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Published on: October 3, 2025
Identifying disease-associated signaling pathways through a novel effector gene analysis
Zhenshen Bao1, Bing Zhang1, Li Li2
1State Key Laboratory of Bioelectronics, School of Biological Sciences and Medical Engineering, Southeast University, Nanjing, Jiangsu, China.
Background:
Signaling pathway analysis methods are commonly used to explain biological behaviors of disease cells. Effector genes typically decide functional attributes (associated with biological behaviors of disease cells) by abnormal signals they received. The signals that the effector genes receive can be quite different in normal vs. disease conditions. However, most of current signaling pathway analysis methods do not take these signal variations into consideration.
Methods:
In this study, we developed a novel signaling pathway analysis method called signaling pathway functional attributes analysis (SPFA) method. This method analyzes the signal variations that effector genes received between two conditions (normal and disease) in different signaling pathways.
Results:
We compared the SPFA method to seven other methods across 33 Gene Expression Omnibus datasets using three measurements: the median rank of target pathways, the median p-value of target pathways, and the percentages of significant pathways. The results confirmed that SPFA was the top-ranking method in terms of median rank of target pathways and the fourth best method in terms of median p-value of target pathways. SPFA's percentage of significant pathways was modest, indicating a good false positive rate and false negative rate. Overall, SPFA was comparable to the other methods. Our results also suggested that the signal variations calculated by SPFA could help identify abnormal functional attributes and parts of pathways. The SPFA R code and functions can be accessed at https://github.com/ZhenshenBao/SPFA.
Insights
A new method, signaling pathway functional attributes analysis (SPFA), analyzes signal variations in effector genes to better understand disease cell behaviors. SPFA shows strong performance compared to existing methods.
Area of Science:
- Bioinformatics
- Computational Biology
- Systems Biology
Background:
- Signaling pathway analysis is crucial for understanding disease cell biology.
- Effector genes determine cell behavior based on received signals, which differ between normal and disease states.
- Current methods often overlook these signal variations.
Purpose of the Study:
- To develop a novel method, Signaling Pathway Functional Attributes analysis (SPFA), for analyzing signal variations.
- To assess the effectiveness of SPFA in identifying differences in effector gene signaling between normal and disease conditions.
Main Methods:
- Developed the Signaling Pathway Functional Attributes analysis (SPFA) method.
- SPFA analyzes signal variations in effector genes across different signaling pathways between normal and disease states.
- Compared SPFA against seven other methods using 33 Gene Expression Omnibus datasets.
Main Results:
- SPFA ranked first in median rank of target pathways and fourth in median p-value across datasets.
- SPFA demonstrated a modest percentage of significant pathways, suggesting balanced false positive and negative rates.
- The method effectively identified abnormal functional attributes and pathway components.
Conclusions:
- The SPFA method provides a valuable approach to analyzing signal variations in biological pathways.
- SPFA's performance is comparable to existing methods, with potential for identifying disease-specific functional attributes.
- The SPFA R code is publicly available for broader research application.
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