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<p>Intervertebral disc degeneration (IVDD) represents a severe chronic condition characterized by diverse programmed cell death (PCD) mechanisms serving as critical pathological features. The identification of key genes associated with cellular demise in IVDD is crucial for enhancing diagnostic and prognostic strategies. We extracted microarray-based transcriptomic multi-datasets from the GEO database, comprising 34 normal specimens (grade I/II) and 38 IVDD cases (grade III/IV). Nineteen PCD-associated genes encompassing multiple death modalities (including apoptosis, pyroptosis, ferroptosis, autophagy, necroptosis, cuproptosis, parthanatos, entotic cell death, netotic cell death, lysosome-dependent cell death, alkaliptosis, oxeiptosis, NETosis, immunogenic cell death, anoikis, paraptosis, methuosis, entosis, and disulfidptosis) were systematically curated from established studies. Pathway enrichment was evaluated through gene set variation analysis (GSVA), while weighted gene co-expression network analysis (WGCNA) facilitated the identification of core cell death-related genes, ultimately constructing a cell death signature (CDS) risk model via LASSO regression. Then, we found the significant upregulation of specific PCD pathways in IVDD specimens, particularly apoptosis, ferroptosis, autophagy, necroptosis, immunogenic cell death, anoikis, and disulfidptosis. Immune profiling revealed substantial infiltration of M0 macrophages in IVDD tissues, contrasting with predominant activated NK cells and M2 macrophages in control groups. Through integrative analysis by limma and WGCNA, we discerned 19 key PCD-related genes, subsequently identifying three gene targets (YWHAB, BID, and GSDME) for IVDD pathogenesis. This investigation culminated in developing a machine learning-driven prognostic model based on these biomarkers. Our study establishes a novel and comprehensive framework integrating IVDD with PCD mechanisms, proposing YWHAB, BID, and GSDME as promising diagnostic biomarkers and therapeutic targets for IVDD management.</p>

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Bioinformatics Analysis Reveals Hub Genes Linked to Programmed Cell Death in Intervertebral Disc Degeneration

  • 期刊论文

  • 发表日期：

    2025年4月30日

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  • Mingyang Zou,
  • Shaobo Wu,
  • Jundan Wang,
  • Wenya Xue,
  • Xince Sun,
  • Luyu Liu,
  • Pan Yin,
  • Dageng Huang

摘要

Intervertebral disc degeneration (IVDD) represents a severe chronic condition characterized by diverse programmed cell death (PCD) mechanisms serving as critical pathological features. The identification of key genes associated with cellular demise in IVDD is crucial for enhancing diagnostic and prognostic strategies. We extracted microarray-based transcriptomic multi-datasets from the GEO database, comprising 34 normal specimens (grade I/II) and 38 IVDD cases (grade III/IV). Nineteen PCD-associated genes encompassing multiple death modalities (including apoptosis, pyroptosis, ferroptosis, autophagy, necroptosis, cuproptosis, parthanatos, entotic cell death, netotic cell death, lysosome-dependent cell death, alkaliptosis, oxeiptosis, NETosis, immunogenic cell death, anoikis, paraptosis, methuosis, entosis, and disulfidptosis) were systematically curated from established studies. Pathway enrichment was evaluated through gene set variation analysis (GSVA), while weighted gene co-expression network analysis (WGCNA) facilitated the identification of core cell death-related genes, ultimately constructing a cell death signature (CDS) risk model via LASSO regression. Then, we found the significant upregulation of specific PCD pathways in IVDD specimens, particularly apoptosis, ferroptosis, autophagy, necroptosis, immunogenic cell death, anoikis, and disulfidptosis. Immune profiling revealed substantial infiltration of M0 macrophages in IVDD tissues, contrasting with predominant activated NK cells and M2 macrophages in control groups. Through integrative analysis by limma and WGCNA, we discerned 19 key PCD-related genes, subsequently identifying three gene targets (YWHAB, BID, and GSDME) for IVDD pathogenesis. This investigation culminated in developing a machine learning-driven prognostic model based on these biomarkers. Our study establishes a novel and comprehensive framework integrating IVDD with PCD mechanisms, proposing YWHAB, BID, and GSDME as promising diagnostic biomarkers and therapeutic targets for IVDD management.