Membrane metalloendopeptidase (MME) is positively correlated with systemic lupus erythematosus and may inhibit the occurrence of breast cancer

Exploration the role of pro-inflammatory fibroblasts and related markers in periodontitis: combing with scRNA-seq and bulk-seq data

Background

Gingival fibroblasts (GFs), as a critical component of periodontal tissue, play a vital role in processes such as collagen synthesis, wound healing, and tissue repair, thereby maintaining the structural integrity of periodontal tissues. Interestingly, recent studies have revealed that GFs also contribute to the pathophysiology of periodontitis by promoting inflammatory responses. However, its specific molecular mechanism and clinical relevance are still not fully understood.

Methods

To find pro-inflammatory gingival fibroblasts (PIGFs) in periodontitis, a comprehensive analysis of single-cell RNA sequencing (scRNA-seq) data from normal and periodontitis patients was conducted. Then, the role of this celltype in periodontitis was further explored by using cell communication. By merging bulk transcriptome data and employing multiple machine learning algorithms, potential feature genes with PIGFs were further screened, which were verified by qPCR and immunofluorescence staining. Lastly, a cell function test was used to examine the part these genes play in the pathogenesis of periodontitis.

Results

Through single-cell sequencing analysis, we identified PIGFs which were closely related to the development of periodontitis. Cell communication analysis revealed the specific role of PIGFs in periodontitis. Differential gene analysis, WGCNA, and machine learning algorithms identified two genes (MME and TSPAN11) as potential therapeutic targets for periodontitis. Immune infiltration analysis demonstrated a significant correlation between these genes and the immune response. Functionally, down-regulation of MME and TSPAN11 promoted the proliferation and migration of GFs and significantly inhibited the release of inflammatory cytokines and chemokines.

Conclusion

This study identified a subpopulation of GFs closely associated with the inflammatory response through scRNA-seq analysis. These cells may contribute to the progression of periodontitis by interacting with various immune and non-immune cell types. Notably, MME and TSPAN11 were identified as key genes associated with this specific GFs subpopulation that may drive disease progression by exacerbating the inflammatory response, suggesting their potential as therapeutic targets for periodontitis.

Comprehensive analysis of IRF8-related genes and immune characteristics in lupus nephritis

Background

There are currently no reliable diagnostic biomarkers or treatments for lupus nephritis (LN), a complication of systemic lupus erythematosus. Objective: We aimed to explore gene networks and potential biomarkers for LN by analyzing the GSE32591 and GSE113342 datasets from the Gene Expression Omnibus database, focusing on IRF8 and IRF8-related genes.

Methods

We used differential expression analysis, functional enrichment, protein-protein interaction (PPI) network construction, and the CIBERSORT algorithm for immune infiltration assessment. To validate the expression levels of the IRF8 gene in the kidneys of lupus mice models, we used quantitative real-time PCR (qRT-PCR) and Western blotting (WB). A diagnostic classifier was built using the RandomForest method to evaluate the diagnostic potential of selected key genes. To bridge our findings with potential therapeutic implications, we used the drug-gene interaction database to predict drugs targeting the identified genes.

Results

Twenty co-differentially expressed genes (DEGs) were identified, with IRF8 exhibiting significant expression differences and potential as a biomarker. Functional enrichment analysis revealed pathways associated with immune response. Validation through qRT-PCR and WB confirmed that the IRF8 gene and its protein exhibited elevated expression levels in the kidneys of lupus mice compared to control groups. The diagnostic classifier revealed impressive accuracy in differentiating LN from control samples, achieving a notable area under the curve values across various datasets. Additionally, immune infiltration analysis indicated significant differences in the immune cell profiles between the LN and control groups.

Conclusion

IRF8 and its related genes show promise as biomarkers and therapeutic targets for LN. These findings contribute to a deeper understanding of the molecular mechanisms involved in LN and may support the development of precision medicine strategies for improved patient outcomes.

Predicting gene signature in breast cancer patients with multiple machine learning models

AIMS

The aim of this study was to predict gene signatures in breast cancer patients using multiple machine learning models.

METHODS

In this study, we first collated and merged the datasets GSE54002 and GSE22820, obtaining a gene expression matrix comprising 16,820 genes (including 593 breast cancer (BC) samples and 26 normal control (NC) samples). Subsequently, we performed enrichment analyses using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Disease Ontology (DO).

RESULTS

We identified 177 differentially expressed genes (DEGs), including 40 up-regulated and 137 down-regulated genes, through differential expression analysis. The GO enrichment results indicated that these genes are primarily involved in extracellular matrix organization, positive regulation of nervous system development, collagen-containing extracellular matrix, heparin binding, glycosaminoglycan binding, and Wnt protein binding, among others. KEGG enrichment analysis revealed that the DEGs were primarily associated with pathways such as focal adhesion, the PI3K-Akt signaling pathway, and human papillomavirus infection. DO enrichment analysis showed that the DEGs play a significant role in regulating diseases such as intestinal disorders, nephritis, and dermatitis. Further, through LASSO regression analysis and SVM-RFE algorithm analysis, we identified 9 key feature DEGs (CF-DEGs): ANGPTL7, TSHZ2, SDPR, CLCA4, PAMR1, MME, CXCL2, ADAMTS5, and KIT. Additionally, ROC curve analysis demonstrated that these CF-DEGs serve as a reliable diagnostic index. Finally, using the CIBERSORT algorithm, we analyzed the infiltration of immune cells and the associations between CF-DEGs and immune cell infiltration across all samples.

CONCLUSIONS

Our findings provide new insights into the molecular functions and metabolic pathways involved in breast cancer, potentially aiding in the discovery of new diagnostic and immunotherapeutic biomarkers.

Key genes and molecular mechanisms related to Paclitaxel Resistance

Paclitaxel is commonly used to treat breast, ovarian, lung, esophageal, gastric, pancreatic cancer, and neck cancer cells. Cancer recurrence is observed in patients treated with paclitaxel due to paclitaxel resistance emergence. Resistant mechanisms are observed in cancer cells treated with paclitaxel, docetaxel, and cabazitaxel including changes in the target molecule β-tubulin of mitosis, molecular mechanisms that activate efflux drug out of the cells, and alterations in regulatory proteins of apoptosis. This review discusses new molecular mechanisms of taxane resistance, such as overexpression of genes like the multidrug resistance genes and EDIL3, ABCB1, MRP1, and TRAG-3/CSAG2 genes. Moreover, significant lncRNAs are detected in paclitaxel resistance, such as lncRNA H19 and cross-resistance between taxanes. This review contributed to discovering new treatment strategies for taxane resistance and increasing the responsiveness of cancer cells toward chemotherapeutic drugs.

Study on Potential Differentially Expressed Genes in Idiopathic Pulmonary Fibrosis by Bioinformatics and Next-Generation Sequencing Data Analysis

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive lung disease with reduced quality of life and earlier mortality, but its pathogenesis and key genes are still unclear. In this investigation, bioinformatics was used to deeply analyze the pathogenesis of IPF and related key genes, so as to investigate the potential molecular pathogenesis of IPF and provide guidance for clinical treatment. Next-generation sequencing dataset GSE213001 was obtained from Gene Expression Omnibus (GEO), and the differentially expressed genes (DEGs) were identified between IPF and normal control group. The DEGs between IPF and normal control group were screened with the DESeq2 package of R language. The Gene Ontology (GO) and REACTOME pathway enrichment analyses of the DEGs were performed. Using the g:Profiler, the function and pathway enrichment analyses of DEGs were performed. Then, a protein-protein interaction (PPI) network was constructed via the Integrated Interactions Database (IID) database. Cytoscape with Network Analyzer was used to identify the hub genes. miRNet and NetworkAnalyst databaseswereused to construct the targeted microRNAs (miRNAs), transcription factors (TFs), and small drug molecules. Finally, receiver operating characteristic (ROC) curve analysis was used to validate the hub genes. A total of 958 DEGs were screened out in this study, including 479 up regulated genes and 479 down regulated genes. Most of the DEGs were significantly enriched in response to stimulus, GPCR ligand binding, microtubule-based process, and defective GALNT3 causes HFTC. In combination with the results of the PPI network, miRNA-hub gene regulatory network and TF-hub gene regulatory network, hub genes including LRRK2, BMI1, EBP, MNDA, KBTBD7, KRT15, OTX1, TEKT4, SPAG8, and EFHC2 were selected. Cyclothiazide and rotigotinethe are predicted small drug molecules for IPF treatment. Our findings will contribute to identification of potential biomarkers and novel strategies for the treatment of IPF, and provide a novel strategy for clinical therapy.