A new topology-guided computational framework extracts sharper and more biologically coherent cancer-associated gene signatures from single-cell sequencing data than conventional gene-selection methods.
What was known
Cancer transcriptomics faces a fundamental challenge: conventional gene-selection methods capture statistical variance but fail to decode the intrinsic geometric architecture of high-dimensional single-cell RNA sequencing data. This leaves cancer-specific signals obscured by noise and biological heterogeneity.
What this work adds
The authors propose a topology-guided framework that uses persistent homology to extract structurally invariant gene signatures. The method integrates highly variable feature selection and dimensionality reduction with a gene correlation topology based on Vietoris-Rips filtration, followed by a two-stage stability-driven classification strategy. Topologically significant genes were validated through differential expression analysis, ROC/AUC evaluation, KEGG pathway enrichment, protein-protein interaction network analysis and literature evidence.
Main results
Against conventional selection based on highly variable features plus PCA, the topological framework delivered markedly superior discriminative power, higher literature-supported biological relevance and dramatically more focused cancer-specific pathway enrichment, converging on compact, functionally coherent gene sets. In breast cancer, the method revealed a dominant mitotic regulatory module centered on cell-cycle dysregulation; in colorectal cancer, by contrast, extracellular matrix remodeling and tumor microenvironment mechanisms predominated. The framework also identified new candidate biomarkers absent from standard pathway databases.
What it means
The study positions persistent homology as a promising tool for transcriptomic biomarker discovery and offers a methodological foundation for precision oncology. This is computational, exploratory work: the proposed candidate genes would require experimental validation before any clinical use.
Reference: Gogoi S, Bandyopadhyay S, Bera S, Roy S, Chakraborty A. A topology-based framework for robust cancer-associated gene signature identification from scRNA-seq data. Computational Biology and Chemistry, 2026. DOI.
