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Updated: Mar 8, 2026

Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
Published on: October 3, 2025
Identification of potential therapeutic targets for colorectal cancer by bioinformatics analysis
Ming Yan1, Maomin Song1, Rixing Bai1
1Department of General Surgery, Beijing Tiantan Hospital, Capital Medical University, Beijing 100050, P.R. China.
Abstract:
The aim of the present study was to identify potential therapeutic targets for colorectal cancer (CRC). The gene expression profile GSE32323, containing 34 samples, including 17 specimens of CRC tissues and 17 of paired normal tissues from CRC patients, was downloaded from the Gene Expression Omnibus database. Following data preprocessing using the Affy and preprocessCore packages, the differentially-expressed genes (DEGs) between the two types of samples were identified with the Linear Models for Microarray Analysis package. Next, functional and pathway enrichment analysis of the DEGs was performed using the Database for Annotation Visualization and Integrated Discovery. The protein-protein interaction (PPI) network was established using the Search Tool for the Retrieval of Interacting Genes database. Utilizing WebGestalt, the potential microRNAs (miRNAs/miRs) of the DEGs were screened and the integrated miRNA-target network was built. A cohort of 1,347 DEGs was identified, the majority of which were mainly enriched in cell cycle-related biological processes and pathways. Cyclin-dependent kinase 1 (CDK1), cyclin B1 (CCNB1), MAD2 mitotic arrest deficient-like 1 (MAD2L1) and BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B) were prominent in the PPI network, while the over-represented genes in the integrated miRNA-target network were SRY (sex determining region Y)-box 4 (SOX4; targeted by hsa-mir-129), v-myc avian myelocytomatosis viral oncogene homolog (MYC; targeted by hsa-let-7c and hsa-mir-145) and cyclin D1 (CCND1; targeted by hsa-let-7b). CDK1, CCNB1 and CCND1 were also associated with the p53 signaling pathway. Overall, several genes associated with the cell cycle and p53 pathway were identified as biomarkers for CRC. CDK1, CCNB1, MAD2L1, BUB1B, SOX4, collagen type I α2 chain and MYC may play significant roles in CRC progression by affecting the cell cycle-related pathways, while CDK1, CCNB1 and CCND1 may serve as crucial regulators in the p53 signaling pathway. Furthermore, SOX4, MYC and CCND1 may be targets of miR-129, hsa-mir-145 and hsa-let-7c, respectively. However, further validation of these data is required.
Insights
This study identified 1,347 differentially expressed genes in colorectal cancer (CRC), many linked to cell cycle regulation. Key genes like CDK1 and MYC emerged as potential therapeutic targets for CRC treatment.
Area of Science:
- Genomics
- Oncology
- Bioinformatics
Background:
- Colorectal cancer (CRC) remains a significant global health challenge.
- Identifying novel therapeutic targets is crucial for improving CRC patient outcomes.
Purpose of the Study:
- To identify potential therapeutic targets for colorectal cancer (CRC).
- To analyze differentially expressed genes (DEGs) and their associated pathways in CRC tissues.
Main Methods:
- Utilized gene expression profile GSE32323 from the Gene Expression Omnibus database.
- Applied bioinformatics tools for data preprocessing, DEG identification, functional enrichment, and network analysis (PPI, miRNA-target).
Main Results:
- Identified 1,347 DEGs, predominantly enriched in cell cycle pathways.
- Highlighted key genes (CDK1, CCNB1, MAD2L1, BUB1B) in the protein-protein interaction network.
- Discovered SOX4, MYC, and CCND1 as significant targets in the miRNA-target network, with potential roles in the p53 signaling pathway.
Conclusions:
- Several cell cycle and p53 pathway-associated genes identified as potential biomarkers for CRC.
- CDK1, CCNB1, MAD2L1, BUB1B, SOX4, collagen type I α2 chain, and MYC may significantly influence CRC progression.
- CDK1, CCNB1, and CCND1 show potential as crucial regulators in the p53 signaling pathway.
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