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Purpose <p>This study investigated the involvement of disulfidptosis in the pathophysiology of sepsis by applying a bioinformatics analysis assisted by large language models (LLMs).</p> Methods <p>Based on DeepSeek R1 and retrieval-augmented generation technology, a deep retrieval architecture was developed for extracting disulfidptosis-related genes. An intersection of genes from LLM extraction, manual extraction, and datasets was included for bioinformatics analyses. Using DeepSeek R1, we synthesized a multi-step bioinformatics protocol from prior publications. The analyses were then performed according to the protocol. Key gene candidates were identified using multiple machine learning models, and validation was performed in a cecal ligation and puncture mouse model of sepsis.</p> Results <p>A total of 21 disulfidptosis-related genes were included for bioinformatics analyses. Nine bioinformatics techniques were integrated based on the&#xa0;LLM summarization of two key references. Thirteen disulfidptosis-related differentially expressed genes (DEGs) were identified in sepsis. Based on these DEGs, sepsis patients were classified into two molecular subgroups with distinct immune profiles. Among the machine learning models evaluated, the support vector machine achieved the highest classification performance (AUC = 0.989). Five hub genes—<i>FSTL1</i>, <i>SELP</i>, <i>PPBP</i>, <i>ITGA2B</i>, and <i>PF4</i>—were selected as key biomarkers. Experimental validation confirmed significantly elevated expression of these genes in the sepsis mice compared with their sham counterparts.</p> Conclusion <p>Our study assisted bioinformatics analysis with large language models and revealed a critical role for disulfidptosis in sepsis. A high-performance diagnostic model was developed, and five genes were validated as potential biomarkers for the diagnosis and treatment of sepsis.</p>

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Disulfidptosis-associated gene signatures in sepsis: a diagnostic model based on an LLM-assisted bioinformatics analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月15日

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  • Tian Liu,
  • Zhi Mao,
  • Jiake Chai,
  • Hui Zhou,
  • Yirui Qu,
  • Chengfeng Xu,
  • Yunfei Chi

摘要

Purpose

This study investigated the involvement of disulfidptosis in the pathophysiology of sepsis by applying a bioinformatics analysis assisted by large language models (LLMs).

Methods

Based on DeepSeek R1 and retrieval-augmented generation technology, a deep retrieval architecture was developed for extracting disulfidptosis-related genes. An intersection of genes from LLM extraction, manual extraction, and datasets was included for bioinformatics analyses. Using DeepSeek R1, we synthesized a multi-step bioinformatics protocol from prior publications. The analyses were then performed according to the protocol. Key gene candidates were identified using multiple machine learning models, and validation was performed in a cecal ligation and puncture mouse model of sepsis.

Results

A total of 21 disulfidptosis-related genes were included for bioinformatics analyses. Nine bioinformatics techniques were integrated based on the LLM summarization of two key references. Thirteen disulfidptosis-related differentially expressed genes (DEGs) were identified in sepsis. Based on these DEGs, sepsis patients were classified into two molecular subgroups with distinct immune profiles. Among the machine learning models evaluated, the support vector machine achieved the highest classification performance (AUC = 0.989). Five hub genes—FSTL1, SELP, PPBP, ITGA2B, and PF4—were selected as key biomarkers. Experimental validation confirmed significantly elevated expression of these genes in the sepsis mice compared with their sham counterparts.

Conclusion

Our study assisted bioinformatics analysis with large language models and revealed a critical role for disulfidptosis in sepsis. A high-performance diagnostic model was developed, and five genes were validated as potential biomarkers for the diagnosis and treatment of sepsis.