METHODS: This study aimed to identify and validate potential glutamine metabolism genes (GlnMgs) associated with OP through comprehensive bioinformatics analysis. The identification of GlnMgs was achieved by integrating the weighted gene co-expression network analysis and a set of 28 candidate GlnMgs. Subsequently, the putative biological functions and pathways associated with GlnMgs were elucidated using gene set variation analysis. The LASSO method was employed to identify key hub genes, and the diagnostic efficacy of five selected GlnMgs in OP detection was assessed. Additionally, the relationship between hub GlnMgs and clinical characteristics was investigated. Finally, the expression levels of the five GlnMgs were validated using independent datasets (GSE2208, GSE7158, GSE56815, and GSE35956).
RESULTS: Five GlnMgs, namely IGKC, TMEM187, RPS11, IGLL3P, and GOLGA8N, were identified in this study. To gain insights into their biological functions, particular emphasis was placed on synaptic transmission GABAergic, inward rectifier potassium channel activity, and the cytoplasmic side of the lysosomal membrane. Furthermore, the diagnostic potential of these five GlnMgs in distinguishing individuals with OP yielded promising results, indicating their efficacy as discriminative markers for OP.
CONCLUSIONS: This study discovered five GlnMgs that are linked to OP. They shed light on potential new biomarkers for OP and tracking its progression.
方法:本研究旨在通过全面的生物信息学分析来鉴定和验证与OP相关的潜在谷氨酰胺代谢基因(GlnMgs)。GlnMgs的鉴定是通过整合加权基因共表达网络分析和一组28个候选GlnMgs来实现的。随后,使用基因集变异分析阐明了与GlnMgs相关的推定生物学功能和途径。LASSO方法用于识别关键枢纽基因,并评估了5种选定的GlnMGs在OP检测中的诊断功效。此外,研究了hubGlnMgs与临床特征之间的关系。最后,使用独立的数据集(GSE2208,GSE7158,GSE56815和GSE35956)验证了5种GlnMGs的表达水平.
结果:五个GlnMgs,即IGKC,TMEM187,RPS11,IGLL3P,GOLGA8N,在这项研究中确定。为了深入了解它们的生物学功能,特别强调突触传递GABA能,内向整流钾通道活性,和溶酶体膜的细胞质侧。此外,这五个GlnMGs在区分患有OP的个体方面的诊断潜力产生了有希望的结果,表明它们作为OP的区别标志物的功效。
结论:本研究发现了5种与OP相关的GlnMGs。他们揭示了OP的潜在新生物标志物并跟踪其进展。