RAB5C, SYNJ1, and RNF19B promote male ankylosing spondylitis by regulating immune cell infiltration

PLCG2 and IFNAR1: The Potential Biomarkers Mediated by Immune Infiltration and Osteoclast Differentiation of Ankylosing Spondylitis in the Peripheral Blood

Objectives

Ankylosing spondylitis (AS) is a chronic inflammatory rheumatic disease characterized by chronic spinal inflammation, arthritis, gut inflammation, and enthesitis. We aimed to identify the key biomarkers related to immune infiltration and osteoclast differentiation in the pathological process of AS by bioinformatic methods.

Methods

GSE25101 from the Gene Expression Omnibus was used to obtain AS-associated microarray datasets. We performed bioinformatics analysis using R software to validate different expression levels. The purpose of the GO and KEGG enrichment analyses of DEGs was to exclude key genes. Using weighted correlation network analysis (WGCNA), we examined all expression profile data and identified differentially expressed genes. The objective was to investigate the interaction between genetic and clinical features and to identify the essential relationships underlying coexpression modules. The CIBERSORT method was used to make a comparison of the immune infiltration in whole blood between the AS group and the control group. The WGCNA R program from Bioconductor was used to identify hub genes. RNA extraction reverse transcription and quantitative polymerase chain reaction were conducted in the peripheral blood collected from six AS patients and six health volunteers matched by age and sex.

Results

125 DEGs were identified, consisting of 36 upregulated and 89 downregulated genes that are involved in the cell cycle and replication processes. In the WGCNA, modules of MCODE with different algorithms were used to find 33 key genes that were related to each other in a strong way. Immune infiltration analysis found that naive CD4+ T cells and monocytes may be involved in the process of AS. PLCG2 and IFNAR1 genes were obtained by screening genes meeting the conditions of immune cell infiltration and osteoclast differentiation in AS patients among IGF2R, GRN, SH2D1A, LILRB3, IFNAR1, PLCG2, and TNFRSF1B. The results demonstrated that the levels of PLCG2 mRNA expression in AS were considerably higher than those in healthy individuals (P=0.003). IFNAR1 mRNA expression levels were considerably lower in AS than in healthy individuals (P < 0.0001).

Conclusions

Dysregulation of PLCG2 and IFNAR1 are key factors in disease occurrence and development of AS through regulating immune infiltration and osteoclast differentiation. Explaining the differences in immune infiltration and osteoclast differentiation between AS and normal samples will contribute to understanding the development of spondyloarthritis.

Comprehensive analysis and validation reveal potential MYCN regulatory biomarkers associated with neuroblastoma prognosis

Neuroblastoma (NB) is an embryonic malignant tumor that occurs in the sympathetic nervous system. The treatment results of patients in the high-risk group are poor, and relapse and treatment failure can occur even with multiple combination treatments. The proto-oncogene MYCN is a BHLH Transcription Factor used as an independent prognostic factor for NB. The proportion of MYCN amplification in tumor tissues of high-risk patients reaches 40-50%. Hence, exploring new MYCN target genes is a meaningful approach in developing treatment for high-risk NB patients. The microarray datasets were obtained from Gene Expression Omnibus (GEO), and differentially expressed genes (DEGs) were identified. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and miRPathDB were used for enrichment analysis. STRING and Cytoscape were used to construct a protein-protein interaction (PPI) network and for modular analysis. The miRNet and NetworkAnalyst databases were used to predict and construct gene-miRNA and gene-TFs networks. The R2 database was used for expression, correlation, and prognostic analyses. The diagnostic value of the biomarkers was predicted by ROC analysis, and RT-qPCR was used to validate the identified hub genes. Finally, using specific MYCN siRNA and overexpressing plasmids, the correlation between the identified hub genes and MYCN was investigated. Our results showed that FBXO9, HECW2, MIB2, RNF19B, RNF213, TRIM36, and ZBTB16 are novel biomarkers that affect the prognosis of the NB patients. In addition, FBXO9, RNF19B, and TRIM36 were preliminarily confirmed as potential target genes of MYCN. Overall, FBXO9, HECW2, MIB2, RNF19B, RNF213, TRIM36, and ZBTB16 are expected to become novel biomarkers for the treatment of high-risk NB patients.Communicated by Ramaswamy H. Sarma.

New Insights into the Regulatory Role of Ferroptosis in Ankylosing Spondylitis via Consensus Clustering of Ferroptosis-Related Genes and Weighted Gene Co-Expression Network Analysis

Background: The pathogenesis of ankylosing spondylitis (AS) remains undetermined. Ferroptosis is a newly discovered form of regulated cell death involved in multiple autoimmune diseases. Currently, there are no reports on the connection between ferroptosis and AS. Methods: AS samples from the Gene Expression Omnibus were divided into two subgroups using consensus clustering of ferroptosis-related genes (FRGs). Weighted gene co-expression network analysis (WGCNA) of the intergroup differentially expressed genes (DEGs) and protein–protein interaction (PPI) analysis of the key module were used to screen out hub genes. A multifactor regulatory network was then constructed based on hub genes. Results: The 52 AS patients in dataset GSE73754 were divided into cluster 1 (n = 24) and cluster 2 (n = 28). DEGs were mainly enriched in pathways related to mitochondria, ubiquitin, and neurodegeneration. Candidate hub genes, screened by PPI and WGCNA, were intersected. Subsequently, 12 overlapping genes were identified as definitive hub genes. A multifactor interaction network with 45 nodes and 150 edges was generated, comprising the 12 hub genes and 32 non-coding RNAs. Conclusions: AS can be divided into two subtypes according to FRG expression. Ferroptosis might play a regulatory role in AS. Tailoring treatment according to the ferroptosis status of AS patients can be a promising direction.