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Identification of Diagnostic Markers Correlated With HIV⁺ Immune Non-response Based on Bioinformatics Analysis.

ABSTRACT: Background: HIV-infected immunological non-responders (INRs) are characterized by their inability to reconstitute CD4⁺ T cell pools after antiretroviral therapy. The risk of non-AIDS-related diseases in INRs is increased, and the outcome and prognosis of INRs are inferior to that of immunological responders (IRs). However, few markers can be used to define INRs precisely. In this study, we aim to identify further potential diagnostic markers associated with INRs through bioinformatic analyses of public datasets. Methods: This study retrieved the microarray data sets of GSE106792 and GSE77939 from the Gene Expression Omnibus (GEO) database. After merging two microarray data and adjusting the batch effect, differentially expressed genes (DEGs) were identified. Gene Ontology (GO) resource and Kyoto Encyclopedia of Genes and Genomes (KEGG) resource were conducted to analyze the biological process and functional enrichment. We performed receiver operating characteristic (ROC) curves to filtrate potential diagnostic markers for INRs. Gene Set Enrichment Analysis (GSEA) was conducted to perform the pathway enrichment analysis of individual genes. Single sample GSEA (ssGSEA) was performed to assess scores of immune cells within INRs and IRs. The correlations between the diagnostic markers and differential immune cells were examined by conducting Spearman's rank correlation analysis. Subsequently, miRNA-mRNA-TF interaction networks in accordance with the potential diagnostic markers were built with Cytoscape. We finally verified the mRNA expression of the diagnostic markers in clinical samples of INRs and IRs by performing RT-qPCR. Results: We identified 52 DEGs in the samples of peripheral blood mononuclear cells (PBMC) between INRs and IRs. A few inflammatory and immune-related pathways, including chronic inflammatory response, T cell receptor signaling pathway, were enriched. FAM120AOS, LTA, FAM179B, JUN, PTMA, and SH3YL1 were considered as potential diagnostic markers. ssGSEA results showed that the IRs had significantly higher enrichment scores of seven immune cells compared with IRs. The miRNA-mRNA-TF network was constructed with 97 miRNAs, 6 diagnostic markers, and 26 TFs, which implied a possible regulatory relationship. Conclusion: The six potential crucial genes, FAM120AOS, LTA, FAM179B, JUN, PTMA, and SH3YL1, may be associated with clinical diagnosis in INRs. Our study provided new insights into diagnostic and therapeutic targets.

SUBMITTER: Bai R

PROVIDER: S-EPMC8727996 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundPrimary Sjögren's syndrome (pSS) is a relatively common diffuse connective tissue disease that often invades exocrine glands, such as the lacrimal and salivary glands, and manifests as dry eyes and dry mouth. At present, the molecular mechanism of pSS is not clear. This study was designed to explore the internal mechanism of pSS from the gene level and screen out the immune-related diagnostic markers of pSS.MethodsThe gene expression profiles GSE84844, GSE7451, and GSE40611 were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified with R software. Then, the DEGs were intersected with the immune genes obtained from the ImmPort database to acquire differentially expressed immune-related genes (DEIRGs), and functional enrichment analyses were performed. The DEIRGs were screened through the least absolute shrinkage and selection operator (LASSO) logistic regression algorithm to obtain the optimal immune-related genes (IRGs). Expression levels of the optimal IRGs were verified by quantitative reverse transcription-polymerase chain reaction (qRT-PCR) to obtain the key genes. Next, gene chips GSE7451 and GSE40611, from other tissues, were selected as the training sets to verify the sensitivity and specificity of the diagnosis of the key genes by receiver operating characteristic (ROC) analysis.ResultsA total of 54 DEIRGs were obtained. The functional enrichment analysis results showed that they play an important role in immune and inflammatory responses. Nine optimal IRGs were screened from the DEIRGs by the LASSO logistic regression algorithm. After qRT-PCR verification, eight out of nine optimal IRGs (IL-18, JAK2, TBK1, EED, TNFSF10, TNFSF13B, CYSLTR1, and ICOS) were significantly highly expressed in pSS patients and were defined as key genes. ROC analysis identified that TNFSF13B and CYSLTR1 had high sensitivity and specificity. Finally, the lack of previous research on EED and CYSLTR1 in pSS suggests that these IRGs may be regarded as new gateways to explore the diagnosis and pathogenesis of pSS.ConclusionsThe key DEIRGs play a decisive role during the occurrence and development of pSS.

Project description:Purpose HIV-infected immunological non-responders (INRs) failed to achieve the normalization of CD4+ T cell counts despite their undetectable viral load. INRs have an increased risk of clinical progressions of Acquired Immunodeficiency Syndrome (AIDS) and non-AIDS events, accompanied by higher mortality rates than immunological responders (IRs). This study aimed to discover the genes, which help to distinguish INRs from IRs and explore the possible mechanism of INRs. Methods Screening DEGs between INRs and IRs using GEO microarray dataset GSE143742. DEG biological functions were investigated using GO and KEGG analysis. DEGs and WGCNA linked modules were intersected to find common genes. Key genes were identified using SVM-RFE and LASSO regression models. ROC analysis was done to evaluate key gene diagnostic effectiveness using GEO database dataset GSE106792. Cytoscape created a miRNA-mRNA-TF network for diagnostic genes. CIBERSORT and flow cytometry examined the INRs and IRs immune microenvironments. In 10 INR and 10 IR clinical samples, diagnostic gene expression was verified by RT-qPCR and Western blot. Results We obtained 190 DEGs between the INR group and IR group. Functional enrichment analysis found a significant enrichment in mitochondria and apoptosis-related pathways. CD69 and ZNF207 were identified as potential diagnostic genes. CD69 and ZNF207 shared a transcription factor, NCOR1, in the miRNA-mRNA-TF network. Immune microenvironment analysis by CIBERSORT showed that IRs had a higher level of resting memory CD4+ T cells, lower level of activated memory CD4+ T cells and resting dendritic cells than INRs, as confirmed by flow cytometry analysis. In addition, CD69 and ZNF207 were correlated with immune cells. Experiments confirmed the expression of the diagnostic genes in INRs and IRs. Conclusion CD69 and ZNF207 were identified as potential diagnostic genes to discriminate INRs from IRs. Our findings offered new clues to diagnostic and therapeutic targets for INRs.

Project description:Periodontitis is the major cause of tooth loss in adults and is linked to systemic illnesses, such as cardiovascular disease and stroke. The development of rapid point-of-care (POC) chairside diagnostics has the potential for the early detection of periodontal infection and progression to identify incipient disease and reduce health care costs. However, validation of effective diagnostics requires the identification and verification of biomarkers correlated with disease progression. This clinical study sought to determine the ability of putative host- and microbially derived biomarkers to identify periodontal disease status from whole saliva and plaque biofilm.One hundred human subjects were equally recruited into a healthy/gingivitis group or a periodontitis population. Whole saliva was collected from all subjects and analyzed using antibody arrays to measure the levels of multiple proinflammatory cytokines and bone resorptive/turnover markers.Salivary biomarker data were correlated to comprehensive clinical, radiographic, and microbial plaque biofilm levels measured by quantitative polymerase chain reaction (qPCR) for the generation of models for periodontal disease identification. Significantly elevated levels of matrix metalloproteinase (MMP)-8 and -9 were found in subjects with advanced periodontitis with Random Forest importance scores of 7.1 and 5.1, respectively. The generation of receiver operating characteristic curves demonstrated that permutations of salivary biomarkers and pathogen biofilm values augmented the prediction of disease category. Multiple combinations of salivary biomarkers (especially MMP-8 and -9 and osteoprotegerin) combined with red-complex anaerobic periodontal pathogens (such as Porphyromonas gingivalis or Treponema denticola) provided highly accurate predictions of periodontal disease category. Elevated salivary MMP-8 and T. denticola biofilm levels displayed robust combinatorial characteristics in predicting periodontal disease severity (area under the curve = 0.88; odds ratio = 24.6; 95% confidence interval: 5.2 to 116.5).Using qPCR and sensitive immunoassays, we identified host- and bacterially derived biomarkers correlated with periodontal disease. This approach offers significant potential for the discovery of biomarker signatures useful in the development of rapid POC chairside diagnostics for oral and systemic diseases. Studies are ongoing to apply this approach to the longitudinal predictions of disease activity.

Project description:Background and aimGiven the ongoing problems of hypertension and endothelial dysfunction in the HIV population, the primary objective of the study was to assess the cardiovascular, endothelial function, and immune markers in response to rice bran arabinoxylan compound (RBAC) treatment in a sample of HIV+ adults on antiretroviral therapy (ART).Study designA randomized, double-blind placebo-controlled trial of 6 months was used to execute the study.Materials and methodsForty-seven subjects were enrolled and randomly assigned to one of two study conditions (n=22 RBAC and n=25 placebo) for 6 months with assessments at baseline and 3 and 6 months. A multivariate repeated measures analysis of variance model was used to assess the differences between RBAC and placebo groups in cardiovascular (systolic blood pressure), endothelial function (skin blood flow in response to nitric oxide), and immune (CD4+ cell count) markers from baseline to 6 months.ResultsThe effect of treatment (RBAC versus placebo) was significant (Wilks' λ=0.92, F[3, 102]=3.07, P=0.03). The effect of time was significant (Wilks' λ=0.10, F[2, 103]=474.6, P<0.001). The overall interaction between treatment and time was significant (Wilks' λ=0.92, F[2, 103]=4.58, P=0.01). Time contrasts showed that a difference in the overall dependent variable did not occur from baseline to 3 months (F[1, 104]=2.7, P=0.10), marginally occurred from baseline to 6 months (F[1, 104]=3.2, P=0.08), and was significant from 3 to 6 months (F[1, 104]=6.43, P=0.01).ConclusionsThe overall significant interaction suggests varying responses in the dependent variables between RBAC and placebo over time, which is being driven by systolic blood pressure, as it decreased in the RBAC group, but increased in the placebo group. In addition, CD4+ manifested a non-significant increase from baseline to 3 months then decreased from 3 to 6 months in the RBAC group, whereas it decreased at 3 months followed by a slight increase at 6 months in the placebo group. Skin blood flow in response to nitric oxide improved non-significantly overall in both groups, but worsened from 3 to 6 months in the placebo group. Thus, RBAC treatment may contribute to modest short-term improvements in systolic blood pressure, endothelial function, and CD4+ cell count, which could help improve the overall health profile of HIV+ adults.Relevance for patientsPersons with HIV on ART suffer disproportionately from hypertension and endothelial dysfunction compared to the non-infected population, and conventional medical therapy does not alleviate these issues. RBAC is a safe, low-risk alternative that may help to improve the overall quality of life of these patients through modest improvements in these biomarkers plus CD4+ cell count.

Project description:Background: Atopic dermatitis (AD) is a dermatological disorder characterized by symptoms such as chronically inflamed skin and frequently intolerable itching. The mechanism underlying AD development is still unclear. Our study aims to identify the diagnostic and therapeutic biomarkers for AD and provide insight into immune mechanisms at the molecular level through bioinformatics analysis. Methods: The GSE6012, GSE32924, and GSE36842 gene expression profiles were obtained for analysis from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were segregated using the “Batch correction” and “RobustRankAggreg” methods. Weighted gene co-expression network analysis (WGCNA) was performed to screen for module genes with AD traits. Then, common DEGs (co-DEGs) were screened out via combined differential expression analysis and WGCNA. Functional enrichment analysis was performed for these co-DEGs using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), followed by protein-protein interaction network analysis. Candidate hub genes were identified using the “cytoHubba” plugin in Cytoscape, and their value for AD diagnosis was validated using receiver operating characteristic curve analysis in the external database GSE120721. Immunohistochemical staining was performed for further validation. The CIBERSORT algorithm was used to evaluate skin samples obtained from healthy controls (HCs) and lesions of AD patients, to determine the extent of immune cell infiltration. The association between the identified hub genes and significant differential immune cells was analyzed using Pearson correlation analysis. Results: A total of 259 DEGs were acquired from the intersection of DEGs obtained by the two independent procedures, and 331 AD-trait module genes were separated out from the blue module via WGCNA analysis. Then, 169 co-DEGs arising from the intersection of the 259 DEGs and the 331 AD-trait module genes were obtained. We found that co-DEGs were significantly enhanced in the type I interferon and IL-17 signal transduction pathways. Thirteen potential hub genes were identified using Cytoscape. Five hub genes (CCR7, CXCL10, IRF7, MMP1, and RRM2) were identified after screening via external dataset validation and immunohistochemical analysis. We also identified four significant differential immune cells, i.e., activated dendritic cells, plasma cells, resting mast cells, and CD4+ naïve T cells, between AD patients and HCs. Moreover, the relationship between the identified hub genes and significant differential immune cells was analyzed. The results showed that the CCR7 expression level was positively correlated with the number of CD4+ naïve T cells (R = 0.42, p = 0.011). Conclusion: CCR7, CXCL10, IRF7, MMP1, and RRM2 could be potential diagnostic and therapeutic biomarkers for AD. CCR7 expression level was positively correlated with the number of CD4+ naïve T cells in AD. These findings need to be corroborated in future studies.

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Identification of Diagnostic Markers Correlated With HIV⁺ Immune Non-response Based on Bioinformatics Analysis.

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Identification of Diagnostic Markers Correlated With HIV+ Immune Non-response Based on Bioinformatics Analysis.

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OmicsDI is part of the ELIXIR infrastructure

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Identification of Diagnostic Markers Correlated With HIV⁺ Immune Non-response Based on Bioinformatics Analysis.