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Identifying Lymph Node Metastasis-Related Factors in Breast Cancer Using Differential Modular and Mutational Structural Analysis

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Abstract

Complex diseases are generally caused by disorders of biological networks and/or mutations in multiple genes. Comparisons of network topologies between different disease states can highlight key factors in their dynamic processes. Here, we propose a differential modular analysis approach that integrates protein–protein interactions with gene expression profiles for modular analysis, and introduces inter-modular edges and date hubs to identify the “core network module” that quantifies the significant phenotypic variation. Then, based on this core network module, key factors, including functional protein–protein interactions, pathways, and driver mutations, are predicted by the topological–functional connection score and structural modeling. We applied this approach to analyze the lymph node metastasis (LNM) process in breast cancer. The functional enrichment analysis showed that both inter-modular edges and date hubs play important roles in cancer metastasis and invasion, and in metastasis hallmarks. The structural mutation analysis suggested that the LNM of breast cancer may be the outcome of the dysfunction of rearranged during transfection (RET) proto-oncogene-related interactions and the non-canonical calcium signaling pathway via an allosteric mutation of RET. We believe that the proposed method can provide new insights into disease progression such as cancer metastasis.

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Data Availability

The code for this work is available at https://github.com/CSB-SUDA/DMA.

Abbreviations

LNM:

Lymph node metastasis

RET:

Rearranged during transfection

PPIN:

Protein–protein interaction network

EGFR:

Epidermal growth factor receptor

BRCA:

Breast cancer

DEGs:

Differentially expressed genes

aPCC:

Average Pearson’s correlation coefficient

CPL:

Characteristic path length

TFC:

Topological–functional connection

TCGA:

The Cancer Genome Atlas

KEGG:

Kyoto Encyclopedia of Genes and Genomes

GSEA:

Gene set enrichment analysis

COSMIC:

Catalogue of Somatic Mutations in Cancer

PDB:

Protein Data Bank

PRISM:

Protein Interactions by Structural Matching

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Funding

This work was supported by the National Natural Science Foundation of China (32271292, 31872723), a project funded by the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions, the China Postdoctoral Science Foundation (2016M590495), and the Jiangsu Planned Projects for Postdoctoral Research Funds (1601168C).

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  1. Xingyi Liu and Bin Yang contributed equally to this work.

Authors and Affiliations

  1. Center for Systems Biology, Department of Bioinformatics, School of Biology and Basic Medical Sciences, Soochow University, Suzhou, 215123, Jiangsu, China

    Xingyi Liu, Bin Yang, Xinpeng Huang, Wenying Yan & Guang Hu

  2. Jiangsu Province Engineering Research Center of Precision Diagnostics and Therapeutics Development, Suzhou, 215123, Jiangsu, China

    Wenying Yan & Guang Hu

  3. Experimental Center of Suzhou Medical College, Soochow University, Suzhou, 215123, Jiangsu, China

    Yujuan Zhang

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  1. Xingyi Liu
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Correspondence to Wenying Yan, Yujuan Zhang or Guang Hu.

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Liu, X., Yang, B., Huang, X. et al. Identifying Lymph Node Metastasis-Related Factors in Breast Cancer Using Differential Modular and Mutational Structural Analysis. Interdiscip Sci Comput Life Sci 15, 525–541 (2023). https://doi.org/10.1007/s12539-023-00568-w

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