Abstract
BACKGROUND: Hypoxia is a hallmark of the colorectal cancer (CRC) tumor microenvironment (TME) that drives malignant progression, chemoresistance, and immune evasion. However, the cellular heterogeneity underpinning hypoxic responses in CRC and its impact on prognosis remain incompletely understood. METHODS: We integrated single-cell RNA sequencing data from 15 CRC samples (GSE166555 and GSE221575) to delineate hypoxic and normoxic cell populations and identify hypoxia-related genes (HRGs). Weighted gene co-expression network analysis (WGCNA) and enrichment profiling elucidated key biological processes associated with hypoxia. Cell-cell communication networks were inferred using CellChat, and transcription factor regulatory modules were reconstructed via SCENIC and GRNBoost2. A hypoxia-based prognostic signature was developed from unique H3 cluster genes using univariate Cox and Lasso regression on The Cancer Genome Atlas (TCGA; n = 606) and validated in GSE39582 (n = 579). Drug sensitivity correlations were derived from the GDSCv2 database. Finally, in vitro assays assessed the functional role of GIPC2, a model gene, in CRC cell lines. RESULTS: Single-cell profiling uncovered eight distinct hypoxic clusters, with H3 exhibiting the highest hypoxia scores and enrichment in glycoprotein metabolic and angiogenesis pathways. The eight-gene prognostic model stratified patients into high- and low-risk groups with significantly different overall survival in both TCGA (P = 0.0026) and validation cohorts (P = 0.011). Drug analysis highlighted associations of model genes with PI3K/MTOR and apoptosis pathways. GIPC2 knockdown in LS180 and HT-29 cells markedly inhibited proliferation, migration, and invasion, while inducing apoptosis and reversing EMT phenotypes. CONCLUSIONS: We present a robust hypoxia-related gene signature that accurately predicts CRC patient prognosis and nominate GIPC2 as a potential biomarker and therapeutic target, offering new insights into hypoxia-driven CRC biology and personalized treatment strategies.