scholarly journals A sepsis gene expression signature in the whole blood.

2020 ◽  
Author(s):  
Shahan Mamoor
Keyword(s):  
Gene Expression ◽  
Septic Shock ◽  
Differential Expression ◽  
Whole Blood ◽  
Clinical Relevance ◽  
Gene Expression Signature ◽  
Infectious Agent ◽  
Gene Signature ◽  
Sepsis And Septic Shock ◽  
Microarray Datasets

Sepsis is a leading cause of mortality (1). We mined published and public microarray datasets (2, 3) to identify differentially expressed genes in the whole blood of patients with sepsis. We observed significant changes in the expression of 78 genes in patients with sepsis and septic shock. Differential expression of these genes was associated with the septic state (independent of infectious agent) and observed in children and adults. The identification of these genes may be useful in the understanding of the biology of sepsis; this gene signature, upon validation, could have clinical relevance as a diagnostic measure.

scholarly journals hnRNP U is differentially expressed in whole blood from patients with sepsis.

2020 ◽  
Author(s):  
Shahan Mamoor
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Whole Blood ◽  
Differentially Expressed ◽  
Significant Differential Expression ◽  
Hnrnp U ◽  
Significant Gene ◽  
Nuclear Ribonucleoprotein ◽  
Microarray Datasets

We probed published and public microarray datasets (1, 2) to discover the most significant gene expression changes in the blood of patients with sepsis. We identified significant differential expression of the heterogenous nuclear ribonucleoprotein hnRNP U in whole blood from patients with sepsis.

scholarly journals Identification of gene expression signature for cigarette smoke exposure response—from man to mouse

2015 ◽  
Vol 34 (12) ◽  
pp. 1200-1211 ◽  
Author(s):  
F Martin ◽  
M Talikka ◽  
J Hoeng ◽  
MC Peitsch
Keyword(s):  
Gene Expression ◽  
Cigarette Smoke ◽  
Whole Blood ◽  
Gene Expression Signature ◽  
High Specificity ◽  
Gene Signature ◽  
Smoke Exposure ◽  
Cigarette Smoke Exposure ◽  
Toxicant Exposure ◽  
Specificity And Sensitivity

Gene expression profiling data can be used in toxicology to assess both the level and impact of toxicant exposure, aligned with a vision of 21st century toxicology. Here, we present a whole blood-derived gene signature that can distinguish current smokers from either nonsmokers or former smokers with high specificity and sensitivity. Such a signature that can be measured in a surrogate tissue (whole blood) may help in monitoring smoking exposure as well as discontinuation of exposure when the primarily impacted tissue (e.g., lung) is not readily accessible. The signature consisted of LRRN3, SASH1, PALLD, RGL1, TNFRSF17, CDKN1C, IGJ, RRM2, ID3, SERPING1, and FUCA1. Several members of this signature have been previously described in the context of smoking. The signature translated well across species and could distinguish mice that were exposed to cigarette smoke from ones exposed to air only or had been withdrawn from cigarette smoke exposure. Finally, the small signature of only 11 genes could be converted into a polymerase chain reaction-based assay that could serve as a marker to monitor compliance with a smoking abstinence protocol.

scholarly journals HNMT is differentially expressed in monocytes from patients with sepsis.

2020 ◽  
Author(s):  
Shahan Mamoor
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Whole Blood ◽  
Blood Cells ◽  
Differentially Expressed ◽  
Significant Differential Expression ◽  
Significant Gene ◽  
Microarray Datasets

We probed published and public microarray datasets (1, 2) to discover the most significant gene expression changes in the blood and blood cells of patients with sepsis. We identified significant differential expression of the histamine N-methyltransferase HNMT in monocytes and whole blood from patients with sepsis.

scholarly journals A novel whole blood gene expression signature for asthma, dermatitis, and rhinitis multimorbidity in children and adolescents

Allergy ◽  
2020 ◽  
Vol 75 (12) ◽  
pp. 3248-3260 ◽  
Author(s):  
Nathanaël Lemonnier ◽  
Erik Melén ◽  
Yale Jiang ◽  
Stéphane Joly ◽  
Camille Ménard ◽  
...  
Keyword(s):  
Gene Expression ◽  
Children And Adolescents ◽  
Whole Blood ◽  
Gene Expression Signature ◽  
Blood Gene Expression ◽  
Expression Signature

scholarly journals A Gene Expression Signature from Peripheral Whole Blood for Stage I Lung Adenocarcinoma

2011 ◽  
Vol 4 (10) ◽  
pp. 1599-1608 ◽  
Author(s):  
Melissa Rotunno ◽  
Nan Hu ◽  
Hua Su ◽  
Chaoyu Wang ◽  
Alisa M. Goldstein ◽  
...  
Keyword(s):  
Gene Expression ◽  
Lung Adenocarcinoma ◽  
Whole Blood ◽  
Gene Expression Signature ◽  
Stage I ◽  
Expression Signature

scholarly journals A Prognostic Gene-Expression Signature and Risk Score for Meningioma Recurrence After Resection

Neurosurgery ◽  
2020 ◽  
Vol 88 (1) ◽  
pp. 202-210 ◽  
Author(s):  
William C Chen ◽  
Harish N Vasudevan ◽  
Abrar Choudhury ◽  
Melike Pekmezci ◽  
Calixto-Hope G Lucas ◽  
...  
Keyword(s):  
Gene Expression ◽  
Risk Score ◽  
Gene Expression Analysis ◽  
Validation Cohort ◽  
Gene Expression Signature ◽  
Gene Signature ◽  
Who Grade ◽  
Independent Validation ◽  
Meningioma Recurrence ◽  
Targeted Gene Expression

Abstract BACKGROUND Prognostic markers for meningioma are needed to risk-stratify patients and guide postoperative surveillance and adjuvant therapy. OBJECTIVE To identify a prognostic gene signature for meningioma recurrence and mortality after resection using targeted gene-expression analysis. METHODS Targeted gene-expression analysis was used to interrogate a discovery cohort of 96 meningiomas and an independent validation cohort of 56 meningiomas with comprehensive clinical follow-up data from separate institutions. Bioinformatic analysis was used to identify prognostic genes and generate a gene-signature risk score between 0 and 1 for local recurrence. RESULTS We identified a 36-gene signature of meningioma recurrence after resection that achieved an area under the curve of 0.86 in identifying tumors at risk for adverse clinical outcomes. The gene-signature risk score compared favorably to World Health Organization (WHO) grade in stratifying cases by local freedom from recurrence (LFFR, P < .001 vs .09, log-rank test), shorter time to failure (TTF, F-test, P < .0001), and overall survival (OS, P < .0001 vs .07) and was independently associated with worse LFFR (relative risk [RR] 1.56, 95% CI 1.30-1.90) and OS (RR 1.32, 95% CI 1.07-1.64), after adjusting for clinical covariates. When tested on an independent validation cohort, the gene-signature risk score remained associated with shorter TTF (F-test, P = .002), compared favorably to WHO grade in stratifying cases by OS (P = .003 vs P = .10), and was significantly associated with worse OS (RR 1.86, 95% CI 1.19-2.88) on multivariate analysis. CONCLUSION The prognostic meningioma gene-expression signature and risk score presented may be useful for identifying patients at risk for recurrence.

Identification of synthetic lethal interactions with the BRAF oncogene in colorectal cancer.

2013 ◽  
Vol 31 (4_suppl) ◽  
pp. 403-403
Author(s):  
Loredana Vecchione ◽  
Valentina Gambino ◽  
Giovanni d'Ario ◽  
Sun Tian ◽  
Iris Simon ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Expression Signature ◽  
Gene Signature ◽  
Braf V600e ◽  
P Value ◽  
Synthetic Lethal ◽  
Braf Mutant

403 Background: Approximately 8-15% of colorectal (CRC) patients carry an activating mutation in BRAF. This CRC subtype is associated with poor outcome and with resistance, both to chemotherapeutic treatments and to tailored drugs. We recently showed that BRAF (V600E) colon cancers (CCs) have a characteristic gene expression signature (1, 2) which is found also in subsets of KRAS mutant and KRAS-BRAF wild type (WT2) tumors. Tumors having this gene signature, referred as “BRAF-like”, have a similar poor prognosis irrespective of the presence of the BRAF (V600E) mutation. By using a shRNA-based genetic screen in BRAF mutant CC cell lines we aimed to identify genes and pathways necessary for survival and growth of BRAFmutant CC. Such studies may reveal additional targets for therapy and potentially provide new biomarkers for patient stratification Methods: We identified 363 genes that are selectively overexpressed in BRAF mutant tumors as compared to WT2 type tumors, based on gene expression profiles of the PETACC3 (1) and Agendia (2) datasets. The TRC human genome-wide shRNA collection (TRC-Hs1.0) was used to generate a 1815 hairpins sub-library targeting those identified genes (BRAF library). BRAF(V600E) CC cell lines were infected with the BRAF library and screened for shRNAs that cause lethality. LIM1215 CC cell line (WT2) was used as a control. Cells stably expressing the shRNA library were cultured for 13 days, after which shRNAs were recovered by PCR. Deep sequencing was applied to determine the specific depletion of shRNA in BRAF(V600E) cells as compared to LIM1215 cells Results: Candidate genes were identified by using following filtering criteria: depletion in BRAF(V600E) cells by at least 50% and depletion in BRAF(V600E) cells 1, 5-fold higher than in control cells with the corresponding p-value to be ≤ 0.1. A total of 34 genes met our criteria of which 6 genes were presented with more than one hairpin and were concordant across the cell lines selected for validation. Conclusions: We identified candidate synthetic lethal genes in BRAF mutant CC cell lines. Functional analysis is ongoing. Data will be presented. References 1. J Clin Oncol 2012 Apr 20;30(12):1288-9 2. Gut (2012). doi:10.1136/gutjnl-2012-302423

scholarly journals Interferon-beta represses cancer stem cell properties in triple-negative breast cancer

2017 ◽  
Vol 114 (52) ◽  
pp. 13792-13797 ◽  
Author(s):  
Mary R. Doherty ◽  
HyeonJoo Cheon ◽  
Damian J. Junk ◽  
Shaveta Vinayak ◽  
Vinay Varadan ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Triple Negative Breast Cancer ◽  
Epithelial To Mesenchymal Transition ◽  
Triple Negative ◽  
Mammary Epithelial Cells ◽  
Gene Expression Signature ◽  
Tumor Infiltrating Lymphocytes ◽  
Gene Signature ◽  
Mesenchymal Transition

Triple-negative breast cancer (TNBC), the deadliest form of this disease, lacks a targeted therapy. TNBC tumors that fail to respond to chemotherapy are characterized by a repressed IFN/signal transducer and activator of transcription (IFN/STAT) gene signature and are often enriched for cancer stem cells (CSCs). We have found that human mammary epithelial cells that undergo an epithelial-to-mesenchymal transition (EMT) following transformation acquire CSC properties. These mesenchymal/CSCs have a significantly repressed IFN/STAT gene expression signature and an enhanced ability to migrate and form tumor spheres. Treatment with IFN-beta (IFN-β) led to a less aggressive epithelial/non–CSC-like state, with repressed expression of mesenchymal proteins (VIMENTIN, SLUG), reduced migration and tumor sphere formation, and reexpression of CD24 (a surface marker for non-CSCs), concomitant with an epithelium-like morphology. The CSC-like properties were correlated with high levels of unphosphorylated IFN-stimulated gene factor 3 (U-ISGF3), which was previously linked to resistance to DNA damage. Inhibiting the expression of IRF9 (the DNA-binding component of U-ISGF3) reduced the migration of mesenchymal/CSCs. Here we report a positive translational role for IFN-β, as gene expression profiling of patient-derived TNBC tumors demonstrates that an IFN-β metagene signature correlates with improved patient survival, an immune response linked with tumor-infiltrating lymphocytes (TILs), and a repressed CSC metagene signature. Taken together, our findings indicate that repressed IFN signaling in TNBCs with CSC-like properties is due to high levels of U-ISGF3 and that treatment with IFN-β reduces CSC properties, suggesting a therapeutic strategy to treat drug-resistant, highly aggressive TNBC tumors.

scholarly journals Gene Expression Signature Associated with Clinical Outcome in ALK-Positive Anaplastic Large Cell Lymphoma

Cancers ◽  
2021 ◽  
Vol 13 (21) ◽  
pp. 5523
Author(s):  
Daugrois Camille ◽  
Bessiere Chloé ◽  
Dejean Sébastien ◽  
Anton Leberre Véronique ◽  
Commes Thérèse ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cell Activation ◽  
Cell Activation ◽  
Predictive Power ◽  
Gene Expression Signature ◽  
Large Cell ◽  
Gene Signature ◽  
Sequencing Data ◽  
Microarray Gene Expression ◽  
Alk Positive

Anaplastic large cell lymphomas associated with ALK translocation have a good outcome after CHOP treatment; however, the 2-year relapse rate remains at 30%. Microarray gene-expression profiling of 48 samples obtained at diagnosis was used to identify 47 genes that were differentially expressed between patients with early relapse/progression and no relapse. In the relapsing group, the most significant overrepresented genes were related to the regulation of the immune response and T-cell activation while those in the non-relapsing group were involved in the extracellular matrix. Fluidigm technology gave concordant results for 29 genes, of which FN1, FAM179A, and SLC40A1 had the strongest predictive power after logistic regression and two classification algorithms. In parallel with 39 samples, we used a Kallisto/Sleuth pipeline to analyze RNA sequencing data and identified 20 genes common to the 28 genes validated by Fluidigm technology—notably, the FAM179A and FN1 genes. Interestingly, FN1 also belongs to the gene signature predicting longer survival in diffuse large B-cell lymphomas treated with CHOP. Thus, our molecular signatures indicate that the FN1 gene, a matrix key regulator, might also be involved in the prognosis and the therapeutic response in anaplastic lymphomas.

scholarly journals Reanalysis And Integration Of Public Microarray Datasets Reveals Novel Host Genes Modulated In Leprosy

2019 ◽  
Author(s):  
Thyago Leal-Calvo ◽  
Milton Ozório Moraes
Keyword(s):  
Gene Expression ◽  
Differential Expression ◽  
Host Gene ◽  
Host Responses ◽  
P Value ◽  
Host Gene Expression ◽  
Novel Genes ◽  
Functional Studies ◽  
Novel Host ◽  
Microarray Datasets

AbstractBackgroundLeprosy is an insidious disease caused primarily by mycobacteria. The difficulties in culturing this slow-growing bacteria together with the chronic progression of the disease have hampered the development of accurate methods for diagnosis. Host gene expression profiling is an important tool to assess overall tissue activity, whether in health or disease conditions. High-throughput gene expression experiments have become popular over the last decade or so, and public databases have been created to easily store and retrieve these data. This has enabled researchers to reuse and reanalyze existing datasets with the aim of generating novel and or more robust information. In this work, after a systematic search, nine microarray datasets evaluating host gene expression in leprosy were reanalyzed and the information was integrated to strengthen evidence of differential expression for several genes.ResultsReanalysis of individual datasets revealed several differentially expressed genes (DEGs). Then, five integration methods were tested, both at the P-value and effect size level. In the end, random effects model (REM) and ratio association (sdef) were selected as the main methods to pinpoint DEGs. Overall, some classic gene/pathways were found corroborating previous findings and validating this approach for analysis. Also, various original DEGs related to poorly understood processes in leprosy were described. Nevertheless, some of the novel genes have already been associated with leprosy pathogenesis by genetic or functional studies, whilst others are, as yet, unrelated or poorly studied in these contexts.ConclusionsThis study reinforces evidences of differential expression of several genes and presents novel genes and pathways associated with leprosy pathogenesis. Altogether, these data are useful in better understanding host responses to the disease and, at the same time, provide a list of potential host biomarkers that could be useful in complementing leprosy diagnosis based on transcriptional levels.

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