Abstract Understanding biological processes based on gene signaling pathways exerts a significant function on exploring the pathogenesis of diseases. Current methods to measure the contribution of pathways to diseases usually rely on the number of differential expression genes in a single pathway, ignoring the effects of upstream and downstream perturbations in the pathway or crosstalk between the pathways. In this paper, a novel crosstalk analysis method based on pathway contribution ranking was proposed to analyze the influence of crosstalk between pathways on the pathogenesis of kidney renal clear cell carcinoma (KIRC). Firstly, the signal pathway impact analysis (SPIA) method was used to rank the KIRC-related pathways. Secondly, the distance correlation (DC) algorithm was applied to calculate the crosstalk between the high-contribution signal pathways in the diseased samples and control samples. Finally, those crosstalk pathways with a crosstalk change value higher than 0.1 were selected. Results showed that in 21 pathways with a crosstalk change value higher than 0.1, the difference of crosstalk relationship between the Epstein-Barr virus pathway and the ErbB signaling pathway was -0.12, the difference between the signal pathway of renal cell carcinoma and ErbB signal pathway was -0.20, the difference between Parkinson′s disease pathway and the pathway of protein processing in endoplasmic reticulum was -0.14. Also, there was a significant change among from 0.1 to 0.3 of crosstalk relationship between the signal pathway of Staphylococcus aureus infection and 11 signaling pathway. At the same time, molecular biological analysis verified that the significant changes of crosstalk between these pathways had an important effect on the occurrence and development of KIRC. This method could effectively explore the known and potential dysregulation-signaling pathway.
Deng Jin,Kong Wei,Wang Shuaiqun, et al. Analysis of Crosstalk Pathways of Renal Clear Cell Carcinoma Based on Contribution Ranking[J]. Chinese Journal of Biomedical Engineering, 2019, 38(4): 424-430.
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