Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
October 3rd, 2025
The current work describes a protocol for running the Pathway2Targets algorithm, an R script that predicts and prioritizes therapeutic targets based on the profile of intracellular signaling pathways generated by comparing case versus control samples from a bulk RNA-sequencing experiment.
The aim of our research is to characterize the intracellular transcriptional response to various conditions and to predict therapeutics and mechanistic or diagnostic markers for those conditions. Computational prediction gives candidates of drugs. Validating drugs'effects is resource-and time-consuming.
However, the incidental method could only improve with high-quality experimental data. The advantage of this work is its ability to identify potential drug targets for repurposing within a signaling pathway rather than just matching differentially expressed genes to known targets. To run the SPIA Pathway Enrichment algorithm, first download the code on the computer system from GitHub.
Open the SPIA_Code. R MD script in RStudio. Select all lines of code, then click the Run or Run Selected Lines button to execute the script.
Wait for the run to complete and verify that a similarly named csv file appears in the Download directory. Open the file as a spreadsheet to manually review and interpret the results. To run the target prioritization algorithm, open the Pathway2Targets.
R script in RStudio by selecting the File menu and clicking Open File, then choose the script name from the directory. In the RStudio code window, go to line 22 and replace the placeholder with the actual SPIA results file name. Select all lines of code and click the Run button to execute the algorithm.
Observe real-time progress messages appearing in the bottom left panel of the screen. After completion, check the Download directory for a similarly named tsv file, which contains the prioritized targets. After generating the file with prioritized targets and their metrics, open it in a spreadsheet application to review.
The SPIA algorithm identified 10 statistically significant signaling pathways with an unadjusted p-value less than 0.05. The Pathway2Targets algorithm identified multiple predicted targets. The predicted therapeutic targets were consistent across both the ranked targets and ranked treatments outputs, including known colorectal cancer-related genes, such as EGFR, TP53, and AKT1.
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Overview
This study focuses on characterizing the intracellular transcriptional response under various conditions and aims to predict therapeutic targets and diagnostic markers. By employing the Pathway2Targets algorithm, the research identifies potential drug targets for colorectal cancer through bulk RNA-sequencing data analysis.
Key Study Components
Research Area
- Target prediction for therapeutic interventions
- Transcriptional profiling of intracellular responses
- Colorectal cancer research
Background
- The need for high-quality experimental data to improve predictions
- Challenges in validating drug effects
- Advantage of identifying drug targets within signaling pathways
Methods Used
- Pathway2Targets algorithm for target prioritization
- Bulk RNA-sequencing data
- R programming for data analysis
Main Results
- Identification of statistically significant signaling pathways
- Multiple predicted therapeutic targets consistent across outputs
- Key genes identified include EGFR, TP53, and AKT1
Conclusions
- The study demonstrates a novel approach to drug repurposing within signaling pathways
- Research is relevant for enhancing therapeutic strategies against colorectal cancer
