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Integrating Protein–Protein Interaction Networks and Somatic Mutation Data to Detect Driver Modules in Pan-Cancer

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Abstract

With the constant update of large-scale sequencing data and the continuous improvement of cancer genomics data, such as International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA), it gains increasing importance to detect the functional high-frequency mutation gene set in cells that causes cancer in the field of medicine. In this study, we propose a new recognition method of driver modules, named ECSWalk to solve the issue of mutated gene heterogeneity and improve the accuracy of driver modules detection, based on human protein–protein interaction networks and pan-cancer somatic mutation data. This study first utilizes high mutual exclusivity and high coverage between mutation genes and topological structure similarity of the nodes in complex networks to calculate interaction weights between genes. Second, the method of random walk with restart is utilized to construct a weighted directed network, and the strong connectivity principle of the directed graph is utilized to create the initial candidate modules with a certain number of genes. Finally, the large modules in the candidate modules are split using induced subgraph method, and the small modules are expanded using a greedy strategy to obtain the optimal driver modules. This method is applied to TCGA pan-cancer data and the experimental results show that ECSWalk can detect driver modules more effectively and accurately, and can identify new candidate gene sets with higher biological relevance and statistical significance than MEXCOWalk and HotNet2. Thus, ECSWalk is of theoretical implication and practical value for cancer diagnosis, treatment and drug targets.

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Data Availability

The data and codes we used can be download from https://github.com/HaoWuLab-Bioinformatics/wu-group.

Abbreviations

ICGC:

International cancer genome consortium.

TCGA:

The cancer genome atlas.

ECSWalk:

A carcinogenic driver module detection method based on a network model.

MEXCOWalk:

Mutual exclusion and coverage based random walk to detect cancer modules.

HotNet2:

An algorithm for finding significantly altered subnetworks in a large gene interaction network.

KL:

Kullback–Leibler, A method of describing the difference between two probability distributions.

JS:

Jensen–Shannon, an improved method based on KL divergence.

PPI:

Protein–protein interaction.

HINT+HI2012:

A combination of high-quality protein–protein interactions from HINT and the recent HI2012 set of protein–protein interactions.

DAVID:

The database for annotation, visualization and integrated discovery.

SCC:

Strongly connected component of the directed graph.

TP:

True positive

FP:

False positive

TN:

True negative

FN:

False negative

GBM:

Glioblastoma multiforme

BLCA:

Bladder urothelial carcinoma

UCEC:

Uterine corpus endometrial carcinoma

NSCLC:

Non-small-cell lung cancer

PAAD:

Pancreatic adenocarcinoma

CML:

Chronic myelocytic leukemia

LAML:

Acute myeloid leukemia

COAD:

Colon adenocarcinoma

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Acknowledgements

We thank Jihua Dong for her careful proofreading, and also thank Bing Zhou, Zhaoheng Ai, Mengdi Liu, Pengyu Zhang and Haoru Zhou for their helpful advice and discussions.

Funding

The work was supported by the National Natural Science Foundation of China (Grant No.61972322), the Natural Science Foundation of Shaanxi Province (Grant No. 2021JM-110), the Humanities and Social Science Fund of Ministry of Education of China (Grant No.18YJCZH190) and the Fundamental Research Funds of Shandong University. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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Author notes

  1. Hao Wu and Zhongli Chen are co-first author of the paper.

Authors and Affiliations

  1. College of Information Engineering, Northwest A&F University, Yangling, 712100, Shaanxi, China

    Hao Wu, Zhongli Chen, Yingfu Wu, Hongming Zhang & Quanzhong Liu

  2. School of Software, Shandong University, Jinan, 250100, Shandong, China

    Hao Wu

  3. Tibet Center for Disease Control and Prevention, Lhasa, 850000, China

    Zhongli Chen

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  1. Hao Wu
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Contributions

Conceive and design the experiments: HW ZC. Perform the experiments: HW ZC. Analyze the data: ZC YW. Contribute reagents/materials/analysis tools: ZC YW QL. Write the paper: HW ZC. Consult on the final version of the paper and edit the paper: HW ZC HZ. The authors read and approve the final version of the manuscript.

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Correspondence to Hao Wu or Hongming Zhang.

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Wu, H., Chen, Z., Wu, Y. et al. Integrating Protein–Protein Interaction Networks and Somatic Mutation Data to Detect Driver Modules in Pan-Cancer. Interdiscip Sci Comput Life Sci 14, 151–167 (2022). https://doi.org/10.1007/s12539-021-00475-y

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