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QNetDiff: a quantitative measurement of network rewiring.

ABSTRACT: Bacteria in the human body, particularly in the large intestine, are known to be associated with various diseases. To identify disease-associated bacteria (markers), a typical method is to statistically compare the relative abundance of bacteria between healthy subjects and diseased patients. However, since bacteria do not necessarily cause diseases in isolation, it is also important to focus on the interactions and relationships among bacteria when examining their association with diseases. In fact, although there are common approaches to represent and analyze bacterial interaction relationships as networks, there are limited methods to find bacteria associated with diseases through network-driven analysis. In this paper, we focus on rewiring of the bacterial network and propose a new method for quantifying the rewiring. We then apply the proposed method to a group of colorectal cancer patients. We show that it can identify and detect bacteria that cannot be detected by conventional methods such as abundance comparison. Furthermore, the proposed method is implemented as a general-purpose tool and made available to the general public.

SUBMITTER: Nose S

PROVIDER: S-EPMC10946107 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature

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QNetDiff: a quantitative measurement of network rewiring.

Nose Shota S Shiroma Hirotsugu H Yamada Takuji T Uno Yushi Y

BMC bioinformatics 20240318 1

Bacteria in the human body, particularly in the large intestine, are known to be associated with various diseases. To identify disease-associated bacteria (markers), a typical method is to statistically compare the relative abundance of bacteria between healthy subjects and diseased patients. However, since bacteria do not necessarily cause diseases in isolation, it is also important to focus on the interactions and relationships among bacteria when examining their association with diseases. In ...[more]

PMID: 38500025

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Project description:BackgroundMining gene regulatory network (GRN) is an important avenue for addressing cancer mechanism. Mutations in cancer genome perturb GRN and cause a rewiring in an orchestrated network. Hence, the exploration of gene regulatory network rewiring is significant to discover potential biomarkers and indicators for discriminating cancer phenotypes.ResultsHere, we propose a new bioinformatics method of identifying biomarkers based on network rewiring in different states. It firstly reconstructs GRN in different phenotypic conditions from gene expression data with a priori background network. We employ the algorithm based on path consistency algorithm and conditional mutual information to delete false-positive regulatory interactions between independent nodes/genes or not closely related gene pairs. And then a differential gene regulatory network (D-GRN) is constructed from the rewiring parts in the two phenotype-specific GRNs. Community detection technique is then applied for D-GRN to detect functional modules. Finally, we apply logistic regression classifier with recursive feature elimination to select biomarker genes in each module individually. The extracted feature genes result in a gene set of biomarkers with impressing ability to distinguish normal samples from controls. We verify the identified biomarkers in external independent validation datasets. For a proof-of-concept study, we apply the framework to identify diagnostic biomarkers of breast cancer. The identified biomarkers obtain a maximum AUC of 0.985 in the internal sample classification experiments. And these biomarkers achieve a maximum AUC of 0.989 in the external validations.ConclusionIn conclusion, network rewiring reveals significant differences between different phenotypes, which indicating cancer dysfunctional mechanisms. With the development of sequencing technology, the amount and quality of gene expression data become available. Condition-specific gene regulatory networks that are close to the real regulations in different states will be established. Revealing the network rewiring will greatly benefit the discovery of biomarkers or signatures for phenotypes. D-GRN is a general method to meet this demand of deciphering the high-throughput data for biomarker discovery. It is also easy to be extended for identifying biomarkers of other complex diseases beyond breast cancer.

Dataset Information

QNetDiff: a quantitative measurement of network rewiring.

Publications

QNetDiff: a quantitative measurement of network rewiring.

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