Detect the early-warning signals of diseases based on signaling pathway perturbations on a single sample

During the pathogenesisof complex diseases, a sudden health deterioration will occur as results of the cumulative effect of various internal or external factors. The prediction of an early warning signal (pre-disease state) before such deterioration is very important in clinical practice, especially for a single sample. The single-sample landscape entropy (SLE) was proposed to tackle this issue. However, the PPI used in SLE was lack of definite biological meanings. Besides, the calculation of multiple correlations based on limited reference samples in SLE is time-consuming and suspect.

Abnormal signals generally exert their effect through the static definite biological functions in signaling pathways across the development of diseases. Thus, it is a natural way to study the propagation of the early-warning signals based on the signaling pathways in the KEGG database. In this paper, we propose a signaling perturbation method named SSP, to study the early-warning signal in signaling pathways for single dynamic time-series data. Results in three real datasets including the influenza virus infection, lung adenocarcinoma, and acute lung injury show that the proposed SSP outperformed the SLE. Moreover, the early-warning signal can be detected by one important signaling pathway PI3K-Akt.

These results all indicate that the static model in pathways could simplify the detection of the early-warning signals.