Genetic alterations and PIK3CA gene mutations and amplifications analysis in cervical cancer by racial groups in the United States

INTRODUCTION: A number of studies indicated racial differences in cervical cancer outcomes and several factors are associated with it such as stage, comorbidities, treatment pattern, and socioeconomic status. However, the associations of tumor genomic patterns such as phosphatidylinositol 3-kinase catalytic subunit alpha (PIK3CA) gene mutations and amplifications with cervical cancer racial disparities are largely unexplored. OBJECTIVES: Therefore, the present investigation aimed to identify genetic alterations (mutations and copy number variations) in cervical cancer and determine whether the PIK3CA gene mutations and amplifications in cervical cancer differ across racial/ethnic groups in the United States. METHODS: This study made use of The Cancer Genome Atlas (TCGA) database. TCGA database is a publicly available database that was created by a joint effort between the National Cancer Institute and the National Human Genome Research Institute. The two-tailed Fisher’s exact test was used to test for associations between the categorical variables, race, and PIK3CA gene mutation as well as PIK3CA gene amplification using the “Fisher test” function in R. RESULTS: There were 309 cervical cancer samples, and of these, 194 samples had gene mutations and 295 samples had copy number alteration data. The top five mutated genes in TCGA dataset were PIK3CA, MUC4, KMT2C, SYNE1, and KMT2D. The top five amplified genes in TCGA dataset were MECOM, TP63, PRKCI, PIK3CA, and TRFC. The PIK3CA gene had the highest number of mutations with 53 mutation counts. The mutation rates were 62.5%, 31.3%, 25.4%, and 21.1% for American Indian, African American, White, and Asian, respectively. The amplification rates were 28.6%, 21.1%, 18.9%, and 12.5% for African American, Asian, White, and American Indian. CONCLUSIONS: There are many significantly mutated or amplified genes implicated in cervical cancer. Some of them are not grouped with the already known genes in relation to cervical cancer. For example, the KMT2C, SYNE1, KMT2D, EP300, RYR2, FLG, DMD, FBXW, MECOM, TRFC, RPL35A, LPP, TBL, FGF12, and SOX2 genes. They can be explored as therapeutic targets to improve cervical cancer treatment.