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Study Design <p>Bioinformatics analysis and experimental validation study.</p> Objectives <p>To investigate the role and expression patterns of disulfidptosis-related genes in spinal cord injury (SCI), identify potential pivotal genes, and explore possible therapeutic targets.</p> Setting <p>Shanghai, China.</p> Methods <p>Data acquisition and pre-processing: Screened 27 disulfidptosis-related genes based on literature and downloaded RNA-sequencing data of ASCI patients from GEO database (GSE151371); Identification of differentially expressed genes (DEGs): Used R package “limma” for differential gene expression analysis between ASCI samples and normal controls; Evaluating immune cell infiltration: Employed ssGSEA algorithm and CIBERSORT to determine immune cell abundance; Identification and functional verification of key genes: Intersected disulfidptosis-related genes with DEGs, and used machine learning techniques (Random Forest, Lasso, Support Vector Machine) to identify hub genes. Validated hub genes expression by real-time PCR; Construction of a diagnostic model: Developed a backpropagation neural network clinical prediction model based on hub genes and clinical features, and evaluated its performance using ROC curve. 6. Subcluster analysis: Performed consensus cluster analysis of ASCI samples and hub genes, and used GSVA to elucidate functional differences between subgroups.</p> Results <p>Identified 7764 DEGs in ASCI, with GO and KEGG enrichment in inflammation and autophagy-related pathways; Found differences in immune cell infiltration between ASCI and control groups, and correlation between immune cells and DRGs; Determined seven hub genes (MYL6, NUBPL, CYFIP1, IQGAP1, FLNB, SLC7A11, CD2AP) through machine learning; Validated the expression of hub genes by qRT-PCR; Constructed a clinical diagnostic model with good predictive accuracy (overall dataset accuracy of 83.3%); Identified two subtypes of ASCI based on hub genes, with different immune infiltration and pathway activity.</p> Conclusion <p>Disulfidptosis is closely related to spinal cord injury. The identified hub genes and subtypes provide new insights for biomarker and therapeutic target research. The diagnostic model has potential for clinical application, but further studies are needed due to limitations such as small sample size.</p> Sponsorship <p>This study was supported in part by the project of Youth Scientific and Technological Talents of PLA (2020QN06125), Changhong Talent Project in First affiliated hospital of Navy Medical University (Wei Xianzhao) and Basic Medical Research Project in First affiliated hospital of Navy Medical University (2023PY17). I want to reiterate that there is no prior publication of figures or tables and no conflict of interest in the submission of this manuscript.</p> <p></p>

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Identification of disulfidptosis-related genes and subgroups in spinal cord injury

  • 期刊论文

  • 发表日期：

    2025年5月4日

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  • Ye Tao,
  • Shanhe Wang,
  • Xiongfei Li,
  • Letian Jin,
  • Chen Liu,
  • Kun Jiao,
  • Xiaoyu Li,
  • Yajun Cheng,
  • Kehan Xu,
  • Xiaoyi Zhou,
  • Xianzhao Wei

摘要

Study Design

Bioinformatics analysis and experimental validation study.

Objectives

To investigate the role and expression patterns of disulfidptosis-related genes in spinal cord injury (SCI), identify potential pivotal genes, and explore possible therapeutic targets.

Setting

Shanghai, China.

Methods

Data acquisition and pre-processing: Screened 27 disulfidptosis-related genes based on literature and downloaded RNA-sequencing data of ASCI patients from GEO database (GSE151371); Identification of differentially expressed genes (DEGs): Used R package “limma” for differential gene expression analysis between ASCI samples and normal controls; Evaluating immune cell infiltration: Employed ssGSEA algorithm and CIBERSORT to determine immune cell abundance; Identification and functional verification of key genes: Intersected disulfidptosis-related genes with DEGs, and used machine learning techniques (Random Forest, Lasso, Support Vector Machine) to identify hub genes. Validated hub genes expression by real-time PCR; Construction of a diagnostic model: Developed a backpropagation neural network clinical prediction model based on hub genes and clinical features, and evaluated its performance using ROC curve. 6. Subcluster analysis: Performed consensus cluster analysis of ASCI samples and hub genes, and used GSVA to elucidate functional differences between subgroups.

Results

Identified 7764 DEGs in ASCI, with GO and KEGG enrichment in inflammation and autophagy-related pathways; Found differences in immune cell infiltration between ASCI and control groups, and correlation between immune cells and DRGs; Determined seven hub genes (MYL6, NUBPL, CYFIP1, IQGAP1, FLNB, SLC7A11, CD2AP) through machine learning; Validated the expression of hub genes by qRT-PCR; Constructed a clinical diagnostic model with good predictive accuracy (overall dataset accuracy of 83.3%); Identified two subtypes of ASCI based on hub genes, with different immune infiltration and pathway activity.

Conclusion

Disulfidptosis is closely related to spinal cord injury. The identified hub genes and subtypes provide new insights for biomarker and therapeutic target research. The diagnostic model has potential for clinical application, but further studies are needed due to limitations such as small sample size.

Sponsorship

This study was supported in part by the project of Youth Scientific and Technological Talents of PLA (2020QN06125), Changhong Talent Project in First affiliated hospital of Navy Medical University (Wei Xianzhao) and Basic Medical Research Project in First affiliated hospital of Navy Medical University (2023PY17). I want to reiterate that there is no prior publication of figures or tables and no conflict of interest in the submission of this manuscript.