Meta-analysis Of Gene Expression Profiles Research Articles

The anti-hepatocellular carcinoma effect of Aidi injection was related to the synergistic action of cantharidin, formononetin, and isofraxidin through BIRC5, FEN1, and EGFR

Ethnopharmacological relevanceAidi injection (ADI) is a popular anti-tumor Chinese patent medicine, widely used in clinics for the treatment of hepatocellular carcinoma (HCC) with remarkable therapeutic effects through multiple targets and pathways. However, the scientific evidence of the synergistic role of the complex chemical component system and the potential mechanism for treating diseases are ignored and remain to be elucidated. Aim of the studyThis study aimed to elucidate and verify the cooperative association between the potential active ingredient of ADI, which is of significance to enlarge our understanding of its anti-HCC molecular mechanisms. Materials and methodsFirstly, the anti-HCC effect of ADI was evaluated in various HCC cells and the zebrafish xenograft model. Subsequently, a variety of bioinformatic technologies, including network pharmacology, weighted gene co-expression network analysis (WGCNA), meta-analysis of gene expression profiles, and pathway enrichment analysis were performed to construct the competitive endogenous RNA (ceRNA) network of ADI intervention in HCC and to establish the relationship between the critical targets/pathways and the key corresponding components, which were involved in ADI against HCC in a synergistic way and were validated by molecular biology experiments. ResultsADI exerted remarkable anti-HCC in vitro cells and in vivo zebrafish model, especially that the Hep 3B2.1-7 cell showed substantial sensibility to ADI. The ceRNA network revealed that the EGFR/PI3K/AKT signaling pathway was identified as the promising pathway. Furthermore, the meta-analysis also demonstrated the critical role of BIRC5 and FEN1 as key targets. Finally, the synergistic effect of ADI was revealed by discovering the inhibitory effect of cantharidin on BIRC5, formononetin on FEN1 and EGFR, as well as isofraxidin on EGFR. ConclusionOur study unveiled that the incredible protective effect of ADI on HCC resulted from the synergistic inhibition effect of cantharidin, formononetin, and isofraxidin on multiple targets/pathways, including BIRC5, FEN1, and EGFR/PI3K/AKT, respectively, providing a scientific interpretation of ADI against HCC and a typical example of pharmacodynamic evaluation of other proprietary Chinese patent medicine.

Identification of lncRNAs Deregulated in Epithelial Ovarian Cancer Based on a Gene Expression Profiling Meta-Analysis.

Epithelial ovarian cancer (EOC) is one of the deadliest gynecological cancers worldwide, mainly because of its initially asymptomatic nature and consequently late diagnosis. Long non-coding RNAs (lncRNA) are non-coding transcripts of more than 200 nucleotides, whose deregulation is involved in pathologies such as EOC, and are therefore envisaged as future biomarkers. We present a meta-analysis of available gene expression profiling (microarray and RNA sequencing) studies from EOC patients to identify lncRNA genes with diagnostic and prognostic value. In this meta-analysis, we include 46 independent cohorts, along with available expression profiling data from EOC cell lines. Differential expression analyses were conducted to identify those lncRNAs that are deregulated in (i) EOC versus healthy ovary tissue, (ii) unfavorable versus more favorable prognosis, (iii) metastatic versus primary tumors, (iv) chemoresistant versus chemosensitive EOC, and (v) correlation to specific histological subtypes of EOC. From the results of this meta-analysis, we established a panel of lncRNAs that are highly correlated with EOC. The panel includes several lncRNAs that are already known and even functionally characterized in EOC, but also lncRNAs that have not been previously correlated with this cancer, and which are discussed in relation to their putative role in EOC and their potential use as clinically relevant tools.

Comparative transcriptome meta-analysis reveals a set of genes involved in the responses to multiple pathogens in maize.

Maize production is constantly threatened by the presence of different fungal pathogens worldwide. Genetic resistance is the most favorable approach to reducing yield losses resulted from fungal diseases. The molecular mechanism underlying disease resistance in maize remains largely unknown. The objective of this study was to identify key genes/pathways that are consistently associated with multiple fungal pathogen infections in maize. Here, we conducted a meta-analysis of gene expression profiles from seven publicly available RNA-seq datasets of different fungal pathogen infections in maize. We identified 267 common differentially expressed genes (co-DEGs) in the four maize leaf infection experiments and 115 co-DEGs in all the seven experiments. Functional enrichment analysis showed that the co-DEGs were mainly involved in the biosynthesis of diterpenoid and phenylpropanoid. Further investigation revealed a set of genes associated with terpenoid phytoalexin and lignin biosynthesis, as well as potential pattern recognition receptors and nutrient transporter genes, which were consistently up-regulated after inoculation with different pathogens. In addition, we constructed a weighted gene co-expression network and identified several hub genes encoding transcription factors and protein kinases. Our results provide valuable insights into the pathways and genes influenced by different fungal pathogens, which might facilitate mining multiple disease resistance genes in maize.

A review and meta-analysis of gene expression profiles in suicide

Suicide claims over 800,000 deaths worldwide, making it a serious public health problem. The etiopathophysiology of suicide remains unclear and is highly complex, and postmortem gene expression studies can offer insights into the molecular biological mechanism underlying suicide. In the current study, we conducted a meta-analysis of postmortem brain gene expression in relation to suicide. We identified five gene expression datasets for postmortem orbitofrontal, prefrontal, or dorsolateral prefrontal cortical brain regions from the Gene Expression Omnibus repository. After quality control, the total sample size was 380 (141 suicide deaths and 239 deaths from other causes). We performed the analyses using two meta-analytic approaches. We further performed pathway and cell-set enrichment analyses. We found reduced expression of the KCNJ2 (Potassium Inwardly Rectifying Channel Subfamily J Member 2), A2M (Alpha-2-Macroglobulin), AGT (Angiotensinogen), PMP2 (Peripheral Myelin Protein 2), and VEZF1 (Vascular Endothelial Zinc Finger 1) genes (FDR p<0.05). Our findings support the involvement of astrocytes, stress response, immune system, and microglia in suicide. These findings will require further validation in additional large datasets.

Integrative meta-analysis of gene expression profiles identifies FEN1 and ENDOU as potential diagnostic biomarkers for cervical squamous cell carcinoma.

Cervical carcinoma is a global public health burden. Given that it is usually asymptomatic at potentially curative stages, the development of clinically accurate tests is critical for early detection and individual risk stratification. The present study performed an integrative meta-analysis of the transcriptomes from 10 cervical carcinoma cohorts, with the aim of identifying biomarkers that are associated with malignant transformation of cervical epithelium, and establish their clinical applicability. From among the top ranked differentially expressed genes, flap structure-specific endonuclease 1 (FEN1) and poly (U)-specific endoribonuclease (ENDOU) were selected for further validation, and their clinical applicability was assessed using immunohistochemically stained microarrays comprising 110 tissue cores, using p16 and Ki67 staining as the comparator tests. The results demonstrated that FEN1 expression was significantly upregulated in 65% of tumor specimens (P=0.0001), with no detectable expression in the non-tumor tissues. Furthermore, its expression was significantly associated with Ki67 staining in tumor samples (P<0.0001), but no association was observed with p16 expression or the presence of human papilloma virus types 16/18, patient age, tumor grade or stage. FEN1 staining demonstrated lower sensitivity than p16 (69.3 vs. 96.8%) and Ki67 (69.3 vs. 76.3%); however, the specificity was identical to p16 and higher than that of Ki67 (100 vs. 71.4%).ENDOU staining was consistent with the microarray results, demonstrating 1% positivity in tumors and 40% positivity in non-tumor tissues. Gene set enrichment analysis of cervical tumors overexpressing FEN1 revealed its association with enhanced growth factor signaling, immune response inhibition and extracellular matrix remodeling, whereas tumors with low ENDOU expression exhibited inhibition of epithelial development and differentiation processes. Taken together, the results of the present study demonstrate the feasibility of the integrative meta-analysis approach to identify relevant biomarkers associated with cervical carcinogenesis. Thus, FEN1 and ENDOU may be useful diagnostic biomarkers for squamous cervical carcinoma. However, further studies are required to determine their diagnostic performance in larger patient cohorts and validate the results presented here.

Meta-analysis of gene expression profiling reveals novel basal gene signatures in MCF-10A cells transformed with cadmium.

Cadmium (Cd2+) is an environmental toxicant and a human carcinogen. Several studies show an association of Cd2+ exposure to the development of breast cancer. Previously, we have transformed the immortalized non-tumorigenic cell line MCF-10A with Cd2+ and have demonstrated that the transformed cells have anchorage independent growth. In a separate study, we showed that transformation of the immortalized urothelial cells with the environmental carcinogen arsenite (As3+) results in an increase in expression of genes associated with the basal subtype of bladder cancer. In this study, we determined if transformation of the MCF-10A cells with Cd2+ would have a similar effect on the expression of basal genes. The results of our study indicate that there is a decrease in expression of genes associated with keratinization and cornification and this gene signature includes the genes associated with the basal subtype of breast cancer. An analysis of human breast cancer databases indicates an increased expression of this gene signature is associated with a positive correlation to patient survival whereas a reduced expression/absence of this gene signature is associated with poor patient survival. Thus, our study suggests that transformation of the MCF-10A cells with Cd2+ produces a decreased basal gene expression profile that correlates to patient outcome.

A Systematic Review and Meta-Analysis of Gene Expression Profiling for Primary Cutaneous Melanoma Prognosis

To decrease morbidity and mortality from melanoma, it is imperative to identify patients who are at high risk for developing widespread disease. Gene expression profiling (GEP) technology may impact melanoma management as physicians are better equipped to measure prognosis. Many different GEP signatures have been investigated. We searched Pubmed, Cochrane CENTRAL, and Embase for studies on GEP in primary melanoma prognosis and assessed GEP signatures for prognostic and analytic validity and clinical impact. The relationship between GEP and survival was measured using hazard ratios (HR) and odds ratios (OR). We found twenty-nine articles comprising 9 gene signatures meeting inclusion criteria and conducted a meta-analysis on 6 studies on a 31-gene signature. High-risk GEP status was associated with poorer recurrence-free survival (HR=7.22; 95% CI, 4.75-10.98), distant metastasis-free survival (HR=6.62; 95% CI, 4.91-8.91), and overall survival (HR=7.06; 95% CI, 4.44-11.22); as well as sentinel lymph node biopsy positivity (OR=2.99; 95% CI, 2.15-4.15). With recent improvements in treating advanced melanoma, accurately assessing prognosis is important. This study has clinical implications for melanoma patients who may benefit from prognostic testing. These results may be useful to clinicians when ordering GEP testing and help them make better management decisions.

Meta-analysis of gene expression profiles of lean and obese PCOS to identify differentially regulated pathways and risk of comorbidities

Polycystic ovary syndrome (PCOS) is a complex multigenic disorder and women with PCOS suffer from several comorbidities. Although, obesity is a known risk factor for PCOS, the incidence of lean women with PCOS is on the rise. A systematic and comparative study on lean and obese PCOS with respect to genes, pathways and comorbidity analysis has not been attempted so far. Analysis of differentially expressed genes (DEGs) across tissue types for lean and obese PCOS revealed that the majority of them were downregulated for lean and obese PCOS. Ovarian and endometrial tissues shared several commonly dysregulated genes, suggesting shared PCOS pathophysiology mechanisms exist across tissues. Several pathways for cellular homeostasis, such as inflammation and immune response, insulin signaling, steroidogenesis, hormonal and metabolic signaling, regulation of gonadotrophic hormone secretion, cell structure and signaling that are known to be affected in PCOS were found to be enriched in our gene expression analysis of lean and obese PCOS. The gene-disease network is denser for obese PCOS with a higher comorbidity score as compared to lean PCOS.

Meta-analysis of gene expression profiles in preeclampsia.

Preeclampsia (PE) is a serious complication of pregnancy. It is considered a complex condition influenced by maternal genes, environmental factors and a deregulated immune response of the mother, but the etiology is largely unknown. The aim of this study is to identify differentially expressed genes (DEGs) in PE, to help elucidate the identification of the disease etiological mechanisms. The databases Pubmed and GEO were searched according to PRISMA guidelines for the existence of gene expression data on placental samples from case-control studies. After meta-analysis the identified DEGs were further analyzed with STRING and PANTHER to retrieve interaction networks and overrepresented biochemical pathways. Only 10 gene expression datasets and articles fulfilled inclusion criteria, containing data on 195 patients and 231 controls, and were analyzed. Meta-analysis identified 629 DEGs to be associated with PE at a False Discovery Rate p-value of 0.01. Network analysis showed few highly interconnected genes involved in innate immunity and signal transduction pathways indicative of a multifaceted disease with etiological heterogeneity. over representation analysis revealed that these genes participate mainly in carbohydrates, amino acids and pyrimidine metabolism, circadian clock system and signal transduction pathways. This work, combining rigorous methods of meta-analysis and the use of modern bioinformatics tools, proposes the existence of novel, overlooked so far, biochemical pathways and mechanisms to contribute to PE development such as carbohydrate, aminoacids and pyrimidine metabolism. Our findings pave the way for further investigation of the above pathways in experimental efforts to decipher the orchestrating mechanisms for PE development.

Gene expression profiling meta-analysis reveals novel gene signatures and pathways shared between tuberculosis and rheumatoid arthritis.

Tuberculosis (TB) is among the leading causes of death by infectious diseases. An epidemiological association between Mycobacterium tuberculosis infection and autoimmune diseases like rheumatoid arthritis (RA) has been reported but it remains unclear if there is a causal relationship, and if so, which molecular pathways and regulatory mechanisms contribute to it. Here we used a computational biology approach by global gene expression meta-analysis to identify candidate genes and pathways that may link TB and RA. Data were collected from public expression databases such as NCBI GEO. Studies were selected that analyzed mRNA-expression in whole blood or blood cell populations in human case control studies at comparable conditions. Six TB and RA datasets (41 active TB patients, 33 RA patients, and 67 healthy controls) were included in the downstream analysis. This approach allowed the identification of deregulated genes that had not been identified in the single analysis of TB or RA patients and that were co-regulated in TB and RA patients compared to healthy subjects. The genes encoding TLR5, TNFSF10/TRAIL, PPP1R16B/TIMAP, SIAH1, PIK3IP1, and IL17RA were among the genes that were most significantly deregulated in TB and RA. Pathway enrichment analysis revealed ‘T cell receptor signaling pathway’, ‘Toll-like receptor signaling pathway,’ and ‘virus defense related pathways’ among the pathways most strongly associated with both diseases. The identification of a common gene signature and pathways substantiates the observation of an epidemiological association of TB and RA and provides clues on the mechanistic basis of this association. Newly identified genes may be a basis for future functional and epidemiological studies.

Meta-analysis of gene expression profiles in long-term non-progressors infected with HIV-1

BackgroundIn the absence of antiretroviral treatments (ARTs), a small group of individuals infected with HIV, including long-term non-progressors (LTNPs) who maintain high levels of CD4+ T cells for more than 7–10 years in the absence of ART and in particular a subgroup of LTNPs, elite controllers (ECs), who have low levels of viremia, remain clinically and/or immunologically stable for years. However, the mechanism of stable disease progression in LTNPs and ECs needs to be elucidated to help those infected with HIV-1 remain healthy. In this study, to identify the characteristics of gene expression profiles and biomarkers in LTNPs, we performed a meta-analysis using multiple gene expression profiles among LTNPs, individuals infected with HIV-1 without ART, individuals infected with HIV-1 with ART, and healthy controls.MethodsThe gene expression profiles obtained from the Gene Expression Omnibus (GEO) microarray data repositories were classified into three groups: LTNPs versus healthy controls (first group, 3 studies), LTNPs versus patients infected with HIV-1 without ART (second group, 3 studies), and LTNPs versus patients infected with HIV-1 with ART (third group, 3 studies). In addition, we considered a fourth group, patients infected with HIV-1 without ART versus healthy controls (3 studies), to exclude genes associated with HIV-1 infection in the three groups. For each group, we performed a meta-analysis using the RankProd method to identify and compare the differentially expressed genes (DEGs) in the three groups.ResultsWe identified the 14 common DEGs in the three groups when comparing them with each other. Most belonged to immune responses, antigen processing and presentation, the interferon-gamma-mediated signaling pathway, and T cell co-stimulation. Of these DEGs, PHLDA1 was up-regulated and ACTB and ACTG1 were down-regulated in all three groups. However, the rest of the up- or down-regulated genes were discordant in the three groups. Additionally, ACTB and ACTG1 are known to inhibit viral assembly and production, and THBS1 is known to inhibit HIV-1 infection.ConclusionsThese results suggest that significant genes identified in a meta-analysis provide clues to the cause of delayed disease progression and give a deeper understanding of HIV pathogenesis in LTNPs.

Transcriptome meta-analysis identifies immune signature comprising of RNA binding proteins in ulcerative colitis patients

Ulcerative colitis (UC) is a persistent inflammatory illness, which is clinically categorised as Inflammatory bowel disease (IBD), affecting millions of people worldwide. The precise cause behind the pathology of the disease remains unknown. However, the involvement of multiple factors including genetic predisposition, immunological deregulations, microbiota imbalance, and environmental triggers has been suggested. Amongst all these factors, the over-active immunological response reported in UC patients seems to be a promising target for therapy. Moreover, identification of gene signatures associated with disease onset and progression would help in better understanding of the molecular mechanisms involved in the disease pathogenesis. Here, we have conducted meta-analysis of gene expression profiles of UC patient microarray datasets accessible in public databases and further validated the in-silico findings in UC patients’ blood samples. Our study reveals that UC pathogenesis perturbs expression of several inflammatory genes. In addition, we report a novel gene signature comprising of TIA1 (T cell restricted intracellular antigen) and TIAR (TIA1 related protein; also known as TIAL1), which were found to be significantly downregulated in UC patients. TIA1 and TIAR are RNA-binding proteins (RBPs), which function as a translational represser by binding to ARE sequences in the 3′ UTR of mRNAs encoding inflammatory mediators including cytokines. Our findings demonstrate that deletion of TIAR using gene specific siRNAs in-vitro results in enhanced production of inflammatory cytokine IL-1β. In conclusion, the findings of this study reveal that down regulation of TIA1/TIAR genes could be responsible for UC associated inflammation. This study highlights the usefulness of the meta-analysis approach in the identification of unique gene signatures that might deliver mechanistic insights into UC pathogenesis and possibly assist in discovery of prognostic markers and therapeutic interventions.

Meta-analysis of gene expression profiles of peripheral blood cells in systemic lupus erythematosus

Our purpose was to identify differentially expressed (DE) genes and biological processes associated with gene expression changes in systemic lupus erythematosus (SLE). We performed a meta-analysis using the INMEX program (integrative meta-analysis of expression data) on publicly available microarray Genetic Expression Omnibus (GEO) datasets of peripheral blood mononuclear cells (PBMCs) of SLE patients and healthy controls. We performed Gene Ontology (GO) enrichment analysis by using hypergeometric tests. In total, five comparisons (2 B cells, 2 CD4 T cells, and 1 myeloid cell) from two GEO datasets containing 51 cases and 46 controls were included in the meta-analysis. We identified 483 genes consistently differentially expressed in SLE (260 up-regulated and 223 down-regulated). The up-regulated gene with the lowest P-value (P-value = 7.33E-10) was TAP1 (transporter 1, ATP-binding cassette, sub-family B (MDR/TAP)). The up-regulated gene with the largest effect size (ES = 2.7799, P-value = 8.28E-06) was STAT1 (signal transducer and activator of transcription 1, 91 kDa). The down-regulated gene with the lowest P-value (P-value = 2.53E-06) was EIF3F (eukaryotic translation initiation factor 3, subunit F), and the down-regulated gene with the largest ES (ES = -1.8543, P-value = 8.56E-06) was FBL (fibrillarin). The most significant enrichment among 317 GO categories was the type I interferon (IFN)-mediated signaling pathway with P = 1.93E-20. Other significant GO categories included cellular response to type I IFN (P = 1.93E-20) and response to type I IFN (P = 2.86E-20). Our meta-analysis demonstrates that up-regulated genes mediate IFN-regulated and cytokine-mediate signaling pathways, innate immune response, and antigen processing and presentation. These results provide insights into molecular mechanisms associated to the pathophysiology of SLE.

A meta-analysis of transcriptome datasets characterizes malignant transformation from melanocytes and nevi to melanoma.

Melanoma represents one of the most aggressive malignancies and has a high tendency to metastasize. The present study aims to investigate the molecular mechanisms of two pathways to cancer transformation with the purpose of identifying potential biomarkers. Our approach is based on a meta-analysis of gene expression profiling contrasting two scenarios: A model that describes a transformation pathway from melanocyte to melanoma and a second model where transformation occurs through an intermediary nevus. Data consists of three independent, publicly available microarray datasets from the Gene Expression Omnibus (GEO) database comprising samples from melanocytes, nevi and melanoma. The present analysis identified 808 differentially expressed genes (528 upregulated and 360 downregulated) in melanoma compared with nevi, and 2,331 differentially expressed genes (946 upregulated and 1,385 downregulated) in melanoma compared with melanocytes. Further analysis narrowed down this list, since 682 differentially expressed genes were found in both models (417 upregulated and 265 downregulated). Enrichment analysis identified relevant dysregulated pathways. This article also presented a discussion on significant genes including ADAM like decysin 1, neudesin neurotrophic factor, MMP19, apolipoprotein L6, C-X-C motif chemokine ligand (CXCL)8, basic, immunoglobulin-like variable motif containing and CXCL16. These are of particular interest because they encode secreted proteins hence represent potential blood biomarkers for the early detection of malignant transformation in both scenarios. Cytotoxic T-lymphocyte associated protein 4, an important therapeutic target in melanoma treatment, was also upregulated in both comparisons indicating a potential involvement in immune tolerance, not only at advanced stages but also during the early transformation to melanoma. The results of the present study may provide a research direction for studying the mechanisms underlying the development of melanoma, depending on its origin.

Integrative multi-platform meta-analysis of gene expression profiles in pancreatic ductal adenocarcinoma patients for identifying novel diagnostic biomarkers.

Applying differentially expressed genes (DEGs) to identify feasible biomarkers in diseases can be a hard task when working with heterogeneous datasets. Expression data are strongly influenced by technology, sample preparation processes, and/or labeling methods. The proliferation of different microarray platforms for measuring gene expression increases the need to develop models able to compare their results, especially when different technologies can lead to signal values that vary greatly. Integrative meta-analysis can significantly improve the reliability and robustness of DEG detection. The objective of this work was to develop an integrative approach for identifying potential cancer biomarkers by integrating gene expression data from two different platforms. Pancreatic ductal adenocarcinoma (PDAC), where there is an urgent need to find new biomarkers due its late diagnosis, is an ideal candidate for testing this technology. Expression data from two different datasets, namely Affymetrix and Illumina (18 and 36 PDAC patients, respectively), as well as from 18 healthy controls, was used for this study. A meta-analysis based on an empirical Bayesian methodology (ComBat) was then proposed to integrate these datasets. DEGs were finally identified from the integrated data by using the statistical programming language R. After our integrative meta-analysis, 5 genes were commonly identified within the individual analyses of the independent datasets. Also, 28 novel genes that were not reported by the individual analyses (‘gained’ genes) were also discovered. Several of these gained genes have been already related to other gastroenterological tumors. The proposed integrative meta-analysis has revealed novel DEGs that may play an important role in PDAC and could be potential biomarkers for diagnosing the disease.

1269P - Integrative multi-platform meta-analysis of gene expression profiles in pancreatic ductal adenocarcinoma patients for identifying novel diagnostic biomarkers

Background: Applying differentially expressed genes (DEGs) to identify feasible biomarkers in diseases can be a hard task when working with heterogeneous datasets. Expression data are strongly influenced by technology, sample preparation processes, and/or labeling methods. The proliferation of different microarray platforms for measuring gene expression increases the need to develop models able to compare their results, especially when different technologies can lead to signal values that vary greatly. Integrative meta-analysis can significantly improve the reliability and robustness of DEG detection. The objective of this work was to develop an integrative approach for identifying potential cancer biomarkers by integrating gene expression data from two different platforms. Pancreatic ductal adenocarcinoma (PDAC), where there is an urgent need to find new biomarkers due its late diagnosis, is an ideal candidate for testing this technology. Methods: Expression data from two different datasets, namely Affymetrix and Illumina (18 and 36 PDAC patients, respectively), as well as from 18 healthy controls, was used for this study. A meta-analysis based on an empirical Bayesian methodology (ComBat) was then proposed to integrate these datasets. DEGs were finally identified from the integrated data by using the statistical programming language R. Results: After our integrative meta-analysis, 5 genes were commonly identified within the individual analyses of the independent datasets. Also, 28 novel genes that were not reported by the individual analyses ('gained' genes) were also discovered. Several of these gained genes have been already related to other gastroenterological tumors. Conclusions: The proposed integrative meta-analysis has revealed novel DEGs that may play an important role in PDAC and could be potential biomarkers for diagnosing the disease. Legal entity responsible for the study: Grupo de Tratamiento de Tumores Digestivos Funding: Grupo de Tratamiento de Tumores Digestivos Disclosure: M. Benavides: Received honoraria from Roche and honoraria for advisory role from Roche. C. Guillen-Ponce: Received honoraria from Roche; honoraria for advisory role from Roche Pharma, Bayer, Merck Serono, Sanofi Aventis, Celgene y Novocure; other remuneration from Celgene, Roche Pharma, Sanofi Aventis, Merck Serono. E. Aranda Aguilar: Received honoraria for advisory role from Amgen, Bayer, Celgene, Merck, Roche, Sanofi. All other authors have declared no conflicts of interest.

Abstract LB-281: Meta analysis of gene transcripts from fatty livers and liver cancers of C57BL6 mice identifies potential drivers of HCC

Abstract Rates of obesity are increasing and 640 million adults were considered obese in 2014 an increase of 27.5% over the past three decades. Obesity is strongly linked with increased cancer risk and particularly so for liver cancer in men. Although virus infection had been the leading cause of hepatocellular carcinoma (HCC) in the past, the incidence of obesity induced Non-Alcoholic Fatty Liver Disease (NAFLD) a precursor to HCC is increasing and the number of new HCC cases increased 38% and HCC deaths 56% in the last decade. In this study, we conducted meta-analysis of gene expression profiles obtained from livers of high fat diet fed and chemically induced liver cancers of male C57BL/6 mice in order to elucidate the characteristics of fatty liver and HCC. Three separate studies fed mice high fat diets with 60% kcal% fat (from lard and soy bean oil) diet starting at 7 or 8 week old, and continued for 7-10 weeks (7 weeks, GSE40638; 10 weeks, GSE52333; and 8 weeks, GSE79434). We compared these three studies with a liver cancer study (GSE51188) which induced cancers following a single dose of 25 mg/kg diethyl nitrosamine. Affymetrix Mouse Gene 1.0 ST Array was used for high fat diet studies, and Illumina MouseRef-8 x2.0 BeadChips was used for cancer study. We analyzed each study data with GEO2R (comparison against respective normal liver) and performed meta-analysis of the four data sets by Advaita Pathway Guide software. The analysis identified 118 genes ≥0.2 log2 fold-change (Log2FC) and &amp;lt;0.05 p-value (Allowed &amp;gt;0.05 for 1/3 high fat diet studies to increase the candidate genes, but the direction of changes is the same with other two studies). Among these 118 genes, 22 genes were up-regulated and another 17 genes were down-regulated in all four data sets. 29 genes were elevated in tumor, but lowered in high fat diet. On the other hand, 50 genes were lowered in tumor and elevated in high fat diet. We identified Annexin A2 (Anxa2) among the 22 genes which were up-regulated in all data sets, Anxa2 has consistently been reported to be increased in liver tumors and is associated with poor prognosis. The Anxa2 finding suggests our methodology is robust. Besides Anxa2, Nucleoporin 62 (Nup62), Lactate Dehydrogenase A (Ldha), Oxysterol Binding Protein-Like 3 (Osbpl3) were also up-regulated in all studies (Log2FC of liver tumor: Anxa2=2.75, Nup62=0.35, Ldha=0.53, Osbpl3=1.82). Osbpl3 had higher Log2FC value following Anxa2, and its expression was found to be upregulated in some cancers. Importantly, all these were significantly associated with lower overall survival rates in male HCC patients of The Cancer Genome Atlas (TCGA Genes; ANXA2, p=0.00032; NUP62, p=0.000003; LDHA, p=0.0154; OSBPL3, p=0.0255). This data analysis identified several new genes that may be involved in developing cancer from fatty liver, and they might be useful cancer biomarkers based on their association with poor survival. Citation Format: Kumiko Kato-Shoji, Noriyuki Miyoshi, Miki Igarashi, John I. Risinger, Yutaka Shoji. Meta analysis of gene transcripts from fatty livers and liver cancers of C57BL6 mice identifies potential drivers of HCC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-281. doi:10.1158/1538-7445.AM2017-LB-281

Meta-analysis of gene expression profiles identifies differential biomarkers for hepatocellular carcinoma and cholangiocarcinoma.

Hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA) are the members of hepatobiliary diseases. Both types of cancer often exert high levels of similarity in terms of phenotypic characteristics, thus leading to difficulties in HCC and CCA differential diagnoses. In this study, a transcriptome meta-analysis was performed on HCC and CCA microarray data to identify differential transcriptome networks and potential biomarkers for HCC and CCA. Raw data from nine gene expression profiling datasets, consisting of 1,185 samples in total, were methodologically compiled and analyzed. To evaluate differentially expressed (DE) genes in HCC and CCA, the levels of gene expression were compared between cancer and its normal counterparts (i.e., HCC versus normal liver and CCA versus normal bile duct) using t test (P<0.05) and k-fold validation. A total of 226 DE genes were specific to HCC, 249 DE genes specific to CCA, and 41 DE genes in both HCC and CCA. Gene ontology and pathway enrichment analyses revealed different patterns between functional transcriptome networks of HCC and CCA. Cell cycle and glycolysis/gluconeogenesis pathways were exclusively dysregulated in HCC whereas complement and coagulation cascades as well as glycine, serine, and threonine metabolism were prodominantly differentially expressed in CCA. Our meta-analysis revealed distinct dysregulation in transcriptome networks between HCC and CCA. Certain genes in these networks were discussed in the context of HCC and CCA transition, unique characteristics of HCC and CCA, and their potentials as HCC and CCA differential biomarkers.

Meta-analysis of gene expression profiles in granulosa cells during folliculogenesis

Folliculogenesis involves coordinated profound changes in different follicular compartments and significant modifications of their gene expression patterns, particularly in granulosa cells. Huge datasets have accumulated from the analyses of granulosa cell transcriptomic signatures in predefined physiological contexts using different technological platforms. However, no comprehensive overview of folliculogenesis is available. This would require integration of datasets from numerous individual studies. A prerequisite for such integration would be the use of comparable platforms and experimental conditions. The EmbryoGENE program was created to study bovine granulosa cell transcriptomics under different physiological conditions using the same platform. Based on the data thus generated so far, we present here an interactive web interface called GranulosaIMAGE (Integrative Meta-Analysis of Gene Expression), which provides dynamic expression profiles of any gene of interest and all isoforms thereof in granulosa cells at different stages of folliculogenesis. GranulosaIMAGE features two kinds of expression profiles: gene expression kinetics during bovine folliculogenesis from small (6 mm) to pre-ovulatory follicles under different hormonal and physiological conditions and expression profiles of granulosa cells of dominant follicles from post-partum cows in different metabolic states. This article provides selected examples of expression patterns along with suggestions for users to access and generate their own patterns using GranulosaIMAGE. The possibility of analysing gene expression dynamics during the late stages of folliculogenesis in a mono-ovulatory species such as bovine should provide a new and enriched perspective on ovarian physiology.

Meta-analysis of gene expression profiles indicates genes in spliceosome pathway are up-regulated in hepatocellular carcinoma (HCC).

Hepatocellular carcinoma (HCC) is among the commonest kind of malignant tumors, which accounts for more than 500,000 cases of newly diagnosed cancer annually. Many microarray studies for identifying differentially expressed genes (DEGs) in HCC have been conducted, but results have varied across different studies. Here, we performed a meta-analysis of publicly available microarray Gene Expression Omnibus datasets, which covers five independent studies, containing 753 HCC samples and 638 non-tumor liver samples. We identified 192 DEGs that were consistently up-regulated in HCC vs. normal liver tissue. For the 192 up-regulated genes, we performed Kyoto Encyclopedia of Genes and Genomes pathway analysis. To our surprise, besides several cell growth-related pathways, spliceosome pathway was also up-regulated in HCC. For further exploring the relationship between spliceosome pathway and HCC, we investigated the expression data of spliceosome pathway genes in 15 independent studies in Nextbio database ( https://www.nextbio.com/b/nextbioCorp.nb ). It was found that many genes of spliceosome pathway such as HSPA1A, SNRPE, SF3B2, SF3B4 and TRA2A genes which we identified to be up-regulated in our meta-analysis were generally overexpressed in HCC. At last, using real-time PCR, we also found that BUD31, SF3B2, SF3B4, SNRPE, SPINK1, TPA2A and HSPA1A genes are significantly up-regulated in clinical HCC samples when compared to the corresponding non-tumorous liver tissues. Our study for the first time indicates that many genes of spliceosome pathway are up-regulated in HCC. This finding might put new insights for people's understanding about the relationship of spliceosome pathway and HCC.