Table 3_Identification of diagnostic biomarkers of and immune cell infiltration analysis in bovine respiratory disease.xlsx

BackgroundBovine respiratory disease (BRD) is a prevalent and costly condition in the cattle industry, impacting long-term productivity, antibioticusage, and global food safety. Thus, identifying reliable biomarkers for BRD is crucial for early diagnosis, effective treatment, and monitoring therapeutic outcomes.
MethodsThis study identified differentially expressed genes (DEGs) associated with BRD by analyzing a blood RNA-seq expression dataset associated with BRD, and conducted a Kyoto Encyclopedia of Genes and Genomes (KEGG) approach enrichment and Gene Ontology (GO) annotation analysis on these DEGs. Meanwhile, the key modules related to BRD were screened by weighted gene co-expression network analysis (WGCNA), and the genes in the module were intersected with DEGs. Subequently, least absolute shrinkage and selection operator (LASSO) and random forest (RF) analysis were employed to identify potential biomarkers. Finally, gene set enrichment analysis (GSEA) was performed to explore the potential mechanisms of the identified biomarkers, and their diagnostic significance was assessed using receiver operator characteristic (ROC) curve analysis and real-time fluorescent quantitative PCR (RT-qPCR). In addition, immune cell infiltration in BRD was evaluated using the CIBERSORT algorithm and the correlation between biomarkers and immune cell infiltration was analyzed.
ResultsThe results showed that a total of 1,097 DEG were screened. GO and KEGG analysis showed that DEGs was mainly enriched in inflammatory response, defense response, Complement and coagulation cascades and Antigen processing and presentation pathways. WGCNA analysis determined that the cyan module had the highest correlation with BRD. A total of 833 overlapping genes were identified through Venn analysis of the differential and WGCNA results. Lasso and RF analyses identified five potential biomarkers for BRD. RT-qPCR testing and data set analysis showed that the expression levels of these five potential biomarkers in nasal mucus and blood of BRD cattle were significantly higher than those of healthy cattle. In addition, ROC curve analysis showed that potential biomarkers had high diagnostic value. GSEA analysis revealed that potential biomarkers are mainly involved in Neutrophil extracellular trap formation, Complement and coagulation cascades, T cell receptor signaling pathway, B cell receptor signaling pathway, Fc gamma R-mediated phagocytosis and IL-17 signaling pathway. The results from the CIBERSORT algorithm demonstrated a significant difference in immune cell composition between the BRD group and the healthy group, indicating that the diagnostic biomarkers were closely associated with immune cells.
ConclusionThis study identified ADGRG3, CDKN1A, CA4, GGT5, and SLC26A8 as potential diagnostic markers for BRD, providing significant insights for the development of new immunotherapy targets and improving disease prevention and treatment strategies.

背景：牛呼吸道疾病（Bovine respiratory disease, BRD）是肉牛产业中流行广泛且经济成本高昂的疾病，会长期影响生产性能、抗生素使用情况及全球食品安全。因此，筛选可靠的牛呼吸道疾病生物标志物，对于该病的早期诊断、有效治疗及治疗效果监测均具有重要意义。
方法：本研究通过分析与牛呼吸道疾病相关的血液RNA测序（RNA-seq）表达数据集，筛选出与该病关联的差异表达基因（differentially expressed genes, DEGs），并对这些差异表达基因开展京都基因与基因组百科全书（Kyoto Encyclopedia of Genes and Genomes, KEGG）富集分析及基因本体（Gene Ontology, GO）注释分析。同时，本研究通过加权基因共表达网络分析（weighted gene co-expression network analysis, WGCNA）筛选与牛呼吸道疾病相关的关键模块，并将模块内基因与差异表达基因取交集。随后，采用最小绝对收缩和选择算子（least absolute shrinkage and selection operator, LASSO）与随机森林（random forest, RF）分析筛选潜在生物标志物。最后，通过基因集富集分析（gene set enrichment analysis, GSEA）探究潜在生物标志物的作用机制，并利用受试者工作特征（receiver operator characteristic, ROC）曲线分析及实时荧光定量聚合酶链式反应（real-time fluorescent quantitative PCR, RT-qPCR）评估其诊断价值。此外，本研究采用CIBERSORT算法评估牛呼吸道疾病患病牛的免疫细胞浸润情况，并分析生物标志物与免疫细胞浸润的相关性。
结果：研究结果显示，共筛选得到1097个差异表达基因。GO与KEGG富集分析结果表明，差异表达基因主要富集于炎症反应、防御反应、补体与凝血级联反应以及抗原加工呈递通路。WGCNA分析显示，青色模块与牛呼吸道疾病的相关性最高。通过对差异表达基因与WGCNA关键模块基因进行韦恩分析，共得到833个重叠基因。LASSO与RF分析共筛选出5个牛呼吸道疾病潜在生物标志物。RT-qPCR实验与数据集分析结果显示，这5个潜在生物标志物在牛呼吸道疾病患病牛的鼻黏液与血液中的表达水平显著高于健康牛。此外，ROC曲线分析表明，潜在生物标志物具有较高的诊断价值。GSEA分析显示，潜在生物标志物主要参与中性粒细胞胞外陷阱形成、补体与凝血级联反应、T细胞受体信号通路、B细胞受体信号通路、Fcγ受体介导的吞噬作用及IL-17信号通路。CIBERSORT算法分析结果显示，患病组与健康组牛的免疫细胞组成存在显著差异，表明诊断生物标志物与免疫细胞密切相关。
结论：本研究筛选得到ADGRG3、CDKN1A、CA4、GGT5及SLC26A8作为牛呼吸道疾病的潜在诊断标志物，为新型免疫治疗靶点的开发及疾病防治策略的优化提供了重要参考。