Related Experiment Video For Exosome
Updated: May 26, 2026
Molecular Profiling of the Invasive Tumor Microenvironment in a 3-Dimensional Model of Colorectal Cancer Cells and Ex vivo Fibroblasts
Published on: April 29, 2014
Construction of a diagnostic model for colorectal cancer based on exosome-related genes: integration of immune cell
Yulai Yin1, Li Li2,3, Shuang Liu4
1School of Medicine, Nankai University, Tianjin, China.
Background:
Colorectal cancer (CRC) is one of the most common malignancies of the digestive tract, with conventional clinical diagnoses often made at advanced stages. There is an urgent need for a genetic diagnostic model to predict the onset of CRC at early stages, thereby reducing the disease burden associated with it. This study aimed to construct and validate a CRC diagnostic model based on exosome-related genes and to explore potential target drugs.
Methods:
Gene expression differences between CRC and normal groups were first analyzed using datasets from the Gene Expression Omnibus (GEO) database, and CRC-related genes were identified. Exosome-related genes were then obtained through GeneCards and literature searches. The intersection of CRC-related genes and exosome-related genes was analyzed, and the intersection genes underwent Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) enrichment analysis. Three machine learning models were used to screen the model genes. The model was validated using both the training and validation datasets. Potential target drugs were identified based on the number of enriched model genes and were subjected to molecular docking analysis.
Results:
Gene expression analysis from the GEO dataset identified 316 differentially expressed genes associated with CRC. A total of 1,052 exosome-related genes were found through GeneCards and literature searches. The intersection of CRC-related genes and exosome-related genes yielded 21 intersection genes. These intersection genes were enriched in pathways such as interleukin-17 (IL-17) signaling and microRNA in cancer. Three machine learning methods identified four model intersection genes: EXOSC4, MMP9, ABCB1, and SOX2. Based on the number of enriched model genes, butyrate, dimethyl sulfoxide, and vorinostat were identified as potential target drugs.
Conclusions:
EXOSC4, MMP9, ABCB1, and SOX2 are important exosome-related biomarkers for the diagnosis of CRC, and butyrate, dimethyl sulfoxide, and vorinostat have the potential to serve as targeted therapeutic drugs for CRC.
