The Role of CCL24 in Systemic Sclerosis

Machine Learning Identifies Key Proteins in Primary Sclerosing Cholangitis Progression and Links High CCL24 to Cirrhosis

Primary sclerosing cholangitis (PSC) is a rare, progressive disease, characterized by inflammation and fibrosis of the bile ducts, lacking reliable prognostic biomarkers for disease activity. Machine learning applied to broad proteomic profiling of sera allowed for the discovery of markers of disease presence, severity, and cirrhosis and the exploration of the involvement of CCL24, a chemokine with fibro-inflammatory activity. Sera from 30 healthy controls and 45 PSC patients were profiled with proximity extension assay, quantifying the expression of 2870 proteins, and used to train an elastic net model. Proteins that contributed most to the model were tested for correlation to enhanced liver fibrosis (ELF) score and used to perform pathway analysis. Statistical modeling for the presence of cirrhosis was performed with principal component analysis (PCA), and receiver operating characteristics (ROC) curves were used to assess the useability of potential biomarkers. The model successfully predicted the presence of PSC, where the top-ranked proteins were associated with cell adhesion, immune response, and inflammation, and each had an area under receiver operator characteristic (AUROC) curve greater than 0.9 for disease presence and greater than 0.8 for ELF score. Pathway analysis showed enrichment for functions associated with PSC, overlapping with pathways enriched in patients with high levels of CCL24. Patients with cirrhosis showed higher levels of CCL24. This data-driven approach to characterize PSC and its severity highlights potential serum protein biomarkers and the importance of CCL24 in the disease, implying its therapeutic potential in PSC.

Single-Cell Sequencing Combined with Transcriptome Sequencing Constructs a Predictive Model of Key Genes in Multiple Sclerosis and Explores Molecular Mechanisms Related to Cellular Communication

Background

Multiple sclerosis (MS) causes chronic inflammation and demyelination of the central nervous system and comprises a class of neurodegenerative diseases in which interactions between multiple immune cell types mediate the involvement of MS development. However, the early diagnosis and treatment of MS remain challenging.

Methods

Gene expression profiles of MS patients were obtained from the Gene Expression Omnibus (GEO) database. Single-cell and intercellular communication analyses were performed to identify candidate gene sets. Predictive models were constructed using LASSO regression. Relationships between genes and immune cells were analyzed by single sample gene set enrichment analysis (ssGSEA). The molecular mechanisms of key genes were explored using gene enrichment analysis. An miRNA network was constructed to search for target miRNAs related to key genes, and related transcription factors were searched by transcriptional regulation analysis. We utilized the GeneCard database to detect the correlations between disease-regulated genes and key genes. We verified the mRNA expression of 4 key genes by reverse transcription-quantitative PCR (RT‒qPCR).

Results

Monocyte marker genes were selected as candidate gene sets. CD3D, IL2RG, MS4A6A, and NCF2 were found to be the key genes by LASSO regression. We constructed a prediction model with AUC values of 0.7569 and 0.719. The key genes were closely related to immune factors and immune cells. We explored the signaling pathways and molecular mechanisms involving the key genes by gene enrichment analysis. We obtained and visualized the miRNAs associated with the key genes using the miRcode database. We also predicted the transcription factors involved. We used validated key genes in MS patients, several of which were confirmed by RT‒qPCR.

Conclusion

The prediction model constructed with the CD3D, IL2RG, MS4A6A, and NCF2 genes has good diagnostic efficacy and provides new ideas for the diagnosis and treatment of MS.