A resampling-based meta-analysis for detection of differential gene expression in breast cancer

MiRNA-miRNA interaction network in peripheral blood of patients with myocardial infarction: a gene expression meta-analysis

In recent years, investigations have revealed that microRNAs (miRNAs) can bind together and form a miRNA-miRNA-mRNA regulatory network that alters the consequence of miRNA-mRNA interaction. If we consider the miRNA that binds to mRNA as the primary miRNA and the miRNA that binds to the primary miRNA as the secondary one, secondry miRNAs can act as master regulators upstream of primary miRNAs and their target mRNAs. One of the distinguishing characteristics of secondary miRNAs as master regulators within a diverse set of differentially expressed genes is the absence of direct target mRNA for them. Instead, these master regulators exclusively govern the regulation of miRNAs that target specific mRNAs. Through in silico analysis, we identified 18 miRNAs among 385 differentially expressed miRNAs (DEmiRNAs) with no direct target mRNAs among 58 differentially expressed mRNAs (DEmRNAs) in peripheral blood of patients with myocardial infarction (MI). Instead, these secondary miRNAs targeted 9 primary miRNAs that had 36 direct targets among 58 DEmRNAs. We found that one primary miRNA might be regulated by more than one secondary miRNAs and each secondary miRNA can target more than one primary miRNAs. Among identified miRNA-miRNA-mRNA networks miR-188-5p/miR-299-3p/natural killer cell granule protein (NKG7), miR-200a-3p/miR-199b-5p/granzyme B (GZMB), and miR-377-3p/miR-581/oviductal glycoprotein 1 (OVGP1) exhibited higher scors in terms of expression levels (>2-fold increase or decrease) and strengh of interactions (ΔG < -5). Given the extensive network of miRNA interactions, focusing on master regulators opens up avenues for identifying key regulatory nodes for more effective therapeutic strategies.

Functional analysis of co-expression networks of zebrafish ace2 reveals enrichment of pathways associated with development and disease

Human Angiotensin I Converting Enzyme 2 (ACE2) plays an essential role in blood pressure regulation and SARS-CoV-2 entry. ACE2 has a highly conserved, one-to-one ortholog (ace2) in zebrafish, which is an important model for human diseases. However, the zebrafish ace2 expression profile has not yet been studied during early development, between genders, across different genotypes, or in disease. Moreover, a network-based meta-analysis for the extraction of functionally enriched pathways associated with differential ace2 expression is lacking in the literature. Herein, we first identified significant development-, tissue-, genotype-, and gender-specific modulations in ace2 expression via meta-analysis of zebrafish Affymetrix transcriptomics datasets (n_datasets = 107); and the correlation analysis of ace2 meta-differential expression profile revealed distinct positively and negatively correlated local functionally enriched gene networks. Moreover, we demonstrated that ace2 expression was significantly modulated under different physiological and pathological conditions related to development, tissue, gender, diet, infection, and inflammation using additional RNA-seq datasets. Our findings implicate a novel translational role for zebrafish ace2 in organ differentiation and pathologies observed in the intestines and liver.

DECO: decompose heterogeneous population cohorts for patient stratification and discovery of sample biomarkers using omic data profiling

Motivation

Patient and sample diversity is one of the main challenges when dealing with clinical cohorts in biomedical genomics studies. During last decade, several methods have been developed to identify biomarkers assigned to specific individuals or subtypes of samples. However, current methods still fail to discover markers in complex scenarios where heterogeneity or hidden phenotypical factors are present. Here, we propose a method to analyze and understand heterogeneous data avoiding classical normalization approaches of reducing or removing variation.

Results

DEcomposing heterogeneous Cohorts using Omic data profiling (DECO) is a method to find significant association among biological features (biomarkers) and samples (individuals) analyzing large-scale omic data. The method identifies and categorizes biomarkers of specific phenotypic conditions based on a recurrent differential analysis integrated with a non-symmetrical correspondence analysis. DECO integrates both omic data dispersion and predictor–response relationship from non-symmetrical correspondence analysis in a unique statistic (called h-statistic), allowing the identification of closely related sample categories within complex cohorts. The performance is demonstrated using simulated data and five experimental transcriptomic datasets, and comparing to seven other methods. We show DECO greatly enhances the discovery and subtle identification of biomarkers, making it especially suited for deep and accurate patient stratification.

Availability and implementation

DECO is freely available as an R package (including a practical vignette) at Bioconductor repository (http://bioconductor.org/packages/deco/).

Supplementary information

Supplementary data are available at Bioinformatics online.

Differentially Expressed Genes in Matched Normal, Cancer, and Lymph Node Metastases Predict Clinical Outcomes in Patients With Breast Cancer

Genome-wide screening of transcriptional changes among normal, cancer, and nodal metastases provides insights into the molecular basis of breast cancer (BC) progression and metastasis. To identify transcriptional changes and differentially expressed genes (DEGs) in the metastatic progression of BC and to determine the prognostic role of these DEGs in clinical outcome, we compared transcriptome profiling in matched normal, cancer, and lymph node metastatic tissues of 7 patients with estrogen receptor-positive, HER2-negative BC by using massive parallel RNA sequencing. The global profiles of gene expression in cancer and nodal metastases were highly correlated (r=0.962, P<0.001). In 6 (85.8%) patients, cancer and corresponding nodal metastases from the same patient clustered together. We identified 1522 and 664 DEGs between normal and cancer and between cancer and nodal metastases, respectively. The DEGs in normal versus cancer and cancer versus nodal metastases were significantly clustered in 1 and 8 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, respectively. The chemokine signaling pathway was the most significant pathway in the cancer-to-nodal metastasis transition (false discovery rate=2.15E-13). The expression of 2 dysregulated RAC2 and PTGDS genes was confirmed by quantitative real-time polymerase chain reaction and immunohistochemistry. Interestingly, the lower RAC2 and PTGDS expression were associated with significantly worse disease-free survival in patients with BC. Our results show a high concordance of gene expression in BC and their nodal metastases, and identify DEGs associated with the metastatic progression of BC. The DEGs identified in this study represent novel biomarkers for predicting the prognosis of patients with BC.

Time-course data prediction for repeatedly measured gene expression

Variability in time course gene expression data is a natural phenomenon. The intention of this work is to predict the future time point data through observed sample data point. The Bayesian inference is carried to serve the objective. A total of 6 replicates 3 time point’s data of 218 genes expression is adopted to illustrate the method. The estimates are found consistent with HPD interval to predict the future time point gene expression value. This proposed method can be adopted in other gene expression data setup to predict the future time course data.

Profiling plasma extracellular vesicle by pluronic block-copolymer based enrichment method unveils features associated with breast cancer aggression, metastasis and invasion

Extracellular vesicle (EV)-based liquid biopsies have been proposed to be a readily obtainable biological substrate recently for both profiling and diagnostics purposes. Development of a fast and reliable preparation protocol to enrich such small particles could accelerate the discovery of informative, disease-related biomarkers. Though multiple EV enrichment protocols are available, in terms of efficiency, reproducibility and simplicity, precipitation-based methods are most amenable to studies with large numbers of subjects. However, the selectivity of the precipitation becomes critical. Here, we present a simple plasma EV enrichment protocol based on pluronic block copolymer. The enriched plasma EV was able to be verified by multiple platforms. Our results showed that the particles enriched from plasma by the copolymer were EV size vesicles with membrane structure; proteomic profiling showed that EV-related proteins were significantly enriched, while high-abundant plasma proteins were significantly reduced in comparison to other precipitation-based enrichment methods. Next-generation sequencing confirmed the existence of various RNA species that have been observed in EVs from previous studies. Small RNA sequencing showed enriched species compared to the corresponding plasma. Moreover, plasma EVs enriched from 20 advanced breast cancer patients and 20 age-matched non-cancer controls were profiled by semi-quantitative mass spectrometry. Protein features were further screened by EV proteomic profiles generated from four breast cancer cell lines, and then selected in cross-validation models. A total of 60 protein features that highly contributed in model prediction were identified. Interestingly, a large portion of these features were associated with breast cancer aggression, metastasis as well as invasion, consistent with the advanced clinical stage of the patients. In summary, we have developed a plasma EV enrichment method with improved precipitation selectivity and it might be suitable for larger-scale discovery studies.

Gene expression variability and the analysis of large-scale RNA-seq studies with the MDSeq

Rapidly decreasing cost of next-generation sequencing has led to the recent availability of large-scale RNA-seq data, that empowers the analysis of gene expression variability, in addition to gene expression means. In this paper, we present the MDSeq, based on the coefficient of dispersion, to provide robust and computationally efficient analysis of both gene expression means and variability on RNA-seq counts. The MDSeq utilizes a novel reparametrization of the negative binomial to provide flexible generalized linear models (GLMs) on both the mean and dispersion. We address challenges of analyzing large-scale RNA-seq data via several new developments to provide a comprehensive toolset that models technical excess zeros, identifies outliers efficiently, and evaluates differential expressions at biologically interesting levels. We evaluated performances of the MDSeq using simulated data when the ground truths are known. Results suggest that the MDSeq often outperforms current methods for the analysis of gene expression mean and variability. Moreover, the MDSeq is applied in two real RNA-seq studies, in which we identified functionally relevant genes and gene pathways. Specifically, the analysis of gene expression variability with the MDSeq on the GTEx human brain tissue data has identified pathways associated with common neurodegenerative disorders when gene expression means were conserved.

Meta-Analysis of Microarray Data of Rainbow Trout Fry Gonad Differentiation Modulated by Ethynylestradiol

Sex differentiation in fish is a highly labile process easily reversed by the use of exogenous hormonal treatment and has led to environmental concerns since low doses of estrogenic molecules can adversely impact fish reproduction. The goal of this study was to identify pathways altered by treatment with ethynylestradiol (EE2) in developing fish and to find new target genes to be tested further for their possible role in male-to-female sex transdifferentiation. To this end, we have successfully adapted a previously developed bioinformatics workflow to a meta-analysis of two datasets studying sex reversal following exposure to EE2 in juvenile rainbow trout. The meta-analysis consisted of retrieving the intersection of the top gene lists generated for both datasets, performed at different levels of stringency. The intersecting gene lists, enriched in true positive differentially expressed genes (DEGs), were subjected to over-representation analysis (ORA) which allowed identifying several statistically significant enriched pathways altered by EE2 treatment and several new candidate pathways, such as progesterone-mediated oocyte maturation and PPAR signalling. Moreover, several relevant key genes potentially implicated in the early transdifferentiation process were selected. Altogether, the results show that EE2 has a great effect on gene expression in juvenile rainbow trout. The feminization process seems to result from the altered transcription of genes implicated in normal female gonad differentiation, resulting in expression similar to that observed in normal females (i.e. the repression of key testicular markers cyp17a1, cyp11b, tbx1), as well as from other genes (including transcription factors) that respond specifically to the EE2 treatment. The results also showed that the bioinformatics workflow can be applied to different types of microarray platforms and could be generalized to (eco)toxicogenomics studies for environmental risk assessment purposes.

A ranking-based meta-analysis reveals let-7 family as a meta-signature for grade classification in breast cancer

Breast cancer is one of the most important causes of cancer-related deaths worldwide in women. In addition to gene expression studies, the progressing work in the miRNA area including miRNA microarray studies, brings new aspects to the research on the cancer development and progression. Microarray technology has been widely used to find new biomarkers in research and many transcriptomic microarray studies are available in public databases. In this study, the breast cancer miRNA and mRNA microarray studies were collected according to the availability of their data and clinical information, and combined by a newly developed ranking-based meta-analysis approach to find out candidate miRNA biomarkers (meta-miRNAs) that classify breast cancers according to their grades and explain the relation between miRNAs and mRNAs. This approach provided meta-miRNAs specific to breast cancer grades, pointing out let-7 family members as grade classifiers. The qRT-PCR studies performed with independent breast tumors confirmed the potential biomarker role of let-7 family members (meta-miRNAs). The concordance between the meta-mRNAs and miRNA target genes specific to tumor grade (common genes) supported the idea of mRNAs as miRNA targets. The pathway analysis results showed that most of the let-7 family miRNA targets, and also common genes, were significantly taking part in cancer-related pathways. The qRT-PCR studies, together with bioinformatic analyses, confirmed the results of meta-analysis approach, which is dynamic and allows combining datasets from different platforms.

Application of microarray in breast cancer: An overview

There are more than 1.15 million cases of breast cancer diagnosed worldwide annually. At present, only small numbers of accurate prognostic and predictive factors are used clinically for managing the patients with breast cancer. DNA microarrays have the potential to assess the expression of thousands of genes simultaneously. Recent preliminary researches indicate that gene expression profiling based on DNA microarray can offer potential and independent prognostic information in patients with newly diagnosed breast cancer. In this paper, an overview upon the applications of microarray techniques in breast cancer is presented.

Prioritizing genes associated with prostate cancer development

Background

The genetic control of prostate cancer development is poorly understood. Large numbers of gene-expression datasets on different aspects of prostate tumorigenesis are available. We used these data to identify and prioritize candidate genes associated with the development of prostate cancer and bone metastases. Our working hypothesis was that combining meta-analyses on different but overlapping steps of prostate tumorigenesis will improve identification of genes associated with prostate cancer development.

Methods

A Z score-based meta-analysis of gene-expression data was used to identify candidate genes associated with prostate cancer development. To put together different datasets, we conducted a meta-analysis on 3 levels that follow the natural history of prostate cancer development. For experimental verification of candidates, we used in silico validation as well as in-house gene-expression data.

Results

Genes with experimental evidence of an association with prostate cancer development were overrepresented among our top candidates. The meta-analysis also identified a considerable number of novel candidate genes with no published evidence of a role in prostate cancer development. Functional annotation identified cytoskeleton, cell adhesion, extracellular matrix, and cell motility as the top functions associated with prostate cancer development. We identified 10 genes--CDC2, CCNA2, IGF1, EGR1, SRF, CTGF, CCL2, CAV1, SMAD4, and AURKA--that form hubs of the interaction network and therefore are likely to be primary drivers of prostate cancer development.

Conclusions

By using this large 3-level meta-analysis of the gene-expression data to identify candidate genes associated with prostate cancer development, we have generated a list of candidate genes that may be a useful resource for researchers studying the molecular mechanisms underlying prostate cancer development.

The ability to generate senescent progeny as a mechanism underlying breast cancer cell heterogeneity

BACKGROUND

Breast cancer is a remarkably heterogeneous disease. Luminal, basal-like, "normal-like", and ERBB2+ subgroups were identified and were shown to have different prognoses. The mechanisms underlying this heterogeneity are poorly understood. In our study, we explored the role of cellular differentiation and senescence as a potential cause of heterogeneity.

METHODOLOGY/PRINCIPAL FINDINGS

A panel of breast cancer cell lines, isogenic clones, and breast tumors were used. Based on their ability to generate senescent progeny under low-density clonogenic conditions, we classified breast cancer cell lines as senescent cell progenitor (SCP) and immortal cell progenitor (ICP) subtypes. All SCP cell lines expressed estrogen receptor (ER). Loss of ER expression combined with the accumulation of p21(Cip1) correlated with senescence in these cell lines. p21(Cip1) knockdown, estrogen-mediated ER activation or ectopic ER overexpression protected cells against senescence. In contrast, tamoxifen triggered a robust senescence response. As ER expression has been linked to luminal differentiation, we compared the differentiation status of SCP and ICP cell lines using stem/progenitor, luminal, and myoepithelial markers. The SCP cells produced CD24+ or ER+ luminal-like and ASMA+ myoepithelial-like progeny, in addition to CD44+ stem/progenitor-like cells. In contrast, ICP cell lines acted as differentiation-defective stem/progenitor cells. Some ICP cell lines generated only CD44+/CD24-/ER-/ASMA- progenitor/stem-like cells, and others also produced CD24+/ER- luminal-like, but not ASMA+ myoepithelial-like cells. Furthermore, gene expression profiles clustered SCP cell lines with luminal A and "normal-like" tumors, and ICP cell lines with luminal B and basal-like tumors. The ICP cells displayed higher tumorigenicity in immunodeficient mice.

CONCLUSIONS/SIGNIFICANCE

Luminal A and "normal-like" breast cancer cell lines were able to generate luminal-like and myoepithelial-like progeny undergoing senescence arrest. In contrast, luminal B/basal-like cell lines acted as stem/progenitor cells with defective differentiation capacities. Our findings suggest that the malignancy of breast tumors is directly correlated with stem/progenitor phenotypes and poor differentiation potential.

Meta-analysis of archived DNA microarrays identifies genes regulated by hypoxia and involved in a metastatic phenotype in cancer cells

BACKGROUND

Metastasis is a major cancer-related cause of death. Recent studies have described metastasis pathways. However, the exact contribution of each pathway remains unclear. Another key feature of a tumor is the presence of hypoxic areas caused by a lack of oxygen at the center of the tumor. Hypoxia leads to the expression of pro-metastatic genes as well as the repression of anti-metastatic genes. As many Affymetrix datasets about metastasis and hypoxia are publicly available and not fully exploited, this study proposes to re-analyze these datasets to extract new information about the metastatic phenotype induced by hypoxia in different cancer cell lines.

METHODS

Affymetrix datasets about metastasis and/or hypoxia were downloaded from GEO and ArrayExpress. AffyProbeMiner and GCRMA packages were used for pre-processing and the Window Welch t test was used for processing. Three approaches of meta-analysis were eventually used for the selection of genes of interest.

RESULTS

Three complementary approaches were used, that eventually selected 183 genes of interest. Out of these 183 genes, 99, among which the well known JUNB, FOS and TP63, have already been described in the literature to be involved in cancer. Moreover, 39 genes of those, such as SERPINE1 and MMP7, are known to regulate metastasis. Twenty-one genes including VEGFA and ID2 have also been described to be involved in the response to hypoxia. Lastly, DAVID classified those 183 genes in 24 different pathways, among which 8 are directly related to cancer while 5 others are related to proliferation and cell motility. A negative control composed of 183 random genes failed to provide such results. Interestingly, 6 pathways retrieved by DAVID with the 183 genes of interest concern pathogen recognition and phagocytosis.

CONCLUSION

The proposed methodology was able to find genes actually known to be involved in cancer, metastasis and hypoxia and, thus, we propose that the other genes selected based on the same methodology are of prime interest in the metastatic phenotype induced by hypoxia.

Candidate pathways and genes for prostate cancer: a meta-analysis of gene expression data

BACKGROUND

The genetic mechanisms of prostate tumorigenesis remain poorly understood, but with the advent of gene expression array capabilities, we can now produce a large amount of data that can be used to explore the molecular and genetic mechanisms of prostate tumorigenesis.

METHODS

We conducted a meta-analysis of gene expression data from 18 gene array datasets targeting transition from normal to localized prostate cancer and from localized to metastatic prostate cancer. We functionally annotated the top 500 differentially expressed genes and identified several candidate pathways associated with prostate tumorigeneses.

RESULTS

We found the top differentially expressed genes to be clustered in pathways involving integrin-based cell adhesion: integrin signaling, the actin cytoskeleton, cell death, and cell motility pathways. We also found integrins themselves to be downregulated in the transition from normal prostate tissue to primary localized prostate cancer. Based on the results of this study, we developed a collagen hypothesis of prostate tumorigenesis. According to this hypothesis, the initiating event in prostate tumorigenesis is the age-related decrease in the expression of collagen genes and other genes encoding integrin ligands. This concomitant depletion of integrin ligands leads to the accumulation of ligandless integrin and activation of integrin-associated cell death. To escape integrin-associated death, cells suppress the expression of integrins, which in turn alters the actin cytoskeleton, elevates cell motility and proliferation, and disorganizes prostate histology, contributing to the histologic progression of prostate cancer and its increased metastasizing potential.

CONCLUSION

The results of this study suggest that prostate tumor progression is associated with the suppression of integrin-based cell adhesion. Suppression of integrin expression driven by integrin-mediated cell death leads to increased cell proliferation and motility and increased tumor malignancy.