Frontiers in immunology · 2026
Multi-omics analyses related to unfolded protein response in prostate cancer implicate pro-tumor role of IFRD1.
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Xue Yifeng, Huang Enyao, Luo Caichen, Qian Yanrong, Wu Tiange, Chu Chunyan, Shi Fan, Chen Shengrong, Zhang Dakun, Chen Weijun, Huang Weihua, Wang Ping, Chen Huixing, Cheng Yifei, Fan Yunxia
Аннотация
INTRODUCTION: The unfolded protein response (UPR) promotes prostate cancer (PCa) progression, yet its multi-omics landscape and clinical utility remain undefined.
METHODS: We integrated single-cell and bulk transcriptomic datasets, and identified UPR-related genes (UPRRGs) through a combination of differential expression analysis and weighted gene co-expression network analysis (WGCNA), based on which we further developed a consensus UPR-related signature (UPRRS) using a machine learning framework. The UPRRGs were further characterized by functional enrichment, cell-cell communication, and survival analyses. A clinically applicable nomogram integrating UPR-related prognostic genes was constructed for prognostic prediction. Through in silico and in vitro analyses, we validated the clinical relevance between the hub UPRRGs and PCa progression.
RESULTS: Single-cell analyses revealed elevated UPR activity in prostate epithelial cells, most prominently within the LE-KLK3 subpopulation. These cells exhibited enhanced ligand-receptor interactions in TNF, VEGF and NOTCH signaling axes. A seven-UPRRG signature (including IFRD1, DDIT3, HSPA5) demonstrated robust prognostic performance in the TCGA training set and three external validation cohorts (C-index > 0.82; AUC > 0.80). Multivariate Cox analysis confirmed UPRRS as an independent prognostic factor beyond clinical stage and Gleason score. Mechanistically, the UPRRS-high subgroup displayed an immunosuppressive microenvironment and reduced sensitivity to multiple chemotherapeutics. In vitro knock-down of IFRD1 markedly attenuated PCa cell proliferation and migration.
CONCLUSION: We provide the first systematic single-cell atlas of UPR heterogeneity in PCa and develop a clinically translatable UPRRS prognostic model. IFRD1, a key driver, emerges as a dual diagnostic and therapeutic target, offering both theoretical and experimental foundations for precision stratification and individualized management of PCa.
METHODS: We integrated single-cell and bulk transcriptomic datasets, and identified UPR-related genes (UPRRGs) through a combination of differential expression analysis and weighted gene co-expression network analysis (WGCNA), based on which we further developed a consensus UPR-related signature (UPRRS) using a machine learning framework. The UPRRGs were further characterized by functional enrichment, cell-cell communication, and survival analyses. A clinically applicable nomogram integrating UPR-related prognostic genes was constructed for prognostic prediction. Through in silico and in vitro analyses, we validated the clinical relevance between the hub UPRRGs and PCa progression.
RESULTS: Single-cell analyses revealed elevated UPR activity in prostate epithelial cells, most prominently within the LE-KLK3 subpopulation. These cells exhibited enhanced ligand-receptor interactions in TNF, VEGF and NOTCH signaling axes. A seven-UPRRG signature (including IFRD1, DDIT3, HSPA5) demonstrated robust prognostic performance in the TCGA training set and three external validation cohorts (C-index > 0.82; AUC > 0.80). Multivariate Cox analysis confirmed UPRRS as an independent prognostic factor beyond clinical stage and Gleason score. Mechanistically, the UPRRS-high subgroup displayed an immunosuppressive microenvironment and reduced sensitivity to multiple chemotherapeutics. In vitro knock-down of IFRD1 markedly attenuated PCa cell proliferation and migration.
CONCLUSION: We provide the first systematic single-cell atlas of UPR heterogeneity in PCa and develop a clinically translatable UPRRS prognostic model. IFRD1, a key driver, emerges as a dual diagnostic and therapeutic target, offering both theoretical and experimental foundations for precision stratification and individualized management of PCa.