Monocytes are closely associated with tuberculosis (TB). Latent tuberculosis in some patients gradually develops into its active state. This study aimed to investigate the role of hub monocyte-associated genes in distinguishing latent TB infection (LTBI) from active TB. The gene expression profiles of 15 peripheral blood mononuclear cells (PBMCs) samples were downloaded from the gene expression omnibus (GEO) database, GSE54992. The monocyte abundance was high in active TB as evaluated by the Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm. The limma test and correlation analysis documented 165 differentially expressed monocyte-related genes (DEMonRGs) between latent TB and active TB. Functional annotation and enrichment analyses of the DEMonRGs using the database for annotation, visualization, and integration discovery (DAVID) tools showed enrichment of inflammatory response mechanisms and immune-related pathways. A protein-protein interaction network was constructed with a node degree ≥10. The expression levels of these hub DEMonRGs ( SERPINA1, FUCA2 , and HP ) were evaluated and verified using several independent datasets and clinical settings. Finally, a single sample scoring method was used to establish a gene signature for the three DEMonRGs, distinguishing active TB from latent TB. The findings of the present study provide a better understanding of monocyte-related molecular fundamentals in TB progression and contribute to the identification of new potential biomarkers for the diagnosis of active TB.
Idiopathic pulmonary fibrosis (IPF) is a lung disease that is both chronic and progressive and is characterized by glycolysis. However, glycolysis’s function and its clinical significance in IPF are still not well understood. We accessed the Gene Expression Omnibus database to retrieve mRNA expression information for lung tissue and other samples. We identified genes associated with glycolysis that had differential expression levels between IPF and controls. In this work, we conducted a comprehensive bioinformatic analysis to systematically examine the glycolysis-associated genes with differential expression and subsequently investigated the possible prognostic significance of these genes. Additionally, the expression profiles of the associated prognostic genes were further investigated via quantitative real-time polymerase chain reaction in our cohort. In this investigation, we found that the expression of 16 genes involved in glycolysis was differentially expressed. Among them, 12 were upregulated and 4 were downregulated. We found that 3 glycolysis-related genes (stanniocalcin 2, transketolase like 1, artemin) might serve as hub genes for anticipating patient prognosis. The data from these genes were used to generate the prognostic models. The findings confirmed that high-risk IPF patients recorded a shorter overall survival relative to low-risk patients. This prognostic model yielded 1-, 2-, and 3-year survival rates of 0.666, 0.651, and 0.717, correspondingly, based on the area under the curve of the survival-dependent receiver operating characteristic. The GSE27957 and GSE70866 cohorts validated these findings, indicating the model has a good predictive performance. All 3 glycolysis-associated genes were validated to be expressed in our cohort. Finally, we used mRNA levels from 3 genes to produce a nomogram to quantitatively predict the prognosis of IPF individuals. As possible indicators for the prognosis of IPF, the glycolysis-related genes stanniocalcin 2, transketolase like 1, and artemin were shown to be promising candidate markers.
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