scholarly journals OS9.5 Evidence that adult glioblastoma adapts to standard therapy though chromatin remodeling

2019 ◽  
Vol 21 (Supplement_3) ◽  
pp. iii19-iii19
Author(s):  
N Rippaus ◽  
J Manning ◽  
A Droop ◽  
M Al-Jabri ◽  
M Care ◽  
...  
Keyword(s):  
Gene Expression ◽  
Chromatin Remodeling ◽  
Standard Therapy ◽  
Single Cells ◽  
Treatment Resistance ◽  
Enrichment Analysis ◽  
Post Treatment ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Differential Gene

Abstract BACKGROUND Glioblastoma (GBM) tumours recur following standard treatment in almost all cases. We use ‘omics technologies to simultaneously profile pairs of primary and matched recurrent GBM to specifically identify and characterise the cells that resisted treatment, with the aim of determining how to more effectively kill them. MATERIAL AND METHODS We have analysed high coverage RNAseq data from pairs of GBM tumours: primary de novo tumour and matched local recurrence from patients that underwent standard therapy. Our original cohort constituted 23 pairs and our validation cohort was an additional 22 pairs. We also cultured two plates of spheroids directly from a patient’s GBM, treating one with radiation and temozolomide. We monitored growth and captured and sequenced RNA from single cells at two time-points: one week post-treatment when the deviation between untreated and treated spheroid growth curves was most pronounced; and three weeks post-treatment when the growth rate of treated spheroids had recovered. We investigated differential gene expression between primary and recurrent pairs, and single cells pre- and post-treatment, and performed a bespoke per patient gene set enrichment analysis. RESULTS Differential gene expression analysis in 23 tumour pairs indicated a treatment-induced shift in cell states linked to normal neurogenesis and prompted us to develop a novel gene set enrichment analysis approach to identify gene regulatory factors that may orchestrate such a shift. This revealed the significant and universal dysregulation of genes, through therapy, that are targeted by a specific chromatin remodeling machinery. This finding was validated in an independent cohort of 22 further GBM pairs. To understand the therapeutic potential of this finding we must determine whether genes are dysregulated through therapy owing to a) their fixed expression in inherently treatment resistance cells in the primary tumour which get selected during therapy to increase the signal of that profile, or b) changes in expression during the process of cells acquiring treatment resistance. To inspect this, we analysed single cell gene expression data from GBM spheroids pre- and post-treatment. We found that there was significant dysregulation of the genes associated with the chromatin remodeling complex but only at the three-week post-treatment time-point. CONCLUSION Our results indicate that GBM cells are being transcriptionally reprogrammed in response to treatment; the mechanism of which may represent a therapeutic opportunity.

scholarly journals Revealing Biological Pathways Implicated in Lung Cancer from TCGA Gene Expression Data Using Gene Set Enrichment Analysis

2014 ◽  
Vol 13s1 ◽  
pp. CIN.S13882 ◽  
Author(s):  
Binghuang Cai ◽  
Xia Jiang
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Gene Expression Data ◽  
Lung Squamous Cell Carcinoma ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Expression Data ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Pathway Gene

Analyzing biological system abnormalities in cancer patients based on measures of biological entities, such as gene expression levels, is an important and challenging problem. This paper applies existing methods, Gene Set Enrichment Analysis and Signaling Pathway Impact Analysis, to pathway abnormality analysis in lung cancer using microarray gene expression data. Gene expression data from studies of Lung Squamous Cell Carcinoma (LUSC) in The Cancer Genome Atlas project, and pathway gene set data from the Kyoto Encyclopedia of Genes and Genomes were used to analyze the relationship between pathways and phenotypes. Results, in the form of pathway rankings, indicate that some pathways may behave abnormally in LUSC. For example, both the cell cycle and viral carcinogenesis pathways ranked very high in LUSC. Furthermore, some pathways that are known to be associated with cancer, such as the p53 and the PI3K-Akt signal transduction pathways, were found to rank high in LUSC. Other pathways, such as bladder cancer and thyroid cancer pathways, were also ranked high in LUSC.

Differential gene expression in prostate tissue according to vasectomy.

2016 ◽  
Vol 34 (2_suppl) ◽  
pp. 298-298
Author(s):  
Kathryn M Wilson ◽  
Travis Gerke ◽  
Ericka Ebot ◽  
Jennifer A Sinnott ◽  
Jennifer R. Rider ◽  
...  
Keyword(s):  
Gene Expression ◽  
Prostate Cancer ◽  
Cancer Diagnosis ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Prostate Tissue ◽  
Gene Set Enrichment ◽  
Normal Prostate ◽  
Gene Sets ◽  
Normal Prostate Tissue

298 Background: We previously found that vasectomy was associated with an increased risk of prostate cancer, and particularly, risk of lethal prostate cancer in the Health Professionals Follow-up Study (HPFS). However, the possible biological basis for this finding is unclear. In this study, we explored possible biological mechanisms by assessing differences in gene expression in the prostate tissue of men with and without a history of vasectomy prostate cancer diagnosis. Methods: Within the HPFS, vasectomy data and gene expression data (20,254 genes) was available from archival tumor tissue from 263 cases, 124 of whom also had data for adjacent normal tissue. To relate expression of individual genes to vasectomy we used linear regression adjusting for age and year at diagnosis. We ran gene set enrichment analysis to identify pathways of genes associated with vasectomy. Results: Among 263 cases, 67 (25%) reported a vasectomy prior to cancer diagnosis. Mean age at diagnosis was 66 years among men without and 65 years among men with vasectomy. Median time between vasectomy and prostate cancer diagnosis was 25 years. Gene expression in tumor tissue was not associated with vasectomy status. In adjacent normal tissue, three individual genes were associated with vasectomy with Bonferroni-corrected p-values of < 0.10: RAPGEF6, OR4C3, and SLC35F4. Gene set enrichment analysis found five pathways upregulated and seven pathways downregulated in men with vasectomy compared to those without in normal prostate tissue with a FDR < 0.05. Upregulated pathways included several immune-related gene sets and G-protein-coupled receptor gene sets. Conclusions: We identified significant differences in gene expression profiles in normal prostate tissue according to vasectomy status among men treated for prostate cancer. The fact that such differences existed several decades after vasectomy provides support for the idea that vasectomy may play a role in the etiology of prostate cancer.

scholarly journals A Comparative Study of Mouse Hepatic and Intestinal Gene Expression Profiles under PPARαKnockout by Gene Set Enrichment Analysis

PPAR Research ◽  
2011 ◽  
Vol 2011 ◽  
pp. 1-10 ◽  
Author(s):  
Kan He ◽  
Qishan Wang ◽  
Yumei Yang ◽  
Minghui Wang ◽  
Yuchun Pan
Keyword(s):  
Gene Expression ◽  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Enrichment Analysis ◽  
Small Cell ◽  
Gene Set Enrichment Analysis ◽  
Small Cell Lung ◽  
Gene Set Enrichment ◽  
Tissue Specific

Gene expression profiling of PPARαhas been used in several studies, but fewer studies went further to identify the tissue-specific pathways or genes involved in PPARαactivation in genome-wide. Here, we employed and applied gene set enrichment analysis to two microarray datasets both PPARαrelated respectively in mouse liver and intestine. We suggested that the regulatory mechanism of PPARαactivation by WY14643 in mouse small intestine is more complicated than in liver due to more involved pathways. Several pathways were cancer-related such as pancreatic cancer and small cell lung cancer, which indicated that PPARαmay have an important role in prevention of cancer development. 12 PPARαdependent pathways and 4 PPARαindependent pathways were identified highly common in both liver and intestine of mice. Most of them were metabolism related, such as fatty acid metabolism, tryptophan metabolism, pyruvate metabolism with regard to PPARαregulation but gluconeogenesis and propanoate metabolism independent of PPARαregulation. Keratan sulfate biosynthesis, the pathway of regulation of actin cytoskeleton, the pathways associated with prostate cancer and small cell lung cancer were not identified as hepatic PPARαindependent but as WY14643 dependent ones in intestinal study. We also provided some novel hepatic tissue-specific marker genes.

scholarly journals A review of digital cytometry methods: estimating the relative abundance of cell types in a bulk of cells

2020 ◽  
Author(s):  
Trang Le ◽  
Rachel A Aronow ◽  
Arkadz Kirshtein ◽  
Leili Shahriyari
Keyword(s):  
Gene Expression ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Single Cells ◽  
Enrichment Analysis ◽  
Cell Types ◽  
Gene Set Enrichment Analysis ◽  
Single Sample ◽  
Batch Correction ◽  
Signature Matrix

Abstract Due to the high cost of flow and mass cytometry, there has been a recent surge in the development of computational methods for estimating the relative distributions of cell types from the gene expression profile of a bulk of cells. Here, we review the five common ‘digital cytometry’ methods: deconvolution of RNA-Seq, cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT), CIBERSORTx, single sample gene set enrichment analysis and single-sample scoring of molecular phenotypes deconvolution method. The results show that CIBERSORTx B-mode, which uses batch correction to adjust the gene expression profile of the bulk of cells (‘mixture data’) to eliminate possible cross-platform variations between the mixture data and the gene expression data of single cells (‘signature matrix’), outperforms other methods, especially when signature matrix and mixture data come from different platforms. However, in our tests, CIBERSORTx S-mode, which uses batch correction for adjusting the signature matrix instead of mixture data, did not perform better than the original CIBERSORT method, which does not use any batch correction method. This result suggests the need for further investigations into how to utilize batch correction in deconvolution methods.

Gene expression combined with gene set enrichment analysis to identify markers of vinorelbine efficacy in breast cancer patients.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. e21099-e21099
Author(s):  
Robert Audet ◽  
Changyu Shen ◽  
Scooter Willis ◽  
Renata Duchnowska ◽  
Krzysztof Adamowicz ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Transcription Factor ◽  
Chromosomal Location ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Molecular Function ◽  
Time To Progression ◽  
Gene Set Enrichment ◽  
Histone Binding

e21099 Background: Vinorelbine (V) induces mitotic arrest and apoptosis but there are limited data on its effect on gene expression in breast cancer clinical setting. Methods: 43 adult female patients with pathologically confirmed breast cancer and locally advanced or metastatic disease were treated with V 25 mg/m2 days 1, 8, 15 of a 28-day cycle. Gene expression was assessed in archival FFPE tissue using the microarray-based DASL assay (cDNA-mediated Annealing, Selection extension and Ligation) and correlated with time-to-progression (TTP). Using a Gene Set Enrichment Analysis (GSEA), groups of genes that share a common molecular function, chromosomal location, or regulation were identified in patients classified as having either a short (S) (n=25) or a long (L) (n=18) time to progression (TTP) divided by the median (72 days). The GSEA software ( http://www.broadinstitute.org/gsea/index.jsp ) was used for the analysis. Results: GSEA focusing on genes grouped according to similar a) molecular function: 16 out of a set of 43 genes involved in histone binding were enriched in group S (p = 0.002), consistent with higher expression in group S of HIST3H2BB and HIST1H3I as well as a nuclear transcription factor promoting their expression. b) transcription factors: 14 out of 47 genes were enriched in group S (p = 0.004) and corresponds to genes with promoter regions that match c-fos serum response element-binding transcription factor that modulates, for example, ABCC1 and ABCB1 (P-gp/MDR1) solute carriers. c) chromosomal location: in group S, genes were enriched on chromosome 11q21 (20 out of 45 genes p = 0.004) and on chromosome 12p12 (14 out of 22 genes p = 0.002). Conclusions: a) the up-regulation of histone binding genes is consonant with recent discovery of high affinity V binding to histones b) the role of P-gp/MDR1 in V transport is well known c) our observations on chromosome 11q21 and12p12 are novel. DASL expression combined with GSEA highlights gene sets that correlate with clinical outcome and may lead to predictive markers of V efficacy. Further confirmatory analysis is needed due to the limitation of small sample size and multiple comparisons.

scholarly journals Tracking Difference in Gene Expression in a Time-Course Experiment Using Gene Set Enrichment Analysis

PLoS ONE ◽  
2014 ◽  
Vol 9 (9) ◽  
pp. e107629 ◽  
Author(s):  
Pui Shan Wong ◽  
Michihiro Tanaka ◽  
Yoshihiko Sunaga ◽  
Masayoshi Tanaka ◽  
Takeaki Taniguchi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Gene Set Enrichment ◽  
Gene Set ◽  
Time Course Experiment

scholarly journals Construction and Validation of a Reliable Six-Gene Prognostic Signature Based on the TP53 Alteration for Hepatocellular Carcinoma

2021 ◽  
Vol 11 ◽  
Author(s):  
Junyu Huo ◽  
Liqun Wu ◽  
Yunjin Zang
Keyword(s):  
Gene Expression ◽  
Hepatocellular Carcinoma ◽  
Regression Analysis ◽  
Cox Regression ◽  
Enrichment Analysis ◽  
Gene Set Enrichment Analysis ◽  
Prognostic Signature ◽  
Gene Set Enrichment ◽  
Cox Regression Analysis ◽  
Gene Set

BackgroundThe high mutation rate of TP53 in hepatocellular carcinoma (HCC) makes it an attractive potential therapeutic target. However, the mechanism by which TP53 mutation affects the prognosis of HCC is not fully understood.Material and ApproachThis study downloaded a gene expression profile and clinical-related information from The Cancer Genome Atlas (TCGA) database and the international genome consortium (ICGC) database. We used Gene Set Enrichment Analysis (GSEA) to determine the difference in gene expression patterns between HCC samples with wild-type TP53 (n=258) and mutant TP53 (n=116) in the TCGA cohort. We screened prognosis-related genes by univariate Cox regression analysis and Kaplan–Meier (KM) survival analysis. We constructed a six-gene prognostic signature in the TCGA training group (n=184) by Lasso and multivariate Cox regression analysis. To assess the predictive capability and applicability of the signature in HCC, we conducted internal validation, external validation, integrated analysis and subgroup analysis.ResultsA prognostic signature consisting of six genes (EIF2S1, SEC61A1, CDC42EP2, SRM, GRM8, and TBCD) showed good performance in predicting the prognosis of HCC. The area under the curve (AUC) values of the ROC curve of 1-, 2-, and 3-year survival of the model were all greater than 0.7 in each independent cohort (internal testing cohort, n = 181; TCGA cohort, n = 365; ICGC cohort, n = 229; whole cohort, n = 594; subgroup, n = 9). Importantly, by gene set variation analysis (GSVA) and the single sample gene set enrichment analysis (ssGSEA) method, we found three possible causes that may lead to poor prognosis of HCC: high proliferative activity, low metabolic activity and immunosuppression.ConclusionOur study provides a reliable method for the prognostic risk assessment of HCC and has great potential for clinical transformation.

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