Pathway-Based Analysis of the Hidden Genetic Heterogeneities in Cancers

Zhao, Xiaolei; Zhong, Shouqiang; Zuo, Xiaoyu; Lin, Mei-Hua; Qin, Jiheng; Luan, Yizhao; Zhang, Naizun; Liang, Yan; Rao, Shaoqi

doi:10.1016/j.gpb.2013.12.001

Cited by 6 publications

(5 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The genes of the ANDnet showed significant enrichment in nine KEGG pathways. Of these, three pathways are cancer‐related: ABC‐transporters (KEGG: hsa02010; Fletcher, Haber, Henderson, & Norris, ), pathways in cancer (KEGG:hsa05200), hematopoietic cell lineage (KEGG:hsa 04640; Zhao, Zhong, Zuo, Lin, & Qin, ).…”

Section: Discussionmentioning

confidence: 99%

Integration of gene expression and methylation to unravel biological networks in glioblastoma patients

Gadaleta

Bessonov

Steen

2016

Genetic Epidemiology

View full text Add to dashboard Cite

The vast amount of heterogeneous omics data, encompassing a broad range of biomolecular information, requires novel methods of analysis, including those that integrate the available levels of information. In this work we describe Regression2Net, a computational approach that is able to integrate gene expression and genomic or methylome data in two steps. First, penalized regressions are used to build ExpressionExpression (EEnet) and Expression-Genome or -Methylome (EMnet) networks. Second, network theory is used to highlight important communities of genes. When applying our approach Regression2Net to gene expression and methylation profiles for individuals with glioblastoma multiforme, we identified respectively 284 and 447 potentially interesting genes in relation to glioblastoma pathology. These genes showed at least one connection in the integrated networks ANDnet and XORnet derived from aforementioned EEnet and EMnet networks. Whereas the edges in ANDnet occur in both EEnet and EMnet, the edges in XORnet occur in EMnet but not in EEnet. In-depth biological analysis of connected genes in ANDnet and XORnet revealed genes that are related to energy metabolism, cell cycle control (AATF), immune system response and several cancer types. Importantly, we observed significant over-representation of cancer related pathways including glioma, especially in the XORnet network, suggesting a non-ignorable role of methylation in glioblastoma multiforma. In the ANDnet, we furthermore identified potential glioma suppressor genes ACCN3 and ACCN4 linked to the NBPF1 neuroblastoma breakpoint family, as well as numerous ABC transporter genes (ABCA1, ABCB1) suggesting drug resistance of glioblastoma tumors.

show abstract

Section: Discussionmentioning

confidence: 99%

Integration of gene expression and methylation to unravel biological networks in glioblastoma patients

Gadaleta

Bessonov

Steen

2016

Genetic Epidemiology

View full text Add to dashboard Cite

show abstract

“…The LFA-1 absence prevents the leukocyte emigration to inflammation sites, resulting in a severe immunodeficiency (Drillenburg & Pals 2000;Pals et al 2007;Page et al 2012). On the other hand, previous studies have proved that LFA-1 expression provokes cell aggregation limiting its proliferation and its expression is induced by CD21 (complement receptor type 2, CR2, C3d receptor, and mature B-cell marker) (Otsuka et al 2004a;Otsuka et al 2004b;Tanimoto et al 2009;Zhao et al 2014), whose expression is lost in A20 tumour cells. This suggests that A20 B-cell lymphoma line has jointly selected the loss of CD18 and CD21 expression as tumour proliferative and development advantages, reducing the possible cell interaction and aggregation.…”

Section: Molecular and In Vitro Consequences Of The Loss Of Hvem In Amentioning

confidence: 98%

The Role of Hvem and its Interaction with Btla and Cd160 in b-Cell Lymphoma Progression

Juan

2019

Preprint

View full text Add to dashboard Cite

Despite the fact that the cell surface receptor HVEM (TNFRSF14) appears to be implicated in the development and progression of B-cell lymphomas, its specific role in these tumours is still unclear. On the one hand, HVEM overexpression is related to worse prognosis in some types of B-cell lymphoma and other solid tumours. On the other hand, most mutations of HVEM in B-cell lymphomas are thought to promote tumour growth through the loss of function. Here, we used a CRISPR-Cas9 system to study the effect of HVEM loss on gene expression in a murine model of A20 B-cell lymphoma (belonging to the diffuse large Bcell lymphoma group). We show that loss of HVEM does not affect the doubling rate of A20 tumour cells in culture, but leads to a decrease in BTLA expression. HVEM-deficient A20 cells do not present a different pattern of metastatic dissemination to lymphoid organs compared with unmodified A20 cells. However, we observed a significant expansion of endogenous B-cells as a result of A20 tumour implantation in the thymus. Although we found no differences in the dissemination or progression of HVEM-deficient A20 cells, our results reveal that loss of HVEM alters the leukocyte recruitment capacity of A20 cells in hepatic tumour nodules at the intermediate stage of tumour development, which may be of relevance as a mechanism of immune evasion.

show abstract

“…Pathway-based approaches such as GSVA [ 22 ] and DiffRank [ 63 ] are attractive to the subtype discovery task since they can transform gene-expression profiles to pathway-based profiles without any prior subgroup definition or additional information other than gene sets or functional annotations. Such profiles can then be used to compile robust patient stratification results [ 40 , 67 ]. Another interesting approach for pathway-based clustering analysis is integrating (transcript-)omics data with molecular networks to characterize biological pathway activity.…”

Section: Introductionmentioning

confidence: 99%

A systematic comparison of data- and knowledge-driven approaches to disease subtype discovery

Rintala

Federico

Latonen

et al. 2021

Briefings in Bioinformatics

View full text Add to dashboard Cite

Typical clustering analysis for large-scale genomics data combines two unsupervised learning techniques: dimensionality reduction and clustering (DR-CL) methods. It has been demonstrated that transforming gene expression to pathway-level information can improve the robustness and interpretability of disease grouping results. This approach, referred to as biological knowledge-driven clustering (BK-CL) approach, is often neglected, due to a lack of tools enabling systematic comparisons with more established DR-based methods. Moreover, classic clustering metrics based on group separability tend to favor the DR-CL paradigm, which may increase the risk of identifying less actionable disease subtypes that have ambiguous biological and clinical explanations. Hence, there is a need for developing metrics that assess biological and clinical relevance. To facilitate the systematic analysis of BK-CL methods, we propose a computational protocol for quantitative analysis of clustering results derived from both DR-CL and BK-CL methods. Moreover, we propose a new BK-CL method that combines prior knowledge of disease relevant genes, network diffusion algorithms and gene set enrichment analysis to generate robust pathway-level information. Benchmarking studies were conducted to compare the grouping results from different DR-CL and BK-CL approaches with respect to standard clustering evaluation metrics, concordance with known subtypes, association with clinical outcomes and disease modules in co-expression networks of genes. No single approach dominated every metric, showing the importance multi-objective evaluation in clustering analysis. However, we demonstrated that, on gene expression data sets derived from TCGA samples, the BK-CL approach can find groupings that provide significant prognostic value in both breast and prostate cancers.

show abstract

Pathway-Based Analysis of the Hidden Genetic Heterogeneities in Cancers

Cited by 6 publications

References 34 publications

Integration of gene expression and methylation to unravel biological networks in glioblastoma patients

Integration of gene expression and methylation to unravel biological networks in glioblastoma patients

The Role of Hvem and its Interaction with Btla and Cd160 in b-Cell Lymphoma Progression

A systematic comparison of data- and knowledge-driven approaches to disease subtype discovery

Contact Info

Product

Resources

About