Arya Rakesh, Shakya Hemlata, Biswas Viplov Kumar, Kumar Gyanendra, Yogarayan Sumendra, Shakya Harish Kumar, Kim Jong-Joo
Department of Biotechnology, Yeungnam University, Gyeongsan 38541, Republic of Korea.
Department of Biomedical Engineering, Shri G. S. Institute of Technology and Science, Indore 452003, Madhya Pradesh, India.
Genes (Basel). 2025 Jun 18;16(6):716. doi: 10.3390/genes16060716.
Tuberculosis (TB) remains a major global public health challenge, and diagnosing it can be difficult due to issues such as distinguishing active TB from latent TB infection (LTBI), as well as the sample collection process, which is often time-consuming and lacks sensitivity and specificity. Ferroptosis is emerging as an important factor in TB pathogenesis; however, its underlying molecular mechanisms are not fully understood. Thus, there is a critical need to establish ferroptosis-related diagnostic biomarkers for tuberculosis (TB).
This study aimed to identify and validate potential ferroptosis-related genes in TB infection while enhancing clinical diagnostic accuracy through bioinformatics-driven gene identification. The microarray expression profile dataset GSE28623 from the Gene Expression Omnibus (GEO) database was used to identify ferroptosis-related differentially expressed genes (FR-DEGs) associated with TB. Subsequently, these genes were used for immune cell infiltration, Gene Set Enrichment Analysis (GSEA), functional enrichment and correlation analyses. Hub genes were identified using Weighted Gene Co-expression Network Analysis (WGCNA) and validated in independent datasets GSE37250, GSE39940, GSE19437, and GSE31348.
A total of 21 FR-DEGs were identified. Among them, four hub genes (, , , and ) were identified as diagnostic biomarkers. These biomarkers were enriched in immune-response related pathways and were validated. Immune cell infiltration, GSEA, functional enrichment and correlation analyses revealed that multiple immune cell types could be activated by FR-DEGs. Throughout anti-TB therapy, the expression of the four hub gene signatures significantly decreased in patients cured of TB.
In conclusion, ferroptosis plays a key role in TB pathogenesis. These four hub gene signatures are linked with TB treatment effectiveness and show promise as biomarkers for differentiating TB from LTBI.
结核病仍然是全球主要的公共卫生挑战，由于存在诸如区分活动性结核病与潜伏性结核感染（LTBI）等问题，以及样本采集过程通常耗时且缺乏敏感性和特异性，结核病的诊断可能会很困难。铁死亡正在成为结核病发病机制中的一个重要因素；然而，其潜在的分子机制尚未完全明确。因此，迫切需要建立与铁死亡相关的结核病诊断生物标志物。
本研究旨在识别和验证结核病感染中潜在的铁死亡相关基因，同时通过生物信息学驱动的基因识别提高临床诊断准确性。使用来自基因表达综合数据库（GEO）的微阵列表达谱数据集GSE28623来识别与结核病相关的铁死亡相关差异表达基因（FR-DEGs）。随后，将这些基因用于免疫细胞浸润、基因集富集分析（GSEA）、功能富集和相关性分析。使用加权基因共表达网络分析（WGCNA）识别枢纽基因，并在独立数据集GSE37250、GSE39940、GSE19437和GSE31348中进行验证。
共识别出21个FR-DEGs。其中，四个枢纽基因（、、和）被识别为诊断生物标志物。这些生物标志物在免疫反应相关途径中富集并得到验证。免疫细胞浸润、GSEA、功能富集和相关性分析表明，多种免疫细胞类型可被FR-DEGs激活。在整个抗结核治疗过程中，治愈结核病患者的四个枢纽基因特征的表达显著下降。
总之，铁死亡在结核病发病机制中起关键作用。这四个枢纽基因特征与结核病治疗效果相关，并有望作为区分结核病与LTBI的生物标志物。
