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Identification of Liver Immune Microenvironment-Related Hub Genes in Liver of Biliary Atresia

ABSTRACT: Background: Biliary atresia (BA) is one of the most common and fatal abnormalities of newborns. Increasing evidences indicated that immunology was the critical part of the etiology. This research used a public gene expression database to explore the immune microenvironment of BA liver. Methods: The gene expression profiles GSE46960, GSE159720, and GSE15235, containing BA and normal liver gene expression data, were obtained from the Expression Omnibus Gene. We applied CIBERSORTx to quantify 22 subsets of immune cells in BA liver. The differentially expressed genes (DEGs) and immune cells were used to further explore their relationship with liver fibrosis and the inflammation status of BA. Results: The expression of immune-related genes CXCL6, CXCL8, CXCL10, CCL20, IL32, TGFB2, SPP1, and SLIT2 was significantly different between BA and normal liver, among which CXCL8 was the hub gene. Six of 22 immune cell proportions were significantly different between BA and normal liver. Specifically, M0 macrophages and resting memory CD4+ T cells were upregulated in BA liver compared with normal liver. Meanwhile, monocytes, resting natural killer (NK) cells, plasma cells, and regulatory T (Treg) cells were downregulated. A further correlation analysis revealed that SLIT2 and CXCL6 owned high positive correlation coefficients with fibrosis grade, while the proportion of resting NK cells was negatively correlated. Proportions of resting CD4+ memory T cells were strongly related to the inflammation grade of BA liver. Conclusion: Biliary atresia is a disease strongly correlated with immune response. Our results might provide a clue for further exploration of BA etiology, which may promote a potential prediction model based on immune infiltration features.

SUBMITTER: Zhang J

PROVIDER: S-EPMC8802228 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundEndometriosis, a common gynecological condition, can cause symptoms such as dysmenorrhea, infertility, and abnormal bleeding, which can negatively affect a woman's quality of life. In the current study, the pathophysiological mechanisms of endometriosis are unknown, but this study suggests that endometriosis is associated with dysregulation of the autoimmune system. This study identify hub genes involved in the prevalence, identification and diagnostic value of endometriosis and autoimmune diseases, and explore the central genes and immune infiltrates, the diagnosis of endometriosis provides a new sight of thinking about diagnosis and treatment.Methods and resultsThe relevant datasets for endometriosis GSE141549, GSE7305 and autoimmune disease-related genes (AIDGs) were downloaded from online database. Using the "limma" package and WGCNA to screen out the autoimmune disease related genes and endometriosis related genes, the autoimmune disease gene-related differential genes (AID-DEGs) progressive GO, KEGG enrichment analysis, and then using the protein interaction network and Cytoscape software to select hub genes (CXCL12, PECAM1, NGF, CTGF, WNT5A), using the "pROC" package to analyze the hub genes for the diagnostic value of endometriosis. The difference in the importance of hub genes for the diagnosis of endometriosis was analyzed by machine learning random forest, and the combined diagnostic value of hub genes was analyzed by using the Support Vector Machine (SVM) algorithm. The eutopic (EU) and ectopic endometrium (EC) immune microenvironment of endometriosis was evaluated using CIBERSORT, the correlation of hub genes to the immune microenvironment was analyzed.ConclusionThe hub genes associated with AIDGs are differentially expressed in EC and EU of endometriosis and possess important value for the diagnosis of endometriosis. The hub genes have a very important impact on the immune microenvironment of endometriosis, which is important for exploring the connection between endometriosis and autoimmune diseases and provides a new insight for the subsequent study of immunotherapy and diagnosis of endometriosis.

Project description:IntroductionPeriodontitis is an inflammatory disease and its molecular mechanisms is not clear. A recently discovered cell death pathway called cuproptosis, may related to the disease.MethodsThe datasets GSE10334 of human periodontitis and control were retrieved from the Gene Expression Omnibus database (GEO) for analysis.Following the use of two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature removal (SVM-RFE) were used to find CRG-based signature. Then the Receiver operating characteristic (ROC) curves was used to evaluate the gene signature's discriminatory ability. The CIBERSORT deconvolution algorithm was used to study the link between hub genes and distinct types of immune cells. Next, the association of the CRGs with immune cells in periodontitis and relevant clusters of cuproptosis were found. The link between various clusters was ascertained by the GSVA and CIBERSORT deconvolution algorithm. Finally, An external dataset (GSE16134) was used to confirm the diagnosis capacity of the identified biomarkers. In addition, clinical samples were examined using qRT-PCR and immunohistochemistry to verifiy the expression of genes related to cuprotosis in periodontitis and the signature may better predict the periodontitis.Results15 periodontitis-related DE-CRGs were found,then 11-CRG-based signature was found by using of LASSO and SVM-RFE. ROC curves also supported the value of signature. CIBERSORT results of immune cell signature in periodontitis showed that signature genes is a crucial component of the immune response.The relevant clusters of cuproptosis found that the NFE2L2, SLC31A1, FDX1,LIAS, DLD, DLAT, and DBT showed a highest expression levels in Cluster2 ,while the NLRP3, MTF1, and DLST displayed the lowest level in Cluster 2 but the highest level in Cluster1. The GSVA results also showed that the 11 cuproptosis diagnostic gene may regulate the periodontitis by affecting immune cells. The external dataset (GSE16134) confirm the diagnosis capacity of the identified biomarkers, and clinical samples examined by qRT-PCR and immunohistochemistry also verified that these cuprotosis related signiture genes in periodontitis may better predict the periodontitis.ConclusionThese findings have important implications for the cuproptosis and periodontitis, and highlight further research is needed to better understand the mechanisms underlying this relationship between the cuproptosis and periodontitis.

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Identification of Liver Immune Microenvironment-Related Hub Genes in Liver of Biliary Atresia

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