COMPARISON OF RNA-SEQ AND MICROARRAY OF GENE EXPRESSION SIGNATURES AS A PREDICTOR OF SURVIVAL IN SIX SELECTED CANCERS

Objectives. Glioblastoma (GBM) is a malignant brain tumor which is the most common and aggressive type of central nervous system cancer, with high morbidity and mortality. Despite lots of systematic studies on the molecular mechanism of glioblastoma, the pathogenesis is still unclear, and effective therapies are relatively rare with surgical resection as the frequently therapeutic intervention. Identification of fundamental molecules and gene networks associated with initiation is critical in glioblastoma drug discovery. In this study, an approach for the prediction of potential drug was developed based on perturbation-induced gene expression signatures. Methods. We first collected RNA-seq data of 12 pairs of glioblastoma samples and adjacent normal samples from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified by DESeq2, and coexpression networks were analyzed with weighted gene correlation network analysis (WGCNA). Furthermore, key driver genes were detected based on the differentially expressed genes and potential chemotherapeutic drugs and targeted drugs were found by correlating the gene expression profiles with drug perturbation database. Finally, RNA-seq data of glioblastoma from The Cancer Genome Atlas (TCGA) dataset was collected as an independent validation dataset to verify our findings. Results. We identified 1771 significantly DEGs with 446 upregulated genes and 1325 downregulated genes. A total of 24 key drivers were found in the upregulated gene set, and 81 key drivers were found in the downregulated gene set. We screened the Crowd Extracted Expression of Differential Signatures (CREEDS) database to identify drug perturbations that could reverse the key factors of glioblastoma, and a total of 354 drugs were obtained with p value < 10-10. Finally, 7 drugs that could turn down the expression of upregulated factors and 3 drugs that could reverse the expression of downregulated key factors were selected as potential glioblastoma drugs. In addition, similar results were obtained through the analysis of TCGA as independent dataset. Conclusions. In this study, we provided a framework of workflow for potential therapeutic drug discovery and predicted 10 potential drugs for glioblastoma therapy.

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AB0108 USING RNA SEQUENCING TO IDENTIFY GENE EXPRESSION SIGNATURES ASSOCIATED WITH RESPONSE TO ABATACEPT IN PATIENTS WITH RHEUMATOID ARTHRITIS

Annals of the Rheumatic Diseases ◽

10.1136/annrheumdis-2021-eular.896 ◽

2021 ◽

Vol 80 (Suppl 1) ◽

pp. 1082-1083

Author(s):

H. H. Chen ◽

W. C. Chao ◽

J. R. Wang ◽

T. M. Ko

Keyword(s):

Rheumatoid Arthritis ◽

Gene Expression ◽

Mhc Class Ii ◽

Whole Blood ◽

Antigen Processing ◽

Cytokine Production ◽

Rna Seq ◽

Eular Response ◽

Gene Expression Signatures ◽

Identify Gene Expression

Background:Rheumatoid arthritis (RA) is a common chronic autoimmune disease. Abatacept (CTLA4-immunoglobulin) is one of the biological disease-modifying antirheumatic drug (bDMARD) for RA patients with indequate response to methotrexate. Recently, Yokoyama-Kokuryo et al. compared gene expression levels between abatacept responders and non-responders in RA patients using a microarray and found that type I IFN score and expression levels of nine genes may be used as a biomarker to predict response to abatacept. However, little study used RNA sequencing (RNA-seq) to identify whole blood gene expression signatures to predict therapeutic response to abatacept.Objectives:The aim of this study is to identify gene expression signatures to predict therapeutic responses to abatacept in RA patients using RNA-seq.Methods:This study is a single-center, prospective study. We used a PAX gene Blood RNA kit to collect whole blood at baseline and 4 weeks after abatacept treatment from RA patients. We also measured DAS28, physician global assessment, HAQ, ESR, CRP at baseline and 12 week to calculate EULAR response at 12 week. Patients with good EULAR response were defined as responders and those with moderate or no EULAR response were defined as non-responders.Results:We finally conducted RNA-seq for whole blood from 7 RA patients initiating abatacept therapy. Of the 7 RA patients, one was non-responder and 6 were responders. We first use DESeq2 to analyze the differentially expressed genes of non-responder and responder before taking the drug. We used hierarchical clustering and PCA to evaluate the overall similarity of the samples, and group the patient data, and find that the nonresponder can be distinguished from responders. Subsequently, we analyzed the differentially expressed genes of the two groups of non-responder and responder patients before taking the drug. Before treatment, we found that 72 genes had a higher expression in the non-responder, and 23 genes had a higher expression level in responders. Figure 1 showed the top 20 DEG Heatmap between the non-responder and responders.Using these two sets of genes for GO analysis, we found that most of the pathways in the non-responder are related to immune response and cytokine production, and most of the pathways in the responders are related to antigen processing and MHC class II.Figure 1.Top 20 DEG Heatmap between non-responder and respondersConclusion:The study showed that most of the pathways in RA patients with no EULAR response to abatacept are related to immune response and cytokine production; while most of the pathways in RA patients with moderate/good response to abatacept are related to antigen processing and MHC class II.References:[1]Yokoyama-Kokuyo W, Yamazaki H, Takeuchi T, et al. Identification of molecules associated with response to abatacept in patients with rheumatoid arthritis. Arthritis Research & Therapy. 2020;22:46.Disclosure of Interests:Hsin-Hua Chen Grant/research support from: This is an investigator-sponsored trial with Bristol-Myers Squibb who provides funding support., Wen-Cheng Chao: None declared, Jing-Rong Wang: None declared, Tai-Ming Ko: None declared

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The landscape of mouse epididymal cells defined by the single-cell RNA-Seq

10.1101/2020.01.05.895052 ◽

2020 ◽

Author(s):

Jianwu Shi ◽

Mengmeng Sang ◽

Gangcai Xie ◽

Hao Chen

Keyword(s):

Gene Expression ◽

Single Cell ◽

Cell Types ◽

Principal Cell ◽

Sperm Maturation ◽

Specific Cell ◽

Rna Seq ◽

Cell Type ◽

Gene Expression Signatures ◽

Mouse Epididymis

ABSTRACTSpermatozoa acquire their fertilizing ability and forward motility properties during epididymal transit. Although lots of attempts elucidating the functions of different cell types in epididymis, the composition of epididymal tubal and cell types are still largely unknown. Using single-cell RNA sequence, we analyzed the cell constitutions and their gene expression profiles of adult epididymis derived from caput, corpus and cauda epididymis with a total of 12,597 cells. This allowed us to elucidate the full range of gene expression changes during epididymis and derive region-specific gene expression signatures along the epididymis. A total of 7 cell populations were identified with all known constituent cells of mouse epididymis, as well as two novel cell types. Our analyses revealed a segment to segment variation of the same cell type in the three different part of epididymis and generated a reference dataset of epididymal cell gene expression. Focused analyses uncovered nine subtypes of principal cell. Two subtypes of principal cell, c0.3 and c.6 respectively, in our results supported with previous finding that they mainly located in the caput of mouse epididymis and play important roles during sperm maturation. We also showed unique gene expression signatures of each cell population and key pathways that may concert epididymal epithelial cell-sperm interactions. Overall, our single-cell RNA seq datasets of epididymis provide a comprehensive potential cell types and information-rich resource for the studies of epididymal composition, epididymal microenvironment regulation by the specific cell type, or contraceptive development, as well as a gene expression roadmap to be emulated in efforts to achieve sperm maturation regulation in the epididymis.

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Gene Expression Signatures of Sporadic ALS Motor Neuron Populations

10.1101/038448 ◽

2016 ◽

Cited By ~ 9

Author(s):

Ranjan Batra ◽

Kasey Hutt ◽

Anthony Vu ◽

Stuart J Rabin ◽

Michael W Baughn ◽

...

Keyword(s):

Gene Expression ◽

Motor Neurons ◽

Molecular Mechanisms ◽

Interaction Analysis ◽

Molecular Networks ◽

Specific Gene ◽

Rna Seq ◽

Tissue Samples ◽

Sporadic Als ◽

Gene Expression Signatures

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease primarily affecting motor neurons (MNs) to cause progressive paralysis. Ninety percent of cases are sporadic (sALS) and ten percent are familial (fALS). The molecular mechanisms underlying neurodegeneration remain elusive and there is a lack of promising biomarkers that define ALS phenotypes and progression. To date, most expression studies have focused on either complex whole tissues that contain cells other than MNs or induced pluripotent derived MNs (iMNs). Furthermore, as human tissue samples have high variability, estimation of differential gene-expression is not a trivial task. Here, we report a battery of orthogonal computational analyses to discover gene-expression defects in laser capture microdissected and enriched MN RNA pools from sALS patient spinal cords in regions destined for but not yet advanced in neurodegenerative stage. We used total RNA-sequencing (RNA-seq), applied multiple percentile rank (MPR) analysis to analyze MN-specific gene-expression signatures, and used high-throughput qPCR to validate RNA-seq results. Furthermore, we used a systems-level approach that identified molecular networks perturbed in sALS MNs. Weighted gene co-expression correlation network (WGCNA) analysis revealed defects in neurotransmitter biosynthesis and RNA-processing pathways while gene-gene interaction analysis showed abnormalities in networks that pertained to cell-adhesion, immune response and wound healing. We discover gene-expression signatures that distinguish sALS from control MNs and our findings illuminate possible mechanisms of cellular toxicity. Our systematic and comprehensive analysis serves as a framework to reveal expression signatures and disrupted pathways that will be useful for future mechanistic studies and biomarker based therapeutic research. *Corresponding authors: [email protected], [email protected]

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RNA-seq and network analysis reveal unique glial gene expression signatures during prion infection

Molecular Brain ◽

10.1186/s13041-020-00610-8 ◽

2020 ◽

Vol 13 (1) ◽

Cited By ~ 3

Author(s):

James A. Carroll ◽

Brent Race ◽

Katie Williams ◽

James Striebel ◽

Bruce Chesebro

Keyword(s):

Gene Expression ◽

Network Analysis ◽

Rna Seq ◽

Prion Infection ◽

Gene Expression Signatures

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Systematic evaluation of transcriptomic disease risk and diagnostic biomarker overlap between COVID-19 and tuberculosis: a patient-level meta-analysis

10.1101/2020.11.25.20236646 ◽

2020 ◽

Author(s):

Dylan Sheerin ◽

Abhimanyu ◽

Xutao Wang ◽

W Evan Johnson ◽

Anna Coussens

Keyword(s):

Gene Expression ◽

Disease Risk ◽

Meta Analysis ◽

Risk Scores ◽

Systematic Evaluation ◽

Rna Seq ◽

Sequencing Data ◽

Transcriptional Responses ◽

Patient Level ◽

Gene Expression Signatures

AbstractBackgroundThe novel coronavirus, SARS-CoV-2, has increased the burden on healthcare systems already strained by a high incidence of tuberculosis (TB) as co-infection and dual presentation are occurring in syndemic settings. We aimed to understand the interaction between these diseases by profiling COVID-19 gene expression signatures on RNA-sequencing data from TB-infected individuals.MethodsWe performed a systematic review and patient-level meta-analysis by querying PubMed and pre-print servers to derive eligible COVID-19 gene expression signatures from human whole blood (WB), PBMCs or BALF studies. A WB influenza dataset served as a control respiratory disease signature. Three large TB RNA-seq datasets, comprising multiple cohorts from the UK and Africa and consisting of TB patients across the disease spectrum, were chosen to profile these signatures. Putative “COVID-19 risk scores” were generated for each sample in the TB datasets using the TBSignatureProfiler package. Risk was stratified by time to TB diagnosis in progressors and contacts of pulmonary and extra-pulmonary TB. An integrative analysis between TB and COVID-19 single-cell RNA-seq data was performed and a population-level meta-analysis was conducted to identify shared gene ontologies between the diseases and their relative enrichment in COVID-19 disease severity states.Results35 COVID-19 gene signatures from nine eligible studies comprising 98 samples were profiled on TB RNA-seq data from 1181 samples from 853 individuals. 25 signatures had significantly higher COVID-19 risk in active TB (ATB) compared with latent TB infection (p <0·005), 13 of which were validated in two independent datasets. FCN1- and SPP1-expressing macrophages enriched in BALF during severe COVID-19 were identified in circulation during ATB. Shared perturbed ontologies included antigen presentation, epigenetic regulation, platelet activation, and ROS/RNS production were enriched with increasing COVID-19 severity. Finally, we demonstrate that the overlapping transcriptional responses may complicate development of blood-based diagnostic signatures of co-infection.InterpretationOur results identify shared dysregulation of immune responses in COVID-19 and TB as a dual risk posed by co-infection to COVID-19 severity and TB disease progression. These individuals should be followed up for TB in the months subsequent to SARS-CoV-2 diagnosis.

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A gp78/AMFR protein-driven gene signature that predicts breast cancer outcome.

Journal of Clinical Oncology ◽

10.1200/jco.2021.39.15_suppl.553 ◽

2021 ◽

Vol 39 (15_suppl) ◽

pp. 553-553

Author(s):

Sandeep K. Singhal ◽

Jung Byun ◽

Samson Park ◽

Tingfen Yan ◽

Ryan Yancey ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Protein Expression ◽

Gene Expression Data ◽

Independent Predictor ◽

Response To Therapy ◽

Expression Data ◽

Rna Seq ◽

Gene Expression Signatures ◽

Gene Modules

553 Background: gp78, also known as the autocrine motility factor receptor (AMFR) or RNF45, is a polytopic RING-type E3 ubiquitin ligase resident to the endoplasmic reticulum (ER) that plays major role in the cellular response to stress by regulating ER homeostasis and signaling through its participation in the unfolded protein response (UPR) and ER associated degradation. We used machine learning (ML) and statistical modeling (SM) to assess gp78 as a protein biomarker that is an independent predictor of breast cancer (bc) survival exclusively in women of self-reported African descent as opposed to European ancestry. Methods: We examined a cohort of racially diverse 555 BC bc patients who underwent surgery for their primary BC in Greenville, NC using ML and SM approach. We leveraged the availability of RNA-seq gene expression data on a portion of our bc cohort (N=136 of 555) to construct gene expression signatures. Results: Using antibodies developed in the Weissman lab and established methods for quantitative IHC, we have found that gp78 expression is significantly increased in the tumors of bc patients compared to normal breast epithelia. In addition, we found that gp78 is expressed at significantly higher levels in bc of non-Hispanic black women (NHB) compared to non-Hispanic white women (NHW) (p=0.0038), and that bc subtypes known to be more aggressive and associated with higher grades like, Basal (p=1.6e-12), Luminal B (p=2.3e-4) and HER2(8.3e-4), display significantly higher levels of gp78 compare to Luminal A. Moreover, Kaplan-Meier survival curve analyses show that gp78 protein expression is more significantly associated with poor survival in NHB women (HR:1.65, p=0.073) compared to NHW women (HR:2.01, p=0.004). Finally, multivariate analysis reveals that gp78 protein expression, based on quantitative IHC, is an independent predictor of poor bc survival exclusively in women of African (NHB) ancestry (HR:1.99, p=0.017). We leveraged the availability of RNA-seq gene expression data on a portion of our bc cohort to construct gene expression signatures or gene modules. An analysis of pooled publicly available data from 845 patients that underwent neoadjuvant chemotherapy for bc (primarily taxane and anthracycline based), reveals that gp78 gene modules are highly predictive of patient response to therapy. gp78-derived gene modules show both high fold difference and significance in predicting response to therapy (AUC:0.72) which is very similar to other multi-gene panels that are currently in clinical use including Prosigna, MammaPrint, and Oncotype Dx. Conclusions: Our results show that gp78/AMFR is an independent predictor of bc survival and response to therapy, based on race, thus implicating a role for this protein, and potentially the UPR, as underlying biological differences in tumor properties linked to genetic ancestry.

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