Machine learning-based integration develops biomarkers initial the crosstalk between inflammation and immune in acute myocardial infarction patients

Machine Learning-Driven Transcriptome Analysis of Keratoconus for Predictive Biomarker Identification

Background: Keratoconus (KTCN) is a multifactorial disease characterized by progressive corneal degeneration. Recent studies suggest that a gene expression analysis of corneas may uncover potential novel biomarkers involved in corneal matrix remodeling. However, identifying reliable combinations of biomarkers that are linked to disease risk or progression remains a significant challenge. Objective: This study employed multiple machine learning algorithms to analyze the transcriptomes of keratoconus patients, identifying feature gene combinations and their functional associations, with the aim of enhancing the understanding of keratoconus pathogenesis. Methods: We analyzed the GSE77938 (PRJNA312169) dataset for differential gene expression (DGE) and performed gene set enrichment analysis (GSEA) using Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways to identify enriched pathways in keratoconus (KTCN) versus controls. Machine learning algorithms were then used to analyze the gene sets, with SHapley Additive exPlanations (SHAP) applied to assess the contribution of key feature genes in the model's predictions. Selected feature genes were further analyzed through Gene Ontology (GO) enrichment to explore their roles in biological processes and cellular functions. Results: Machine learning models, including XGBoost, Random Forest, Logistic Regression, and SVM, identified a set of important feature genes associated with keratoconus, with 15 notable genes appearing across multiple models, such as IL1R1, JUN, CYBB, CXCR4, KRT13, KRT14, S100A8, S100A9, and others. The under-expressed genes in KTCN were involved in the mechanical resistance of the epidermis (KRT14, KRT15) and in inflammation pathways (S100A8/A9, IL1R1, CYBB, JUN, and CXCR4), as compared to controls. The GO analysis highlighted that the S100A8/A9 complex and its associated genes were primarily involved in biological processes related to the cytoskeleton organization, inflammation, and immune response. Furthermore, we expanded our analysis by incorporating additional datasets from PRJNA636666 and PRJNA1184491, thereby offering a broader representation of gene features and increasing the generalizability of our results across diverse cohorts. Conclusions: The differing gene sets identified by XGBoost and SVM may reflect distinct but complementary aspects of keratoconus pathophysiology. Meanwhile, XGBoost captured key immune and chemotactic regulators (e.g., IL1R1, CXCR4), suggesting upstream inflammatory signaling pathways. SVM highlighted structural and epithelial differentiation markers (e.g., KRT14, S100A8/A9), possibly reflecting downstream tissue remodeling and stress responses. Our findings provide a novel research platform for the evaluation of keratoconus using machine learning-based approaches, offering valuable insights into its pathogenesis and potential therapeutic targets.

Metabolomics Insights into Gut Microbiota and Functional Constipation

Background: The composition and metabolic activity of the gut microbiota play a crucial role in various health conditions, including the occurrence and development of chronic constipation. Recent metabolomic advances reveal that gut microbiota-derived metabolites-such as SCFAs, bile acids, neurotransmitters, and microbial gases-play critical roles in regulating intestinal function. Methods: We systematically analyzed the current literature on microbial metabolomics in chronic constipation. This review consolidates findings from high-throughput metabolomic techniques (GC-MS, LC-MS, NMR) comparing metabolic profiles of constipated patients with healthy individuals. It also examines diagnostic improvements and personalized treatments, including fecal microbiota transplantation and neuromodulation, guided by these metabolomic insights. Results: This review shows that reduced SCFA levels impair intestinal motility and promote inflammation. An altered bile acid metabolism-with decreased secondary bile acids like deoxycholic acid-disrupts receptor-mediated signaling, further affecting motility. Additionally, imbalances in amino acid metabolism and neurotransmitter production contribute to neuromuscular dysfunction, while variations in microbial gas production (e.g., methane vs. hydrogen) further modulate gut transit. Conclusions: Integrating metabolomics with gut microbiota research clarifies how specific microbial metabolites regulate gut function. These insights offer promising directions for precision diagnostics and targeted therapies to restore microbial balance and improve intestinal motility.

Integrating single-cell RNA-Seq and machine learning to dissect tryptophan metabolism in ulcerative colitis

BACKGROUND

Ulcerative colitis (UC) is a persistent inflammatory bowels disease (IBD) characterized by immune response dysregulation and metabolic disruptions. Tryptophan metabolism has been believed as a significant factor in UC pathogenesis, with specific metabolites influencing immune modulation and gut microbiota interactions. However, the precise regulatory mechanisms and key genes involved remain unclear.

METHODS

AUCell, Ucell, and other functional enrichment algorithms were utilized to determine the activation patterns of tryptophan metabolism at the UC cell level. Differential analysis identified key genes associated with tryptophan metabolism. Five machine learning algorithms, including Random Forest, Boruta algorithm, LASSO, SVM-RFE, and GBM were integrated to identify and categorize disease-specific characteristic genes.

RESULTS

We observed significant heterogeneity in tryptophan metabolism activity across cell types in UC, with the highest activity levels in macrophages and fibroblasts. Among the key tryptophan metabolism-related genes, CTSS, S100A11, and TUBB were predominantly expressed in macrophages and significantly upregulated in UC, highlighting their involvement in immune dysregulation and inflammation. Cross-analysis with bulk RNA data confirmed the consistent upregulation of these genes in UC samples, highly indicating their relevance in UC pathology and potential as targets for therapeutic intervention.

CONCLUSIONS

This study is the first to reveal the heterogeneity of tryptophan metabolism at the single-cell level in UC, with macrophages emerging as key contributors to inflammatory processes. The identification of CTSS, S100A11, and TUBB as key regulators of tryptophan metabolism in UC underscores their potential as biomarkers and therapeutic targets.

Identification of Immuno-Inflammation-Related Biomarkers for Acute Myocardial Infarction Based on Bioinformatics

Purpose

Previous studies have confirmed that inflammation and immunity are involved in the pathogenesis of acute myocardial infarction (AMI). However, only few related genes are identified as biomarkers for the diagnosis and treatment of AMI.

Patients and Methods

GSE48060 and GSE60993 datasets were retrieved from Gene Expression Omnibus. The differentially expressed immuno-inflammation-related genes (DEIIRGs) were obtained from GSE48060, and the biomarkers for AMI were screened and validated using the "Neuralnet" package and GSE60993 dataset. Further, the biomarker-based nomogram was constructed, and miRNAs, transcription factors (TFs), and potential drugs targeting the biomarkers were explored. Furthermore, immune infiltration analysis was analyzed in AMI. Finally, the biomarkers were verified by assessing their mRNA levels using real-time quantitative PCR (RT-qPCR).

Results

First, eight biomarkers were screened via bioinformatics, and the artificial neural network model indicated a higher prediction accuracy for AMI even in the validation dataset. Nomogram had accurate forecasting ability for AMI as well. The TFs GTF2I, PHOX2B, RUNX1, and FOS targeting hsa-miR-1297 could regulate the expressions of ADM and CBLB, and RORA could effectively interact with melatonin and citalopram. RT-qPCR results for ADM, PI3, MMP9, NRG1 and CBLB were consistent with those of bioinformatic analysis.

Conclusion

In conclusion, eight key immuno-inflammation-related genes, namely, SH2D1B, ADM, PI3, MMP9, NRG1, CBLB, RORA, and FASLG, may serve as the potential biomarkers for AMI, in which the downregulation of CBLB and upregulation of ADM, PI3, and NRG1 in AMI was detected for the first time, providing a new strategy for the diagnosis and treatment of AMI.