Gene Expression Profiling Technologies Research Articles

Diagnosis progress of carcinoma of unknown primary

Carcinoma of unknown primary (CUP) is a common and complex type of tumor in clinical practice, where the primary site cannot be determined through conventional diagnostic methods, posing significant challenges for clinical diagnosis and treatment. In recent years, advancements in gene expression profiling and genetic testing technologies have provided new perspectives for CUP research, driving progress in this field. By analyzing gene expression profiles, researchers can more effectively identify the tissue origin of tumors, thereby improving diagnostic accuracy. At the same time, the potential application of genetic testing is continuously being explored, offering new possibilities for personalized treatment. This article aims to discuss the latest advancements in the diagnosis of CUP, analyze the importance of gene expression profiling and genetic testing in tumor origin identification and their clinical applications, and summarize current research progress and future research directions, with the goal of providing a theoretical basis for the early diagnosis and treatment of CUP.

HCC spatial transcriptomic profiling reveals significant and potentially targetable cancer-endothelial interactions.

HCC is a highly vascular tumor, and many effective drug regimens target the tumor blood vessels. Prior bulk HCC subtyping data used bulk transcriptomes, which contained a mixture of parenchymal and stromal contributions. We utilized computational deconvolution and cell-cell interaction analyses to cell type-specific (tumor-enriched and vessel-enriched) spatial transcriptomic data collected from 41 resected HCC tissue specimens. We report that the prior Hoshida bulk transcriptional subtyping schema is driven largely by an endothelial fraction, show an alternative tumor-specific schema has potential prognostic value, and use spatially paired ligand-receptor analyses to identify known and novel (LGALS9 tumor-HAVCR2 vessel) signaling relationships that drive HCC biology in a subtype-specific and potentially targetable manner. Our study leverages spatial gene expression profiling technologies to dissect HCC heterogeneity and identify heterogeneous signaling relationships between cancer cells and their endothelial cells. Future validation and expansion of these findings may validate novel cancer-endothelial cell interactions and related drug targets.

The classification of melanocytic gene signatures.

Gene expression profiling technologies have revolutionized cell biology, enabling researchers to identify gene signatures linked to various biological attributes of melanomas, such as pigmentation status, differentiation state, proliferative versus invasive capacity, and disease progression. Although the discovery of gene signatures has significantly enhanced our understanding of melanocytic phenotypes, reconciling the numerous signatures reported across independent studies and different profiling platforms remains a challenge. Current methods for classifying melanocytic gene signatures depend on exact gene overlap and comparison with unstandardized baseline transcriptomes. In this study, we aimed to categorize published gene signatures into clusters based on their similar patterns of expression across clinical cutaneous melanoma specimens. We analyzed nearly 800 melanoma samples from six gene expression repositories and developed a classification framework for gene signatures that is resilient against biases in gene identification across profiling platforms and inconsistencies in baseline standards. Using 39 frequently cited published gene signatures, our analysis revealed seven principal classes of gene signatures that correlate with previously identified phenotypes: Differentiated, Mitotic/MYC, AXL, Amelanotic, Neuro, Hypometabolic, and Invasive. Each class is consistent with the phenotypes that the constituent gene signatures represent, and our classification method does not rely on overlapping genes between signatures. To facilitate broader application, we created WIMMS (what is my melanocytic signature, available at https://wimms.tanlab.org/), a user-friendly web application. WIMMS allows users to categorize any gene signature, determining its relationship to predominantly cited signatures and its representation within the seven principal classes.

Navigating ethical challenges of integrating genomic medicine into clinical practice: Maximising beneficence in precision oncology

The development of gene expression profiling and next-generation sequencing technologies have steered oncogenomics to the forefront of precision medicine. This created a need for harmonious cooperation between clinicians and researchers to increase access to precision oncology, despite multiple implementation challenges being encountered. The aim is to apply personalised treatment strategies early in cancer management, targeting tumour subtypes and actionable gene variants within the individual’s broader clinical risk profile and wellbeing. A knowledge-generating database linked to the South African Medical Research Council’s Genomic Centre has been created for the application of personalised medicine, using an integrated service and research approach. Insights gained from patient experiences related to tumour heterogeneity, access to targeted therapies and incidental findings of pathogenic germline variants in tumour DNA, provided practice-changing evidence for the implementation of a cost-minimisation pathology-supported genetic testing strategy. Integrating clinical care with genomic research through data sharing advances personalised medicine and maximises precision oncology benefits.

Statistical Power Analysis for Designing Bulk, Single-Cell, and Spatial Transcriptomics Experiments: Review, Tutorial, and Perspectives.

Gene expression profiling technologies have been used in various applications such as cancer biology. The development of gene expression profiling has expanded the scope of target discovery in transcriptomic studies, and each technology produces data with distinct characteristics. In order to guarantee biologically meaningful findings using transcriptomic experiments, it is important to consider various experimental factors in a systematic way through statistical power analysis. In this paper, we review and discuss the power analysis for three types of gene expression profiling technologies from a practical standpoint, including bulk RNA-seq, single-cell RNA-seq, and high-throughput spatial transcriptomics. Specifically, we describe the existing power analysis tools for each research objective for each of the bulk RNA-seq and scRNA-seq experiments, along with recommendations. On the other hand, since there are no power analysis tools for high-throughput spatial transcriptomics at this point, we instead investigate the factors that can influence power analysis.

Molecular subtypes of leiomyosarcoma: Moving toward a consensus

Molecular subtypes of leiomyosarcoma: Moving toward a consensus

TIGER: A Web Portal of Tumor Immunotherapy Gene Expression Resource

Immunotherapy is a promising cancer treatment method; however, only a few patients benefit from it. The development of new immunotherapy strategies and effective biomarkers of response and resistance is urgently needed. Recently, high-throughput bulk and single-cell gene expression profiling technologies have generated valuable resources. However, these resources are not well organized and systematic analysis is difficult. Here, we present TIGER, a tumor immunotherapy gene expression resource, which contains bulk transcriptome data of 1508 tumor samples with clinical immunotherapy outcomes and 11,057 tumor/normal samples without clinical immunotherapy outcomes, as well as single-cell transcriptome data of 2,116,945 immune cells from 655 samples. TIGER provides many useful modules for analyzing collected and user-provided data. Using the resource in TIGER, we identified a tumor-enriched subset of CD4+ T cells. Patients with melanoma with a higher signature score of this subset have a significantly better response and survival under immunotherapy. We believe that TIGER will be helpful in understanding anti-tumor immunity mechanisms and discovering effective biomarkers. TIGER is freely accessible at http://tiger.canceromics.org/.

The single-cell stereo-seq reveals region-specific cell subtypes and transcriptome profiling in Arabidopsis leaves.

Understanding the complex functions of plant leaves requires a thorough characterization of discrete cell features. Although single-cell gene expression profiling technologies have been developed, their application in characterizing cell subtypes has not been achieved yet. Here, we present scStereo-seq (single-cell spatial enhanced resolution omics sequencing) that enabled us to show the bona fide single-cell spatial transcriptome profiles of Arabidopsis leaves. Subtle but significant transcriptomic differences between upper and lower epidermal cells have been successfully distinguished. Furthermore, we discovered cell-type-specific gene expression gradients from the main vein to the leaf edge, which led to the finding of distinct spatial developmental trajectories of vascular cells and guard cells. Our study showcases the importance of physical locations of individual cells for exerting complex biological functions in plants and demonstrates that scStereo-seq is a powerful tool to integrate single-cell location and transcriptome information for plant biology study.

Abstract PD2-07: Impact of using cross-platform gene expression profiling technologies and computational methods for intrinsic breast cancer subtyping in PALOMA-2 and PALLET

Abstract Background Intrinsic breast cancer molecular subtyping (IBCMS) provides significant prognostic information for patients (pts) with breast cancer (BC) treated with chemotherapy, human epidermal growth factor receptor 2 (HER2) targeted therapies, and endocrine therapies (ETs). Classifying tumors into intrinsic subtypes to determine optimal treatment is often applied using PAM50, commercially known as Prosigna. Meanwhile, Absolute Assignment of Breast Cancer Intrinsic Molecular Subtypes (AIMS) computational method was trained to predict PAM50-based IBCMS. As the PAM50 algorithm was developed to capture the major subtypes in a general pt population, clinicopathologic distribution of the study cohort and technology platform calibration should be considered in IBCMS analyses. This study compared different next-generation sequencing technologies and methodologic approaches of PAM50 on tumor samples from 2 randomized trials of postmenopausal women with estrogen receptor-positive (ER+)/HER2-negative (HER2-) BC. Methods PALOMA-2 is a double-blind, randomized study of first-line palbociclib (PAL) + letrozole (LET) for ER+/HER2- advanced BC (ABC). Tumor samples from consented pts were subtyped using the validated RUO PAM50 assay (ruoProsigna, NanoString); results were compared with published subtype results using AIMS on EdgeSeq Oncology Biomarker Panel (HTG Molecular Diagnostics). PALLET is a phase 2, randomized trial of PAL+LET as neoadjuvant therapy in pts with ER+ HER2- BC. Baseline frozen tumor biopsies underwent whole transcriptome mRNA-sequencing (mRNA-seq). IBCMS was performed using AIMS; PAM50 subtyping was performed on data normalised with subgroup-specific gene centering and microarray-RNA-sequencing calibration. Results In PALOMA-2, 222 pts had both ruoProsigna and AIMS data; an overall 54% agreement rate between methods was observed, with 46% (56/121) of Luminal B (LumB) subtype by ruoProsigna assigned as Luminal A (LumA) by AIMS and 67% (6/9) of basal-like by ruoProsigna as HER2-enriched (HER2-E) by AIMS (Table 1). In PALLET, 224 pts had mRNA-seq data; a 69% agreement between the two approaches (AIMS and PAM50) was observed, with only 4% (2/49) of LumB assigned as LumA by AIMS but 17% (26/156) and 16% (25/156) of LumA considered LumB or normal-like by AIMS, respectively. Progression-free survival (PFS) by ruoProsigna-derived subtype in PALOMA-2 showed that PAL+LET benefited all pts but those with a basal-like subtype (Table 2). With AIMS, PAL+LET provided a PFS benefit in pts with LumA and LumB subtypes, but was less effective in the HER2-E subtype. Conclusion Intrinsic subtyping has potential clinical utility. PAL+ET should be considered for ER+/HER2- ABC, except possibly in pts with a basal-like tumor, consistent with previous reports. A standardized clinical PAM50 assay and bioinformatics approach should be used as discrepancies in gene expression platforms and algorithms lead to different results and could misguide treatment decisions. Clinical trial identification: Pfizer (NCT01740427) Table 1.Intrinsic Subtyping by IBCMS MethodsPALOMA-2PALLETMethodruoProsignaPAM50 mRNAseqAIMS BasalHER2LumALumBGrand TotalBasalHER2LumALumBNormalGrand TotalBasal-like, n (%)1 (11)NANANA13 (75)0001 (8)4HER2-E, n (%)6 (67)6 (30)6 (8)13 (10)311 (25)3 (100)8 (5)6 (12)1 (8)19LumA, n (%)NA2 (10)60 (83)56 (46)1180097 (62)2 (4)099LumB, n (%)2 (22)12 (60)3 (4)52 (43)690026 (17)41 (84)067Normal-like, n (%)NANA3 (4)NA30025 (16)010 (83)35Grand Total9 (100)20 (100)72 (100)121 (100)2224 (100)3 (100)156 (100)49 (100)12 (100)224NA=Not available Table 2.Median PFS statistics by subtype in PALOMA-2PAL+LET PFS, monthsPBO+LET PFS, monthsHazard Ratio(95% CI)P ValueruoProsignaBasal-like8.2 (n=5)3.6 (n=4)0.39 (0.09-1.77)0.206HER2-E11.0 (n=12)5.1 (n=8)0.41 (0.15-1.11)0.071LumA37.2 (n=52)13.6 (n=20)0.42 (0.21-0.84)0.011LumB27.6 (n=79)13.8 (n=42)0.63 (0.40-1.00)0.049AIMSBasal-likeNANANANAHER2-E16.4 (n=21)8.4 (n=10)0.82 (0.32-2.1)0.684LumA30.6 (n=84)16.5 (n=34)0.56 (0.33-0.95)0.029LumB19.3 (n=41)8.8 (n=28)0.39 (0.23-0.67)<0.001NA=Not available; PBO=placebo Citation Format: Maggie Cheang, Mitch Dowsett, Mothaffar Rimawi, Stephen Johnston, Samuel Jacobs, Judith Bliss, Katherine Pogue-Geile, Lucy Kilburn, Zhou Zhu, Eugene F. Schuster, Hui Xiao, Lisa Swaim, Shibing Deng, Dongrui R. Lu, Eric Gauthier, Jennifer Tursi, Dennis J. Slamon, Hope S. Rugo, Richard S. Finn, Yuan Liu. Impact of using cross-platform gene expression profiling technologies and computational methods for intrinsic breast cancer subtyping in PALOMA-2 and PALLET [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD2-07.

Gene expression profiling signatures for immunophenotyping of tumor microenvironment using HTG EdgeSeq Precision Immuno-Oncology Panel.

e14528 Background: Immunotherapy has revolutionized cancer treatment by harnessing the power of the patient’s immune system to fight the disease. While immunotherapies have become increasingly effective, the proportion of patients not responding to immunotherapy ranges between only 20-50%. New breakthroughs in the understanding of the tumor immune biology have led to the development of new, more effective therapies; however, these breakthroughs often hinge on tools that can elucidate the role and abundance of individual components of the patient’s immune system in the tumor microenvironment. Methods: This poster describes the performance of 26 gene enrichment signatures that allow for the measurement of the relative abundance of 23 individual immune and stromal cells in the tumor environment, in addition to providing composite inflammation, stroma and tumor microenvironment scores. These Research Use Only (RUO) signatures are based on gene expression profiling (GEP) data for 1,392 genes implicated in tumor-immune interaction generated using the HTG EdgeSeq Precision Immuno-Oncology Panel (RUO). The signatures were developed and validated using GEP data from 1,073 colorectal, gastric and ovarian cancer formalin-fixed paraffin-embedded (FFPE) tissue specimens. Results: The signature accuracy was independently verified by spiking known numbers of representative immune and stromal cells into an FFPE tissue lysate; the signature outputs were highly correlated to the number of immune and stromal cells present in the sample, with Pearson correlation coefficients ranging from 0.71 to 0.98. In addition, the outputs of some of the signatures were compared to multiple commonly used immunohistochemistry markers of immune cells in 61 non-small-cell lung carcinoma FFPE samples. The results showed a high degree of correlation, with Pearson correlation coefficients of 0.77 and 0.81 for CD4 and CD8, respectively. Conclusions: These immunophenotyping signatures provide a suite of robust tools for the characterization of the tumor microenvironment in order to better understand the tumor immune biology and mechanisms of resistance to immunotherapy. The signature scores can typically be generated from a single section of FFPE tissue using HTG’s extraction-free GEP technology and a fully automated bioinformatic pipeline available in the HTG EdgeSeq Reveal software.

Identification of transcriptional subtypes in lung adenocarcinoma and squamous cell carcinoma through integrative analysis of microarray and RNA sequencing data

Classification of tumors into subtypes can inform personalized approaches to treatment including the choice of targeted therapies. The two most common lung cancer histological subtypes, lung adenocarcinoma and lung squamous cell carcinoma, have been previously divided into transcriptional subtypes using microarray data, and corresponding signatures were subsequently used to classify RNA-seq data. Cross-platform unsupervised classification facilitates the identification of robust transcriptional subtypes by combining vast amounts of publicly available microarray and RNA-seq data. However, cross-platform classification is challenging because of intrinsic differences in data generated using the two gene expression profiling technologies. In this report, we show that robust gene expression subtypes can be identified in integrated data representing over 3500 normal and tumor lung samples profiled using two widely used platforms, Affymetrix HG-U133 Plus 2.0 Array and Illumina HiSeq RNA sequencing. We tested and analyzed consensus clustering for 384 combinations of data processing methods. The agreement between subtypes identified in single-platform and cross-platform normalized data was then evaluated using a variety of statistics. Results show that unsupervised learning can be achieved with combined microarray and RNA-seq data using selected preprocessing, cross-platform normalization, and unsupervised feature selection methods. Our analysis confirmed three lung adenocarcinoma transcriptional subtypes, but only two consistent subtypes in squamous cell carcinoma, as opposed to four subtypes previously identified. Further analysis showed that tumor subtypes were associated with distinct patterns of genomic alterations in genes coding for therapeutic targets. Importantly, by integrating quantitative proteomics data, we were able to identify tumor subtype biomarkers that effectively classify samples on the basis of both gene and protein expression. This study provides the basis for further integrative data analysis across gene and protein expression profiling platforms.

Quantifying 35 transcripts in a single tube: model-based calibration of the GeXP multiplex RT-PCR assay

BackgroundQuantitative analysis of differential gene expression is of central importance in molecular life sciences. The Gene eXpression Profiling technology (GeXP) relies on multiplex RT-PCR and subsequent capillary electrophoretic separation of the amplification products and allows to quantify the transcripts of at least 35 genes with a single reaction and one dye.ResultsWe provide a kinetic model of primer binding and PCR product formation as the rational basis for taking and evaluating calibration curves. The calibration procedure and the model predictions were validated with the help of a purposefully designed data processing workflow supported by easy-to-use Perl scripts for calibration, data evaluation, and quality control. We further demonstrate the robustness and linearity of quantification of individual transcripts at variable relative abundance of other co-amplified transcripts in a complex mixture of RNAs isolated from differentiating Physarum polycephalum plasmodial cells.ConclusionsWe conclude that GeXP analysis is a robust, sensitive, and useful method when the transcripts of tens to few hundred genes are to be precisely quantified in a high number of samples.

High-Resolution Transcriptomic and Proteomic Profiling of Heterogeneity of Brain-Derived Microglia in Multiple Sclerosis.

Microglia are important for central nervous system (CNS) homeostasis and first to respond to tissue damage and perturbations. Microglia are heterogeneous cells; in case of pathology, microglia adopt a range of phenotypes with altered functions. However, how these different microglia subtypes are implicated in CNS disease is largely unresolved. Multiple sclerosis (MS) is a chronic demyelinating disease of the CNS, characterized by inflammation and axonal degeneration, ultimately leading to neurological decline. One way microglia are implicated in MS is through stimulation of remyelination. They facilitate efficient remyelination by phagocytosis of myelin debris. In addition, microglia recruit oligodendrocyte precursor cells (OPCs) to demyelinated areas and stimulate remyelination. The development of high-resolution technologies to profile individual cells has greatly contributed to our understanding of microglia heterogeneity and function under normal and pathological conditions. Gene expression profiling technologies have evolved from whole tissue RNA sequencing toward single-cell or nucleus sequencing. Single microglia proteomic profiles are also increasingly generated, offering another layer of high-resolution data. Here, we will review recent studies that have employed these technologies in the context of MS and their respective advantages and disadvantages. Moreover, recent developments that allow for (single) cell profiling while retaining spatial information and tissue context will be discussed.

Characterization of the DREBA4-Type Transcription Factor (SlDREBA4), Which Contributes to Heat Tolerance in Tomatoes.

Dehydration-responsive element binding (DREB) transcription factors play crucial regulatory roles in abiotic stress. The only DREB transcription factor in tomato (Solanum lycopersicum), SlDREBA4 (Accession No. MN197531), which was determined to be a DREBA4 subfamily member, was isolated from cv. Microtom using high-temperature-induced digital gene expression (DGE) profiling technology. The constitutive expression of SlDREBA4 was detected in different tissues of Microtom plants. In addition to responding to high temperature, SlDREBA4 was up-regulated after exposure to abscisic acid (ABA), cold, drought and high-salt conditions. Transgenic overexpression and silencing systems revealed that SlDREBA4 could alter the resistance of transgenic Microtom plants to heat stress by altering the content of osmolytes and stress hormones, and the activities of antioxidant enzymes at the physiologic level. Moreover, SlDREBA4 regulated the downstream gene expression of many heat shock proteins (Hsp), as well as calcium-binding protein enriched in the pathways of protein processing in endoplasmic reticulum (ko04141) and plant-pathogen interaction (ko04626) at the molecular level. SlDREBA4 also induces the expression of biosynthesis genes in jasmonic acid (JA), salicylic acid (SA), and ethylene (ETH), and specifically binds to the DRE elements (core sequence, A/GCCGAC) of the Hsp genes downstream from SlDREBA4. This study provides new genetic resources and rationales for tomato heat-tolerance breeding and the heat-related regulatory mechanisms of DREBs.

Whole-blood dysregulation of actin-cytoskeleton pathway in adult spinal muscular atrophy patients.

ObjectiveRecent advances in therapeutics have improved prognosis for severely affected spinal muscular atrophy (SMA) type 1 and 2 patients, while the best method of treatment for SMA type 3 patients with later onset of disease is unknown. To better characterize the SMA type 3 population and provide potential therapeutic targets, we aimed to understand gene expression differences in whole blood of SMA type 3 patients (n = 31) and age‐ and gender‐matched controls (n = 34).MethodsWe performed the first large‐scale whole blood transcriptomic screen with L1000, a rapid, high‐throughput gene expression profiling technology that uses 978 landmark genes to capture a representation of the transcriptome and predict expression of 9196 additional genes.ResultsThe primary downregulated KEGG pathway in adult SMA type 3 patients was "Regulation of Actin Cytoskeleton,” and downregulated expression of key genes in this pathway, including ROCK1, RHOA, and ACTB, was confirmed in the same whole blood samples using RT‐qPCR. SMA type 3 patient‐derived fibroblasts had lower expression of these genes compared to control fibroblasts from unaffected first‐degree relatives. Overexpression of SMN levels using an AAV vector in fibroblasts did not normalize ROCK1, RHOA, and ACTB mRNA expression, indicating the involvement of additional genes in cytoskeleton dynamic regulation.InterpretationOur findings from whole blood and patient‐derived fibroblasts suggest SMA type 3 patients have decreased expression of actin cytoskeleton regulators. These observations provide new insights and potential therapeutic targets for SMA patients with longstanding denervation and secondary musculoskeletal pathophysiology.

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.

WD Repeat Domain 5 Promotes Invasion, Metastasis and Tumor Growth in Glioma Through Up-Regulated Zinc Finger E-Box Binding Homeobox 1 Expression.

BackgroundGlioma is one of the important diseases that threaten human survival in today’s society. WD repeat domain 5 (WDR5) belongs to the components of the lysine methyltransferase complex. WDR5 is involved in gene transcription regulation, cell senescence, cancer and other biological events through methylation modification. However, its expression and function in glioma are still unclear.Materials and MethodsThe expression of WDR5 was observed in glioma cells, and then a glioma cell line SW1783 knocked down WDR5 was established. The effects of the decrease of WDR5 expression on proliferation, migration, invasion and EMT of glioma cells were detected, respectively. The downstream regulators of WDR5 were identified by gene expression profiling technology, and the possible molecular mechanisms were identified.ResultsIn this study, we found that WDR5 could promote glioma cell’s proliferation, migration, invasion and tumor metastasis. In glioma, especially in metastatic glioma tissues, WDR5 levels were significantly increased, the higher expression level could also cause a significant reduction in overall survival of glioma patients. Second, the ability of cells’ proliferation, migration, invasion and tumor metastasis was significantly reduced in WDR5 knockdown cell lines. We also found a significant change in the expression level of epithelial and mesenchymal markers in WDR5 knockdown cell lines. Furthermore, we found that knockdown of WDR5 could inhibit the expression of zinc finger E-box binding homeobox 1 (ZEB1), and knockdown of ZEB1 could inhibit invasion, migration and epithelial–mesenchymal transformation (EMT) in WDR5 over-expression cell line. We also found that WDR5 may regulate ZEB1’s expression through H3K4me3.ConclusionIn summary, in this study, we have studied the relationship between WDR5 and glioma, and found that WDR5’s expression is positively correlated with the proliferation, migration, and invasion of glioma cells, which will help find the potential therapeutic target for glioma patients.

The Gene Ontology Differs in Bursa of Fabricius Between Two Breeds of Ducks Post Hatching by Enriching the Differentially Expressed Genes

ABSTRACT The bursa of Fabricius (BF) is the central humoral immune organ unique to birds. The present study investigated the possible difference on a molecular level between two duck breeds. The digital gene expression profiling (DGE) technology was used to enrich the differentially expressed genes (DEGs) in BF between the Jianchang and Nonghua-P strains of ducks. DGE data identified 195 DEGs in the bursa. Gene Ontology (GO) analysis suggested that DEGs were mainly enriched in the metabolic pathways and ribosome components. Pathways analysis identified the spliceosome, RNA transport, RNA degradation process, Jak-STAT signaling pathway, TNF signaling pathway and B cell receptor signaling pathway. The results indicated that the main difference in the BF between the two duck strains was in the capabilities of protein formation and B cell development. These data have revealed the main divergence in the BF on a molecular level between genetically different duck breeds and may help to perform molecular breeding programs in poultry in the future.

RNA-Seq and Expression Arrays: Selection Guidelines for Genome-Wide Expression Profiling.

The development of genome-wide gene expression profiling technologies over the past two decades has produced great opportunity for researchers to explore the transcriptome and to better understand biological systems and their perturbation. In this chapter we provide an overview of microarray and massively parallel sequencing technologies and their application to gene expression analysis. We discuss factors that impact expression data generation and analysis that which should be considered in the application of these technology platforms. We further present the results of a simple illustration study to highlight performance similarities and differences in expression profiling of protein-coding mRNAs with each platform. Based on technical and analytical differences between the two platforms, reports in the literature comparing arrays and RNA-Seq for gene expression, and our own example study and experience, we provide recommendations for platform selection for gene expression studies.

SPRING: a kinetic interface for visualizing high dimensional single-cell expression data.

MotivationSingle-cell gene expression profiling technologies can map the cell states in a tissue or organism. As these technologies become more common, there is a need for computational tools to explore the data they produce. In particular, visualizing continuous gene expression topologies can be improved, since current tools tend to fragment gene expression continua or capture only limited features of complex population topologies.ResultsForce-directed layouts of k-nearest-neighbor graphs can visualize continuous gene expression topologies in a manner that preserves high-dimensional relationships and captures complex population topologies. We describe SPRING, a pipeline for data filtering, normalization and visualization using force-directed layouts and show that it reveals more detailed biological relationships than existing approaches when applied to branching gene expression trajectories from hematopoietic progenitor cells and cells of the upper airway epithelium. Visualizations from SPRING are also more reproducible than those of stochastic visualization methods such as tSNE, a state-of-the-art tool. We provide SPRING as an interactive web-tool with an easy to use GUI.Availability and implementation https://kleintools.hms.harvard.edu/tools/spring.html, https://github.com/AllonKleinLab/SPRING/.Supplementary information Supplementary data are available at Bioinformatics online.