Dynamic Quantum Clustering of Gliomas RNA-seq Identifies Diagnostic Separation and Survival Gradients

Public RNAseq sample sets can refine pertumor diagnosis and risk, but heterogeneous biology and analytic drift often obscure structure. Dynamic Quantum Clustering (DQC), an unsupervised geometry preserving method requiring no clinical labels or preset cluster counts, addresses both challenges. Applied to RNAseq from 692 TCGA gliomas (524 low-grade gliomas (LGG), 168 glioblastomas (GBM); 20,057 protein coding genes), DQC produced two dominant clusters with 90.9% posthoc diagnostic concordance and clear survival time separation. Filtering genes by intercluster mean differences yielded a 554 gene subset that improved accuracy to 97.3%. Rank ordering these genes identified [~]90 genes that, under DQC, produced three LGG pure subclusters with ordered, but different survival outcomes and one GBM rich cluster (PPV 97.1%)--the RNA-based clustering without clinical information thereby inherently reveals molecular groupings which mirror critically important clinical features. Comparing these clusters defined four non overlapping gene modules and assigned four "BioCoords" per tumor. DQC with Biocoords recapitulated the LGG-to-GBM continuum with a mesenchymal/invasion-extracellular matrix axis exhibiting a monotonic survival gradient, illustrating how geometry-aware unsupervised learning can translate bench and computational discovery into meaningful biology-based patient stratification and prognosis. HighlightsO_LISignificant clusters discovered among glioma tumors using 554 RNAs. Overlaying histology labels on these clusters showed 97% discrimination accuracy between low-grade gliomas and glioblastomas. C_LIO_LIUsing 90 RNAs, three separate low-grade glioma clusters are identified with markedly different progression-free survival times. C_LIO_LIThe mesenchymal/invasion-extracellular matrix axis plays a substantial role in the clustering and survival gradients align with expression profiles along this biological axis. C_LI