Machine learning on multiple epigenetic features reveals H3K27Ac as a driver of gene expression prediction across patients with glioblastoma.
Epigenetic mechanisms play a crucial role in driving transcript expression and shaping the phenotypic plasticity of glioblastoma stem cells (GSCs), contributing to tumor heterogeneity and therapeutic resistance. These mechanisms dynamically regulate the expression of key oncogenic and stemness-assoc...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2025-08-01
|
| Series: | PLoS Computational Biology |
| Online Access: | https://doi.org/10.1371/journal.pcbi.1012272 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849228263032881152 |
|---|---|
| author | Yusuke Suita Hardy Bright Yuan Pu Merih Deniz Toruner Jordan Idehen Nikos Tapinos Ritambhara Singh |
| author_facet | Yusuke Suita Hardy Bright Yuan Pu Merih Deniz Toruner Jordan Idehen Nikos Tapinos Ritambhara Singh |
| author_sort | Yusuke Suita |
| collection | DOAJ |
| description | Epigenetic mechanisms play a crucial role in driving transcript expression and shaping the phenotypic plasticity of glioblastoma stem cells (GSCs), contributing to tumor heterogeneity and therapeutic resistance. These mechanisms dynamically regulate the expression of key oncogenic and stemness-associated genes, enabling GSCs to adapt to environmental cues and evade targeted therapies. Importantly, epigenetic reprogramming allows GSCs to transition between cellular states, including therapy-resistant mesenchymal-like phenotypes, underscoring the need for epigenetic-targeting strategies to disrupt these adaptive processes. Understanding these epigenetic drivers of gene expression provides a foundation for novel therapeutic interventions aimed at eradicating GSCs and improving glioblastoma outcomes. Using machine learning (ML), we employ cross-patient prediction of transcript expression in GSCs by combining epigenetic features from various sources, including ATAC-seq, CTCF ChIP-seq, RNAPII ChIP-seq, H3K27Ac ChIP-seq, and RNA-seq. We investigate different ML and deep learning (DL) models for this task and ultimately build our final pipeline using XGBoost. The model trained on one patient generalizes to other 11 patients with high performance. Notably, H3K27Ac alone from a single patient is sufficient to predict gene expression in all 11 patients. Furthermore, the distribution of H3K27Ac peaks across the genomes of all patients is remarkably similar. These findings suggest that GSCs share a common distributional pattern of enhancer activity characterized by H3K27Ac, which can be utilized to predict gene expression in GSCs across patients. In summary, while GSCs are known for their transcriptomic and phenotypic heterogeneity, we propose that they share a common epigenetic pattern of enhancer activation that defines their underlying transcriptomic expression pattern. This pattern can predict gene expression across patient samples, providing valuable insights into the biology of GSCs. |
| format | Article |
| id | doaj-art-ea4aa1bcc07545649664e650120f4dc2 |
| institution | Kabale University |
| issn | 1553-734X 1553-7358 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj-art-ea4aa1bcc07545649664e650120f4dc22025-08-23T05:31:15ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582025-08-01218e101227210.1371/journal.pcbi.1012272Machine learning on multiple epigenetic features reveals H3K27Ac as a driver of gene expression prediction across patients with glioblastoma.Yusuke SuitaHardy BrightYuan PuMerih Deniz TorunerJordan IdehenNikos TapinosRitambhara SinghEpigenetic mechanisms play a crucial role in driving transcript expression and shaping the phenotypic plasticity of glioblastoma stem cells (GSCs), contributing to tumor heterogeneity and therapeutic resistance. These mechanisms dynamically regulate the expression of key oncogenic and stemness-associated genes, enabling GSCs to adapt to environmental cues and evade targeted therapies. Importantly, epigenetic reprogramming allows GSCs to transition between cellular states, including therapy-resistant mesenchymal-like phenotypes, underscoring the need for epigenetic-targeting strategies to disrupt these adaptive processes. Understanding these epigenetic drivers of gene expression provides a foundation for novel therapeutic interventions aimed at eradicating GSCs and improving glioblastoma outcomes. Using machine learning (ML), we employ cross-patient prediction of transcript expression in GSCs by combining epigenetic features from various sources, including ATAC-seq, CTCF ChIP-seq, RNAPII ChIP-seq, H3K27Ac ChIP-seq, and RNA-seq. We investigate different ML and deep learning (DL) models for this task and ultimately build our final pipeline using XGBoost. The model trained on one patient generalizes to other 11 patients with high performance. Notably, H3K27Ac alone from a single patient is sufficient to predict gene expression in all 11 patients. Furthermore, the distribution of H3K27Ac peaks across the genomes of all patients is remarkably similar. These findings suggest that GSCs share a common distributional pattern of enhancer activity characterized by H3K27Ac, which can be utilized to predict gene expression in GSCs across patients. In summary, while GSCs are known for their transcriptomic and phenotypic heterogeneity, we propose that they share a common epigenetic pattern of enhancer activation that defines their underlying transcriptomic expression pattern. This pattern can predict gene expression across patient samples, providing valuable insights into the biology of GSCs.https://doi.org/10.1371/journal.pcbi.1012272 |
| spellingShingle | Yusuke Suita Hardy Bright Yuan Pu Merih Deniz Toruner Jordan Idehen Nikos Tapinos Ritambhara Singh Machine learning on multiple epigenetic features reveals H3K27Ac as a driver of gene expression prediction across patients with glioblastoma. PLoS Computational Biology |
| title | Machine learning on multiple epigenetic features reveals H3K27Ac as a driver of gene expression prediction across patients with glioblastoma. |
| title_full | Machine learning on multiple epigenetic features reveals H3K27Ac as a driver of gene expression prediction across patients with glioblastoma. |
| title_fullStr | Machine learning on multiple epigenetic features reveals H3K27Ac as a driver of gene expression prediction across patients with glioblastoma. |
| title_full_unstemmed | Machine learning on multiple epigenetic features reveals H3K27Ac as a driver of gene expression prediction across patients with glioblastoma. |
| title_short | Machine learning on multiple epigenetic features reveals H3K27Ac as a driver of gene expression prediction across patients with glioblastoma. |
| title_sort | machine learning on multiple epigenetic features reveals h3k27ac as a driver of gene expression prediction across patients with glioblastoma |
| url | https://doi.org/10.1371/journal.pcbi.1012272 |
| work_keys_str_mv | AT yusukesuita machinelearningonmultipleepigeneticfeaturesrevealsh3k27acasadriverofgeneexpressionpredictionacrosspatientswithglioblastoma AT hardybright machinelearningonmultipleepigeneticfeaturesrevealsh3k27acasadriverofgeneexpressionpredictionacrosspatientswithglioblastoma AT yuanpu machinelearningonmultipleepigeneticfeaturesrevealsh3k27acasadriverofgeneexpressionpredictionacrosspatientswithglioblastoma AT merihdeniztoruner machinelearningonmultipleepigeneticfeaturesrevealsh3k27acasadriverofgeneexpressionpredictionacrosspatientswithglioblastoma AT jordanidehen machinelearningonmultipleepigeneticfeaturesrevealsh3k27acasadriverofgeneexpressionpredictionacrosspatientswithglioblastoma AT nikostapinos machinelearningonmultipleepigeneticfeaturesrevealsh3k27acasadriverofgeneexpressionpredictionacrosspatientswithglioblastoma AT ritambharasingh machinelearningonmultipleepigeneticfeaturesrevealsh3k27acasadriverofgeneexpressionpredictionacrosspatientswithglioblastoma |