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. 2025 Jun;63(6):306-318.
doi: 10.1038/s41393-025-01081-1. Epub 2025 May 4.

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

Ye Tao #  1 , Shanhe Wang #  1 , Xiongfei Li #  2 , Letian Jin  3 , Chen Liu  2 , Kun Jiao  2 , Xiaoyu Li  2 , Yajun Cheng  2 , Kehan Xu  4 , Xiaoyi Zhou  5 , Xianzhao Wei  6
Affiliations

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

Ye Tao et al. Spinal Cord. 2025 Jun.

Abstract

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. The graphical abstract is divided into two parts. The upper section sequentially illustrates the occurrence of disulfidptosis and changes in the immune microenvironment in the human body after SCI. The lower section displays the construction of a diagnostic model for SCI through the detection of changes in disulfidptosis-related genes, combined with patient clinical information.

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Conflict of interest statement

Competing interests: The authors declare no competing interests. Ethical statement: Ethical approval was granted for this study by institute’s ethics committee of Changhai Hospital. (Ethical number: CHEC2002-077) After review, this research strictly follows the principles of fairness and justice, fully reflects the rights and interests of the subjects, and ensures that the research will not put the subjects at unreasonable risk. The whole project will not involve animal experiments. This project conforms to the current medical ethical research policies and regulations of China. The privacy rights of human subjects always be observed. Transparency, Rigor and Reproducibility Summary: The study was pre-registered at institute’s ethics committee of Changhai Hospital. The analysis plan was registered prior to beginning data collection at institute’s ethics committee of Changhai Hospital. A total sample size of 30 subjects was planned to allow a model development set of 25 participants and an independent validation set of 5 participants, yielding >80% prognostic accuracy for (primary clinical outcome) with a p-value < 0.05. Participants were not told the results of their prognostic assessments. Final clinical outcome assessments and adjudications were performed by team members blinded to relevant characteristics of the participants. All data used to develop prognostic models are available from GEO database (GSE151371). The key inclusion criteria and outcome evaluations are established standards. Replication by the study group was performed as part of this study. The datasets underlying this article were derived from sources in the public domain: Gene Expression Omnibus, https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE151371 ; Open Data Commons for Spinal Cord Injury, https://odc-sci.org/data/405 . Author(s)/Creator(s): R Core Team Date: 2023 Title/Software name: R: A Language and Environment for Statistical Computing Version: 4.3.1 Publication venue: R Foundation for Statistical Computing URL: https://www.R-project.org/ . Author(s)/Creator(s): Python Software Foundation Date: 2014 Title / Software name: Python Version: 3.4.0 Publication venue: Python Software Foundation URL: https://www.python.org/downloads/release/python-340/ . This paper will be published under a Creative Commons Open Access license, and upon publication will be freely available at https://www.liebertpub.com/loi/neu .

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