scholarly journals Can we assume the gene expression profile as a proxy for signaling network activity?

2019 ◽  
Author(s):  
Mehran Piran ◽  
Reza Karbalaei ◽  
Mehrdad Piran ◽  
Jehad Aldahdooh ◽  
Mehdi Mirzaie ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Enrichment Analysis ◽  
Transcript Level ◽  
Signaling Network ◽  
Network Activity ◽  
Expression Data ◽  
Disease Etiology ◽  
Gene Pairs

AbstractStudying relationships among gene-products by gene expression profile analysis is a common approach in systems biology. Many studies have generalized the outcomes to the different levels of central dogma information flow and assumed correlation of transcript and protein expression levels. All these efforts partook in the current understanding of signaling network models and expanded the signaling databases. In fact, due to the unavailability or high-cost of the experiments, most of the studies do not usually look for direct interactions, and some parts of these networks are contradictory. Besides, it is now a standard step to accomplish enrichment analysis on biological annotations, to make claims about the potentially implicated biological pathways in any perturbation. Explicitly, upon identifying differentially expressed genes, they are spontaneously presumed the corresponding dysregulated pathways. Then, molecular mechanistic insights are proposed for disease etiology and drug discovery based on statistically enriched biological processes. In this study, using four common and comprehensive databases, we extracted all relevant gene expression data and all relationships among directly linked gene pairs. We aimed to evaluate the ratio of coherency or sign consistency between the expression level and the causal relationships among the gene pairs. We illustrated that the signaling network was not more consistent or coherent with the recorded expression profile compared to the random relationships. Finally, we provided the pieces of evidence and concluded that gene-product expression data, especially at the transcript level, are not reliable or at least insufficient to infer causal biological relationships among genes and in turn, describe cellular behavior.

scholarly journals Can We Assume the Gene Expression Profile as a Proxy for Signaling Network Activity?

Biomolecules ◽  
2020 ◽  
Vol 10 (6) ◽  
pp. 850 ◽  
Author(s):  
Mehran Piran ◽  
Reza Karbalaei ◽  
Mehrdad Piran ◽  
Jehad Aldahdooh ◽  
Mehdi Mirzaie ◽  
...  
Keyword(s):  
Gene Expression ◽  
Signaling Pathways ◽  
Differentially Expressed Genes ◽  
Expression Profile ◽  
Signaling Network ◽  
Differentially Expressed ◽  
Network Activity ◽  
Gene Products ◽  
Expression Data ◽  
Gene Pairs

Studying relationships among gene products by expression profile analysis is a common approach in systems biology. Many studies have generalized the outcomes to the different levels of central dogma information flow and assumed a correlation of transcript and protein expression levels. However, the relation between the various types of interaction (i.e., activation and inhibition) of gene products to their expression profiles has not been widely studied. In fact, looking for any perturbation according to differentially expressed genes is the common approach, while analyzing the effects of altered expression on the activity of signaling pathways is often ignored. In this study, we examine whether significant changes in gene expression necessarily lead to dysregulated signaling pathways. Using four commonly used and comprehensive databases, we extracted all relevant gene expression data and all relationships among directly linked gene pairs. We aimed to evaluate the ratio of coherency or sign consistency between the expression level as well as the causal relationships among the gene pairs. Through a comparison with random unconnected gene pairs, we illustrate that the signaling network is incoherent, and inconsistent with the recorded expression profile. Finally, we demonstrate that, to infer perturbed signaling pathways, we need to consider the type of relationships in addition to gene-product expression data, especially at the transcript level. We assert that identifying enriched biological processes via differentially expressed genes is limited when attempting to infer dysregulated pathways.

Confirmation of the distinct molecular phenotype of inflammatory breast cancer compared to non-inflammatory breast cancer using Affymetrix-based genome-wide gene expression analysis

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 21055-21055
Author(s):  
S. J. Van Laere ◽  
I. Van der Auwera ◽  
G. G. Van den Eynden ◽  
X. Trinh ◽  
P. Van Hummelen ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Gene Expression ◽  
Gene Expression Profile ◽  
Expression Profile ◽  
Inflammatory Breast Cancer ◽  
Principle Component Analysis ◽  
Expression Data ◽  
Data Set ◽  
Principle Component ◽  
Intrinsic Gene

21055 Background: We have shown with cDNA microarrays that inflammatory breast cancer (IBC) and non-IBC are distinct biological entities. The purpose of this study was to confirm our previous results using Affymetrix chips. Methods: RNA was extracted from 19 IBC samples and 42 non-stage matched non-IBC samples. RNA was hybridized onto Affymetrix HG U133 Plus 2.0 chips. Gene expression data were normalized using GCRMA and genes with a gene expression of at least 250 in 50% of the cases were filtered in. Hierarchical clustering and principle component analysis was executed. Identification of the different cell-of-origin subtypes in our expression data set was done using the intrinsic gene list. A NFkB signature, a MAPK signature and our own IBC signature were tested by clustering analysis. Results: Clustering using 11341 genes resulted in the identification of two clusters: one containing 14/19 IBC samples and a second containing 32/42 non-IBC (Pearson χ2; p<0.0001). Principle component analysis separated IBC from non-IBC samples along the first principle component. Interestingly, IBC samples more closely resemble T1 - T2 tumours than T3 - T4 tumours. Application of the intrinsic gene set to our IBC/non-IBC data set resulted in the classification of 14/19 IBC samples as basal-like or ErbB2-overexpressing tumours compared to only 4/42 non-IBC tumours (Pearson χ2; p<0.0001). Our own IBC signature was confronted with the new data set and performed well in separating IBC specimens form non-IBC specimens. Clustering identified three clusters from which one cluster contained 18 samples, including 12 IBC specimens (p<0.0001). Using the NFkB and MAPK signatures, similar results were obtained. Conclusions: These results confirm our findings that IBC is a distinct biologic phenotype, characterized by activation of NFkB, possibly through activation of MAPK's. IBC tumours more often demonstrate characteristics from basal-like and ErbB2-overexpressing breast tumours. The fact that IBC tumours are rapidly developing tumours instead of longstanding tumourigenic processes might explain the close resemblance of the IBC gene expression profile to the gene expression profile of T1 and T2 tumours. No significant financial relationships to disclose.

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.

scholarly journals Analyzing Gene Expression Profile in K562 Cells Exposed to Sodium Valproate Using Microarray Combined with the Connectivity Map Database

2012 ◽  
Vol 2012 ◽  
pp. 1-8
Author(s):  
Xiang-Zhong Zhang ◽  
Ai-Hua Yin ◽  
Dong-Jun Lin ◽  
Xiao-Yu Zhu ◽  
Qian Ding ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profile ◽  
Differentially Expressed Genes ◽  
Sodium Valproate ◽  
Expression Profile ◽  
Therapeutic Potential ◽  
Enrichment Analysis ◽  
K562 Cells ◽  
Differentially Expressed ◽  
Connectivity Map

To explore the mechanism underlying antileukaemia effect of sodium valproate, the growth and survival of the K562 cell line were investigated. Global profiles of gene expression in K562 cells exposed to sodium valproate were assessed and validated. The differentially expressed genes identified were further used to query the connectivity map database to retrieve a ranked list of compounds that act on the same intracellular targets as sodium valproate. A significant increase in cell apoptosis and a change in gene expression profile were observed in valproate-exposed K562 cells. The significant enrichment analysis of gene ontology terms for the differentially expressed genes showed that these genes were involved in many important biological processes. Eight differentially expressed genes involved in apoptosis were verified by quantitative real-time PCR. The connectivity map analysis showed gene expression profile in K562 cells exposed to sodium valproate was most similar to that of HDACi and PI3K inhibitors, suggesting that sodium valproate might exert antileukaemic action by inhibiting HDAC as well as inhibiting PI3K pathway. In conclusion, our data might provide clues to elucidate the molecular and therapeutic potential of VPA in leukaemia treatment, and the connectivity map is a useful tool for exploring the molecular mechanism of drug action.

Gene Expression Profile in Persons Exposed to the WTC With and Without PTSD

2009 ◽  
Author(s):  
Rachel Yehuda ◽  
Julia Golier ◽  
Sandro Galea ◽  
Marcus Ising ◽  
Florian Holsborer ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gene Expression Profile ◽  
Expression Profile
Sign in / Sign up

Export Citation Format

Share Document