Identification and Validation of Mannose Metabolism-Related Biomarkers in COPD Through Integrated Bioinformatics and Machine Learning Analysis: A Pilot Study
Aug 2026· International Journal of COPD· Vol 21· 0 citations· 51 references
Medicine
TL;DR
The potential roles of MMRGs in COPD are revealed and novel biomarkers are identified, providing new insights and research foundations for the early diagnosis, personalized treatment, and drug development of COPD.
Abstract
Background Chronic obstructive pulmonary disease (COPD) represents a progressive respiratory disorder marked by sustained airflow restriction and ongoing inflammatory processes. Recently, mannose metabolism has emerged as a significant factor in chronic disease development. This investigation explored how mannose metabolism-related genes (MMRGs) contribute to COPD pathogenesis and evaluated their utility as candidate diagnostic and therapeutic targets. Methods We obtained blood sample gene expression data from COPD patients and healthy controls via the GEO database. Differentially expressed genes (DEGs) were identified and intersected with MMRGs to obtain candidate genes. Three machine learning algorithms combined with expression validation across independent datasets were applied to identify biomarkers. A nomogram prediction model was constructed and its diagnostic performance was assessed using receiver operating characteristic (ROC) curve analysis. Subsequently, gene set enrichment analysis (GSEA), immune infiltration analysis, drug prediction, and molecular docking were performed. Results Nineteen candidate genes were identified from 1685 DEGs and subsequently screened for two biomarkers: MAN1C1 and MAN2B2. A nomogram model constructed on the basis of the two showed moderate discriminatory efficacy (area under the curve (AUC) = 0.701). In addition, GSEA analysis showed that both were co-enriched in pathways such as TNF’s target up-regulated gene sets. The immune infiltration results revealed significant differences (p < 0.05) between COPD and controls in a total of 12 categories of immune cells, such as activated B cells. Finally, drug prediction revealed 12 and 3 potential drugs for MAN1C1 and MAN2B2, respectively, with trichostatin A showing a potential binding conformation. Conclusion This study revealed the potential roles of MMRGs in COPD and identified novel biomarkers. These findings provided new insights and research foundations for the early diagnosis, personalized treatment, and drug development of COPD.
Findings support the five genes as candidate PH biomarkers and VX-745 as a computational drug-repositioning hypothesis requiring experimental validation and VX-745 as a computational drug-repositioning hypothesis requiring experimental validation.
Bile acids are shown to participate in inflammatory responses. This study was designed to investigate the functions of bile acid metabolism-associated genes (BAMGs) in ulcerative colitis (UC), identify the potential biomarkers based on eleven machine learning algorithms.
Seven independent UC transcriptomic datasets were retrieved from the GEO database. Differentially expressed genes, weighted gene co-expression network analysis (WGCNA), and multiple machine learning algorithms were integrated to identify key BAMGs. Subsequently, enrichment analysis, immune cell analysis and single cell analysis were performed to explore the biological functions and immunological characteristics. The dextran sulfate sodium (DSS) induced colitis model in mice was then established and validated the results through western blot and immunohistochemical (IHC) analysis. In addition, peripheral blood samples were collected from UC patients for the detection of feature gene expression by quantitative real-time PCR (RT-qPCR).
Through integrative analysis, three feature BAMGs (
CH25H
,
SLC23A1
and
PHYH
) were identified. Unsupervised clustering based on the three-gene signature stratified UC patients into two distinct subgroups exhibiting divergent immune status. In DSS-treated mice, western blot and IHC confirmed significantly reduced SLC23A1 and PHYH protein levels and elevated CH25H protein expression in colonic tissues. RT-qPCR analysis of PBMCs from UC patients showed consistent gene expression. Immune cell analysis showed obvious association between the key BAMGs and inflammatory cells including naïve B cells, neutrophils, monocytes, CD8 T cells, and macrophages. Single-cell analysis revealed that the three feature genes were differentially expressed across T- and B-cell subsets, indicating their potential involvement in UC.
This study identified a novel of BAMGs and preliminary revealed their interaction with immune cells in the development of UC. Downregulation of
SLC23A1
and
PHYH
and upregulation of
CH25H
may contribute to UC pathogenesis and represent potential biomarkers.
Yuqing Wu, Danyan Gu, Jin Liu et al.· Frontiers in Immunology· 0 citations
Idiopathic pulmonary fibrosis (IPF) is a progressive chronic lung disease with an unclear etiology, and the contribution of glycosylation-related genes (GRGs) to its pathogenesis remains poorly understood. This study focuses on elucidating the potential mechanisms of GRGs in IPF, identifying key biomarkers, and developing a diagnostic model using bioinformatics and machine learning. Transcriptomic data from the GEO database were integrated with GRGs to investigate their role in IPF. Differentially expressed genes (DEGs) were identified and subjected to functional enrichment and protein-protein interaction (PPI) analyses. A machine learning workflow combining LASSO regression, support vector machine-recursive feature elimination (SVM-RFE), and XGBoost was applied to identify key genes and construct a diagnostic model using the GSE150910 training dataset. Model performance was subsequently evaluated in four independent validation datasets. Additional analyses, including gene set enrichment analysis (GSEA), immune infiltration analysis, drug-gene interaction analysis, molecular docking, and RT-qPCR validation using peripheral blood samples from IPF patients and healthy controls, were performed to investigate the biological relevance of the identified genes. A total of 126 glycosylation-related DEGs were identified, and 10 key genes were selected. The diagnostic model achieved area under the curve (AUC) values of 0.963 and 0.814 on lung tissue datasets, and 0.681 and 0.706 on blood datasets. Immune infiltration analysis revealed differences in B-cell abundance between patient subgroups, and RT-qPCR validation confirmed the differential expression of selected genes in clinical samples. These findings provide insight into the potential involvement of GRGs in IPF and support their relevance as candidate diagnostic genes.
Xiaoyun Fan, Gaoqi Zhu, Xiaowen Zhang et al.· Journal of Visualized Experi...· 0 citations
Emerging evidence suggests that sphingolipid metabolism is involved in respiratory diseases, including idiopathic pulmonary fibrosis (IPF). This study aimed to evaluate the diagnostic and prognostic value of sphingolipid-related genes in IPF and to identify potential sphingolipid-associated biomarkers.
Sphingolipid-related genes were obtained from the GeneCards database. Non-negative matrix factorization (NMF) was performed to identify molecular clusters and differentially expressed genes (DEGs). High-dimensional weighted gene co-expression network analysis (hdWGCNA) was used to determine fibroblast-associated genes. The intersecting genes were used to construct diagnostic and prognostic models using multiple machine learning algorithms. Hub genes were identified from the overlap between the diagnostic and prognostic signatures. In silico gene knockout was conducted using scTenifoldKnk. Functional validation was performed using Cell Counting Kit-8 (CCK-8) assay, wound healing assay, quantitative real-time polymerase chain reaction (RT-qPCR), and western blotting (WB).
Three molecular clusters with significant prognostic differences were identified. hdWGCNA revealed 60 key fibroblast-associated genes. Diagnostic and prognostic models constructed from these genes showed promising diagnostic and prognostic utility across independent cohorts, while the prognostic model exhibited some degree of cohort-dependent variability. CCDC80 was identified as a hub gene associated with sphingolipid-defined molecular heterogeneity and fibroblast-related transcriptional programs. scTenifoldKnk analysis indicated that simulated CCDC80 knockout affected fibrosis-related signaling pathways. In vitro experiments demonstrated that CCDC80 knockdown attenuated fibroblast proliferation, migration, and fibrotic activation.
This study identified sphingolipid-related molecular signatures with potential diagnostic and prognostic value in IPF and highlighted CCDC80 as a candidate profibrotic regulator. Further validation in larger and clinically standardized cohorts is required before clinical translation.
Not applicable.
Yi Liao, Lingjing Yang, Xiaoshu Liu et al.· Orphanet Journal of Rare Dis...· 0 citations
It is demonstrated that LMRGs are critically involved in the pathogenesis of AS, providing a novel molecular framework for early diagnosis and mechanistic insight and imply that lactate metabolism-related pathways are intertwined with inflammatory and immune responses, offering new insights into AS mechanisms.
Xiao-Ying Wang, Xiumin Hou, Hang Yu et al.· Current Medicinal Chemistry· 0 citations
Gallbladder cancer (GBC) is a malignant digestive system tumor, and patients are often only diagnosed at an advanced stage, resulting in high mortality and poor prognosis. Insufficiently specific and sensitive biomarkers impede early screening and diagnosis, highlighting the need to improve early diagnostic capabilities and identify reliable molecular biomarkers. Two GBC datasets and 3,252 acetylation-related genes were compiled from GeneCards and PubMed. Key genes were then identified using four machine learning algorithms, and their diagnostic performance and correlations with immune cell infiltration were evaluated. There were 848 identified differentially expressed genes (DEGs), of which 203 were acetylation-related DEGs. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed associations with biological processes. Machine learning approaches identified seven key genes that were used to develop a diagnostic model. The model’s performance was assessed using receiver operating characteristic curves and decision curve analysis. Immune infiltration analysis revealed links between the principal genes and immune cell populations, with NCOA1 showing the strongest positive correlation with activated natural killer cells. This study systematically characterizes acetylation-related genes in GBC, develops a high-accuracy diagnostic model, and provides preliminary insights into potential links between epigenetic regulation and the immune microenvironment.
