Public articles linked to the same research event.
Experimental hematology & oncology This study integrated six public single-cell RNA sequencing datasets, used inferCNV to infer copy number variations at the single-cell level and identify a high-CNV (HCNV) malignant subpopulation, built a multimodal deep-learning AI prognostic model on routine H&E-stained sections with HCNV activity as the biological anchor for pathology feature selection, and through multiomics screening plus in vitro and in vivo experiments identified RTN3 as the core driver gene, showing that RTN3 activates JAK2/STAT3 to transcriptionally upregulate glycolytic enzymes PKM2, GLUT1 and LDHA, driving glycolytic metabolic reprogramming and conferring gemcitabine resistance in bladder cancer.
This study integrated six public single-cell RNA sequencing datasets, used inferCNV to infer copy number variations at the single-cell level and identify a high-CNV (HCNV) malignant subpopulation, built a multimodal deep-learning AI prognostic model on routine H&E-stained sections with HCNV activity as the biological anchor for pathology feature selection, and through multiomics screening plus in vitro and in vivo experiments identified RTN3 as the core driver gene, showing that RTN3 activates JAK2/STAT3 to transcriptionally upregulate glycolytic enzymes PKM2, GLUT1 and LDHA, driving glycolytic metabolic reprogramming and conferring gemcitabine resistance in bladder cancer.
This study integrated six public single-cell RNA sequencing datasets, used inferCNV to infer copy number variations at the single-cell level and identify a high-CNV (HCNV) malignant subpopulation, built a multimodal deep-learning AI prognostic model on routine H&E-stained sections with HCNV activity as the biological anchor for pathology feature selection, and through multiomics screening plus in vitro and in vivo experiments identified RTN3 as the core driver gene, showing that RTN3 activates JAK2/STAT3 to transcriptionally upregulate glycolytic enzymes PKM2, GLUT1 and LDHA, driving glycolytic metabolic reprogramming and conferring gemcitabine resistance in bladder cancer.
This study integrated six public single-cell RNA sequencing datasets, used inferCNV to infer copy number variations at the single-cell level and identify a high-CNV (HCNV) malignant subpopulation, built a multimodal deep-learning AI prognostic model on routine H&E-stained sections with HCNV activity as the biological anchor for pathology feature selection, and through multiomics screening plus in vitro and in vivo experiments identified RTN3 as the core driver gene, showing that RTN3 activates JAK2/STAT3 to transcriptionally upregulate glycolytic enzymes PKM2, GLUT1 and LDHA, driving glycolytic metabolic reprogramming and conferring gemcitabine resistance in bladder cancer.