Jian Du, Tian Zhou, Ran Meng, Wei Zhang, Jin Zhou, Wei Peng
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引用次数: 0
Abstract
Background: Osteoporosis (OP) is a systemic metabolic bone disease that may increase the risk of disability or death. Increasing evidence suggests that circadian rhythms play an important role in OP, yet the specific mechanisms remain unclear. Therefore, this study aims to utilize bioinformatics and machine learning algorithms to identify novel diagnostic biomarkers related to the circadian rhythm in OP, providing new targets for early diagnosis and treatment of OP.
Methods: The OP dataset GSE56815 was downloaded from the GEO database, differential expression analysis was performed to identify differentially expressed genes (DEGs) between OP and control samples. DEGs were intersected with circadian rhythm-related genes (CRRGs) to obtain circadian rhythm-related differentially expressed genes (CRRDEGs), which were subjected to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Four machine learning algorithms were applied to identify key genes for constructing a diagnostic model. The diagnostic performance of the model was validated by plotting receiver operating characteristic (ROC) curves using the GSE7158 dataset. Gene set enrichment analysis (GSEA) was performed on the key genes. Single-sample gene set enrichment analysis (ssGSEA) was used to analyze immune cell infiltration and explore the correlation between key genes and immune cells. Drug-gene interaction networks and competitive endogenous RNA (ceRNA) networks were constructed using the key genes.
Results: A total of 140 CRRDEGs were identified. By comparing four machine learning algorithms, the top five genes from the SVM algorithm (ECE1, FLT3, APPL1, RAB5C and FCGR2A) were determined as key genes for OP. The diagnostic model based on these five key genes demonstrated high diagnostic performance, with AUC of 0.904 for the training set and 0.887 for the validation set. Immune cell infiltration analysis revealed that Type 2 T helper cells and CD56dim natural killer cells were significantly upregulated in the OP group, while activated dendritic cells were significantly downregulated. The drug-gene interaction network and ceRNA network constructed based on the key genes revealed potential therapeutic targets for OP.
Conclusion: This study identified ECE1, FLT3, APPL1, RAB5C and FCGR2A as circadian rhythm-related novel diagnostic biomarkers for OP, providing new insights for further understanding the early diagnosis and treatment of OP.
期刊介绍：
Much of contemporary investigation in the life sciences is devoted to the molecular-scale understanding of the relationships between genes and the environment — in particular, dynamic alterations in the levels, modifications, and interactions of cellular effectors, including proteins. Frontiers in Molecular Biosciences offers an international publication platform for basic as well as applied research; we encourage contributions spanning both established and emerging areas of biology. To this end, the journal draws from empirical disciplines such as structural biology, enzymology, biochemistry, and biophysics, capitalizing as well on the technological advancements that have enabled metabolomics and proteomics measurements in massively parallel throughput, and the development of robust and innovative computational biology strategies. We also recognize influences from medicine and technology, welcoming studies in molecular genetics, molecular diagnostics and therapeutics, and nanotechnology.
Our ultimate objective is the comprehensive illustration of the molecular mechanisms regulating proteins, nucleic acids, carbohydrates, lipids, and small metabolites in organisms across all branches of life.
In addition to interesting new findings, techniques, and applications, Frontiers in Molecular Biosciences will consider new testable hypotheses to inspire different perspectives and stimulate scientific dialogue. The integration of in silico, in vitro, and in vivo approaches will benefit endeavors across all domains of the life sciences.
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