Meta-analysis Of Gene Expression Data Research Articles

The cytochrome P450 subfamilies CYP392A and CYP392D are key players in acaricide metabolism in Tetranychusurticae

The spider mite Tetranychus urticae is a major agricultural pest with a global distribution, extremely diverse host range and a remarkable ability to develop resistance to a wide variety of acaricides. P450 mono‐oxygenases have been frequently associated with resistance development in this species. In particular enzymes of the CYP392A-subfamily were shown to metabolize a number of key acaricides, including abamectin, amitraz, fenpyroximate and the active metabolite of pyflubumide. However, transcriptomic studies comparing highly resistant and susceptible populations have often revealed high expression of members of the CYP392D-subfamily, but these have been only poorly studied. Here, we conducted a meta-analysis of gene expression data of 20 populations and identified two key enzymes of this family, CYP392D2 and CYP392D8, whose expression is associated with resistance. We subsequently functionally expressed these enzymes, together with CYP392A11 and CYP392A16 as known metabolizers, and compared their potential to accept a wide diversity of acaricides as substrate. This study overall confirms previous discovered substrates for CYP392A11 and CYP392A16, but also reveals unreported metabolic activity towards new acaricides. These include carbaryl, chlorpyrifos and etoxazole for CYP392A16 and carbaryl, chlorpyrifos and NNI-0711-NH pyflubumide for CYP392A11. For the newly studied CYP392D-family, we show that CYP392D2 metabolizes pyridaben, fenpyroximate, etoxazole and chlorpyrifos, while CYP392D8 metabolizes carbaryl, fenazaquin and tebufenpyrad. Last, we observed that both CYP392A- and CYP392D-subfamily enzymes activate chlorpyrifos to its corresponding oxon. Our study indicates that there is both overlap and specificity in the activity of A- and D-subfamily enzymes against acaricides and model substrates. With the recent advent of highly efficient CRISPR/Cas9 gene editing protocols in T. urticae, the way is now paved to conduct further genetic experiments revealing and quantifying the role of these enzymes in the resistance phenotype in field populations.

The Role of over-Expressed β Globin in Driving Relapsed B - Cell Acute Lymphoblastic Leukemia (B-ALL)

Background: The prognosis for patients with relapsed acute lymphoblastic leukemia (ALL) remains suboptimal despite intensification of chemotherapy, immunotherapy and stem cell transplant. We and others have identified genetic and epigenetic alterations enriched at relapse that drive drug resistance and relapse. In our recent work mapping the epigenetic landscape of B-ALL, we discovered novel super-enhancers (SEs), or regions with increased H3K27 acetylation, enriched at relapse in a majority of patients, implicating a role in clonal evolution. One of these SEs was upstream of the β globin locus and lead to aberrant expression of hemoglobin beta subunit ( HBB) mRNA. HBB upregulation at relapse was also found in a large meta-analysis of gene expression data from pediatric diagnosis/relapse pairs. Of note, b globin is normally found in erythrocytes that interacts with other subunits to form functional hemoglobin. We hypothesized that HBB upregulation in blasts plays a role in relapse and aimed to determine whether it leads to drug tolerance, an early recovery phenotype and/or increased clonal potential. Methods: B-lineage REH, RS4;11 and 697 ALL cell lines were transduced with lentiviral vectors overexpressing either wild-type (WT) HBB or an empty vector (EV) control. Real time PCR was performed to confirm RNA expression and Western Blot analysis was performed to detect protein expression. We generated additional cell lines with a mutation in the translation start codon and another leading to a premature stop codon to unequivocally interrupt protein expression. The generated cell lines were then plated and evaluated for differences in apoptosis via Annexin V assay and proliferation, in response to chemotherapeutic agents commonly used in the treatment of ALL. Clonogenic growth in vitro was also assessed using MethoCult TM media. To test the possibility that HBB acted in generating a persistor cell phenotype, cell lines were placed in mercaptopurine and prednisone for 10-14 days at two different concentrations and counted throughout drug exposure and for an additional 10-14 days following removal of drug. Results: We did not observe significant differences in chemosensitivity to various chemotherapies used in the treatment of B-ALL. However, we did observe less cells undergoing apoptosis in HBB expressing cells compared to control following prolonged treatment with 6-mercaptopurine, suggesting an increase in a small population of drug tolerant cells. However, this did not result in a difference in recovery time following removal of the drug. We also demonstrated that β globin overexpression led to increased clonal growth in methylcellulose compared to control (Fig. 1A, REH) and this observation was replicated in a second cell line (Fig. 1B, RS4;11). While we confirmed mRNA expression, we were not able to confirm protein expression by Western blot or by immunoprecipitation. We therefore hypothesized that b globin mRNA, rather than protein, mediates the observed increase in clonal growth. Interestingly, we observed the same phenotype of increased clonal growth (Fig. 1) in mutated cell lines, suggesting a catalytic role for β globin RNA in expanding a leukemia stem cell population. Conclusions: The increased clonal potential upon overexpression of HBB could be indicative of the ability to reconstitute a tumor from a small subpopulation following treatment. Additionally, our data suggests that protein is not required for increased clonal growth leading us to hypothesize that catalytic RNA is responsible. We are also currently analyzing RNAseq data from the cell lines and will compare differential gene expression between the empty vector and HBB expressing cells to discover downstream pathways impacted by HBB. We will be testing this hypothesis further by performing limiting dilution analysis in an immunocompromised mouse model (with David Teachey, Children's Hospital of Philadelphia).

A multi-platform normalization method for meta-analysis of gene expression data.

Transcriptomic profiling is a mainstay of translational cancer research and is often used to identify cancer subtypes, stratify responders vs. non-responders patients, predict survival, and identify potential targets for therapeutic intervention. Analysis of gene expression data gathered by RNA sequencing (RNA-seq) and microarray is generally the first step in identifying and characterizing cancer-associated molecular determinants. The methodological advancements and reduced costs associated with transcriptomic profiling have increased the number of publicly available gene expression profiles for cancer subtypes. Data integration from multiple datasets is routinely done to increase the number of samples, improve statistical power, and provide better insight into the heterogeneity of the biological determinant. However, utilizing raw data from multiple platforms, species, and sources introduces systematic variations due to noise, batch effects, and biases. As such, the integrated data is mathematically adjusted through normalization, which allows direct comparison of expression measures among studies while minimizing technical and systemic variations. This study applied meta-analysis to multiple independent Affymetrix microarray and Illumina RNA-seq datasets available through the Gene Expression Omnibus (GEO) and The Cancer Gene Atlas (TCGA). We have previously identified a tripartite motif containing 37 (TRIM37), a breast cancer oncogene, that drives tumorigenesis and metastasis in triple-negative breast cancer. In this article, we adapted and assessed the validity of Stouffer's z-score normalization method to interrogate TRIM37 expression across different cancer types using multiple large-scale datasets.

Breast cancer stage prediction: a computational approach guided by transcriptome analysis.

Breast cancer is the second leading cancer among women in terms of mortality rate. In recent years, its incidence frequency has been continuously rising across the globe. In this context, the new therapeutic strategies to manage the deadly disease attracts tremendous research focus. However, finding new prognostic predictors to refine the selection of therapy for the various stages of breast cancer is an unattempted issue. Aberrant expression of genes at various stages of cancer progression can be studied to identify specific genes that play a critical role in cancer staging. Moreover, while many schemes for subtype prediction in breast cancer have been explored in the literature, stage-wise classification remains a challenge. These observations motivated the proposed two-phased method: stage-specific gene signature selection and stage classification. In the first phase, meta-analysis of gene expression data is conducted to identify stage-wise biomarkers that were then used in the second phase of cancer classification. From the analysis, 118, 12 and 4 genes respectively in stage I, stage II and stage III are determined as potential biomarkers. Pathway enrichment, gene network and literature analysis validate the significance of the identified genes in breast cancer. In this study, machine learning methods were combined with principal component and posterior probability analysis. Such a scheme offers a unique opportunity to build a meaningful model for predicting breast cancer staging. Among the machine learning models compared, Support Vector Machine (SVM) is found to perform the best for the selected datasets with an accuracy of 92.21% during test data evaluation. Perhaps, biomarker identification performed here for stage-specific cancer treatment would be a meaningful step towards predictive medicine. Significantly, the determination of correct cancer stage using the proposed 134 gene signature set can possibly act as potential target for breast cancer therapeutics.

Identification of hub genes and potential molecular mechanisms associated with inflammatory bowel diseases using meta-analysis of gene expression data

Inflammatory bowel diseases (IBDs), which primarily include Crohn's disease (CD) and ulcerative colitis (UC), are chronic recurrent diseases of the gastrointestinal tract with increasing prevalence and incidence worldwide. In this study, we aimed to identify key factor genes that control the progression of inflammatory bowel disease, identify common and unique nodal genes, examine gene-protein interactions, assess current advances in the published literature on inflammatory bowel disease, and examine the impact of various biological pathways. Gene expression profiles were obtained from the Gene Expression Omnibus (GEO) database. We performed gene expression analysis to identify deferentially expressed genes. Subsequently, GO and KEGG pathway enrichment analyzes and protein-protein interaction network analyzes (PPI) of DEGs were performed. Text mining was used to examine the frequency of genes in the published IBD literature. Four GEO databases (GSE156044, GSE159751, GSE159008, and GSE102746) were downloaded from GEO databases. A total of 368 DEGs were identified. The results of GO term analysis showed that DEGs were mainly involved in the activity of cytokine receptors, integral components of the plasma membrane, and cytokine-mediated signaling. KEGG pathway analysis showed that DEGs were mainly enriched in bile secretion, mineral absorption, and cytokine-cytokine receptor interaction. The results of PPI analysis showed that about 10 genes were the key genes for the occurrence of CED. Text mining revealed the existence of 399 genes associated with CED. Our results suggest a possible link between CED and other diseases such as triple negative breast cancer (TNBC) and lung adenocarcinoma (LUAD), and provide new insights into the mechanisms of inflammatory bowel disease and new treatment targets.

Meta-Analysis of Immune Induced Gene Expression Changes in Diverse Drosophila melanogaster Innate Immune Responses

Simple SummaryOrganisms can be infected by a wide range of pathogens, including bacteria, viruses, and parasites. Following infection, the host mounts an immune response to attempt to eliminate the pathogen. These responses are often specific to the type of pathogen and mediated by the expression of specialized genes. We have characterized the expression changes induced in host Drosophila fruit flies following infection by multiple types of pathogens, and identified a small number of genes that show expression changes in each infection. This includes genes that are known to be involved in pathogen resistance, and others that have not been previously studied as immune response genes. These findings provide new insight into transcriptional changes that accompany Drosophila immunity. They may suggest possible roles for the differentially expressed genes in innate immune responses to diverse classes of pathogens, and serve to identify candidate genes for further empirical study of these processes.Organisms are commonly infected by a diverse array of pathogens and mount functionally distinct responses to each of these varied immune challenges. Host immune responses are characterized by the induction of gene expression, however, the extent to which expression changes are shared among responses to distinct pathogens is largely unknown. To examine this, we performed meta-analysis of gene expression data collected from Drosophila melanogaster following infection with a wide array of pathogens. We identified 62 genes that are significantly induced by infection. While many of these infection-induced genes encode known immune response factors, we also identified 21 genes that have not been previously associated with host immunity. Examination of the upstream flanking sequences of the infection-induced genes lead to the identification of two conserved enhancer sites. These sites correspond to conserved binding sites for GATA and nuclear factor κB (NFκB) family transcription factors and are associated with higher levels of transcript induction. We further identified 31 genes with predicted functions in metabolism and organismal development that are significantly downregulated following infection by diverse pathogens. Our study identifies conserved gene expression changes in Drosophila melanogaster following infection with varied pathogens, and transcription factor families that may regulate this immune induction.

Meta-analysis of gene expression data in adipose tissue reveals new obesity associated genes.

High-throughput transcriptomic and proteomic data like microarray data are deposited in public databases such as Gene Expression Omnibus (GEO). Omics data integration and processing from different and independent studies is achieved by using efficient and effective computational tools through meta-analysis. Meta-analysis is a statistical powerful tool combining data from numerous studies, minimizes bias and increases statistical power by increasing sample size compared to individual studies. Therefore, we performed a meta-analysis of gene expression data in adipose tissue to identify genes that are differentially expressed between obese and non-obese subjects as well as to detect gene expression signatures, pathways and networks associated with obesity. We identified 821 differentially expressed genes (DEGs) in adipose tissue of obese subjects compared to non-obese. A protein-protein interactions (PPIs) network was reconstructed consisting of 168 proteins. Functional enrichment analysis in the network revealed proteins involved in RNA and energy metabolism. The KEGG pathway analysis revealed 15 enriched pathway terms. Furthermore, multiple testing correction methods identified five statistically significant obesity associated genes (NDUFA12, SFI1, SSB, FAR2 and LACE1) that require further investigation.

Epigenetics of COVID-19 infected lung: systematic review and meta-analysis of gene expression data

Epigenetics of COVID-19 infected lung: systematic review and meta-analysis of gene expression data

Site-Specific and Common Prostate Cancer Metastasis Genes as Suggested by Meta-Analysis of Gene Expression Data.

Anticancer therapies mainly target primary tumor growth and little attention is given to the events driving metastasis formation. Metastatic prostate cancer, in comparison to localized disease, has a much worse prognosis. In the work presented here, groups of genes that are common to prostate cancer metastatic cells from bones, lymph nodes, and liver and those that are site-specific were delineated. The purpose of the study was to dissect potential markers and targets of anticancer therapies considering the common characteristics and differences in transcriptional programs of metastatic cells from different secondary sites. To that end, a meta-analysis of gene expression data of prostate cancer datasets from the GEO database was conducted. Genes with differential expression in all metastatic sites analyzed belong to the class of filaments, focal adhesion, and androgen receptor signaling. Bone metastases undergo the largest transcriptional changes that are highly enriched for the term of the chemokine signaling pathway, while lymph node metastasis show perturbation in signaling cascades. Liver metastases change the expression of genes in a way that is reminiscent of processes that take place in the target organ. Survival analysis for the common hub genes revealed involvements in prostate cancer prognosis and suggested potential biomarkers.

Icb-1 expression inhibits growth and fulvestrant response of breast cancer cells and affects survival of breast cancer patients.

Human gene icb-1 recently has been reported to be part of a gene expression score predicting response to antiestrogen fulvestrant in breast cancer patients. In the present study, we examined to what extent icb-1 expression would affect the response of breast cancer cells to this antiestrogen in vitro and investigated underlying molecular mechanisms. Using open access mRNA data, we elucidated the significance of icb-1 expression for survival of breast cancer patients. Icb-1 gene expression was knocked down by RNAi. Breast cancer cell growth after treatment with fulvestrant was assessed using the Cell Titer Blue assay. Gene expression was analyzed by Western blot analysis or RT-qPCR. Survival analyses were performed using bioinformatical online tools and data. Knockdown of icb-1 in T-47D breast cancer cells significantly increased growth of this cell line and also elevated the growth-stimulatory effect of E2 (p < 0.001). After treatment with different concentrations of fulvestrant, icb-1 knockdown cells exhibited a significantly enhanced response to this drug (p < 0.01). On the molecular level, icb-1 knockdown led to elevated expression of ESR1 and its target gene TFF1 (pS2) and enhanced E2-triggered up-regulation of proliferation genes. Finally, bioinformatical meta-analysis of gene expression data of 3951 breast cancer patients revealed that high icb-1 expression increases their relapse-free survival (HR = 0.87, p < 0.05). The presented data further support a tumor-suppressive role of icb-1 in breast cancer and suggest an inhibitory effect of this gene on fulvestrant action, which both are suggested to be mediated by suppression of cellular E2 response.

Modeling the human aging transcriptome across tissues, health status, and sex.

Aging in humans is an incredibly complex biological process that leads to increased susceptibility to various diseases. Understanding which genes are associated with healthy aging can provide valuable insights into aging mechanisms and possible avenues for therapeutics to prolong healthy life. However, modeling this complex biological process requires an enormous collection of high‐quality data along with cutting‐edge computational methods. Here, we have compiled a large meta‐analysis of gene expression data from RNA‐Seq experiments available from the Sequence Read Archive. We began by reprocessing more than 6000 raw samples—including mapping, filtering, normalization, and batch correction—to generate 3060 high‐quality samples spanning a large age range and multiple different tissues. We then used standard differential expression analyses and machine learning approaches to model and predict aging across the dataset, achieving an R 2 value of 0.96 and a root‐mean‐square error of 3.22 years. These models allow us to explore aging across health status, sex, and tissue and provide novel insights into possible aging processes. We also explore how preprocessing parameters affect predictions and highlight the reproducibility limits of these machine learning models. Finally, we develop an online tool for predicting the ages of human transcriptomic samples given raw gene expression counts. Together, this study provides valuable resources and insights into the transcriptomics of human aging.

DYRK1A Is Required to Alleviate Replication Stress in KMT2A-Rearranged Acute Lymphoblastic Leukemia

Background: KMT2A-rearranged (R) ALL is associated with chemoresistance, relapse, and poor survival with a frequency of 75% in infants and 10% in children and adults with ALL. Current intensive multiagent chemotherapy regimens induce significant side effects, yet fail to cure many patients, demonstrating continued need for novel therapeutic approaches. We performed a kinome-wide CRISPR screen and identified DYRK1A as required for KMT2A-R ALL cell survival, but not in other high risk ALL genetic subtypes. DYRK1A is a member of the dual-specificity tyrosine phosphorylation-regulated kinase family and has been reported as a critical oncoprotein in a murine Down syndrome model of megakaryoblastic leukemia. DYRK1A negatively regulates cell proliferation and induces quiescence. Paradoxically, genetic deletion or pharmacological inhibition of DYRK1A upregulates the cell cycle regulator CCND3 and increased numbers of B cells in S-phase, yet also significantly reduces cell proliferation. The specific role of DYRK1A in ALL has not been reported. Results: We assessed the importance of DYRK1A deletion in a focused screen of 14 previously identified kinases. Meta-analysis of ChIP-Seq data from two KMT2A-AFF1 cell lines and a human KMT2A-Aff1-FLAG transduced ALL model demonstrated direct binding of both N-terminal (KMT2AN) and C-terminal (AFF1C) and the FLAG-tagged KMT2A-fusion to the DYRK1A promoter. To assess if KMT2A fusion directly regulates DYRK1A expression, we treated SEM cells with the menin-KMT2A disrupter MI-503 and identified that the KMT2A fusion protein is a positive regulator of DYRK1A. Pharmacologic inhibition of DYRK1A with EHT1610 demonstrated potent leukemic cell growth inhibition, demonstrating that DYRK1 could be a new therapeutic target in KMT2A-R ALL. To further elucidate the mechanism of DYRK1A function, we treated several KMT2A-R ALL cell lines in vitro with EHT1610, which resulted in accumulation of CCND3 as expected. In addition, we detected upregulation of the positive cell cycle regulator MYC and the replication stress response molecule CHK1. In a second experiment, we validated the upregulation of MYC and identified significant upregulation of the proapoptotic protein BIM. Strikingly, meta-analysis of gene expression data from Dyrk1a-deleted murine pre-B cells isolated from a conditional Dyrk1a knockout mouse model also demonstrated increased levels of MYC and CHK1, validating that the EHT1610 mediated upregulation of MYC or CHK1 is a specific effect induced by DYRK1A inhibition. Western blot analysis demonstrated that KMT2A-R ALL cell lines have constitutive activation of pH2AX. Based on these data, we hypothesize that DYRK1A-mediated upregulation of CCND3 and MYC forces the cells to proliferate, which significantly increases replication stress and causes apoptosis, as evident by upregulation of CHK1 and BIM. To test if targeting the interaction of BIM with BCL2 will have an increased apoptotic effect when combined with EHT1610, we treated two KMT2A-R ALL cell lines with increasing concentrations of EHT1610 and the BCL2 inhibitor venetoclax. Strikingly, we observed a synergistic effect with both drugs, suggesting that combining these inhibitors has superior anti-leukemic activity. Conclusions: DYRK1A and MYC are positively regulated by the KMT2A fusion protein in KMT2A-R ALL and negatively regulate each other. Pharmacologic inhibition of DYRK1A resulted in significant growth disadvantage of KMT2A-R ALL cells due to increased MYC and CHK1 proteins that induce replication stress. While further in vivo studies are needed, we predict that combining DYRK1A inhibition with venetoclax may be a novel precision medicine strategy for KMT2A-R ALL that is translatable to the clinic for patients with these high-risk leukemias. Disclosures Tasian: Gilead Sciences: Research Funding; Aleta Biotherapeutics: Membership on an entity's Board of Directors or advisory committees; Incyte Corporation: Research Funding.

A meta-analysis of gene expression data highlights synaptic dysfunction in the hippocampus of brains with Alzheimer\u2019s disease

Since the world population is ageing, dementia is going to be a growing concern. Alzheimer’s disease is the most common form of dementia. The pathogenesis of Alzheimer’s disease is extensively studied, yet unknown remains. Therefore, we aimed to extract new knowledge from existing data. We analysed about 2700 upregulated genes and 2200 downregulated genes from three studies on the CA1 of the hippocampus of brains with Alzheimer’s disease. We found that only the calcium signalling pathway enriched by 48 downregulated genes was consistent between all three studies. We predicted miR-129 to target nine out of 48 genes. Then, we validated miR-129 to regulate six out of nine genes in HEK cells. We noticed that four out of six genes play a role in synaptic plasticity. Finally, we confirmed the upregulation of miR-129 in the hippocampus of brains of rats with scopolamine-induced amnesia as a model of Alzheimer’s disease. We suggest that future research should investigate the possible role of miR-129 in synaptic plasticity and Alzheimer’s disease. This paper presents a novel framework to gain insight into potential biomarkers and targets for diagnosis and treatment of diseases.

The role of associated genes of Wnt signaling pathway in chronic obstructive pulmonary disease (COPD)

Chronic obstructive pulmonary disease (COPD), as a chronic lung disease with two common types, emphysema and chronic bronchitis, is a key factor in lung cancer development which mostly appears in tobacco smoking people, hence the necessity of an early detection protocol for COPD patients based on genome-wide association studies. The NCBI-GEO database was systematically searched for COPD smoking patients with available mRNA datasets. A meta-analysis of gene expression data was performed to identify the significant genes among extracted GEO datasets while considering the genes involved in the Wnt signaling pathway. To further validate the target gene, the Kaplan-Meier (KM) tool was utilized for assessing three types of survival rates, namely first progression (FP), overall survival (OS), and post progression survival (PPS).The obtained results showed that JUN gene was significantly over-expressed among the GEO datasets (p-value < 0.05) between COPD smoking and normal smoking patients. Further assessment using KM tool presented the significant role of JUN gene in lung cancer development, while FP and OS were also studied. However, the expression of JUN was not significant regarding PPS rate. Based on the outcomes of the current study, genome wide association studies are highly recommended for an early diagnosis of COPD. Noticeably, novel therapeutic agents can be developed through understanding the cellular functions in the Wnt signaling pathway.

Integrated Network Analysis Reveals FOXM1 and MYBL2 as Key Regulators of Cell Proliferation in Non-small Cell Lung Cancer.

Background: Loss of control on cell division is an important factor for the development of non-small cell lung cancer (NSCLC), however, its molecular mechanism and gene regulatory network are not clearly understood. This study utilized the systems bioinformatics approach to reveal the “driver-network” involve in tumorigenic processes in NSCLC.Methods: A meta-analysis of gene expression data of NSCLC was integrated with protein-protein interaction (PPI) data to construct an NSCLC network. MCODE and iRegulone were used to identify the local clusters and its upstream transcription regulators involve in NSCLC. Pair-wise gene expression correlation was performed using GEPIA. The survival analysis was performed by the Kaplan-Meier plot.Results: This study identified a local “driver-network” with highest MCODE score having 26 up-regulated genes involved in the process of cell proliferation in NSCLC. Interestingly, the “driver-network” is under the regulation of TFs FOXM1 and MYBL2 as well as miRNAs. Furthermore, the overexpression of member genes in “driver-network” and the TFs are associated with poor overall survival (OS) in NSCLC patients.Conclusion: This study identified a local “driver-network” and its upstream regulators responsible for the cell proliferation in NSCLC, which could be promising biomarkers and therapeutic targets for NSCLC treatment.

Relapse-associated AURKB blunts the glucocorticoid sensitivity of B cell acute lymphoblastic leukemia

Glucocorticoids (GCs) are used in combination chemotherapies as front-line treatment for B cell acute lymphoblastic leukemia (B-ALL). Although effective, many patients relapse and become resistant to chemotherapy and GCs in particular. Why these patients relapse is not clear. We took a comprehensive, functional genomics approach to identify sources of GC resistance. A genome-wide shRNA screen identified the transcriptional coactivators EHMT2, EHMT1, and CBX3 as important contributors to GC-induced cell death. This complex selectively supports GC-induced expression of genes contributing to cell death. A metaanalysis of gene expression data from B-ALL patient specimens revealed that Aurora kinase B (AURKB), which restrains GC signaling by phosphorylating EHMT1-2, is overexpressed in relapsed B-ALL, suggesting it as a potential contributor to relapse. Inhibition of AURKB enhanced GC-induced expression of cell death genes, resulting in potentiation of GC cytotoxicity in cell lines and relapsed B-ALL patient samples. This function for AURKB is distinct from its canonical role in the cell cycle. These results show the utility of functional genomics in understanding mechanisms of resistance and rapidly identifying combination chemotherapeutics.

SMAGEXP: a galaxy tool suite for transcriptomics data meta-analysis

BackgroundWith the proliferation of available microarray and high-throughput sequencing experiments in the public domain, the use of meta-analysis methods increases. In these experiments, where the sample size is often limited, meta-analysis offers the possibility to considerably enhance the statistical power and give more accurate results. For those purposes, it combines either effect sizes or results of single studies in an appropriate manner. R packages metaMA and metaRNASeq perform meta-analysis on microarray and next generation sequencing (NGS) data, respectively. They are not interchangeable as they rely on statistical modeling specific to each technology.ResultsSMAGEXP (Statistical Meta-Analysis for Gene EXPression) integrates metaMA and metaRNAseq packages into Galaxy. We aim to propose a unified way to carry out meta-analysis of gene expression data, while taking care of their specificities. We have developed this tool suite to analyze microarray data from the Gene Expression Omnibus database or custom data from Affymetrix© microarrays. These data are then combined to carry out meta-analysis using metaMA package. SMAGEXP also offers to combine raw read counts from NGS experiments using DESeq2 and metaRNASeq package. In both cases, key values, independent from the technology type, are reported to judge the quality of the meta-analysis. These tools are available on the Galaxy main tool shed. A dockerized instance of galaxy containing SMAGEXP and its dependencies is available on Docker hub. Source code, help, and installation instructions are available on GitHub.ConclusionThe use of Galaxy offers an easy-to-use gene expression meta-analysis tool suite based on the metaMA and metaRNASeq packages.

DDB2 regulates Epithelial-to-Mesenchymal Transition (EMT) in Oral/Head and Neck Squamous Cell Carcinoma.

DDB2 is a sensor of DNA damage and it plays an important role in Global Genomic Repair (GG-NER). Our previous studies show that DDB2 is involved in the regulation of metastasis in colon adenocarcinoma. Squamous Cell Carcinomas in the Oral/Head & Neck region (HNSCC) are particularly aggressive due to high incidence of recurrence and distant metastasis. In this study, we show that DDB2 expression is downregulated in advanced HNSCCs and loss of DDB2 expression coincides with reduced survival. Recent meta-analysis of gene expression data characterized the mesenchymal-type (EMT-type) as one most aggressive cancer cluster in HNSCC. Here, we report that DDB2 constitutively represses mRNA expression of the EMT- regulatory transcription factors SNAIL, ZEB1, and angiogenic factor VEGF in HNSCC cells. As a result, re-expression of DDB2 in metastatic cells reversed EMT with transcriptional upregulation of epithelial marker E-cadherin, and downregulation of mesenchymal markers N-cadherin, Vimentin, and Fibronectin. Interestingly, in a reverse assay, depletion of DDB2 in non-metastatic cells induced expression of the same EMT-regulatory transcription factors. TGFβs are major regulators of Snail and Zeb1, and we observed that DDB2 transcriptionally regulates expression of TGFB2 in HNSCC cells. Re-expression of DDB2 in mouse embryonic fibroblasts (MEFs) isolated from Ddb2 (−/−) knockout-mice resulted in repression of EMT-regulatory factors Zeb1, Snail and Tgfb2. Taken together, these results support the active role of DDB2 as a candidate suppressor of the EMT-process in HNSCC. Early detection leads to significantly higher survival in HNSCC and DDB2 expression in tumors can be a predictor of EMT progression.

Decentralized Learning Framework of Meta-Survival Analysis for Developing Robust Prognostic Signatures.

PurposeA significant hurdle in developing reliable gene expression–based prognostic models has been the limited sample size, which can cause overfitting and false discovery. Combining data from multiple studies can enhance statistical power and reduce spurious findings, but how to address the biologic heterogeneity across different datasets remains a major challenge. Better meta-survival analysis approaches are needed.Material and MethodsWe presented a decentralized learning framework for meta-survival analysis without the need for data aggregation. Our method consisted of a series of proposals that together alleviated the influence of data heterogeneity and improved the performance of survival prediction. First, we transformed the gene expression profile of every sample into normalized percentile ranks to obtain platform-agnostic features. Second, we used Stouffer’s meta-z approach in combination with Harrell’s concordance index to prioritize and select genes to be included in the model. Third, we used survival discordance as a scale-independent model loss function. Instead of generating a merged dataset and training the model therein, we avoided comparing patients across datasets and individually evaluated the loss function on each dataset. Finally, we optimized the model by minimizing the joint loss function.ResultsThrough comprehensive evaluation on 31 public microarray datasets containing 6,724 samples of several cancer types, we demonstrated that the proposed method has outperformed (1) single prognostic genes identified using conventional meta-analysis, (2) multigene signatures trained on single datasets, (3) multigene signatures trained on merged datasets as well as by other existing meta-analysis methods, and (4) clinically applicable, established multigene signatures.ConclusionThe decentralized learning approach can be used to effectively perform meta-analysis of gene expression data and to develop robust multigene prognostic signatures.

Differential co-expression analysis reveals a novel prognostic gene module in ovarian cancer

Ovarian cancer is one of the most significant disease among gynecological disorders that women suffered from over the centuries. However, disease-specific and effective biomarkers were still not available, since studies have focused on individual genes associated with ovarian cancer, ignoring the interactions and associations among the gene products. Here, ovarian cancer differential co-expression networks were reconstructed via meta-analysis of gene expression data and co-expressed gene modules were identified in epithelial cells from ovarian tumor and healthy ovarian surface epithelial samples to propose ovarian cancer associated genes and their interactions. We propose a novel, highly interconnected, differentially co-expressed, and co-regulated gene module in ovarian cancer consisting of 84 prognostic genes. Furthermore, the specificity of the module to ovarian cancer was shown through analyses of datasets in nine other cancers. These observations underscore the importance of transcriptome based systems biomarkers research in deciphering the elusive pathophysiology of ovarian cancer, and here, we present reciprocal interplay between candidate ovarian cancer genes and their transcriptional regulatory dynamics. The corresponding gene module might provide new insights on ovarian cancer prognosis and treatment strategies that continue to place a significant burden on global health.