PDF(Pubmed)
Abstract：
BACKGROUND: Osteoporosis (OP), the \"silent epidemic\" of our century, poses a significant challenge to public health, predominantly affecting postmenopausal women and the elderly. It evolves from mild symptoms to pronounced severity, stabilizing eventually. Unique among OP\'s characteristics is the altered metabolic profile of affected cells, particularly in pyrimidine metabolism (PyM), a crucial pathway for nucleotide turnover and pyrimidine decomposition. While metabolic adaptation is acknowledged as a therapeutic target in various diseases, the specific role of PyM genes (PyMGs) in OP\'s molecular response remains to be clarified.
METHODS: In pursuit of elucidating and authenticating PyMGs relevant to OP, we embarked on a comprehensive bioinformatics exploration. This entailed the integration of Weighted Gene Co-expression Network Analysis (WGCNA) with a curated list of 37 candidate PyMGs, followed by the examination of their biological functions and pathways via Gene Set Variation Analysis (GSVA). The Least Absolute Shrinkage and Selection Operator (LASSO) technique was harnessed to identify crucial hub genes. We evaluated the diagnostic prowess of five PyMGs in OP detection and explored their correlation with OP\'s clinical traits, further validating their expression profiles through independent datasets (GSE2208, GSE7158, GSE56815, and GSE35956).
RESULTS: Our analytical rigor unveiled five PyMGs-IGKC, TMEM187, RPS11, IGLL3P, and GOLGA8N-with significant ties to OP. A deeper dive into their biological functions highlighted their roles in estrogen response modulation, cytosolic calcium ion concentration regulation, and GABAergic synaptic transmission. Remarkably, these PyMGs emerged as potent diagnostic biomarkers for OP, distinguishing affected individuals with substantial accuracy.
CONCLUSIONS: This investigation brings to light five PyMGs intricately associated with OP, heralding new avenues for biomarker discovery and providing insights into its pathophysiological underpinnings. These findings not only deepen our comprehension of OP\'s complexity but also herald the advent of more refined diagnostic and therapeutic modalities.
METHODS: In pursuit of elucidating and authenticating PyMGs relevant to OP, we embarked on a comprehensive bioinformatics exploration. This entailed the integration of Weighted Gene Co-expression Network Analysis (WGCNA) with a curated list of 37 candidate PyMGs, followed by the examination of their biological functions and pathways via Gene Set Variation Analysis (GSVA). The Least Absolute Shrinkage and Selection Operator (LASSO) technique was harnessed to identify crucial hub genes. We evaluated the diagnostic prowess of five PyMGs in OP detection and explored their correlation with OP\'s clinical traits, further validating their expression profiles through independent datasets (GSE2208, GSE7158, GSE56815, and GSE35956).
RESULTS: Our analytical rigor unveiled five PyMGs-IGKC, TMEM187, RPS11, IGLL3P, and GOLGA8N-with significant ties to OP. A deeper dive into their biological functions highlighted their roles in estrogen response modulation, cytosolic calcium ion concentration regulation, and GABAergic synaptic transmission. Remarkably, these PyMGs emerged as potent diagnostic biomarkers for OP, distinguishing affected individuals with substantial accuracy.
CONCLUSIONS: This investigation brings to light five PyMGs intricately associated with OP, heralding new avenues for biomarker discovery and providing insights into its pathophysiological underpinnings. These findings not only deepen our comprehension of OP\'s complexity but also herald the advent of more refined diagnostic and therapeutic modalities.
摘要：
背景：骨质疏松症（OP），我们这个世纪的“无声流行病”，对公共卫生构成重大挑战，主要影响绝经后妇女和老年人。它从轻微的症状发展到明显的严重，最终稳定。在OP的特征中，独特的是受影响细胞的代谢谱改变，特别是在嘧啶代谢（PyM）中，核苷酸周转和嘧啶分解的关键途径。虽然代谢适应被认为是各种疾病的治疗靶点，PyM基因（PyMGs）在OP的分子反应中的具体作用仍有待阐明。
方法：为了阐明和验证与OP相关的PyMG，我们开始了全面的生物信息学探索。这需要将加权基因共表达网络分析（WGCNA）与37个候选PyMGs的精选列表整合，然后通过基因集变异分析（GSVA）检查其生物学功能和途径。利用最小绝对收缩和选择算子（LASSO）技术来识别关键的枢纽基因。我们评估了五种PyMGs在OP检测中的诊断能力，并探讨了它们与OP临床特征的相关性。通过独立的数据集(GSE2208,GSE7158,GSE56815和GSE35956)进一步验证其表达谱.
结果：我们的分析严谨性公布了五个PyMGs-IGKC，TMEM187,RPS11,IGLL3P,和GOLGA8N-与OP有重要的联系。深入研究它们的生物学功能，强调了它们在雌激素反应调节中的作用，胞浆钙离子浓度调节，和GABA能突触传递。值得注意的是,这些PyMGs作为OP的有效诊断生物标志物出现，以相当高的准确性区分受影响的个体。
结论：这项调查揭示了与OP复杂相关的五个PyMG，预示着发现生物标志物的新途径，并提供对其病理生理基础的见解。这些发现不仅加深了我们对OP的复杂性的理解，而且预示着更精细的诊断和治疗方式的出现。
方法：为了阐明和验证与OP相关的PyMG，我们开始了全面的生物信息学探索。这需要将加权基因共表达网络分析（WGCNA）与37个候选PyMGs的精选列表整合，然后通过基因集变异分析（GSVA）检查其生物学功能和途径。利用最小绝对收缩和选择算子（LASSO）技术来识别关键的枢纽基因。我们评估了五种PyMGs在OP检测中的诊断能力，并探讨了它们与OP临床特征的相关性。通过独立的数据集(GSE2208,GSE7158,GSE56815和GSE35956)进一步验证其表达谱.
结果：我们的分析严谨性公布了五个PyMGs-IGKC，TMEM187,RPS11,IGLL3P,和GOLGA8N-与OP有重要的联系。深入研究它们的生物学功能，强调了它们在雌激素反应调节中的作用，胞浆钙离子浓度调节，和GABA能突触传递。值得注意的是,这些PyMGs作为OP的有效诊断生物标志物出现，以相当高的准确性区分受影响的个体。
结论：这项调查揭示了与OP复杂相关的五个PyMG，预示着发现生物标志物的新途径，并提供对其病理生理基础的见解。这些发现不仅加深了我们对OP的复杂性的理解，而且预示着更精细的诊断和治疗方式的出现。
