Screening of feature genes of the ovarian cancer epithelia with DNA microarray

OBJECTIVE: We aimed to screen differentially expressed genes (DEGs) of ovarian surface epithelia in order to provide beneficial help for early diagnosis and treatment of ovarian cancer with DNA microarrays. METHODS: We extracted the microarray expression profile GSE14407 from Gene Expression Omnibus database which conducted gene expression profiling analysis of 12 ovarian surface epithelia (OSE) and 12 laser capture microdissected serous ovarian cancer epithelia (CEPI) samples. The DEGs between OSE and CEPI were identified by Limma package of R language. Cluster analysis was employed to compare the differences of gene expression patterns between OSE and CEPI. Furthermore, DEGs were analyzed with Functional classification tool, GenMAPP software and GENECODIS. RESULTS: We identified 1229 DEGs including 592 down-regulated genes and 637 up-regulated genes. Pathway analysis showed that cell cycle was the most significant pathway and the DEGs related with cell cycle were almost up-regulated. Module mining analysis showed that the up-regulated DEGs were related with signal transduction while the down-regulated DEGs were related with lipid metabolism pathway and cytoskeletal structure. CONCLUSION: The genes related with cell cycle, lipid metabolism and cytoskeletal structure may be the treatment targets for ovarian cancer.