Concordant integrative gene set enrichment analysis of multiple large-scale two-sample expression data sets

BackgroundPrognostic markers specific to a particular cancer type can assist in the evaluation of survival probability of patients and help clinicians to assess the available treatment modalities.MethodsGene expression data was analyzed from three independent colon cancer microarray gene expression data sets (N = 1052). Survival analysis was performed for the three data sets, stratified by the expression level of the LINE-1 type transposase domain containing 1 (L1TD1). Correlation analysis was performed to investigate the role of the interactome of L1TD1 in colon cancer patients.ResultsWe found L1TD1 as a novel positive prognostic marker for colon cancer. Increased expression of L1TD1 associated with longer disease-free survival in all the three data sets. Our results were in contrast to a previous study on medulloblastoma, where high expression of L1TD1 was linked with poor prognosis. Notably, in medulloblastoma L1TD1 was co-expressed with its interaction partners, whereas our analysis revealed lack of co-expression of L1TD1 with its interaction partners in colon cancer.ConclusionsOur results identify increased expression of L1TD1 as a prognostic marker predicting longer disease-free survival in colon cancer patients.

Osteosarcoma (OSA) is one of the most common malignancies in humans (especially children) and dogs. Canine OSA is considered an ideal model of human OSA because OSA in both species shares comparable oncogenic properties—including histopathological features, tumor biology, therapeutic approaches, and molecular mechanisms. In this study, we sought to compare the expression profiles of embryonic stem cell (ES) gene signatures in canine and human OSA tissues and cell lines. The expression patterns of three major ES gene signatures (modules or gene sets)—namely Myc, Core, and PRC modules—were primarily analyzed through the gene set enrichment analysis method in three gene expression datasets. For verification of the primary results, an additional 13 ES gene sets which were categorized into four groups—namely NOS targets, ES expressed, Myc targets, and Polycomb targets—were evaluated in expression datasets. Additionally, the prognostic efficacy of the gene sets with a similar enrichment pattern in humans and dogs was evaluated using the Cox proportional hazard model. Our results revealed that there were similar ES module expression patterns in the human OSA tissue and cell line, where the MYC modules, Myc targets, ES exp1, and NOS target modules were upregulated and the Polycomb target modules were downregulated in both entities. The canine OSA cell line showed ES enrichment patterns more similar to the patterns in its human counterpart, where we were able to detect the upregulation of the MYC modules, Myc targets, ES exp1, and NOS target modules and the downregulation of the Polycomb targets, including H3K27 bound and PRC2 targets. However, the canine OSA tissues presented a similar enrichment pattern at a lower degree than the canine OSA cell line. Furthermore, survival analysis identified the NOS targets as a more robust prognostic gene signature that efficiently predicted survival time in the human and canine OSA samples.

BackgroundAlpha-2-glycoprotein 1, zinc-binding (ZAG), a secreted protein encoded by the AZGP1 gene, is structurally similar to HLA class I. Despite its presumed immunological function, little is known about its role in tumor immunity. In this study, we thus aimed to determine the relationship between the expression of AZGP1/ZAG and the immunological profiles of breast cancer tissues at both the gene and protein level.MethodsUsing a publicly available gene expression dataset from a large-scale breast cancer cohort, we conducted gene set enrichment analysis (GSEA) to screen the biological processes associated with AZGP1. We analyzed the correlation between AZGP1 expression and immune cell composition in breast cancer tissues, estimated using CIBERSORTx. Previously, we evaluated the infiltration of 11 types of immune cells for 45 breast cancer tissues using flow cytometry (FCM). ZAG expression was evaluated by immunohistochemistry on these specimens and analyzed for its relationship with immune cell infiltration. The action of ZAG in M1/M2 polarization models using primary cultures of human peripheral blood mononuclear cells (PBMC)-derived macrophage (Mφ) was analyzed based on the expression of M1/M2 markers (CD86, CD80/CD163, MRC1) and HLA class I/II by FCM.ResultsAZGP1 expression was negatively correlated with multiple immunological processes and specific immune cell infiltration including Mφ M1 using GSEA and CIBERSORTx. ZAG expression was associated with decreased infiltration of monocytes/macrophages, non-classical monocytes, and myeloid-derived suppressor cells in tumor tissues assessed using FCM. In in vitro analyses, ZAG decreased the expression of CD80, CD163, MRC1, and HLA classes I/II in the M1 polarization model and the expression of CD163 and MRC1 in the M2 polarization model.ConclusionZAG is suggested to be a novel immunoregulatory factor affecting the Mφ phenotype in breast cancer tissues.

High-throughout genomic data provide an opportunity for identifying pathways and genes that are related to various clinical phenotypes. Besides these genomic data, another valuable source of data is the biological knowledge about genes and pathways that might be related to the phenotypes of many complex diseases. Databases of such knowledge are often called the metadata. In microarray data analysis, such metadata are currently explored in post hoc ways by gene set enrichment analysis but have hardly been utilized in the modeling step. We propose to develop and evaluate a pathway-based gradient descent boosting procedure for nonparametric pathways-based regression (NPR) analysis to efficiently integrate genomic data and metadata. Such NPR models consider multiple pathways simultaneously and allow complex interactions among genes within the pathways and can be applied to identify pathways and genes that are related to variations of the phenotypes. These methods also provide an alternative to mediating the problem of a large number of potential interactions by limiting analysis to biologically plausible interactions between genes in related pathways. Our simulation studies indicate that the proposed boosting procedure can indeed identify relevant pathways. Application to a gene expression data set on breast cancer distant metastasis identified that Wnt, apoptosis, and cell cycle-regulated pathways are more likely related to the risk of distant metastasis among lymph-node-negative breast cancer patients. Results from analysis of other two breast cancer gene expression data sets indicate that the pathways of Metalloendopeptidases (MMPs) and MMP inhibitors, as well as cell proliferation, cell growth, and maintenance are important to breast cancer relapse and survival. We also observed that by incorporating the pathway information, we achieved better prediction for cancer recurrence.

Neuropathic pain (NP), a chronic condition resulting from nerve injury or dysfunction, represents considerable therapeutic difficulties owing to its intricate and multifactorial pathophysiology. Recent studies indicate that dysregulated fatty acid metabolism (FAM) plays a pivotal role in the onset and persistence of NP. Nonetheless, the molecular mechanisms underpinning this association remain incompletely elucidated. This research aims to characterize the expression profiles of FAM-related genes in NP and explore their relationship with immune cell infiltration (ICI). Publicly available gene expression data sets (GSE24982 and GSE2884) were analyzed using bioinformatics approaches to identify differentially expressed genes (DEGs) related to FAM in NP. Differential expression analysis was performed using the limma package in R. Functional enrichment analyses, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA), were carried out to elucidate relevant biological pathways. ICI was assessed using computational deconvolution methods. Overall, 116 FAM-related DEGs were identified in NP. Enrichment analyses highlighted key pathways such as FAM, arachidonic acid metabolism, and the PPAR signal transduction. Among these, Cyp3a2, Hadha, and Scp2 emerged as key genes potentially involved in NP pathogenesis, with Cyp3a2 exhibiting high mRNA levels in validated animal models. ICI analysis revealed significant correlations between the expression of these genes and various immune cell populations, including activated B cells and CD8+ T cells, indicating a strong inflammatory component in NP. Moreover, stratification by FAM scores demonstrated distinct gene expression and immune infiltration profiles between low- and high-risk groups. This study highlights the essential role of FAM in the molecular landscape of NP and identifies Cyp3a2, Hadha, and Scp2 as potential biomarkers or therapeutic targets. These findings advance our understanding of NP pathogenesis and offer insights into novel strategies for its management.

Background Lung adenocarcinoma (LUAD) is the most prevalent tumor in lung carcinoma cases and threatens human life seriously worldwide. Here we attempt to identify a prognostic biomarker and potential therapeutic target for LUAD patients. Methods Differentially expressed genes (DEGs) shared by GSE18842, GSE75037, GSE101929 and GSE19188 profiles were determined and used for protein-protein interaction analysis, enrichment analysis and clinical correlation analysis to search for the core gene, whose expression was further validated in multiple databases and LUAD cells (A549 and PC-9) by quantitative real-time PCR (qRT-PCR) and western blot analyses. Its prognostic value was estimated using the Kaplan-Meier method, meta-analysis and Cox regression analysis based on the Cancer Genome Atlas (TCGA) dataset and co-expression analysis was conducted using the Oncomine database. Gene Set Enrichment Analysis (GSEA) was performed to illuminate the potential functions of the core gene. Results A total of 115 shared DEGs were found, of which 24 DEGs were identified as candidate hub genes with potential functions associated with cell cycle and FOXM1 transcription factor network. Among these candidates, HMMR was identified as the core gene, which was highly expressed in LUAD as verified by multiple datasets and cell samples. Besides, high HMMR expression was found to independently predict poor survival in patients with LUAD. Co-expression analysis showed that HMMR was closely related to FOXM1 and was mainly involved in cell cycle as suggested by GSEA. Conclusion HMMR might be served as an independent prognostic biomarker for LUAD patients, which needs further validation in subsequent studies.

Among primary brain tumors, gliomas are associated with a poor prognosis and a median survival that varies depending on the tumor grade and subtype. As the most malignant form of glioma, glioblastoma (GBM) constitutes a significant health concern. Alteration in granulin(GRN) has been proved to be accountable for several diseases. However, the relationship between GRN and GBM remains unclear. We evaluated the role of GRN in GBM through The Cancer Genome Atlas (TCGA) database. First, we assessed the relationship between GRN and GBM through the GEPIA database. Next, the relationship between GRN and GBM prognosis was analyzed by logistic regression and multivariate cox methods. Using CIBERSORT and the GEPIA correlation module, we also investigated the link between GRN and immune infiltrates in cancer. Using the TCGA data, a gene set enrichment analysis (GSEA) was performed. We also employed Tumor Immune Estimation Resource (TIMER) to examine the data set of GRN expression and immune infiltration level in GBM and investigate the cumulative survival in GBM. We also validated tissues from GBM patients by Western blotting, RT-qPCR, and immunohistochemistry. Increased GRN expression was shown to have a significant relationship to tumor grade in a univariate study utilizing logistic regression. Furthermore, multivariate analysis disclosed that GRN expression down-regulation is an independent predictive factor for a favorable outcome. GRN expression level positively correlates with the number of CD4+ T cells, neutrophils, macrophages, and dendritic cells (DCs) that infiltrate a GBM. The GSEA also found that the high GRN expression phenotype pathway was enriched for genes involved in immune response molecular mediator production, lymphocyte-mediated immunity, cytokine-mediated signaling pathway, leukocyte proliferation, cell chemotaxis, and CD4+ alpha beta T cell activation. Differentially enriched pathways in the Kyoto Encyclopedia of Genes and Genomes (KEGG) include lysosome, apoptosis, primary immunodeficiency, chemokine signaling pathway, natural killer cell-mediated cytotoxicity, and B cell receptor signaling pathway. Validated result showed that GRN was upregulated in GBM tissues. These results suggested that GRN was a potential indicator for the status of GBM. GRN is a prognostic biomarker and correlated with immune infiltrates in GBM.

First responders (FR) exposed to the World Trade Center (WTC) Ground Zero air over the first week after the 9/11 disaster have an increased heart disease incidence compared to unexposed FR and the general population. To test if WTC dusts were causative agents, rats were exposed to WTC dusts (under isoflurane [ISO] anesthesia) 2 h/day on 2 consecutive days; controls received air/ISO or air only. Hearts were collected 1, 30, 240, and 360 d post-exposure, left ventricle total RNA was extracted, and transcription profiles were obtained. The data showed that differentially expressed genes (DEG) for WTC vs. ISO rats did not reach any significance with a false discovery rate (FDR) < 0.05 at days 1, 30, and 240, indicating that the dusts did not impart effects beyond any from ISO. However, at day 360, 14 DEG with a low FDR were identified, reflecting potential long-term effects from WTC dust alone, and the majority of these DEG have been implicated as having an impact on heart functions. Furthermore, the functional gene set enrichment analysis (GSEA) data at day 360 showed that WTC dust could potentially impact the myocardial energy metabolism via PPAR signaling and heart valve development. This is the first study showing that WTC dust could significantly affect some genes that are associated with the heart/CV system, in the long term. Even > 20 years after the 9/11 disaster, this has potentially important implications for those FR exposed repeatedly at Ground Zero over the first week after the buildings collapsed.

The gene expression profile of tissue in the colonic mucosa is highly altered in ulcerative colitis (UC) compared with healthy tissue. Analyses of microarray data taken from colonic mucosa biopsies shows upregulation of immune-related genes and may help to indicate molecular pathways that may be targeted for therapeutic intervention. At the same time, genome-wide association studies (GWAS) of ulcerative colitis have identified genetic susceptibility loci highly enriched in immune-related genes as well. We sought to establish the extent of overlap between pathways upregulated in gene expression with genetic susceptibility loci in order to generate a list of core gene sets that can guide functional investigation of UC pathobiology. We conducted a Gene Set Enrichment Analysis (GSEA) in 3 sets of colonic biopsy studies assayed on the Affymetrix U133 Plus 2 array that were publically available through the Gene Expression Omnibus. We also performed a GSEA on genotype data from 8 European-ancestry cohorts consisting of 5584 cases and 11587 controls using a hypergeometric test with the same gene set database of manually-curated pathways from BioCarta, Reactome, and Kyoto Encyclopedia of Genes and Genomes. GSEA revealed that all 3 gene expression datasets share 87 gene sets upregulated in UC lesions and 8 gene sets downregulated (false discovery rate <0.05). The upregulated pathways were dominated by gene sets involved in immune function and signaling, as well as the control of mitosis. GSEA in the GWAS study produced 56 gene sets, 33 of which were concordant with the gene-expression-derived pathways (hypergeometric P = 1.54 × 10−18 for this overlap). The identified pathways were dominated by immune system function, control of mitosis, signaling pathways, oxidative phosphorylation, ubiquitin/proteasome, and extracellular matrix interaction. This study reveals that a concordant set of pathways are altered both at the transcriptome level as well as having risk-conferring genetic variation. These common pathways can serve as a useful guide to prioritizing targets for therapeutic development or establishing personalized treatment of ulcerative colitis.

Identification of key pathways and biomarkers in rheumatoid arthritis synovial tissue through comprehensive transcriptomic integration.

Atherosclerosis (AS) is a lipid-induced, chronic inflammatory, autoimmune disease affecting multiple arteries. Although much effort has been put into AS research in the past decades, it is still the leading cause of death worldwide. The complex genetic network regulation underlying the pathogenesis of AS still needs further investigation to provide effective targeted therapy for AS. We performed a bioinformatic microarray data analysis at different atherosclerotic plaque stages from the Gene Expression Omnibus database with accession numbers GSE43292 and GSE28829. Using gene set enrichment analysis, we further confirmed the immune-related pathways that play an important role in the development of AS. We are reporting, for the first time, that the metabolism of the three branched-chain amino acids (BCAAs; leucine, isoleucine, and valine) and short-chain fatty acids (SCFA; propanoate, and butanoate) are involved in the progression of AS using microarray data of atherosclerotic plaque tissue. Immune and muscle system-related pathways were further confirmed as highly regulated pathways during the development of AS using gene expression pattern analysis. Furthermore, we also identified four modules mainly involved in histone modification, immune-related processes, macroautophagy, and B cell activation with modular differential connectivity in the dataset of GSE43292, and three modules related to immune-related processes, B cell activation, and nuclear division in the dataset of GSE28829 also display modular differential connectivity based on the weighted gene co-expression network analysis. Finally, we identified eight key genes related to the pathways of immune and muscle system function as potential therapeutic biomarkers to distinguish patients with early or advanced stages in AS, and two of the eight genes were validated using the gene expression dataset from gene-deficient mice. The results of the current study will improve our understanding of the molecular mechanisms in the progression of AS. The key genes and pathways identified could be potential biomarkers or new drug targets for AS management.

The clinical value of Fibulin-1 for prognosis and its prospective mechanism in intrahepatic cholangiocarcinoma

Network propagation algorithms have proved useful for the analysis of high-dimensional genomic data. One limitation is that the current formulation only allows network propagation on positively weighted graphs. In this paper, we explore two signed network propagation algorithms and general optimization frameworks for detecting differential gene expressions and DNA copy number variations (CNV). The proposed algorithms consider both positive and negative relations in graphs to model gene up/down-regulation or amplification/deletion CNV events. The first algorithm (Signed-NP) integrates gene co-expressions and differential expressions for consistent and robust gene selection from microarray datasets by propagation on gene correlation graphs. The second algorithm (Signed-NPBi) identifies gene or CNV markers by propagation on sample-feature bipartite graphs to capture bi-clusters between samples and genomic features. Large scale experiments on several microarray gene expression datasets and CNV datasets validate that Signed-NP and Signed-NPBi perform better classification of gene expression and CNV data than standard network propagation. The experiments also demonstrate that Signed-NP is capable of selecting genes that are more biologically interpretable and consistent across multiple datasets, and Signed-NPBi can detect hidden CNV patterns in bi-clusters by smoothing on correlations between adjacent probes.

In this paper, we explore several network propagation methods for gene selection from microarray gene expression datasets. The network propagation methods capture gene co-expression and differential expression with unified machine learning frameworks. Large scale experiments on five breast cancer datasets validated that the network propagation methods are capable of selecting genes that are more biologically interpretable and more consistent across multiple datasets, compared with the existing approaches.

The lncRNA GAS5 upregulates ANXA2 to mediate the macrophage inflammatory response during atherosclerosis development