Intervertebral disc degeneration, the leading cause of chronic low back pain, remains incurable with traditional conservative therapies limited to symptomatic alleviation. We present an ECM-mimetic injectable hydrogel (HPTC) synthesized via dynamic crosslinking of hyaluronic acid-phenylboronic acid (HA-PBA) and tannic acid-cerium(III) metal-polyphenol networks (TA-Ce³⁺ MPNs), which faithfully recapitulates native nucleus pulposus ECM to enable functional tissue regeneration. In vitro, HPTC presented broad-spectrum reactive oxygen species scavenging and downregulated pro-inflammatory cytokine expression (TNF-α, IL-1β, IL-6), while upregulating anti-inflammatory markers (IL-4, IL-10). Crucially, Ce³⁺ effectively reduced dissolved oxygen levels (to 105% vs. 115% in control at 15 min), thereby promoting HIF-1α signal expression and mitigating nucleus pulposus cells senescence under H₂O₂-induced oxidative stress. In rat and rabbit intervertebral disc degeneration models, a single, minimally invasive injection of the ECM-mimetic HPTC hydrogel preserved the disc height index and magnetic resonance imaging signal intensity, enhanced aggrecan and collagen II deposition, suppressed inflammatory mediators, and elevated HIF-1α while reducing p21 expression in situ. Transcriptomic analysis further implicated HIF-1α pathways in ECM regeneration. All these findings demonstrate that the HPTC hydrogel leverages metal-polyphenol chemistry within an ECM-inspired framework to synergistically modulate oxygen homeostasis, oxidative stress, and inflammation, offering a bifunctional and biomimetic platform for disc regeneration.
Yifan Wang, Minglang Zou, Junyao Cheng et al.· Journal of Nanobiotechnology· 0 citations
Background: Prostate cancer (PCa) is a leading cause of cancer-related mortality worldwide, highlighting the need for improved prognostic tools. The integration of artificial intelligence (AI) and machine learning (ML) with multi-omics data offers new opportunities for biomarker discovery and risk stratification. Methods: We integrated bulk transcriptomic data from GSE116918 (training, n = 248) and three cross-cohort consistency evaluation cohorts (TCGA-PRAD, GSE70769, GSE46602), focusing on 1087 epithelial–mesenchymal transition (EMT)-associated genes. Using consensus clustering, weighted gene co-expression network analysis (WGCNA), and 91 machine learning algorithm combinations (including Random Forest, Lasso, and CoxBoost), we constructed a prognostic signature. SHAP analysis was used for model interpretability. Single-cell RNA sequencing (scRNA-seq, GSE268307, 10,672 cells) and spatial transcriptomics (10× Genomics Visium FFPE) provided hypothesis-generating evidence; spatial analysis was based on one tissue section. Results: A three-gene signature (INHBA, FAP, ITGBL1) effectively stratified patients into high- and low-risk groups, with the high-risk group showing significantly worse metastasis-free survival (HR = 1.61, 95% CI: 1.39–1.87; 4-year AUC = 0.93 in the training cohort; external AUCs ranged from 0.62 to 0.77). CytoTRACE inferred high differentiation potential of COMP+ fibroblasts, and Monocle3 inferred a transcriptional transition from COMP+ toward NELL2+ fibroblasts. BayesPrism deconvolution suggested that high inferred COMP+ fibroblast abundance was associated with poor prognosis and advanced T stage. NicheNet analysis prioritized BMP7 as a key upstream ligand, with downstream targets enriched in TGF-β signaling and stem cell pluripotency pathways. Conclusions: This study presents a machine learning-based multi-omics framework for prostate cancer risk stratification. The three-gene signature provides a new exploratory prognostic model while inferring a COMP+ to NELL2+ transcriptional transition. These findings may inform future hypothesis-driven studies of treatment sensitivity, pending experimental validation, and demonstrate the value of AI-driven multi-omics integration for precision oncology.
Xueqian Zhang, Wei Zhang, Zheng Wang et al.· Genes· 0 citations
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