| Autor: |
Liu, Xingyi, Yang, Bin, Huang, Xinpeng, Yan, Wenying, Zhang, Yujuan, Hu, Guang |
| Předmět: |
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| Zdroj: |
Interdisciplinary Sciences: Computational Life Sciences; Dec2023, Vol. 15 Issue 4, p525-541, 17p |
| Abstrakt: |
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. [ABSTRACT FROM AUTHOR] |
| Databáze: |
Complementary Index |
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