Background Systemic lupus erythematosus (SLE) is a complex heterogeneous systemic autoimmune disease. Previous studies have shown that SLE may be related to diffuse large B cell lymphoma (DLBCL), but the mechanism of their relationship is still unclear. The present study aimed to explore the common genetic molecular mechanisms, core shared genes, and miRNAs between SLE and DLBCL as well as to investigate the diagnostic markers of DLBCL. Methods The SLE and DLBCL microarray data were downloaded from the comprehensive Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was used to identify co-expression modules. Four core shared genes were screened out by various algorithms and validated in other cohorts. Finally, we constructed a common core gene-miRNA network using the human microRNA disease database (HMDD) and TarBase. Results Using WGCNA, four modules were identified as important modules for SLE and DLBCL. Enrichment analysis of the shared genes showed that the highly activated NF-κB pathway was a common feature of the pathophysiology. Four core shared genes, namely, PSMB10, PSMB4, TAF10, and NFΚBIA, were screened out. These core shared genes were significantly upregulated in both diseases, and they may be potential diagnostic markers of DLBCL. The core gene-miRNA network showed that miR-155–5p, regulating the shared NF-κB pathway, may play an important role in the susceptibility of SLE patients to DLBCL. Conclusion The present study revealed that NF-κB pathway in SLE may be a crucial susceptible factor for DLBCL. In addition, we identified PSMB10, PSMB4, TAF10, NFΚBIA and miR-155 involved in the common pathogenesis as potential biomarkers and therapeutic targets for DLBCL.
Objective Lupus nephritis (LN) is the most common complication of systemic lupus erythematosus (SLE), which is an autoimmune disease involving multiple organs. This study aimed to explore the potential molecular mechanism of LN by bioinformatic analysis. Method In total, 130 LN, 25 living healthy donors were included to explore the differentially expressed genes (DEGs). The protein-protein interaction (PPI) network of DEGs were developed using the STRING database. Additionally, five algorithms were used to find the hub genes and their diagnostic effectiveness was predicted using receiver operator characteristic curve (ROC) analysis. CIBERSORT was used to evaluate the infiltration of immune cells in LN. Furthermore, the mRNA-miRNA network was constructed. Finally, we explored the landscape of N6-methyladenosine (m6A) regulators in LN. Result Two hub genes—FOS and IGF1—were found by the intersection of five different algorithms. FOS and IGF1 could jointly diagnose LN with the most excellent specificity and sensitivity. LN patients had lower activated and resting dendritic cells (DCs) while higher M1 macrophages and activated NK cells than HC. FOS had a positive correlation with activated mast cells and a negative correlation with resting mast cells. IGF1 had a positive correlation with activated DCs and a negative correlation with monocytes. MiR-155 is considered as the hub miRNA. And m6A modification is related to the severity of renal injury and involved in the pathogenesis of LN. MiRNA may affect the occurrence and development of LN by targeting m6A regulators. Conclusion Activated DCs, resting DCs, M1 macrophages, and activated NK cells may play a role in LN pathogenesis. FOS, IGF1 and miR-155 may be new potential molecular markers for the pathogenesis, progression and new molecular targets for treatment of LN. MiRNA may affect the occurrence and development of LN by targeting m6A regulators.
Background Lupus nephritis (LN) is the most common complication of systemic lupus erythematosus (SLE). This study aimed to explore biomarkers, mechanisms, and potential novel agents regarding LN through bioinformatic analysis. Method Four expression profiles were downloaded from the Gene Expression Omnibus (GEO) database and differentially expressed genes (DEGs) were acquired. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGGs) pathway enrichment analyses of DEGs were performed using the R software. The protein-protein interaction (PPI) network was developed using the STRING database. Additionally, five algorithms were used to screen out the hub genes. Expression of the hub genes were validated using Nephroseq v5. CIBERSORT was used to evaluate the infiltration of immune cells. Finally, The Drug-Gene Interaction Database was used to predict potential targeted drugs. Result FOS and IGF1 were identified as hub genes, with excellent specificity and sensitivity diagnosis of LN. FOS was also related to renal injury. LN patients had lower activated and resting dendritic cells (DCs) and higher M1 macrophages and activated NK cells than healthy control (HC). FOS had a positive correlation with activated mast cells and a negative correlation with resting mast cells. IGF1 had a positive correlation with activated DCs and a negative correlation with monocytes. The targeted drugs were dusigitumab and xentuzumab target for IGF1. Conclusion We analyzed the transcriptomic signature of LN along with the landscape of the immune cell. FOS and IGF1 are promising biomarkers for diagnosing and evaluating the progression of LN. The drug-gene interaction analyses provide a list of candidate drugs for the precise treatment of LN.
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