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Updated: Mar 28, 2026
Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
Published on: October 3, 2025
In silico pathway analysis in cervical carcinoma reveals potential new targets for treatment
Peter A van Dam1,2, Pieter-Jan H H van Dam1, Christian Rolfo1,2,3
1Antwerp University Hospital, Centre of Oncologic Research (CORE) Antwerp University, Edegem, Belgium.
Abstract:
An in silico pathway analysis was performed in order to improve current knowledge on the molecular drivers of cervical cancer and detect potential targets for treatment. Three publicly available Affymetrix gene expression data-sets (GSE5787, GSE7803, GSE9750) were retrieved, vouching for a total of 9 cervical cancer cell lines (CCCLs), 39 normal cervical samples, 7 CIN3 samples and 111 cervical cancer samples (CCSs). Predication analysis of microarrays was performed in the Affymetrix sets to identify cervical cancer biomarkers. To select cancer cell-specific genes the CCSs were compared to the CCCLs. Validated genes were submitted to a gene set enrichment analysis (GSEA) and Expression2Kinases (E2K). In the CCSs a total of 1,547 probe sets were identified that were overexpressed (FDR < 0.1). Comparing to CCCLs 560 probe sets (481 unique genes) had a cancer cell-specific expression profile, and 315 of these genes (65%) were validated. GSEA identified 5 cancer hallmarks enriched in CCSs (P < 0.01 and FDR < 0.25) showing that deregulation of the cell cycle is a major component of cervical cancer biology. E2K identified a protein-protein interaction (PPI) network of 162 nodes (including 20 drugable kinases) and 1626 edges. This PPI-network consists of 5 signaling modules associated with MYC signaling (Module 1), cell cycle deregulation (Module 2), TGFβ-signaling (Module 3), MAPK signaling (Module 4) and chromatin modeling (Module 5). Potential targets for treatment which could be identified were CDK1, CDK2, ABL1, ATM, AKT1, MAPK1, MAPK3 among others. The present study identified important driver pathways in cervical carcinogenesis which should be assessed for their potential therapeutic drugability.
Insights
This study analyzed gene expression data to find molecular drivers of cervical cancer. It identified cell cycle deregulation and potential drug targets like CDK1 and AKT1 for new treatments.
Area of Science:
- Oncology
- Bioinformatics
- Molecular Biology
Background:
- Cervical cancer remains a significant global health issue.
- Understanding its molecular drivers is crucial for developing effective treatments.
- Current knowledge on cervical cancer's molecular landscape requires further elucidation.
Purpose of the Study:
- To identify molecular drivers of cervical cancer.
- To detect potential therapeutic targets for cervical cancer treatment.
- To analyze gene expression data for novel biomarkers.
Main Methods:
- In silico pathway analysis of publicly available Affymetrix gene expression datasets (GSE5787, GSE7803, GSE9750).
- Comparative analysis of cervical cancer samples (CCSs) and cervical cancer cell lines (CCCLs) to identify cancer cell-specific genes.
- Gene Set Enrichment Analysis (GSEA) and Expression2Kinases (E2K) to analyze gene expression profiles and protein-protein interaction (PPI) networks.
Main Results:
- 1,547 overexpressed probe sets identified in cervical cancer samples.
- 560 probe sets (481 unique genes) showed cancer cell-specific expression, with 315 validated.
- GSEA revealed 5 enriched cancer hallmarks, highlighting cell cycle deregulation.
- E2K identified a PPI network with 5 signaling modules and 20 druggable kinases.
Conclusions:
- Cell cycle deregulation is a major component of cervical cancer biology.
- Key signaling pathways including MYC, cell cycle, TGFβ, MAPK, and chromatin modeling are implicated.
- Potential therapeutic targets such as CDK1, CDK2, ABL1, ATM, AKT1, MAPK1, and MAPK3 were identified for further investigation.
