Predicting diagnostic biomarkers associated with immune infiltration in Crohn's disease based on machine learning and bioinformatics

Integrative multi-omics analysis of Crohn's disease and metabolic syndrome: Unveiling the underlying molecular mechanisms of comorbidity

OBJECTIVES

The focus of this study is on identifying a potential association between Crohn's disease (CD), a chronic inflammatory bowel condition, and metabolic syndrome (Mets), characterized by a cluster of metabolic abnormalities, including high blood pressure, abnormal lipid levels, and overweight. While the link between CD and MetS has been suggested in the medical community, the underlying molecular mechanisms remain largely unexplored.

METHODS

Using microarray data from the Gene Expression Omnibus (GEO) database, we conducted a differential gene expression analysis and applied Weighted Gene Co-expression Network Analysis (WGCNA) to identify genes shared between CD and MetS. To further elucidate the functions of these shared genes, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses and constructed protein-protein interaction (PPI) networks. For key gene screening, we used Random Forest and Least Absolute Shrinkage and Selection Operator (LASSO) regression and constructed a diagnostic prediction model with the Extreme Gradient Boosting (XGBoost) algorithm. Additionally, CIBERSORT and Gene Set Variation Analysis (GSVA) were employed to examine the relationships between these genes and immune cell infiltration, as well as metabolic pathways. Mendelian randomization and colocalization analyses were also conducted to explore causal links between genes and disease. Lastly, single-cell RNA sequencing (scRNA-seq) was used to validate the functionality of these key genes.

RESULTS

Through the use of the limma R package and WGCNA, we identified 1767 co-expressed genes common to both CD and MetS, which are notably enriched in pathways related to immune responses and metabolic regulation. After thorough analysis, 34 key genes were highlighted, demonstrating their potential utility in prognostic models. These genes were closely linked to tissue immune responses and metabolic functions. Subsequent scRNA-seq analysis confirmed the strong diagnostic potential of PIM2 and PBX2, with especially prominent expression in T and B cells.

CONCLUSION

This study identifies shared regulatory genes between CD and MetS, advancing the development of precise diagnostic tools. In particular, PIM2 and PBX2 were found to be positively associated with hypoxia and hemoglobin metabolism pathways, suggesting their involvement in the modulation of cellular processes. These findings improve our understanding of the molecular mechanisms underlying the comorbidity of CD and MetS, offering novel targets for integrated therapeutic interventions.

Lipocalin-2 and intestinal diseases

Dysfunction of the intestinal barrier is a prevalent phenomenon observed across a spectrum of diseases, encompassing conditions such as mesenteric artery dissection, inflammatory bowel disease, cirrhosis, and sepsis. In these pathological states, the integrity of the intestinal barrier, which normally serves to regulate the selective passage of substances between the gut lumen and the bloodstream, becomes compromised. This compromised barrier function can lead to a range of adverse consequences, including increased permeability to harmful substances, the translocation of bacteria and their products into systemic circulation, and heightened inflammatory responses within the gut and beyond. Understanding the mechanisms underlying intestinal barrier dysfunction in these diverse disease contexts is crucial for the development of targeted therapeutic interventions aimed at restoring barrier integrity and ameliorating disease progression. Lipocalin-2 (LCN2) expression is significantly upregulated during episodes of intestinal inflammation, making it a pivotal indicator for gauging the extent of such inflammatory processes. Notably, however, LCN2 derived from distinct cellular sources, whether intestinal epithelial cells or immune cells, exhibits notably divergent functional characteristics. Furthermore, the multifaceted nature of LCN2 is underscored by its varying roles across different diseases, sometimes even demonstrating contradictory effects.

A New Differential Gene Expression Based Simulated Annealing for Solving Gene Selection Problem: A Case Study on Eosinophilic Esophagitis and Few Other Gastro-intestinal Diseases

Identifying the set of genes collectively responsible for causing a disease from differential gene expression data is called gene selection problem. Though many complex methodologies have been applied to solve gene selection, formulated as an optimization problem, this study introduces a new simple, efficient, and biologically plausible solution procedure where the collective power of the targeted gene set to discriminate between diseased and normal gene expression profiles was focused. It uses Simulated Annealing to solve the underlying optimization problem and termed here as Differential Gene Expression Based Simulated Annealing (DGESA). The Ranked Variance (RV) method has been applied to prioritize genes to form reference set to compare with the outcome of DGESA. In a case study on Eosinophilic Esophagitis (EoE) and other gastrointestinal diseases, RV identified the top 40 high-variance genes, overlapping with disease-causing genes from DGESA. DGESA identified 40 gene pathways each for EoE, Crohn's Disease (CD), and Ulcerative Colitis (UC), with 10 genes for EoE, 8 for CD, and 7 for UC confirmed in literature. For EoE, confirmed genes include KRT79, CRISP2, IL36G, SPRR2B, SPRR2D, and SPRR2E. For CD, validated genes are NPDC1, SLC2A4RG, LGALS8, CDKN1A, XAF1, and CYBA. For UC, confirmed genes include TRAF3, BAG6, CCDC80, CDC42SE2, and HSPA9. RV and DGESA effectively elucidate molecular signatures in gastrointestinal diseases. Validating genes like SPRR2B, SPRR2D, SPRR2E, and STAT6 for EoE demonstrates DGESA's efficacy, highlighting potential targets for future research.

PROS1 is a crucial gene in the macrophage efferocytosis of diabetic foot ulcers: a concerted analytical approach through the prisms of computer analysis

BACKGROUND

Diabetic foot ulcers (DFUs) pose a serious long-term threat because of elevated mortality and disability risks. Research on its biomarkers is still, however, very limited. In this paper, we have effectively identified biomarkers linked with macrophage excretion in diabetic foot ulcers through the application of bioinformatics and machine learning methodologies. These findings were subsequently validated using external datasets and animal experiments. Such discoveries are anticipated to offer novel insights and approaches for the early diagnosis and treatment of DFU.

METHODS

In this work, we used the Gene Expression Omnibus (GEO) database's datasets GSE68183 and GSE80178 as the training dataset to build a gene model using machine learning methods. After that, we used the training and validation sets to validate the model (GSE134431). On the model genes, we performed enrichment analysis using both gene set variant analysis (GSVA) and gene set enrichment analysis (GSEA). Additionally, the model genes were subjected to immunological association and immune function analyses.

RESULTS

In this study, PROS1 was identified as a potential key target associated with macrophage efflux in DFU by machine learning and bioinformatics approaches. Subsequently, the key biomarker status of PROS1 in DFU was also confirmed by external datasets. In addition, PROS1 also plays a key role in macrophage exudation in DFU. This gene may be associated with macrophage M1, CD4 memory T cells, naïve B cells, and macrophage M2, and affects IL-17, Rap1, hedgehog, and JAK-STAT signaling pathways.

CONCLUSIONS

PROS1 was identified and validated as a biomarker for DFU. This finding has the potential to provide a target for macrophage clearance of DFU.

Perspective from single-cell sequencing: Is inflammation in acute ischemic stroke beneficial or detrimental?

BACKGROUND

Acute ischemic stroke (AIS) is a common cerebrovascular event associated with high incidence, disability, and poor prognosis. Studies have shown that various cell types, including microglia, astrocytes, oligodendrocytes, neurons, and neutrophils, play complex roles in the early stages of AIS and significantly affect its prognosis. Thus, a comprehensive understanding of the mechanisms of action of these cells will be beneficial for improving stroke prognosis. With the rapid development of single-cell sequencing technology, researchers have explored the pathophysiological mechanisms underlying AIS at the single-cell level.

METHOD

We systematically summarize the latest research on single-cell sequencing in AIS.

RESULT

In this review, we summarize the phenotypes and functions of microglia, astrocytes, oligodendrocytes, neurons, neutrophils, monocytes, and lymphocytes, as well as their respective subtypes, at different time points following AIS. In particular, we focused on the crosstalk between microglia and astrocytes, oligodendrocytes, and neurons. Our findings reveal diverse and sometimes opposing roles within the same cell type, with the possibility of interconversion between different subclusters.

CONCLUSION

This review offers a pioneering exploration of the functions of various glial cells and cell subclusters after AIS, shedding light on their regulatory mechanisms that facilitate the transformation of detrimental cell subclusters towards those that are beneficial for improving the prognosis of AIS. This approach has the potential to advance the discovery of new specific targets and the development of drugs, thus representing a significant breakthrough in addressing the challenges in AIS treatment.

Identifying and Validating GSTM5 as an Immunogenic Gene in Diabetic Foot Ulcer Using Bioinformatics and Machine Learning

Background

A diabetic foot ulcer (DFU) is a serious, long-term condition associated with a significant risk of disability and mortality. However, research on its biomarkers is still limited. This study utilizes bioinformatics and machine learning methods to identify immune-related biomarkers for DFU and validates them through external datasets and animal experiments.

Methods

This study used bioinformatics and machine learning to analyze microarray data from the Gene Expression Omnibus (GEO) database to identify key genes associated with DFU. Animal experiments were conducted to validate these findings. This research employs the datasets GSE68183 and GSE80178 retrieved from the GEO database as the training dataset for building a gene machine learning model, and after conducting differential analysis on the data, this study used package glmnet and package e1071 to construct LASSO and SVM-RFE machine learning models, respectively. Subsequently, we validated the model using the training set and validation set (GSE134431). We conducted enrichment analysis, including GSEA and GSVA, on the model genes. We also performed immune functional analysis and immune-related analysis on the model genes. Finally, we conducted immunohistochemistry (IHC) validation on the model genes.

Results

This study identifies GSTM5 as a potential immune-related key target in DFU using machine learning and bioinformatics methods. Subsequent validation through external datasets and IHC experiments also confirms GSTM5 as a critical biomarker for DFU. The gene may be associated with T cells regulatory (Tregs) and T cells follicular helper, and it influences the NF-κB, GnRH, and MAPK signaling pathway.

Conclusion

This study identified and validated GSTM5 as a biomarker for DFU. This finding may potentially provide a target for immune therapy for DFU.