Scholarlink

<p>This study aims to identify and validate key genes associated with brucellosis. Due to diagnostic challenges, we focused on a bioinformatics-driven approach to construct a robust diagnostic model, providing a theoretical basis for clinical diagnosis. We specifically investigated Prosaposin-related genes (PRGs) due to their role in host-pathogen interactions. The brucellosis dataset GSE69597 was downloaded from the GEO database. After processing, differentially expressed genes were identified and intersected with PRGs to obtain Prosaposin-Related Differentially Expressed Genes (PRDEGs). We employed Random Forest and LASSO regression to screen for key genes and construct a multivariate logistic regression model. Model performance was evaluated using ROC curves. Finally, the expression of the key genes was validated by qPCR in an independent cohort of clinical peripheral blood samples (16 patients, 11 controls). A total of 19 PRDEGs were identified, from which 5 key genes (SKAP2, EIF2B1, PRKAB1, IRF8, RPN1) were selected. The logistic regression model based on these genes demonstrated good diagnostic performance with an Area Under the Curve (AUC) value of 0.844. Crucially, qPCR validation confirmed that the expression of all five genes was significantly different between patients and controls (all <i>p</i> &lt; 0.05), consistent with the bioinformatic findings. Through comprehensive bioinformatic analysis and experimental validation, this study identified key genes closely associated with brucellosis and constructed an effective diagnostic model. This work provides new insights and a promising foundation for the early diagnosis and targeted therapy of brucellosis.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Bioinformatic analysis of brucellosis and construction of a diagnostic model based on key genes

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月6日

查看全文
  • Xia Wang,
  • Qing-Cao Fei,
  • Le Zhang,
  • Xiao-Rong Mao,
  • Zi-min Ma,
  • Li-Na Ma,
  • Xiang-Chun Ding

摘要

This study aims to identify and validate key genes associated with brucellosis. Due to diagnostic challenges, we focused on a bioinformatics-driven approach to construct a robust diagnostic model, providing a theoretical basis for clinical diagnosis. We specifically investigated Prosaposin-related genes (PRGs) due to their role in host-pathogen interactions. The brucellosis dataset GSE69597 was downloaded from the GEO database. After processing, differentially expressed genes were identified and intersected with PRGs to obtain Prosaposin-Related Differentially Expressed Genes (PRDEGs). We employed Random Forest and LASSO regression to screen for key genes and construct a multivariate logistic regression model. Model performance was evaluated using ROC curves. Finally, the expression of the key genes was validated by qPCR in an independent cohort of clinical peripheral blood samples (16 patients, 11 controls). A total of 19 PRDEGs were identified, from which 5 key genes (SKAP2, EIF2B1, PRKAB1, IRF8, RPN1) were selected. The logistic regression model based on these genes demonstrated good diagnostic performance with an Area Under the Curve (AUC) value of 0.844. Crucially, qPCR validation confirmed that the expression of all five genes was significantly different between patients and controls (all p < 0.05), consistent with the bioinformatic findings. Through comprehensive bioinformatic analysis and experimental validation, this study identified key genes closely associated with brucellosis and constructed an effective diagnostic model. This work provides new insights and a promising foundation for the early diagnosis and targeted therapy of brucellosis.