The role of T cells and shared genes in psoriasis and inflammatory bowel disease based on single-cell RNA and comprehensive analysis.

Psoriasis and inflammatory bowel disease (IBD) have a similar etiology, including abnormal activation of T cells. Differentially expressed genes (DEGs) analysis was used to search for shared genes. GO (Gene Ontology) and KEGG (Kyoto Encyclopedia of Genes and Genomes) analysis were then performed. Secondly, single-cell RNA analysis (scRNA-seq) and immune infiltration were employed to explore the immune imbalance of the diseases. By weighted gene co expression network analysis (WGCNA), we obtained hub shared genes. Furthermore, we analyzed the diagnostic performance and immune association with the hub genes. Finally, functional enrichment of miRNAs related to hub shared genes was carried out. Single-cell analysis showed a high proportion of T cells among infiltrated immune cells and immune infiltration showed CD4+ T and γδ T cells were significantly elevated in diseases. Hub shared genes, LCN2, CXCL1 and PI3 had excellent diagnostic properties and were positively correlated with neutrophils, CD4+ T and γδ T cells. IL17 and TNF signaling pathway were the common pathway. In conclusion, CD4+ and γδ T cells and hub shared genes may play a crucial part in common mechanism between psoriasis and IBD. Moreover, hub shared genes may be potential diagnostic markers.

Identification of prognostic genes for breast cancer related to systemic lupus erythematosus by integrated analysis and machine learning.

BACKGROUND: Systemic Lupus Erythematosus (SLE) is an autoimmune disease with multi-organ involvement, and some studies have found that SLE has a reduced risk of breast cancer (BRCA). So, we tried to find prognostic genes for BRCA related to SLE by integrated analysis and machine learning. METHOD: First, we downloaded 2 SLE datasets from Gene Expression Omnibus (GEO) and BRCA data from the Cancer Genome Atlas (TCGA). Subsequently, we performed differentially expressed genes (DEGs) and functional enrichment analysis by Metascape in SLE. Genes that were differentially expressed in both datasets were the validated DEGs. And after constructing PPI network, genes with nodes >30 were intersected with survival genes in BRCA to obtain candidate genes. Then, the candidate genes were validated by lasso regression in both training and validation sets to obtain prognostic genes. Afterwards, we investigated the diagnostic potential of prognostic genes for SLE and the predictive efficacy for BRCA prognosis. Moreover, GSEA analysis and immune infiltration were performed for SLE and BRCA. Finally, we constructed a prognostic gene-miRNAs network and did functional enrichment of the shared genes. RESULT: DEGs for SLE were mainly enriched with neutrophil degranulation and IFN pathways. After the lasso model of BRCA was established, IRF7, IFI35 and EIF2AK2, were identified as prognostic genes for BRCA related to SLE and had good predictive ability for the prognosis of BRCA. Prognostic genes had excellent diagnostic potential for SLE, with IFI35 and EIF2AK2 positively associated with SLE activity and IRF7 positively associated with IFI35. GSEA showed that both SLE and BRCA were associated with ubiquitinated degradation. Immune infiltrates suggest that plasma cells, dendritic cells (DC), neutrophils and monocyte were elevated in SLE. DC, NK and CD8+ T cells were elevated in the BRCA low-risk group. Finally, 5 shared miRNAs were confirmed, which were mainly enriched in the IFN pathway. CONCLUSION: IRF7, IFI35 and EIF2AK2, were identified as prognostic genes for BRCA related to SLE. IFN pathway played an important role in the etiology of SLE and the prognosis of BRCA.

Genes associated with cellular senescence favor melanoma prognosis by stimulating immune responses in tumor microenvironment.

PURPOSE: Skin cutaneous melanoma (SKCM), a malignant tumor from melanocytes, is the fifth most prevalent tumor. Immune checkpoint inhibitor (ICI) immunotherapy improves prognosis of SKCM, but immune response varies for different populations. Cellular senescence in the tumor microenvironment (TME) promotes antitumor immunity, mediated by dendritic cells (DC) and CD8+ T cells. Therefore, we sought to explore the role of cellular senescence in the TME of SKCM through bioinformatics and machine learning. METHODS: First, we obtained 93 cellular senescence-prognosis genes (CSPGs) by univariate survival analysis. Thereafter, 23 optimal CSPGs were obtained by least absolute shrinkage and selection operator (lasso) analysis. Based on the riskscore obtained by lasso analysis and clinical information from multivariate cox, we obtained the nomogram of SKCM, which was validated in the validation cohort. Based on the riskscore, the patients were split into low- and high-risk groups. Functional differences between the two groups were analyzed using Metascape and GSEA, and immune infiltration differences were achieved by multiple algorithms. We obtained a risk prediction nomogram for the validated SKCM based on the lasso model by univariate and multivariate cox regression analysis. RESULTS: In the low-risk group, immune responses were in an active state. NK, CD8+ T, DC, macrophages, and neutrophils were significantly upregulated, and ICI-relevant genes were notably upregulated. With the differentially expressed genes (DEGs) and optimal CSPGs, we obtained the hub genes: NOX4, NTN4, PROX1, and TRPM8. The hub genes were mainly expressed by cancer-associated fibroblasts (CAFs) and endothelial cells by single cell analysis, which were mainly associated with angiogenesis. CONCLUSION: Genes associated with cellular senescence favor SKCM prognosis by stimulating immune responses in TME. Patients with high expression of cellular senescence associated genes in the TME might have better benefit from ICI immunotherapy. Cellular senescence functions as a pro-tumor agent in mesenchymal cells and needs further study.

Integrated analysis and clinical correlation analysis of hub genes, immune infiltration, and potential therapeutic agents related to lupus nephritis.

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.

Exploration of the molecular mechanisms, shared gene signatures, and MicroRNAs between systemic lupus erythematosus and diffuse large B cell lymphoma by bioinformatics analysis.

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.