Analysis Of Expression Datasets Research Articles

Cancer Diagnosis through Contour Visualization of Gene Expression Leveraging Deep Learning Techniques.

Prompt diagnostics and appropriate cancer therapy necessitate the use of gene expression databases. The integration of analytical methods can enhance detection precision by capturing intricate patterns and subtle connections in the data. This study proposes a diagnostic-integrated approach combining Empirical Bayes Harmonization (EBS), Jensen-Shannon Divergence (JSD), deep learning, and contour mathematics for cancer detection using gene expression data. EBS preprocesses the gene expression data, while JSD measures the distributional differences between cancerous and non-cancerous samples, providing invaluable insights into gene expression patterns. Deep learning (DL) models are employed for automatic deep feature extraction and to discern complex patterns from the data. Contour mathematics is applied to visualize decision boundaries and regions in the high-dimensional feature space. JSD imparts significant information to the deep learning model, directing it to concentrate on pertinent features associated with cancerous samples. Contour visualization elucidates the model's decision-making process, bolstering interpretability. The amalgamation of JSD, deep learning, and contour mathematics in gene expression dataset analysis diagnostics presents a promising pathway for precise cancer detection. This method taps into the prowess of deep learning for feature extraction while employing JSD to pinpoint distributional differences and contour mathematics for visual elucidation. The outcomes underscore its potential as a formidable instrument for cancer detection, furnishing crucial insights for timely diagnostics and tailor-made treatment strategies.

Integrated bulk and single-cell RNA-sequencing reveals SPOCK2 as a novel biomarker gene in the development of congenital pulmonary airway malformation

BackgroundCongenital pulmonary airway malformation (CPAM) is the most frequent pulmonary developmental malformation and the pathophysiology remains poorly understood. This study aimed to identify the characteristic gene expression patterns and the marker genes essential to CPAM.MethodsTissues from the cystic area displaying CPAM and the area of normal appearance were obtained during surgery. Bulk RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq) were performed for integrating analysis. Iterative weighted gene correlation network analysis (iWGCNA) was used to identify specifically expressed genes to CPAM.ResultsIn total, 2074 genes were significantly differentially expressed between the CPAM and control areas. Of these differentially expressed genes (DEGs), 1675 genes were up-regulated and 399 genes were down-regulated. Gene ontology analysis revealed these DEGs were specifically enriched in ciliated epithelium and involved in immune response. We also identified several CPAM-related modules by iWGCNA, among them, P15_I4_M3 module was the most influential module for distinguishing CPAMs from controls. By combining the analysis of the expression dataset from RNA-seq and scRNA-seq, SPOCK2, STX11, and ZNF331 were highlighted in CPAM.ConclusionsThrough our analysis of expression datasets from both scRNA-seq and bulk RNA-seq of tissues obtained from patients with CPAM, we identified the characteristic gene expression patterns associated with the condition. Our findings suggest that SPOCK2 could be a potential biomarker gene for the diagnosis and therapeutic target in the development of CPAM, whereas STX11 and ZNF331 might serve as prognostic markers for this condition. Further investigations with larger samples and function studies are necessary to confirm the involvement of these genes in CPAM.

Altered Expression of CCR10 Indicates Malignant Transformation of Bone Marrow Plasma Cells

Introduction. Phenotypic analysis of tumor cells by flow cytometry is now essential for diagnosis and treatment of hematological malignancies, including multiple myeloma (MM). Surface molecules such as CD19, CD56, CD27 and CD45 have been used to distinguish normal bone marrow (BM) plasma cells from malignant plasma cells (MM cells). Molecules that are relatively specific to plasma cells (CD38 and SLAMF7) are considered targets for antibody-based therapies in MM. Among the vast numbers of surface molecules, chemokine receptors have been attracting much attention as the regulators of migration and tissue localization of plasma cells and MM cells. CXCR4 (C-X-C chemokine receptor type 4) has been reported to guide the movement of plasma cells and myeloma cells to the BM, where its ligand CXCL12 (C-X-C motif chemokine 12) is abundantly produced. CCR10 (C-C motif chemokine receptor 10) is another chemokine receptor, whose ligand is CCL27 (C-C motif chemokine ligand 27) and CCL28, also known to be involved in cell homing and migration. CCR10 has been reported to be highly expressed on mucosal derived plasma cells, however its expression in MM cells and its role within the BM microenvironment has not been well studied. In this study, we analyzed the expression of CCR10 on BM lymphocytes from patients of plasma cell disorders in order to understand its significance in the BM microenvironment.Materials and methods. We analyzed a public available gene expression dataset (GSE 22886) in order to study CCR10 gene expression in human plasma cells. To examine CCR10 gene expression in myeloma cells, we utilized the gene expression data set of multiple cancer cell lines from the Cancer Cell Line Encyclopedia (CCLE: http://www.broadinstitute.org/ccle). Multi-color flow cytometry was performed to study CCR10 expression on bone marrow lymphocytes. BM plasma cells were determined as CD38++ CD138+ or CD38++ SLAMF7+. Monoclonal plasma cells (myeloma clones) were determined as CD19- CD56- or CD19- CD56+ cells and polyclonal plasma cells (normal plasma cells) as CD19+ CD56- cells. CCR10 expression of BM plasma cells, B cells, CD4 T cells, CD8 T cells and NK cells of MGUS, newly diagnosed MM, relapsed-refractory MM patients were analyzed.Results. Gene expression dataset analysis showed that, among the normal leukocyte subsets, plasma cells had the highest CCR10 gene expression compared to other leukocytes (p<0.001). Myeloma cell lines showed the highest expression of CCR10 gene expression among all the other cancer cell lines, indicating the specificity of CCR10 expression in MM cells. Multi-color flow cytometry showed significantly high expression of CCR10 on BM plasma cells compared to other lymphocytes in the bone marrow of MGUS patients (p<0.0001). Within the MGUS BM plasma cells, monoclonal plasma cells had significantly lower CCR10 expression compared to polyclonal plasma cells (p<0.05). Additionally, monoclonal plasma cells from relapsed-refractory MM patients had significantly lower CCR10 expression compared to those from MGUS and newly diagnosed MM patients. These data indicate a role of CCR10 decrease in malignant plasma cells leading to MM progression. We further analyzed other surface molecules that may be related to CCR10 on monoclonal plasma cells. CCR10 expression did not correlate with CD56 expression, while CD45- monoclonal plasma cells tend to have lower CCR10 expression compared to the CD45+ counterparts in newly diagnosed MM patients (p<0.001).Conclusions. Gene expression dataset analysis and multi-color flow cytometry have showed that CCR10 is highly expressed in normal plasma cells. However, we show here for the first time that plasma cells start to decrease CCR10 expression at malignant transformation and during disease progression. Since, CCL27, the ligand of CCR10, is known to induce chemotaxis to plasma cells, loss of CCR10 may lead to migration or metastasis of MM cells to different sites. Further research is now on-going to elucidate the significance and mechanism of altered CCR10 expression in MM cells. This study may lead to a better understanding of MM biology and to the development of a novel strategy to target malignant plasma cells. DisclosuresNo relevant conflicts of interest to declare.

Nuclear factor IB is downregulated in vulvar squamous cell carcinoma (VSCC): Unravelling differentially expressed genes in VSCC through gene expression dataset analysis.

Vulvar squamous cell carcinoma (VSCC) comprises two distinct etiopathological subtypes: i) Human papilloma virus (HPV)-related VSCC, which arises via the precursor high grade squamous intraepithelial lesion (HSIL); and ii) HPV-independent VSCC, which arises via precursor, differentiated vulvar intraepithelial neoplasia (dVIN), driven by TP53 mutations. However, the mechanism of carcinogenesis of VSCC is poorly understood. The current study aimed to gain insight into VSCC carcinogenesis by identifying differentially expressed genes (DEGs) for each VSCC subtype. The expression of certain DEGs was then further assessed by performing immunohistochemistry (IHC) on whole tissue sections of VSCC and its precursors. Statistical analysis of microarrays was performed on two independent gene expression datasets (GSE38228 and a study from Erasmus MC) on VSCC and normal vulva. DEGs were identified that were similarly (up/down) regulated with statistical significance in both datasets. For HPV-related VSCCs, this constituted 88 DEGs, and for HPV-independent VSCCs, this comprised 46 DEGs. IHC was performed on VSCC (n=11), dVIN (n=6), HSIL (n=6) and normal vulvar tissue (n=7) with i) signal transducer and activator of transcription 1 (STAT1; an upregulated DEGs); ii) nuclear factor IB (NFIB; a downregulated DEG); iii) p16 (to determine the HPV status of tissues); and iv) p53 (to confirm the histological diagnoses). Strong and diffuse NFIB expression was observed in the basal and para-basal layers of normal vulvar tissue, whereas NFIB expression was minimal or completely negative in dVIN and in both subtypes of VSCC. In contrast, no discernable difference was observed in STAT1 expression among normal vulvar tissue, dVIN, HSIL or VSCC. By leveraging bioinformatics, the current study identified DEGs that can facilitate research into VSCC carcinogenesis. The results suggested that NFIB is downregulated in VSCC and its relevance as a diagnostic/prognostic biomarker deserves further exploration.

Multiple Gene Expression Dataset Analysis Reveals Toll-Like Receptor Signaling Pathway is Strongly Associated With Chronic Obstructive Pulmonary Disease Pathogenesis

Chronic obstructive pulmonary disease is a complex pulmonary disease that causes airflow obstruction in humans. To identify the core genes in COPD pathogenesis, seven diverse microarray datasets (GSE475, GSE1122, GSE1650, GSE3212, GSE8823, GSE37768, and GSE22148) were downloaded from the gene expression omnibus database. All the datasets were analyzed independently with the R/Bioconductor package to screen the differentially expressed genes (DEGs). The gene ontology and pathway enrichment analysis were performed for the acquired DEGs using DAVID (Database for Annotation, Visualization, and Integrated Discovery). Further protein-protein interaction network was constructed for the DEGs and their potential hub genes and sub-networks were identified using Cytoscape software. From the selected seven datasets, 188 overlapped DEGs were perceived eventually based on considering the repetitive genes between at-least one dataset. Gene Ontology analysis reveals that most of the DEGs were significantly enriched in immune response, inflammatory response, extracellular region, lipid binding, cytokine, and chemokine activity. Moreover, genes from the sub-network analysis were again submitted to the DAVID server to validate the results which uncover the Toll-like receptor signaling pathway was significantly enriched and all the genes present in this pathway were likewise detected as hub genes from Cytoscape software. CXCL9, CXCL10, CXCL11, CCL4, TLR7, and SPP1 hub genes in the toll-like receptor signaling pathway were explored in this study as potential biomarker genes associated with COPD pathogenesis.

PB2098 IDENTIFICATION OF NOVEL REGULATORS OF PLASMA CELLS MIGRATION IN MULTIPLE MYELOMA PATIENTS

Background:High number of peripheral blood circulating aberrant plasma cells in the patients with multiple myeloma (MM) correlates with worse survival prognosis and likely contributes to the development of extramedullary lesions. Molecular mechanisms responsible for egress of tumor cells from the supportive bone marrow niche into the blood stream are largely unknown. In the presented study we focus on deubiquitinating enzymes (DUBs), a group of hydrolytic proteases removing ubiquitin modifications from proteins. DUBs are involved in numerous cellular processes and as such provide very promising drug targets.Aims:Through analysis of expression datasets and progression free survival (PFS) we seek to identify DUBs deregulated specifically in MM, highlight those with relevance to plasma cells migration and finally uncover the responsible molecular mechanisms.MethodsThe GSE2658 dataset was used to analyse PFS of MM patients and other hematological malignancies (GSE12417, GSE10846, GSE16131, GSE22762, GSE2350, GSE4475) in relation to expression levels of all and selected human DUBs, respectively. Pathological bone marrow residing plasma cells from 14 newly diagnosed MM patients identified by the expression pattern of following antigens (CD19, CD56, CD45, CD38) were sorted and total mRNA was isolated for gene‐specific qPCR analysis. In parallel, percentages of aberrant plasma cells circulating in the peripheral blood were determined by flow cytometry. Co‐immunoprecipitation of OTUD1 and Filamin A was performed using HEK293 T cells and specific antibodies. For the gene set enrichment analysis (GSEA) the GSE4581 dataset was used.Results:In the pilot analysis we compared PFS of a large cohort of MM patients (n = 559) in relation to expression levels of all currently annotated human DUBs (n = 100). We have indentified 10 DUBs whose expression significantly affected PFS of MM patients (p &lt; 0,05). Low expression levels of 6 DUBs (USP22, USP48, USP49, YOD1, OTUD6B, FAM105A) correlated with good PFS, while low expression of another 4 DUBs (USP3, USP30, USP35, OTUD1) correlated with significantly worse prognosis. Only 4 of the indentified DUBs (USP22, USP30, USP35 and OTUD1) were affecting PFS exclusively in MM patients and not in other hematological malignancies (AML, CLL, DLBCL, FCL, BL). We focused specifically on OTUD1 as it is the only DUBs from our list that was previously implicated to suppress migration and metastasis (Zhang, 2017). Quantitative PCR revealed that low expression of OTUD1 in bone marrow plasma cells (in 14 newly diagnosed MM patients) correlates with significantly higher number of plasma cells in peripheral blood. We have also identified actin crosslinking protein Filamin A as a novel OTUD1 binding partner. Additionally, GSEA of the GSE4581 dataset reveled that low expression of OTUD1 is associated with induction of genes related to actin polymerization and ER stress response.Summary/Conclusion:Database analysis, patient and cell lines derived data indicate that downregulation of OTUD1 is associated with higher number of peripheral blood circulating plasma cells and significantly worse PFS in a large subset of MM patients. Mechanistical investigation unveils Filamin A as direct OTUD1 binding partner and highlights involvement OTUD1 in actin cytoskeleton remodeling and ER stress response.

Abstract PR15: The oncometabolite R-2-Hydroxyglutarate suppresses the innate immune microenvironment of IDH1-mutated gliomas via aryl hydrocarbon receptor signaling

Abstract Background: IDH1-mutated gliomas are associated with less abundant and phenotypically skewed innate and adaptive immune cell infiltrates compared to IDH1 wild-type tumors. Despite this, the most frequent mutation—IDH1R132H—represents a clonal shared neoantigen and mutations in IDH are associated with a more favorable prognosis. While the tumor cell-intrinsic consequences of the oncometabolite R-2-hydroxyglutarate (R-2-HG) accumulating in IDH1-mutated gliomas as a result of a neomorphic enzymatic function are well-characterized, potential direct paracrine effects of R-2-HG influencing the glioma immune microenvironment remain incompletely understood. Aim: This study aimed at characterizing the impact of the oncometabolite R-2-HG on the innate immune microenvironment of IDH1-mutated gliomas. Methods and Results: By means of comprehensive analyses of expression datasets from human gliomas and syngeneic murine tumor models as well as transporter studies, we demonstrate that R-2-HG is imported by both microglia and macrophages via SLC family transporters and suppresses their function in a paracrine manner. Functional analyses of microglia and macrophages indicate an R-2-HG-driven induction of tolerogenicity as evidenced by accumulation of IL10 and TGFβ and suppression of MHC-II expression, which results in impaired activation of antigen-specific T-cells and activation of immune checkpoint molecules. Multilevel signature profiling of human tumor-infiltrating as well as primary immune cells was complemented by reporter gene assays and pathway analyses and revealed that R-2-HG activates the cytosolic transcription factor aryl hydrocarbon receptor (AHR), a key immunomodulatory target of immunosuppressive tryptophan metabolism. By means of knockout models, the observed immunosuppressive phenotype was shown to be AHR-dependent. Functional relevance of R-2-HG-mediated, AHR-driven impairment of myeloid cell immunity was demonstrated in vivo by pharmacologic AHR inhibition, increasing the efficacy of checkpoint blockade. Conclusion: R-2-HG impairs antitumor immunity in IDH1-mutated gliomas by activating the AHR in innate immune cells, thus suppressing the innate immune microenvironment by compromising antigen presentation and activation of antigen-specific T-cells. This, together with recent findings on inhibitory effects on T-cell immunity, represents a novel mechanism of immune evasion of an immunogenic driver mutation and opens a novel therapeutic approach to IDH1-mutated gliomas. Citation Format: Mirco Friedrich, Lukas Bunse, Theresa Bunse, Edward Green, Tobias Kessler, Stefan Pusch, Katrin Deumelandt, Rafael Carretero, Andreas von Deimling, Francisco J Quintana, Wolfgang Wick, Michael Platten. The oncometabolite R-2-Hydroxyglutarate suppresses the innate immune microenvironment of IDH1-mutated gliomas via aryl hydrocarbon receptor signaling [abstract]. In: Proceedings of the Fourth CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; Sept 30-Oct 3, 2018; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2019;7(2 Suppl):Abstract nr PR15.

FOXP1 negatively regulates tumor infiltrating lymphocyte migration in human breast cancer.

BackgroundFOXP1, a transcriptional regulator of lymphocyte development, is abnormally expressed in some human tumors. This study investigated FOXP1-mediated regulation of tumor infiltrating lymphocytes (TIL) in untreated primary breast cancer (BC).MethodsFOXP1 expression was analyzed in tissues from primary untreated breast tumors, BC cell lines and the METABRIC gene expression BC dataset. Cytokine and chemokine expression and lymphocyte migration in response to primary tumor supernatants (SN) was compared between FOXP1hi and FOXP1lo primary BC.FindingFOXP1 expression was higher in estrogen receptor positive compared to negative BC. FOXP1hi tumors were significantly associated with lower TIL and fewer tertiary lymphoid structures (TLS) compared to FOXP1lo BC. Silencing FOXP1 in BC cell lines positively impacted cytokine and chemokine expression with the inverse effect associated with overexpression. CXCL9, CXCL10, CXCL11, CXCL13, CX3CL, CCL20, IL2, IL21, GZMB and IFNG expression decreased while IL10 and TGFβ increased in FOXP1hi compared to FOXP1lo primary BC. Lymphocyte migration using primary BC supernatants detected decreased mobility toward FOXP1hi supernatants. FOXP1lo BC expresses higher levels of chemokines driving TIL migration. The METABRIC gene expression dataset analysis show FOXP1 expression is associated with unfavorable BC outcomes.InterpretationThese data identify FOXP1 as an important negative regulator of immune responses in BC via its regulation of cytokine and chemokine expression.FundBelgian Fund for Scientific Research (FNRS 3.4513.12F) and Opération Télévie (7.4636.13F and 7.4609.15F), Fonds J.C. Heuson and Fonds Lambeau-Marteaux.

Altered Pathway Analyzer: A gene expression dataset analysis tool for identification and prioritization of differentially regulated and network rewired pathways

Gene connection rewiring is an essential feature of gene network dynamics. Apart from its normal functional role, it may also lead to dysregulated functional states by disturbing pathway homeostasis. Very few computational tools measure rewiring within gene co-expression and its corresponding regulatory networks in order to identify and prioritize altered pathways which may or may not be differentially regulated. We have developed Altered Pathway Analyzer (APA), a microarray dataset analysis tool for identification and prioritization of altered pathways, including those which are differentially regulated by TFs, by quantifying rewired sub-network topology. Moreover, APA also helps in re-prioritization of APA shortlisted altered pathways enriched with context-specific genes. We performed APA analysis of simulated datasets and p53 status NCI-60 cell line microarray data to demonstrate potential of APA for identification of several case-specific altered pathways. APA analysis reveals several altered pathways not detected by other tools evaluated by us. APA analysis of unrelated prostate cancer datasets identifies sample-specific as well as conserved altered biological processes, mainly associated with lipid metabolism, cellular differentiation and proliferation. APA is designed as a cross platform tool which may be transparently customized to perform pathway analysis in different gene expression datasets. APA is freely available at http://bioinfo.icgeb.res.in/APA.

The clinical significance of the MIF homolog d-dopachrome tautomerase (MIF-2) and its circulating receptor (sCD74) in burn

BackgroundWe reported earlier that the cytokine macrophage migration inhibitory factor (MIF) is a potential biomarker in burn injury. In the present study, we investigated the clinical significance of the newly discovered MIF family member d-dopachrome tautomerase (DDT or MIF-2) and their common soluble receptor CD74 (sCD74) in severely burned patients. MethodsDDT and sCD74 serum levels were measured 20 severely burned patients and 20 controls. Serum levels were correlated to the abbreviated burn severity index (ABSI) and total body surface area (TBSA) followed by receiver operating characteristic (ROC) analysis. Data were supported by gene expression dataset analysis of 31 burn patients and 28 healthy controls. ResultsCD74 and DDT were increased in burn patients. Furthermore, CD74 and DDT also were elevated in septic non-survivors when compared to survivors. Serum levels of DDT showed a positive correlation with the ABSI and TBSA in the early stage after burn, and the predictive character of DDT was strongest at 24h. Serum levels of CD74 only correlated with the ABSI 5 days after injury. ConclusionsDDT may assist in the monitoring of clinical outcome and prediction of sepsis during the early post-burn period. Soluble CD74 and MIF, by contrast, have limited value as an early predictor of death due to their delayed response to burn.

Selective Activation of the Non-Classical Estrogen Receptor Gper Elicits Potent Anti-Tumor Activity in Multiple Myeloma

Within the hematopoietic compartment, estrogens influence the differentiation, proliferation and survival of B cells and also increase the number of plasmacells (PCs) and their capacity to produce immunoglobulins. In addition to the classical estrogen receptors, an extensive body of literature has shed light on a 7-transmembrane spanning G-protein coupled estrogen receptor, GPER (also known as GPR30). The discovery of GPER-selective agonists and antagonists has led to the comprehension of the pathophysiological role of GPER and its involvement in cancer biology. However, the functional role of GPER in hematological malignancies has not been previously investigated.We detected GPER expression in 12 MM cell lines, including dexamethasone, bortezomib and carfilzomib-resistant cell lines, as well as in Waldestrom Macroglobulinemia (WM) cell lines either at mRNA and protein level, as assessed by qRT-PCR and western blotting, respectively. Importantly, MM patients' expression dataset analysis revealed a progressive decline of GPER mRNA levels during MM progression. Moreover, adhesion of MM cell lines (NCI-H929, U266) to bone marrow stromal cells (BMSCs) reduced GPER mRNA and protein levels even further, supporting a potential role of the BM microenvironment in regulating GPER expression.To address the role of GPER-mediated signaling on MM cell survival and cell death, first we tested the selective GPER agonist G-1 ((±)-1-[(3aR *,4S *,9bS *)-4-(6-Bromo-1,3-benzodioxol-5-yl) -3a,4,5,9b-tetrahydro-3H cyclopenta [c]quinolin-8-yl] ethanone) in vitro. G-1 inhibited, in a dose-dependent manner, the survival of WM (n=2) and MM cell lines (n=9), as well as of primary MM cells (n=3), with an IC50 at 48 h ranging from 2 to 3 μM, while it did not affect the viability of healthy donors' PBMCs (n=3); moreover, G-1 synergized with bortezomib in MM1S MM cells and with ibrutinib in BCMW1 WM cells. Both in MM and WM cell lines, G-1 treatment increased cells in G2/M phase and induced a potent and dose-dependent apoptotic cell death, as assessed by Annexin V/7AAD staining and western blot analysis of active caspases 3, 7 and 9. G-1-induced apoptosis was reverted by the pan-caspase inhibitor ZVAD-FMK and was not impaired by co-colture with BMSCs or by exogenous IL-6, IGF-1 or HGF. 17β-estradiol (10nM) and the selective GPER-antagonist G-15 (0.5µM) slightly increased the survival of MM and WM cells, and this effect was overcome by G-1 treatment. Moreover, G-1 induced the occurrence of a cytoprotective autophagy, as shown by the expression of autophagic markers, such as beclin-1 and LC3A/B, the cytosolic punctate pattern of LC3B and down-regulation of p62/SQSTM1 expression, and treatment of MM and WM cell lines with the autophagy inhibitor chloroquine potentiated G-1-triggered anti-survival effects. Importantly, intraperitoneal injection of G-1 (2mg/kg) in SCID mice significantly reduced the growth of subcutaneous MM1S and bortezomib-resistant AMO-abzb xenografts, and prolonged survival of treated animals.Since GPER-dependent molecular effects in solid tumors have been attributed to modulation of MAPK activity, by using an antibody array we analyzed the phosphorylation status of 24 protein kinases after GPER activation. Notably, treatment with G-1 (2µM) reduced the phosphorylation of ERK1/2, p38α-γ, AKT and GSK3α-β.Finally, on the light of the emerging role of miRNAs in MM pathobiology, we investigated by TaqMan qPCR the effects of G-1 on the miRNA profile and found that G-1 induced the expression of tumor suppressor miR-29b by down-regulating Sp1, the major miR-29b-negative regulator, thus disrupting the previously reported miR-29b/Sp1 negative feedback loop. Consistently, an inverse correlation between GPER and Sp1 mRNA levels emerged in MM patient PCs. In addition, miR-29b canonical targets, such as CDK6 and MCL-1, were downregulated in G-1-treated MM cells.Altogether, our results indicate that GPER is expressed in MM and WM and its selective activation via G-1 triggers potent anti-tumor activity through inhibition of oncogenic protein kinases and activation of miR-29b, providing the preclinical rationale for clinical investigation of GPER-agonists to treat these hematological malignancies. DisclosuresNo relevant conflicts of interest to declare.

BRCA Mutation-Related and Claudin-Low Breast Cancer: Blood Relatives or Stepsisters?

Background: BRCA mutation-associated (BRCAmut) breast cancer represents a heterogeneous group displaying certain molecular features. Claudin-low breast cancers (CLBC) overlap with characteristics of BRCAmut tumors; therefore, we have investigated whether these are identical subtypes. Methods: Using public gene expression data, CLDN, CDH1, 9-cell line claudin-low predictor (9CLCLP) and PAM50 expression was evaluated in BRCAmut and BRCA wild-type (BRCAwt) breast cancer cases focusing on their possible overlap with the CLBC subtype. A separate formalin-fixed, paraffin-embedded (FFPE) cohort of 22 BRCAmut and 19 BRCAwt tumor tissues was used for immunohistochemical examination of AR, CD24, CD44, CK5/6, claudin-1, -3, -4 and -7, E-cadherin, EGFR, estrogen receptor (ER), EZH2, HER2, Ki67, p53, progesterone receptor (PgR) and vimentin expression. Results: In the data sets, CLDN1 (ROC = 0.785, p < 0.001), CDH1 (ROC = 0.785, p < 0.001), CLDN7 (ROC = 0.723, p < 0.001), CLDN3 (ROC = 0.696, p = 0.020) and CLDN4 (ROC = 0.685, p = 0.027) were expressed at higher level in BRCAmut than BRCAwt tumor tissue. The PAM50 subtype differed from the assigned immunohistochemistry (IHC)-based subtype in 30%. Based on accessible 9CLCLP predictor genes, BRCAmut breast cancer does not display the claudin-low phenotype. Utilizing FFPE samples, claudins were evidently expressed in both BRCAmut and BRCAwt cases. However, at the protein level, only claudin-3 expression was higher in BRCAmut tumors, while claudin-1, -4 and -7 and E-cadherin expression was lower compared to BRCAwt cases. A CD24^low/CD44^high phenotype was found in BRCAmut tumors upon comparison with BRCAwt cases (p < 0.001 and p = 0.001, respectively). Conclusions: There is a prominent correlation between the genes under focus herein and BRCA mutation status. BRCAmut tumors bear stem cell characteristics displaying a distinct cell adhesion molecule profile characterized by high expression of CDH1 and CLDN4 according to public gene expression data set analysis, and higher claudin-3 expression as detected by IHC; thus, BRCAmut breast carcinomas are not identical with the previously identified claudin-low subtype of breast cancer.

Bidirectional crosstalk between PD-L1 expression and epithelial to mesenchymal transition: significance in claudin-low breast cancer cells.

BackgroundThe T-cell inhibitory molecule PD-L1 (B7-H1, CD274) is expressed on tumor cells of a subset of breast cancer patients. However, the mechanism that regulates PD-L1 expression in this group of patients is still not well-identified.MethodsWe have used loss and gain of function gene manipulation approach, multi-parametric flow cytometry, large scale gene expression dataset analysis and immunohistochemistry of breast cancer tissue sections.ResultsInduction of epithelial to mesenchymal transition (EMT) in human mammary epithelial cells upregulated PD-L1 expression, which was dependent mainly on the activation of the PI3K/AKT pathway. Interestingly, gene expression signatures available from large cohort of breast tumors showed a significant correlation between EMT score and the PD-L1 mRNA level (p < 0.001). Strikingly, very strong association (p < 0.0001) was found between PD-L1 expression and claudin-low subset of breast cancer, which is known to have high EMT score. On the protein level, significant correlation was found between PD-L1 expression and standard markers of EMT (p = 0.005) in 67 breast cancer patients. Importantly, specific downregulation of PD-L1 in claudin-low breast cancer cells showed signs of EMT reversal as manifested by CD44 and Vimentin downregulation and CD24 upregulation.ConclusionsWe have demonstrated a bidirectional effect between EMT status and PD-L1 expression especially in claudin-low subtype of breast cancer cells. Our findings highlights the potential dual benefit of anti-PD-L1 particularly in this subset of breast cancer patients that will likely benefit more from anti-PD-L1 targeted therapy as well as in monitoring biological changes upon treatment.Electronic supplementary materialThe online version of this article (doi:10.1186/s12943-015-0421-2) contains supplementary material, which is available to authorized users.

PhenomeExpress: a refined network analysis of expression datasets by inclusion of known disease phenotypes.

We describe a new method, PhenomeExpress, for the analysis of transcriptomic datasets to identify pathogenic disease mechanisms. Our analysis method includes input from both protein-protein interaction and phenotype similarity networks. This introduces valuable information from disease relevant phenotypes, which aids the identification of sub-networks that are significantly enriched in differentially expressed genes and are related to the disease relevant phenotypes. This contrasts with many active sub-network detection methods, which rely solely on protein-protein interaction networks derived from compounded data of many unrelated biological conditions and which are therefore not specific to the context of the experiment. PhenomeExpress thus exploits readily available animal model and human disease phenotype information. It combines this prior evidence of disease phenotypes with the experimentally derived disease data sets to provide a more targeted analysis. Two case studies, in subchondral bone in osteoarthritis and in Pax5 in acute lymphoblastic leukaemia, demonstrate that PhenomeExpress identifies core disease pathways in both mouse and human disease expression datasets derived from different technologies. We also validate the approach by comparison to state-of-the-art active sub-network detection methods, which reveals how it may enhance the detection of molecular phenotypes and provide a more detailed context to those previously identified as possible candidates.

Parallel Clustering of Gene Expression Dataset in Multicore Environment

Clustering has become the powerful and widely used method in gene expression dataset analysis to obtain biological information. Clustering using sequential approach is time consuming task. So as to save time and to increase speedup we have applied parallel clustering on single machine utilizing computational power of multicore processors in the system. In this work, we have also done comparison of sequential clustering and the parallel clustering in terms of time consumed for clustering of yeast gene expression dataset. With the use of multicore processor the speedup gained from 5 to 7% on intel core 2 duo processor with 2Gb RAM.

Biomedical Informatics and Computational Biology for High-Throughput Data Analysis

The last few years have witnessed the emergence of several high-throughput (HTP) platforms that are based on the evolution of omic science through transcriptomics, proteomics, and metabolomics. With the huge data accumulation, the informatics and computational methods have become essential to understand the complexity of biomedical data. We lunched the special issue to address the demand for statistical models, biological omic data analyses, and meta-analysis of biomarker for complex diseases. Statistical models are always important for the understanding of complex data; in this issue, Y. Liang et al. proposed a new adaptive L1/2 shooting regularization method for variable selection based on the Cox's proportional hazards mode. Simulation and the real gene expression dataset analysis showed that the method is more accurate for variable selection than Lasso and adaptive Lasso methods. L. Tian et al. employed a nonlinear model to analyze time course gene expression data. They firstly developed a method for estimating the parameters in the nonlinear model and then utilized the model to perform the significance analysis of individually differentially expressed genes and clustering analysis of a set of gene expression profiles. The simulation and real-life biological data analyses showed that their methods outperform some existing methods. For the application of bioinformatics tools, Z. E et al. analyzed the gene expression in rice leaf blades at different temperatures. They analyzed the next-generation sequencing (NGS) datasets and characterized the transcription profiles of the samples in rice seedling leaf blades at 25°C and 30°C. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were applied to study the effect of the temperature. Finally, they observed that temperature markedly regulates several superfamilies of transcription factors, including bZIP, MYB, and WRKY. Chronobiology is a field of biology that studies biological rhythms. R. Lopes et al. reviewed the bioinformatics or computational systems biology methods, tools, and databases which are very helpful to the understanding of the patterns and biological rhythms found in living organisms and the review will benefit the community for the application of bioinformatics in their future researches. For the understanding of protein structure and structure-function relationship, two papers are selected in this issue. M. Shambhu et al. studied the surface accessibility of hydrophobic residues by considering their conservation score and knowledge of flanking regions. The accuracy of prediction is therefore improved. In the other work, D. Lu et al. applied molecular dynamics to investigate the effects of X-linked agammaglobulinaemia associated with amino acid mutations in pleckstrin homology (PH) domain on the binding of the domain with inositol 1,3,4,5-tetrakisphosphate (Ins(1,3,4,5)P4). The mutations were then classified as “functional mutations”, and “folding mutations.” The result provided new insights into the biological function of the Btk-PH domain and related mutation-causing diseases. Finally, two works in this special issue are medical studies. The first is the work by B. Lv et al. They analyzed the 347 studies for the diagnostic value of tumor necrosis factor-α (TNF-α) and found that the northern hemisphere group in the TNF-α test has higher sensitivity and specificity than those of southern hemisphere group. The second medical study is an association study. Z. Liu et al. reported the significantly association of NCK2 with opiates addiction in African-origin men by a comprehensive analysis of a dataset from the Study of Addiction: Genetics and Environment (SAGE). They employed both SNP and gene based methods of analysis and identified a strong and significant association between a SNP in the NCK2 gene on chromosome 2 with opiates addiction in African-origin men. Bairong Shen Jian Ma Jiajun Wang Junbai Wang

MESAdb: microRNA Expression and Sequence Analysis Database

microRNA expression and sequence analysis database (http://konulab.fen.bilkent.edu.tr/mirna/) (mESAdb) is a regularly updated database for the multivariate analysis of sequences and expression of microRNAs from multiple taxa. mESAdb is modular and has a user interface implemented in PHP and JavaScript and coupled with statistical analysis and visualization packages written for the R language. The database primarily comprises mature microRNA sequences and their target data, along with selected human, mouse and zebrafish expression data sets. mESAdb analysis modules allow (i) mining of microRNA expression data sets for subsets of microRNAs selected manually or by motif; (ii) pair-wise multivariate analysis of expression data sets within and between taxa; and (iii) association of microRNA subsets with annotation databases, HUGE Navigator, KEGG and GO. The use of existing and customized R packages facilitates future addition of data sets and analysis tools. Furthermore, the ability to upload and analyze user-specified data sets makes mESAdb an interactive and expandable analysis tool for microRNA sequence and expression data.

Islet protein profiling

Islet protein profiling is defined as generation of extended protein expression data sets from islets or islet cells. Islets from rodent control and animal models of type 1 and type 2 diabetes mellitus and healthy humans and insulin- and glucagon-producing cell lines have been used. Protein profiling entails separation, differential expression determination, identification and expression analysis. Protein/peptide separation is either gel-based or by chromatography. Differential expression is based on comparison of visualized spots/proteins between gels or by sample labelling in gel-free systems. Identification of proteins is made by tryptic fragmentation of proteins, fragment mass determination and mass comparison with protein databases. Analysis of expression data sets interprets the complex protein changes into cellular mechanisms to generate hypotheses. The importance of such protein expression sets to elucidate islet cellular events is evidenced by the observation that only about 50% of the differentially expressed proteins and transcripts showed concordance when measured in parallel. Using protein profiling, different areas related to islet dysfunction in type 1 and type 2 diabetes mellitus have been addressed, including dysfunction induced by elevated levels of glucose and fatty acids and cytokines. Because islets from individuals with type 1 or type 2 diabetes mellitus have not yet been protein profiled, islets from rat (BB-DP) and mouse (NOD, ob/ob, MKR) models of the disease have been used, and mechanisms responsible for islet dysfunction delineated offering avenues of intervention.

Chromosomal patterns of gene expression from microarray data: methodology, validation and clinical relevance in gliomas

BackgroundExpression microarrays represent a powerful technique for the simultaneous investigation of thousands of genes. The evidence that genes are not randomly distributed in the genome and that their coordinated expression depends on their position on chromosomes has highlighted the need for mathematical approaches to exploit this dependency for the analysis of expression data-sets.ResultsWe have devised a novel mathematical technique (CHROMOWAVE) based on the Haar wavelet transform and applied it to a dataset obtained with the Affymetrix® HG-U133_Plus_2 array in 27 gliomas. CHROMOWAVE generated multi-chromosomal pattern featuring low expression in chromosomes 1p, 4, 9q, 13, 18, and 19q. This pattern was not only statistically robust but also clinically relevant as it was predictive of favourable outcome. This finding was replicated on a data-set independently acquired by another laboratory. FISH analysis indicated that monosomy 1p and 19q was a frequent feature of tumours displaying the CHROMOWAVE pattern but that allelic loss on chromosomes 4, 9q, 13 and 18 was much less common.ConclusionThe ability to detect expression changes of spatially related genes and to map their position on chromosomes makes CHROMOWAVE a valuable screening method for the identification and display of regional gene expression changes of clinical relevance. In this study, FISH data showed that monosomy was frequently associated with diffuse low gene expression on chromosome 1p and 19q but not on chromosomes 4, 9q, 13 and 18. Comparative genomic hybridisation, allelic polymorphism analysis and methylation studies are in progress in order to identify the various mechanisms involved in this multi-chromosomal expression pattern.

Genome-Wide Expression Studies of Atherosclerosis

During the past 6 years, gene expression profiling of atherosclerosis has been used to identify genes and pathways relevant in vascular (patho)physiology. This review discusses some critical issues in the methodology, analysis, and interpretation of the data of gene expression studies that have made use of vascular specimens from animal models and humans. Analysis of gene expression studies has evolved toward the genome-wide expression profiling of large series of individual samples of well-characterized donors. Despite the advances in statistical and bioinformatical analysis of expression data sets, studies have not yet fully exploited the potential of gene expression data sets to obtain novel insights into the molecular mechanisms underlying atherosclerosis. To assess the potential of published expression data, we compared the data of a CC chemokine gene cluster between 18 murine and human gene expression profiling articles. Our analysis revealed that an adequate comparison is mainly hindered by the incompleteness of available data sets. The challenge for future vascular genomic profiling studies will be to further improve the experimental design, statistical, and bioinformatical analysis and to make data sets freely accessible.