Pathway Analysis: State of the Art

García-Campos, Miguel Angel; Espinal-Enríquez, Jesús; Hernández‐Lemus, Enrique

doi:10.3389/fphys.2015.00383

Cited by 217 publications

(164 citation statements)

References 85 publications

(141 reference statements)

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“…Since biological functions depend not only in sets of specific molecules, but also in the interaction patterns among them, the joint consideration of similarities (and dissimilarities also) between gene sets (lists) and interaction sets may broaden our scope as to what are the differences and commonalities of breast cancer subtypes in terms of biological features (García-Campos et al, 2015). …”

Section: Methodsmentioning

confidence: 99%

Transcriptional Network Architecture of Breast Cancer Molecular Subtypes

et al. 2016

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Breast cancer heterogeneity is evident at the clinical, histological and molecular level. High throughput technologies allowed the identification of intrinsic subtypes that capture transcriptional differences among tumors. A remaining question is whether said differences are associated to a particular transcriptional program which involves different connections between the same molecules. In other words, whether particular transcriptional network architectures can be linked to specific phenotypes. In this work we infer, construct and analyze transcriptional networks from whole-genome gene expression microarrays, by using an information theory approach. We use 493 samples of primary breast cancer tissue classified in four molecular subtypes: Luminal A, Luminal B, Basal and HER2-enriched. For comparison, a network for non-tumoral mammary tissue (61 samples) is also inferred and analyzed. Transcriptional networks present particular architectures in each breast cancer subtype as well as in the non-tumor breast tissue. We find substantial differences between the non-tumor network and those networks inferred from cancer samples, in both structure and gene composition. More importantly, we find specific network architectural features associated to each breast cancer subtype. Based on breast cancer networks' centrality, we identify genes previously associated to the disease, either, generally (i.e., CNR2) or to a particular subtype (such as LCK). Similarly, we identify LUZP4, a gene barely explored in breast cancer, playing a role in transcriptional networks with subtype-specific relevance. With this approach we observe architectural differences between cancer and non-cancer at network level, as well as differences between cancer subtype networks which might be associated with breast cancer heterogeneity. The centrality measures of these networks allow us to identify genes with potential biomedical implications to breast cancer.

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Section: Methodsmentioning

confidence: 99%

Transcriptional Network Architecture of Breast Cancer Molecular Subtypes

et al. 2016

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“…Therefore, signaling pathway and interaction network analysis became more attractive to give a new perspective for the classic analysis of gene expression data [14]- [16]. New algorithms have started to highlight important regulator proteins and International Journal of Bioscience, Biochemistry and Bioinformatics cellular processes in pathways; such results had not been found by applying naive differentially expression analysis [9], [17]- [20].…”

Section: Resultsmentioning

confidence: 99%

Estimation of Drug Treatment Effects on the Signaling Pathways

Erce¹,

Işık²

2017

IJBBB

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Developing a computational method to observe cell behaviors can help to understand the expected outcome of a drug treatment even before doing the wet-lab experiments. The main goal of this research is computing the effects of a drug treatment in the signaling level for a better understanding of cellular responses after a drug treatment. The proposed algorithm can work on various biological networks to quantitatively evaluate the effects of a drug treatment in the cell by using gene expression data. The method was applied on the integrated KEGG signaling pathway and the most effected proteins were identified after the application of 14 different drugs on lymphoma cells. The results showed that the most affected proteins are not the direct target proteins of the given drugs. Indeed, the protein activities in the distant parts of signaling pathways are highly changed upon drug treatments. The literature validation showed that some of the proteins, which are commonly effected by the treatment of several drugs, have cancer related cellular activities as well.

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“…The outcomes of the PEA methods can be lead to a definite and limited set of genes and intergenic relations through a pool of biological data, in order to perform later phases of research. These methods can be divided into three major categories under the chronologically sorted list of events [6]. The first generation is over-representation analysis (ORA) which uses just a subset of data (e.g., DEGs); inasmuch as the cut-off threshold is defined by the user, varying results might be obtained via ORA [7,8].…”

Section: Introductionmentioning

confidence: 99%

BNrich: A Bayesian network approach to the pathway enrichment analysis

Maleknia

Sharifi‐Zarchi

et al. 2020

Preprint

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Motivation: One of the most popular techniques in biological studies for analyzing high throughput data is pathway enrichment analysis (PEA). Many researchers apply the existing methods without considering the topology of pathways or at least they have overlooked a significant part of the structure, which may reduce the accuracy and generalizability of the results. Developing a new approach while considering gene expression data and topological features like causal relations regarding edge directions will help the investigators to achieve more accurate results. Results: We proposed a new pathway enrichment analysis based on Bayesian network (BNrich) as an approach in PEA. To this end, the cycles were eliminated in 187 KEGG human signaling pathways concerning intuitive biological rules and the Bayesian network structures were constructed. The constructed networks were simplified by the Least Absolute Shrinkage Selector Operator (LASSO), and their parameters were estimated using the gene expression data. We finally prioritize the impacted pathways by Fisher's Exact Test on significant parameters. Our method integrates both edge and node related parameters to enrich modules in the affected signaling pathway network. In order to evaluate the proposed method, consistency, discrimination, false positive rate and empirical P-value criteria were calculated, and the results are compared to well-known enrichment methods such as signaling pathway impact analysis (SPIA), bi-level meta-analysis (BLMA) and topology-based pathway enrichment analysis (TPEA). Availability: The R package is available on carn. Contact: kkavousi@ut.ac.ir

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Pathway Analysis: State of the Art

Cited by 217 publications

References 85 publications

Transcriptional Network Architecture of Breast Cancer Molecular Subtypes

Transcriptional Network Architecture of Breast Cancer Molecular Subtypes

Estimation of Drug Treatment Effects on the Signaling Pathways

BNrich: A Bayesian network approach to the pathway enrichment analysis

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