ABSTRACT
OBJECTIVE: Ovarian cancer is the most lethal gynecologic cancer worldwide, since most patients are diagnosed at an advanced stage. To improve the early diagnosis and treatment of ovarian cancer, we performed a integrated analysis of transcription profile and genetic variations to study on the molecular pathogenesis in ovarian cancer. METHODS: mRNA expression profiles of ovarian cancer and normal controls downloaded from ArrayExpress database were applied to identify differentially expressed genes (DEGs). The chromosomal distributions of these DEGs were established using DAVID. Then, DNASeq data from the Cancer Genome Atlas (TCGA) were extracted to analyze gene mutational information including the number of mutations (mut), the number of mutational genes (mutG) and chromosomal distributions of mutations. Statistical method was offered to carrying on correlation analysis of gene mutations and differential expression. RESULTS: A total of 1732 DEGs were identified, and the chromosomal distributions of 97 genes were unknown. These DEGs were most significantly distributed on chromosome 4 with p value = 1.34E-7. Chromosome 1 enriched the most DEGs (11.56%). Statistical algorithm showed that DEGs presented significantly positive correlation with mut (p = 0.000009) and mutG (p = 0.00001). In 48.7% DEGs, gene mutations were found. CONCLUSIONS: We conducted scientific analysis on integration of DEGs in expression profiles and genetic mutations in ovarian cancer, displayed the correlation of differential expression and genetic variations. The result indicated that expression profiles were significantly correlated to genotype.
