ArticlePloS one2012
Identification of colorectal cancer related genes with mRMR and shortest path in protein-protein interaction network.
Article in PloS one, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers.
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78 citing papers in PubMed.
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- A hybrid feature selection algorithm and its application in bioinformatics.PeerJ. Computer science · 2022Article
- Computational Identification of Guillain-Barré Syndrome-Related Genes by an mRNA Gene Expression Profile and a Protein-Protein Interaction Network.Frontiers in molecular neuroscience · 2022Article
- Smell Detection Agent Optimisation Framework and Systems Biology Approach to Detect Dys-Regulated Subnetwork in Cancer Data.Biomolecules · 2021Article
- Integration of gene expression data identifies key genes and pathways in colorectal cancer.Medical oncology (Northwood, London, England) · 2021Article
- Predicting Human Protein Subcellular Locations by Using a Combination of Network and Function Features.Frontiers in genetics · 2021Article
- Challenges and emerging systems biology approaches to discover how the human gut microbiome impact host physiology.Biophysical reviews · 2020Review
- Multi-omic profiling reveals associations between the gut mucosal microbiome, the metabolome, and host DNA methylation associated gene expression in patients with colorectal cancer.BMC microbiology · 2020Article
- Comprehensive analysis of transcriptome data for identifying biomarkers and therapeutic targets in head and neck squamous cell carcinoma.Annals of translational medicine · 2020Article
- Inferring novel genes related to colorectal cancer via random walk with restart algorithm.Gene therapy · 2019Article
- Article
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18 more citing papers are in PubMed but not listed here.
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5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
One of the most important and challenging problems in biomedicine and genomics is how to identify the disease genes. In this study, we developed a computational method to identify colorectal cancer-related genes based on (i) the gene expression profiles, and (ii) the shortest path analysis of functional protein association networks. The former has been used to select differentially expressed genes as disease genes for quite a long time, while the latter has been widely used to study the mechanism of diseases. With the existing protein-protein interaction data from STRING (Search Tool for the Retrieval of Interacting Genes), a weighted functional protein association network was constructed. By means of the mRMR (Maximum Relevance Minimum Redundancy) approach, six genes were identified that can distinguish the colorectal tumors and normal adjacent colonic tissues from their gene expression profiles. Meanwhile, according to the shortest path approach, we further found an additional 35 genes, of which some have been reported to be relevant to colorectal cancer and some are very likely to be relevant to it. Interestingly, the genes we identified from both the gene expression profiles and the functional protein association network have more cancer genes than the genes identified from the gene expression profiles alone. Besides, these genes also had greater functional similarity with the reported colorectal cancer genes than the genes identified from the gene expression profiles alone. All these indicate that our method as presented in this paper is quite promising. The method may become a useful tool, or at least plays a complementary role to the existing method, for identifying colorectal cancer genes. It has not escaped our notice that the method can be applied to identify the genes of other diseases as well.
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