scholarly journals A statistical method for identifying differential gene-gene co-expression patterns

2004 ◽  
Vol 20 (17) ◽  
pp. 3146-3155 ◽  
Author(s):  
Y. Lai ◽  
B. Wu ◽  
L. Chen ◽  
H. Zhao
2022 ◽  
Vol 14 (1) ◽  
Author(s):  
Thinh T. Nguyen ◽  
Hyun-Sung Lee ◽  
Bryan M. Burt ◽  
Jia Wu ◽  
Jianjun Zhang ◽  
...  

Abstract Background Lung adenocarcinoma, the most common type of lung cancer, has a high level of morphologic heterogeneity and is composed of tumor cells of multiple histological subtypes. It has been reported that immune cell infiltration significantly impacts clinical outcomes of patients with lung adenocarcinoma. However, it is unclear whether histologic subtyping can reflect the tumor immune microenvironment, and whether histologic subtyping can be applied for therapeutic stratification of the current standard of care. Methods We inferred immune cell infiltration levels using a histological subtype-specific gene expression dataset. From differential gene expression analysis between different histological subtypes, we developed two gene signatures to computationally determine the relative abundance of lepidic and solid components (denoted as the L-score and S-score, respectively) in lung adenocarcinoma samples. These signatures enabled us to investigate the relationship between histological composition and clinical outcomes in lung adenocarcinoma using previously published datasets. Results We found dramatic immunological differences among histological subtypes. Differential gene expression analysis showed that the lepidic and solid subtypes could be differentiated based on their gene expression patterns while the other subtypes shared similar gene expression patterns. Our results indicated that higher L-scores were associated with prolonged survival, and higher S-scores were associated with shortened survival. L-scores and S-scores were also correlated with global genomic features such as tumor mutation burdens and driver genomic events. Interestingly, we observed significantly decreased L-scores and increased S-scores in lung adenocarcinoma samples with EGFR gene amplification but not in samples with EGFR gene mutations. In lung cancer cell lines, we observed significant correlations between L-scores and cell sensitivity to a number of targeted drugs including EGFR inhibitors. Moreover, lung cancer patients with higher L-scores were more likely to benefit from immune checkpoint blockade therapy. Conclusions Our findings provided further insights into evaluating histology composition in lung adenocarcinoma. The established signatures reflected that lepidic and solid subtypes in lung adenocarcinoma would be associated with prognosis, genomic features, and responses to targeted therapy and immunotherapy. The signatures therefore suggested potential clinical translation in predicting patient survival and treatment responses. In addition, our framework can be applied to other types of cancer with heterogeneous histological subtypes.


2019 ◽  
Vol 20 (23) ◽  
pp. 6098 ◽  
Author(s):  
Amarinder Singh Thind ◽  
Kumar Parijat Tripathi ◽  
Mario Rosario Guarracino

The comparison of high throughput gene expression datasets obtained from different experimental conditions is a challenging task. It provides an opportunity to explore the cellular response to various biological events such as disease, environmental conditions, and drugs. There is a need for tools that allow the integration and analysis of such data. We developed the “RankerGUI pipeline”, a user-friendly web application for the biological community. It allows users to use various rank based statistical approaches for the comparison of full differential gene expression profiles between the same or different biological states obtained from different sources. The pipeline modules are an integration of various open-source packages, a few of which are modified for extended functionality. The main modules include rank rank hypergeometric overlap, enriched rank rank hypergeometric overlap and distance calculations. Additionally, preprocessing steps such as merging differential expression profiles of multiple independent studies can be added before running the main modules. Output plots show the strength, pattern, and trends among complete differential expression profiles. In this paper, we describe the various modules and functionalities of the developed pipeline. We also present a case study that demonstrates how the pipeline can be used for the comparison of differential expression profiles obtained from multiple platforms’ data of the Gene Expression Omnibus. Using these comparisons, we investigate gene expression patterns in kidney and lung cancers.


2020 ◽  
Vol 12 (11) ◽  
pp. 1994-2001 ◽  
Author(s):  
Michele Wyler ◽  
Christoph Stritt ◽  
Jean-Claude Walser ◽  
Célia Baroux ◽  
Anne C Roulin

Abstract Transposable elements (TEs) constitute a large fraction of plant genomes and are mostly present in a transcriptionally silent state through repressive epigenetic modifications, such as DNA methylation. TE silencing is believed to influence the regulation of adjacent genes, possibly as DNA methylation spreads away from the TE. Whether this is a general principle or a context-dependent phenomenon is still under debate, pressing for studying the relationship between TEs, DNA methylation, and nearby gene expression in additional plant species. Here, we used the grass Brachypodium distachyon as a model and produced DNA methylation and transcriptome profiles for 11 natural accessions. In contrast to what is observed in Arabidopsis thaliana, we found that TEs have a limited impact on methylation spreading and that only few TE families are associated with a low expression of their adjacent genes. Interestingly, we found that a subset of TE insertion polymorphisms is associated with differential gene expression across accessions. Thus, although not having a global impact on gene expression, distinct TE insertions may contribute to specific gene expression patterns in B. distachyon.


2002 ◽  
Vol 278 (9) ◽  
pp. 7540-7552 ◽  
Author(s):  
Swapnil R. Chhabra ◽  
Keith R. Shockley ◽  
Shannon B. Conners ◽  
Kevin L. Scott ◽  
Russell D. Wolfinger ◽  
...  

2006 ◽  
Vol 72 (10) ◽  
pp. 6607-6614 ◽  
Author(s):  
J. Jacob Parnell ◽  
Joonhong Park ◽  
Vincent Denef ◽  
Tamara Tsoi ◽  
Syed Hashsham ◽  
...  

ABSTRACT The biodegradation of polychlorinated biphenyls (PCBs) relies on the ability of aerobic microorganisms such as Burkholderia xenovorans sp. LB400 to tolerate two potential modes of toxicity presented by PCB degradation: passive toxicity, as hydrophobic PCBs potentially disrupt membrane and protein function, and degradation-dependent toxicity from intermediates of incomplete degradation. We monitored the physiological characteristics and genome-wide expression patterns of LB400 in response to the presence of Aroclor 1242 (500 ppm) under low expression of the structural biphenyl pathway (succinate and benzoate growth) and under induction by biphenyl. We found no inhibition of growth or change in fatty acid profile due to PCBs under nondegrading conditions. Moreover, we observed no differential gene expression due to PCBs themselves. However, PCBs did have a slight effect on the biosurface area of LB400 cells and caused slight membrane separation. Upon activation of the biphenyl pathway, we found growth inhibition from PCBs beginning after exponential-phase growth suggestive of the accumulation of toxic compounds. Genome-wide expression profiling revealed 47 differentially expressed genes (0.56% of all genes) under these conditions. The biphenyl and catechol pathways were induced as expected, but the quinoprotein methanol metabolic pathway and a putative chloroacetaldehyde dehydrogenase were also highly expressed. As the latter protein is essential to conversion of toxic metabolites in dichloroethane degradation, it may play a similar role in the degradation of chlorinated aliphatic compounds resulting from PCB degradation.


2016 ◽  
Vol 21 (2) ◽  
pp. 81-88 ◽  
Author(s):  
Karla Padilla ◽  
David Gonzalez-Mendoza ◽  
Laura C. Berumen ◽  
Jesica E. Escobar ◽  
Ricardo Miledi ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document