Related Experiment Video
Updated: Jun 20, 2026
Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
Published on: October 3, 2025
A novel algorithm for detecting differentially regulated paths based on gene set enrichment analysis
Andreas Keller1, Christina Backes, Andreas Gerasch
1Center for Bioinformatics, Saarland University, Building E.1.1, Saarbrücken, Germany. ack@bioinf.uni-sb.de
Motivation:
Deregulated signaling cascades are known to play a crucial role in many pathogenic processes, among them are tumor initiation and progression. In the recent past, modern experimental techniques that allow for measuring the amount of mRNA transcripts of almost all known human genes in a tissue or even in a single cell have opened new avenues for studying the activity of the signaling cascades and for understanding the information flow in the networks.
Results:
We present a novel dynamic programming algorithm for detecting deregulated signaling cascades. The so-called FiDePa (Finding Deregulated Paths) algorithm interprets differences in the expression profiles of tumor and normal tissues. It relies on the well-known gene set enrichment analysis (GSEA) and efficiently detects all paths in a given regulatory or signaling network that are significantly enriched with differentially expressed genes or proteins. Since our algorithm allows for comparing a single tumor expression profile with the control group, it facilitates the detection of specific regulatory features of a tumor that may help to optimize tumor therapy. To demonstrate the capabilities of our algorithm, we analyzed a glioma expression dataset with respect to a directed graph that combined the regulatory networks of the KEGG and TRANSPATH database. The resulting glioma consensus network that encompasses all detected deregulated paths contained many genes and pathways that are known to be key players in glioma or cancer-related pathogenic processes. Moreover, we were able to correlate clinically relevant features like necrosis or metastasis with the detected paths.
Availability:
C++ source code is freely available, BiNA can be downloaded from http://www.bnplusplus.org/.
Contact:
ack@bioinf.uni-sb.de
Supplementary Information:
Supplementary data are available at Bioinformatics online.
Insights
We developed FiDePa, a new algorithm to find deregulated signaling pathways in tumors by analyzing gene expression. This method aids in identifying tumor-specific features for better cancer therapy.
Area of Science:
- Bioinformatics
- Computational Biology
- Systems Biology
Background:
- Deregulated signaling cascades are critical in tumor initiation and progression.
- High-throughput gene expression profiling enables detailed study of signaling networks.
Purpose of the Study:
- To introduce a novel dynamic programming algorithm, FiDePa (Finding Deregulated Paths), for detecting deregulated signaling cascades.
- To enable the identification of tumor-specific regulatory features for optimizing cancer therapy.
Main Methods:
- Developed the FiDePa algorithm using dynamic programming.
- Interpreted gene expression differences between tumor and normal tissues.
- Utilized gene set enrichment analysis (GSEA) to identify enriched paths in regulatory networks.
Main Results:
- FiDePa efficiently detects significantly enriched paths of differentially expressed genes/proteins.
- Analysis of a glioma dataset revealed known key genes and pathways in cancer.
- Correlated detected paths with clinical features such as necrosis and metastasis.
Conclusions:
- FiDePa is a powerful tool for identifying deregulated signaling pathways in cancer.
- The algorithm facilitates the discovery of tumor-specific targets for therapeutic intervention.
- The approach aids in understanding the molecular basis of cancer progression and heterogeneity.
