Abstract：
Tuberculosis (TB) is caused by Mycobacterium tuberculosis and is a major global health concern. Neutrophils play a significant role in TB infection and patient outcomes. This study aimed to identify gene modules associated with neutrophil infiltration in TB samples using WGCNA. Gene ontology and enrichment analyses were performed, and a random forest model was constructed to identify differentially expressed genes. K-means clustering was used to classify samples into subtypes, and immune-related scores, PD-L1 expression, HLA expression, and gene enrichment analysis were evaluated. The blue module showed significant correlation with neutrophils and enrichment in immune-related processes. The model exhibited good classification performance, and subtype 1 demonstrated higher immune-related scores, PD-L1 expression, HLA class I molecule expression, and immune-related pathway enrichment. These findings enhance our understanding of TB pathogenesis and provide potential targets for diagnosis and treatment strategies.
摘要：
结核病（TB）是由结核分枝杆菌引起的，是全球主要的健康问题。中性粒细胞在TB感染和患者预后中起重要作用。这项研究旨在使用WGCNA鉴定与TB样品中嗜中性粒细胞浸润相关的基因模块。进行了基因本体论和富集分析，并构建了随机森林模型来识别差异表达基因。K均值聚类用于将样本分类为亚型，和免疫相关的分数，PD-L1表达，HLA表达，和基因富集分析进行了评价。蓝色模块在免疫相关过程中显示出与嗜中性粒细胞和富集的显着相关性。该模型表现出良好的分类性能，亚型1表现出更高的免疫相关评分，PD-L1表达，HLAⅠ类分子表达,和免疫相关途径富集。这些发现增强了我们对结核病发病机制的理解，并为诊断和治疗策略提供了潜在的靶标。
