comparison of PVT1 splicing-based gene expression model performance: RMSE boxplot distributions of the 365 gene models trained in BRCA and applied in different cancer types. RMSE boxplot distributions for 365 gene models trained in BRCA, both mean and minimum RMSE from 10 x cross-validation are shown in BRCA, alongside cross-tissue RMSE values from applying the BRCA-trained models to unseen tumor types (prostate, adrenal, ovary, uterus, testis). ( B-F ) Model generalizability across five TCGA tumor cohorts: Elastic net models trained in BRCA for the 365 selected genes were applied to unseen tumor datasets (using PVT1 3’ splice site splicing efficiencies as features and gene expression as response). The resulting per-gene prediction error (RMSE) ( y -axis) was compared to BRCA performance (the minimum RMSE from the best fold of 10 x cross-validation in BRCA, on the x -axis), using Pearson correlation, in prostate (B), ovary (C), uterus (D), testis (E) and adrenal gland (F). Red lines denote linear regression fits with 95% confidence intervals (gray shading), while blue identity lines (y = x) indicate equal performance between BRCA and the test tissue. See also Suppl. Figure S11. ( G-H ) Gene ontology (GO) enrichment analysis and cnetplots of predicted gene sets in (G) prostate (n = 589 with performance exceeding R 2 > 0.1) and (H) BRCA (n = 365 that passed filtering criteria). Shared enriched terms (Biological Process) include RNA processing and translation-related categories.

Article Snippet: Then, nested PCR reactions were performed with “PVT1 Com Fw” and “PVT1 Mid Exon Rv (R4)” or “PVT1 Last Exon Rv (R10)” (NEB, M0273) and got analysed by agarose gel electrophoresis (1%).

Techniques: Gene Expression, Comparison, Biomarker Discovery

Journal: bioRxiv

Article Title: PVT1 splicing activity predicts genome-wide gene expression with miRNA regulatory signatures

doi: 10.1101/2025.07.25.666741

Figure Lengend Snippet: Artificial splicing activation of PVT1modulates its chromatin-associated intronic RNA and alters expression of miR-200 target genes in vitro . ( A ). UCSC overview of the PVT1 locus showing positions of gRNA oligos designed to target four splice sites (ss11, ss17, ss25, s33) for splicing activation with dCasRx-RBM25 (CRISPR Artificial Splicing Factor, CASFx). Positions of exonic and intronic primers used in (B-C) are indicated. ( B ) PCR analysis on cDNA from nuclear chromatin-released dT-primed RNA in MCF-7 cells 48 h after CASFx transfection shows altered PVT1 splicing patterns. In PCR with primer pair common_exonF/R4 a major band of 586 bp (star marked) is shifted to two alternative splicing products 718 and 850 bp by activated 3’ splice site exon inclusion (marked with a red star in UCSC track in (A); see also Suppl. Figure S13F). A lower 420 bp band is not altered. PCR with common_exonF/R10 detects several bands in a range 800-1200 bp corresponding to several alternative transcript isoforms detected in control condition. Upon CASFx, all alternative splicing activity downstream of ‘common_exon’ is likely suppressed and PCR with common_exonF/R10 detects one major splicing isoform with a 725 bp PCR product. ( C ) Upon CASFx, intronic signal from introns ‘1’ and ‘4’ is eliminated on random-primed chromatin-associated RNA (but not on nuclear chromatin-released dT-primed RNA; Suppl. Figure S13B). (D) Semi-quantitative PCR on cDNA from nuclear chromatin-released dT-primed RNA with gene-specific primers detects alterations in GTF2I, VPS13B, SEC61B (see also Suppl. Fig. S13C and S14). ( E ) Model of chromatin-associated intronic ceRNA activity mediated by chromatin-associated PVT1 and expected effect of splicing enhancement. PVT1 intronic miR-200 seed sites sequester miRNAs in association with nuclear Ago. Artificial splicing enhancement of PVT1 with CASFx results in increased turnover of spliced-out intronic sequences and releases miR-200/Ago effective availability.

Article Snippet: Then, nested PCR reactions were performed with “PVT1 Com Fw” and “PVT1 Mid Exon Rv (R4)” or “PVT1 Last Exon Rv (R10)” (NEB, M0273) and got analysed by agarose gel electrophoresis (1%).

Techniques: Activation Assay, Expressing, In Vitro, CRISPR, Transfection, Alternative Splicing, Control, Activity Assay, Random Primed, Real-time Polymerase Chain Reaction

Journal: bioRxiv

Article Title: PVT1 splicing activity predicts genome-wide gene expression with miRNA regulatory signatures

doi: 10.1101/2025.07.25.666741

Figure Lengend Snippet:

Article Snippet: Then, nested PCR reactions were performed with “PVT1 Com Fw” and “PVT1 Mid Exon Rv (R4)” or “PVT1 Last Exon Rv (R10)” (NEB, M0273) and got analysed by agarose gel electrophoresis (1%).

Techniques: