title: Analysis of tumor suppressor genes based on gene ontology and the KEGG pathway.
abstract: Cancer is a serious disease that causes many deaths every year. We urgently need to design effective treatments to cure this disease. Tumor suppressor genes (TSGs) are a type of gene that can protect cells from becoming cancerous. In view of this, correct identification of TSGs is an alternative method for identifying effective cancer therapies. In this study, we performed gene ontology (GO) and pathway enrichment analysis of the TSGs and non-TSGs. Some popular feature selection methods, including minimum redundancy maximum relevance (mRMR) and incremental feature selection (IFS), were employed to analyze the enrichment features. Accordingly, some GO terms and KEGG pathways, such as biological adhesion, cell cycle control, genomic stability maintenance and cell death regulation, were extracted, which are important factors for identifying TSGs. We hope these findings can help in building effective prediction methods for identifying TSGs and thereby, promoting the discovery of effective cancer treatments.
authors.affiliation: The Key Laboratory of Stem Cell Biology, Institute of Health Sciences, Shanghai Jiao Tong University School of Medicine (SJTUSM) and Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai, People's Republic of China.
authors.forename: Jing
authors.initials: J
authors.lastname: Yang
authors.affiliation: College of Information Engineering, Shanghai Maritime University, Shanghai, People's Republic of China.
authors.forename: Lei
authors.initials: L
authors.lastname: Chen
authors.affiliation: The Key Laboratory of Stem Cell Biology, Institute of Health Sciences, Shanghai Jiao Tong University School of Medicine (SJTUSM) and Shanghai Institutes for Biological Sciences (SIBS), Chinese Academy of Sciences (CAS), Shanghai, People's Republic of China.
authors.forename: Xiangyin
authors.initials: X
authors.lastname: Kong
authors.affiliation: Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, New York, United States of America.
authors.forename: Tao
authors.initials: T
authors.lastname: Huang
authors.affiliation: Institute of Systems Biology, Shanghai University, Shanghai, People's Republic of China.
authors.forename: Yu-Dong
authors.initials: YD
authors.lastname: Cai
mesh_terms: D002448:Cell Adhesion / Q000235:genetics; D059447:Cell Cycle Checkpoints / Q000235:genetics; D016923:Cell Death / Q000235:genetics; D019295:Computational Biology; D057225:Data Mining; D063990:Gene Ontology; D016147:Genes, Tumor Suppressor; D015894:Genome, Human; D042822:Genomic Instability; D006801:Humans; D053858:Metabolic Networks and Pathways / Q000235:genetics*; D009363:Neoplasm Proteins / Q000145:classification / Q000235:genetics*; D009369:Neoplasms / Q000235:genetics* / Q000473:pathology
chemical_list: D009363:Neoplasm Proteins