Purine metabolism-related genes and immunization in thyroid eye disease were validated using bioinformatics and machine learning

Mechanism of drug-pairs Astragalus Mongholicus-Largehead Atractylodes on treating knee osteoarthritis investigated by GEO gene chip with network pharmacology and molecular docking

Investigations into the therapeutic potential of Astragalus Mongholicus (AM, huáng qí) and Largehead Atractylodes (LA, bái zhú) reveal significant efficacy in mitigating the onset and progression of knee osteoarthritis (KOA), albeit with an elusive mechanistic understanding. This study delineates the primary bioactive constituents and their molecular targets within the AM-LA synergy by harnessing the comprehensive Traditional Chinese Medicine (TCM) network databases, including TCMSP, TCMID, and ETCM. Furthermore, an analysis of 3 gene expression datasets, sourced from the gene expression omnibus database, facilitated the identification of differential genes associated with KOA. Integrating these findings with data from 5 predominant databases yielded a refined list of KOA-associated targets, which were subsequently aligned with the gene signatures corresponding to AM and LA treatment. Through this alignment, specific molecular targets pertinent to the AM-LA therapeutic axis were elucidated. The construction of a protein-protein interaction network, leveraging the shared genetic markers between KOA pathology and AM-LA intervention, enabled the identification of pivotal molecular targets via the topological analysis facilitated by CytoNCA plugins. Subsequent GO and KEGG enrichment analyses fostered the development of a holistic herbal-ingredient-target network and a core target-signal pathway network. Molecular docking techniques were employed to validate the interaction between 5 central molecular targets and their corresponding active compounds within the AM-LA complex. Our findings suggest that the AM-LA combination modulates key biological processes, including cellular activity, reactive oxygen species modification, metabolic regulation, and the activation of systemic immunity. By either augmenting or attenuating crucial signaling pathways, such as MAPK, calcium, and PI3K/AKT pathways, the AM-LA dyad orchestrates a comprehensive regulatory effect on immune-inflammatory responses, cellular proliferation, differentiation, apoptosis, and antioxidant defenses, offering a novel therapeutic avenue for KOA management. This study, underpinned by gene expression omnibus gene chip analyses and network pharmacology, advances our understanding of the molecular underpinnings governing the inhibitory effects of AM and LA on KOA progression, laying the groundwork for future explorations into the active components and mechanistic pathways of TCM in KOA treatment.

Integration of bioinformatics and machine learning approaches for the validation of pyrimidine metabolism-related genes and their implications in immunotherapy for osteoporosis

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.

Bioinformatic validation and machine learning-based exploration of purine metabolism-related gene signatures in the context of immunotherapeutic strategies for nonspecific orbital inflammation

Background

Nonspecific orbital inflammation (NSOI) represents a perplexing and persistent proliferative inflammatory disorder of idiopathic nature, characterized by a heterogeneous lymphoid infiltration within the orbital region. This condition, marked by the aberrant metabolic activities of its cellular constituents, starkly contrasts with the metabolic equilibrium found in healthy cells. Among the myriad pathways integral to cellular metabolism, purine metabolism emerges as a critical player, providing the building blocks for nucleic acid synthesis, such as DNA and RNA. Despite its significance, the contribution of Purine Metabolism Genes (PMGs) to the pathophysiological landscape of NSOI remains a mystery, highlighting a critical gap in our understanding of the disease's molecular underpinnings.

Methods

To bridge this knowledge gap, our study embarked on an exploratory journey to identify and validate PMGs implicated in NSOI, employing a comprehensive bioinformatics strategy. By intersecting differential gene expression analyses with a curated list of 92 known PMGs, we aimed to pinpoint those with potential roles in NSOI. Advanced methodologies, including Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA), facilitated a deep dive into the biological functions and pathways associated with these PMGs. Further refinement through Lasso regression and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) enabled the identification of key hub genes and the evaluation of their diagnostic prowess for NSOI. Additionally, the relationship between these hub PMGs and relevant clinical parameters was thoroughly investigated. To corroborate our findings, we analyzed expression data from datasets GSE58331 and GSE105149, focusing on the seven PMGs identified as potentially crucial to NSOI pathology.

Results

Our investigation unveiled seven PMGs (ENTPD1, POLR2K, NPR2, PDE6D, PDE6H, PDE4B, and ALLC) as intimately connected to NSOI. Functional analyses shed light on their involvement in processes such as peroxisome targeting sequence binding, seminiferous tubule development, and ciliary transition zone organization. Importantly, the diagnostic capabilities of these PMGs demonstrated promising efficacy in distinguishing NSOI from non-affected states.

Conclusions

Through rigorous bioinformatics analyses, this study unveils seven PMGs as novel biomarker candidates for NSOI, elucidating their potential roles in the disease's pathogenesis. These discoveries not only enhance our understanding of NSOI at the molecular level but also pave the way for innovative approaches to monitor and study its progression, offering a beacon of hope for individuals afflicted by this enigmatic condition.

Causality of blood metabolites and metabolic pathways on Graves' disease and Graves' ophthalmopathy: a two-sample Mendelian randomization study

OBJECTIVES

There is ample that metabolic dysregulation is involved in Graves' disease (GD) and Graves' ophthalmopathy (GO). Recent studies have identified numerous metabolites associated with GD and GO. However, the causal impact of metabolites on GD and GO remains to be investigated.

METHODS

This two-sample Mendelian randomization (MR) analysis investigated the causal relationships between 486 blood metabolites and GD and GO. Sensitivity analysis was also performed to examine heterogeneity and pleiotropy.

RESULTS

MR analysis showed that 9 and 13 metabolites were associated with GD and GO, respectively, each meeting the nominal significance criteria (inverse variance weighted, p < 0.05). Additionally, four metabolic pathways were identified for each condition using network-based MetaboAnalyst 5.0.

CONCLUSIONS

The metabolites and pathways discovered in this study could serve as circulating metabolic biomarkers for clinical screening and prevention of GD and GO. They can be also used for further studies on the mechanisms and drug targets in GD and GO.

A novel diabetic foot ulcer diagnostic model: identification and analysis of genes related to glutamine metabolism and immune infiltration

BACKGROUND

Diabetic foot ulcer (DFU) is one of the most common and severe complications of diabetes, with vascular changes, neuropathy, and infections being the primary pathological mechanisms. Glutamine (Gln) metabolism has been found to play a crucial role in diabetes complications. This study aims to identify and validate potential Gln metabolism biomarkers associated with DFU through bioinformatics and machine learning analysis.

METHODS

We downloaded two microarray datasets related to DFU patients from the Gene Expression Omnibus (GEO) database, namely GSE134431, GSE68183, and GSE80178. From the GSE134431 dataset, we obtained differentially expressed Gln-metabolism related genes (deGlnMRGs) between DFU and normal controls. We analyzed the correlation between deGlnMRGs and immune cell infiltration status. We also explored the relationship between GlnMRGs molecular clusters and immune cell infiltration status. Notably, WGCNA to identify differentially expressed genes (DEGs) within specific clusters. Additionally, we conducted GSVA to annotate enriched genes. Subsequently, we constructed and screened the best machine learning model. Finally, we validated the predictions' accuracy using a nomogram, calibration curves, decision curve analysis (DCA), and the GSE134431, GSE68183, and GSE80178 dataset.

RESULTS

In both the DFU and normal control groups, we confirmed the presence of deGlnMRGs and an activated immune response. From the GSE134431 dataset, we obtained 20 deGlnMRGs, including CTPS1, NAGS, SLC7A11, GGT1, GCLM, RIMKLA, ARG2, ASL, ASNS, ASNSD1, PPAT, GLS2, GLUD1, MECP2, ASS1, PRODH, CTPS2, ALDH5A1, DGLUCY, and SLC25A12. Furthermore, two clusters were identified in DFU. Immune infiltration analysis indicated the presence of immune heterogeneity in these two clusters. Additionally, we established a Support Vector Machine (SVM) model based on 5 genes (R3HCC1, ZNF562, MFN1, DRAM1, and PTGDS), which exhibited excellent performance on the external validation datasetGSE134431, GSE68183, and GSE80178 (AUC = 0.929).

CONCLUSION

This study has identified five Gln metabolism genes associated with DFU, revealing potential novel biomarkers and therapeutic targets for DFU. Additionally, the infiltration of immune-inflammatory cells plays a crucial role in the progression of DFU.