ABSTRACT
Oncogenes are genes that have the potential to cause cancer. Oncogene research can provide insight into the occurrence and development of cancer, thereby helping to prevent cancer and to design effective treatments. This study proposes a network method called the oncogene prediction method based on shortest path algorithm (OPMSP) for the identification of novel oncogenes in a large protein network built using protein-protein interaction data. Novel putative genes were extracted from the shortest paths connecting any two known oncogenes. Then, they were filtered by a randomization test, and the linkages among them and known oncogenes were measured by protein interaction and sequence data. Thirty-seven new putative oncogenes were identified by this method. The enrichment analysis of the 37 putative oncogenes indicated that they are highly associated with several biological processes related to the initiation, progression and metastasis of tumors. Six of these genes-ESR1, CDK9, SEPT2, HOXA10, LMX1B, and NR2C2-are extensively discussed. Several lines of evidence indicate that they may be novel oncogenes.
Subject(s)
Computational Biology/methods , Neoplasms/genetics , Oncogenes/genetics , Protein Interaction Maps/genetics , Algorithms , Amino Acid Sequence , Cyclin-Dependent Kinase 9/genetics , Estrogen Receptor alpha/genetics , Homeobox A10 Proteins , Homeodomain Proteins/genetics , LIM-Homeodomain Proteins/genetics , Receptors, Steroid/genetics , Receptors, Thyroid Hormone/genetics , Septins/genetics , Transcription Factors/geneticsABSTRACT
Apoptosis is the process of programmed cell death (PCD) that occurs in multicellular organisms. This process of normal cell death is required to maintain the balance of homeostasis. In addition, some diseases, such as obesity, cancer, and neurodegenerative diseases, can be cured through apoptosis, which produces few side effects. An effective comprehension of the mechanisms underlying apoptosis will be helpful to prevent and treat some diseases. The identification of genes related to apoptosis is essential to uncover its underlying mechanisms. In this study, a computational method was proposed to identify novel candidate genes related to apoptosis. First, protein-protein interaction information was used to construct a weighted graph. Second, a shortest path algorithm was applied to the graph to search for new candidate genes. Finally, the obtained genes were filtered by a permutation test. As a result, 26 genes were obtained, and we discuss their likelihood of being novel apoptosis-related genes by collecting evidence from published literature.
