Meta-analysis Of Gene Expression Datasets Research Articles

Identification of key genes and biological regulatory mechanisms in diabetic nephropathy: Meta-analysis of gene expression datasets

BackgroundDiabetic nephropathy (DN) which refers to the cases with biopsy proven kidney lesions, is one of the main complications of diabetes all around the world; however, the underlying biological changes causing DN remain to be understood. Studying the alterations in gene expression profiles could give a holistic view of the molecular pathogenicity of DN and aid to discover key molecules as potential therapeutic targets. Here, we performed a meta-analysis study that included microarray gene expression profiles coming from glomerular samples of DN patients in order to acquire a list of consensus Differentially Expressed Genes (meta-DEGs) correlated with DN. MethodsAfter quality control and normalization steps, five gene expression datasets (GES1009, GSE30528, GSE47183, GSE104948, and GSE93804) were entered into the meta-analysis. The meta-analysis was performed by random effect size method and the meta-DEGs were put through network analysis and different pathway enrichment analyses steps. MiRTarBase and TRRUST databases were utilized to predict the meta-DEGs related miRNAs and transcription factors. A co-regulatory network including DEGs, transcription factors and miRNAs was constructed by Cytoscape, and top molecules were identified based on centrality scores in the network. ResultsThe identified meta-DEGs were 1364 DEGs including 665 downregulated and 669 upregulated DEGs. The results of pathway enrichment analysis showed, “immune system”, “extracellular matrix organization”, “hemostasis”, “signal transduction”, and “platelet activation” to be the top enriched terms with involvement of the meta-DEGs. After construction of the multilayer regulatory network, several top DEGs (TP53, MYC, BTG2, VEGFA, PTEN, etc.), as well as top miRNAs (miR-335, miR-16, miR-17, miR-20a, and miR-93), and transcription factors (SP1, STAT3, NF-KB1, RELA, E2F1), were introduced as potential therapeutic targets in DN. Among the regulatory molecules, miR-335-5p and SP1 were the most interactive miRNA and transcription factor molecules with the highest degree scores in the constructed network. ConclusionBy performing a meta-analysis of available DN-related transcriptomics datasets, we reached a consensus list of DEGs for this complicated disorder. Further enrichment and network analyses steps revealed the involved pathways in the DN pathogenesis and marked the most potential therapeutic targets in this disease.

Identification of key genes and biological regulatory mechanisms in diabetic nephropathy: Meta-analysis of gene expression datasets

BackgroundDiabetic nephropathy (DN) which refers to the cases with biopsy proven kidney lesions, is one of the main complications of diabetes all around the world; however, the underlying biological changes causing DN remain to be understood. Studying the alterations in gene expression profiles could give a holistic view of the molecular pathogenicity of DN and aid to discover key molecules as potential therapeutic targets. Here, we performed a meta-analysis study that included microarray gene expression profiles coming from glomerular samples of DN patients in order to acquire a list of consensus Differentially Expressed Genes (meta-DEGs) correlated with DN. MethodsAfter quality control and normalization steps, five gene expression datasets (GES1009, GSE30528, GSE47183, GSE104948, and GSE93804) were entered into the meta-analysis. The meta-analysis was performed by random effect size method and the meta-DEGs were put through network analysis and different pathway enrichment analyses steps. MiRTarBase and TRRUST databases were utilized to predict the meta-DEGs related miRNAs and transcription factors. A co-regulatory network including DEGs, transcription factors and miRNAs was constructed by Cytoscape, and top molecules were identified based on centrality scores in the network. ResultsThe identified meta-DEGs were 1364 DEGs including 665 downregulated and 669 upregulated DEGs. The results of pathway enrichment analysis showed, “immune system”, “extracellular matrix organization”, “hemostasis”, “signal transduction”, and “platelet activation” to be the top enriched terms with involvement of the meta-DEGs. After construction of the multilayer regulatory network, several top DEGs (TP53, MYC, BTG2, VEGFA, PTEN, etc.), as well as top miRNAs (miR-335, miR-16, miR-17, miR-20a, and miR-93), and transcription factors (SP1, STAT3, NF-KB1, RELA, E2F1), were introduced as potential therapeutic targets in DN. Among the regulatory molecules, miR-335-5p and SP1 were the most interactive miRNA and transcription factor molecules with the highest degree scores in the constructed network. ConclusionBy performing a meta-analysis of available DN-related transcriptomics datasets, we reached a consensus list of DEGs for this complicated disorder. Further enrichment and network analyses steps revealed the involved pathways in the DN pathogenesis and marked the most potential therapeutic targets in this disease.

The histone demethylase PHF8 regulates TGFβ signaling and promotes melanoma metastasis.

The contribution of epigenetic dysregulation to metastasis remains understudied. Through a meta-analysis of gene expression datasets followed by a mini-screen, we identified Plant Homeodomain Finger protein 8 (PHF8), a histone demethylase of the Jumonji C protein family, as a previously unidentified prometastatic gene in melanoma. Loss- and gain-of-function approaches demonstrate that PHF8 promotes cell invasion without affecting proliferation in vitro and increases dissemination but not subcutaneous tumor growth in vivo, thus supporting its specific contribution to the acquisition of metastatic potential. PHF8 requires its histone demethylase activity to enhance melanoma cell invasion. Transcriptomic and epigenomic analyses revealed that PHF8 orchestrates a molecular program that directly controls the TGFβ signaling pathway and, as a consequence, melanoma invasion and metastasis. Our findings bring a mechanistic understanding of epigenetic regulation of metastatic fitness in cancer, which may pave the way for improved therapeutic interventions.

Abstract B30: The histone demethylase PHF8 epigenetically regulates the TGFbeta pathway to promote melanoma metastasis

Abstract The specific contribution of epigenetic dysregulation to the metastatic process remains understudied. Through a meta-analysis of gene expression datasets followed by a functional mini-screen, we identified PHF8, a histone demethylase of the Jumonji C proteins family, as a novel prometastatic factor in melanoma. Loss- and gain-of-function approaches demonstrate that PHF8 promotes cell invasion without affecting proliferation in vitro, and increases dissemination but not tumor growth in vivo, thus strengthening its specific contribution to the acquisition of metastatic potential. PHF8 demethylase activity is required for its proinvasive role. Transcriptomic and epigenomic analyses revealed that PHF8 orchestrates molecular changes that directly control the TGFβ signaling pathway and, as a direct consequence, melanoma invasion. Citation Format: Rana Moubarak, Ana De Pablos-Aragoneses, Vanessa Ortiz-Barahona, Richard Von Itter, Yixiao Gong, Michael Gowen, Sebastian Stefan, Diana Argibay, Eleazar De Miera-Vega, Igor Dolgalev, Elena Sokolova, Farbod Darvishian, Aristotelis Tsirigos, Iman Osman, Eva Hernando. The histone demethylase PHF8 epigenetically regulates the TGFbeta pathway to promote melanoma metastasis [abstract]. In: Proceedings of the AACR Special Conference on Melanoma: From Biology to Target; 2019 Jan 15-18; Houston, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(19 Suppl):Abstract nr B30.

Estimating a common covariance matrix for network meta-analysis of gene expression datasets in diffuse large B-cell lymphoma

The estimation of covariance matrices of gene expressions has many applications in cancer systems biology. Many gene expression studies, however, are hampered by low sample size and it has therefore become popular to increase sample size by collecting gene expression data across studies. Motivated by the traditional meta-analysis using random effects models, we present a hierarchical random covariance model and use it for the meta-analysis of gene correlation networks across 11 large-scale gene expression studies of diffuse large B-cell lymphoma (DLBCL). We suggest to use a maximum likelihood estimator for the underlying common covariance matrix and introduce an EM algorithm for estimation. By simulation experiments comparing the estimated covariance matrices by cophenetic correlation and Kullback–Leibler divergence the suggested estimator showed to perform better or not worse than a simple pooled estimator. In a posthoc analysis of the estimated common covariance matrix for the DLBCL data we were able to identify novel biologically meaningful gene correlation networks with eigengenes of prognostic value. In conclusion, the method seems to provide a generally applicable framework for meta-analysis, when multiple features are measured and believed to share a common covariance matrix obscured by study dependent noise.

Expression of LC3B and FIP200/Atg17 in brain metastases of breast cancer.

Macroautophagy/autophagy is considered to play key roles in tumor cell evasion of therapy and establishment of metastases in breast cancer. High expression of LC3, a residual autophagy marker, in primary breast tumors has been associated with metastatic disease and poor outcome. FIP200/Atg17, a multi-functional pro-survival molecule required for autophagy, has been implicated in brain metastases in experimental models. However, expression of these proteins has not been examined in brain metastases from patients with breast cancer. In this retrospective study, specimens from 44 patients with brain metastases of infiltrating ductal carcinoma of the breast (IDC), unpaired samples from 52 patients with primary IDC (primary-BC) and 16 matched-paired samples were analyzed for LC3 puncta, expression of FIP200/Atg17, and p62 staining. LC3-puncta+ tumor cells and FIP200/Atg17 expression were detected in greater than 90% of brain metastases but there were considerable intra- and inter-tumor differences in expression levels. High numbers of LC3-puncta+ tumor cells in brain metastases correlated with a significantly shorter survival time in triple-negative breast cancer. FIP200/Atg17 protein levels were significantly higher in metastases that subsequently recurred following therapy. The percentages of LC3 puncta+ tumor cells and FIP200/Atg17 protein expression levels, but not mRNA levels, were significantly higher in metastases than primary-BC. Meta-analysis of gene expression datasets revealed a significant correlation between higher FIP200(RB1CC1)/Atg17 mRNA levels in primary-BC tumors and shorter disease-free survival. These results support assessments of precision medicine-guided targeting of autophagy in treatment of brain metastases in breast cancer patients.

Global mapping of transcription factor motifs in human aging.

Biological aging is a complex process dependent on the interplay of cell autonomous and tissue contextual changes which occur in response to cumulative molecular stress and manifest through adaptive transcriptional reprogramming. Here we describe a transcription factor (TF) meta-analysis of gene expression datasets accrued from 18 tissue sites collected at different biological ages and from 7 different in-vitro aging models. In-vitro aging platforms included replicative senescence and an energy restriction model in quiescence (ERiQ), in which ATP was transiently reduced. TF motifs in promoter regions of trimmed sets of target genes were scanned using JASPAR and TRANSFAC. TF signatures established a global mapping of agglomerating motifs with distinct clusters when ranked hierarchically. Remarkably, the ERiQ profile was shared with the majority of in-vivo aged tissues. Fitting motifs in a minimalistic protein-protein network allowed to probe for connectivity to distinct stress sensors. The DNA damage sensors ATM and ATR linked to the subnetwork associated with senescence. By contrast, the energy sensors PTEN and AMPK connected to the nodes in the ERiQ subnetwork. These data suggest that metabolic dysfunction may be linked to transcriptional patterns characteristic of many aged tissues and distinct from cumulative DNA damage associated with senescence.

Trefoil factor 3 is required for differentiation of thyroid follicular cells and acts as acontext-dependent tumor suppressor.

Trefoil factor 3 (TFF3) is overexpressed in avariety of solid epithelial cancers, where it has been shown to promote migration, invasion, proliferation, survival and angiogenesis. On the contrary, in the majority of thyroid tumors, it is downregulated, yet its role in the development of thyroid cancer remains unknown. Here we show that TFF3 exhibits strong cytoplasmic staining of normal thyroid follicular cells and colloid and the staining is increased in hyperfunctioning thyroid nodules, while it is decreased in all thyroid cancers of follicular cell origin. By meta-analysis of gene expression datasets, we found that in the thyroid cancer, conversely to the breast cancer, the expression of TFF3 mRNA was downregulated by estrogen signaling and confirmed this by treating thyroid cancer cells with estradiol. Forced expression of TFF3 in anaplastic thyroid cancer cells resulted in decreased cell proliferation, clonal spheroid formation and entry into the Sphase. Furthermore, it induced acquisition of epithelial-like cell morphology and expression of the differentiation markers of thyroid follicular cells and transcription factors implicated in the thyroid morphogenesis and function. Taken together, this study provides the first evidence that TFF3 may act as atumor suppressor or an oncogene depending on the cellular context.

GEOGLE: context mining tool for the correlation between gene expression and the phenotypic distinction

BackgroundIn the post-genomic era, the development of high-throughput gene expression detection technology provides huge amounts of experimental data, which challenges the traditional pipelines for data processing and analyzing in scientific researches.ResultsIn our work, we integrated gene expression information from Gene Expression Omnibus (GEO), biomedical ontology from Medical Subject Headings (MeSH) and signaling pathway knowledge from sigPathway entries to develop a context mining tool for gene expression analysis – GEOGLE. GEOGLE offers a rapid and convenient way for searching relevant experimental datasets, pathways and biological terms according to multiple types of queries: including biomedical vocabularies, GDS IDs, gene IDs, pathway names and signature list. Moreover, GEOGLE summarizes the signature genes from a subset of GDSes and estimates the correlation between gene expression and the phenotypic distinction with an integrated p value.ConclusionThis approach performing global searching of expression data may expand the traditional way of collecting heterogeneous gene expression experiment data. GEOGLE is a novel tool that provides researchers a quantitative way to understand the correlation between gene expression and phenotypic distinction through meta-analysis of gene expression datasets from different experiments, as well as the biological meaning behind. The web site and user guide of GEOGLE are available at:

A-MADMAN: Annotation-based microarray data meta-analysis tool

BackgroundPublicly available datasets of microarray gene expression signals represent an unprecedented opportunity for extracting genomic relevant information and validating biological hypotheses. However, the exploitation of this exceptionally rich mine of information is still hampered by the lack of appropriate computational tools, able to overcome the critical issues raised by meta-analysis.ResultsThis work presents A-MADMAN, an open source web application which allows the retrieval, annotation, organization and meta-analysis of gene expression datasets obtained from Gene Expression Omnibus. A-MADMAN addresses and resolves several open issues in the meta-analysis of gene expression data.ConclusionA-MADMAN allows i) the batch retrieval from Gene Expression Omnibus and the local organization of raw data files and of any related meta-information, ii) the re-annotation of samples to fix incomplete, or otherwise inadequate, metadata and to create user-defined batches of data, iii) the integrative analysis of data obtained from different Affymetrix platforms through custom chip definition files and meta-normalization. Software and documentation are available on-line at .