SEER and Gene Expression Data Analysis Deciphers Racial Disparity Patterns in Prostate Cancer Mortality and the Public Health Implication.
Wensheng ZhangYan DongA Oliver SartorErik K FlemingtonKun ZhangPublished in: Scientific reports (2020)
A major racial disparity in prostate cancer (PCa) is that African American (AA) patients have a higher mortality rate than European American (EA) patients. We filtered the SEER 2009-2011 records and divided them into four groups regarding patient races and cancer grades. On such a partition, we performed a series of statistical analyses to further clarify the aforementioned disparity. Molecular evidence for a primary result of the epidemiological analysis was obtained from gene expression data. The results include: (1) Based on the registry-specific measures, a significant linear regression of total mortality rate (as well as PCa specific mortality rate) on the percentage of (Gleason pattern-based) high-grade cancers (PHG) is demonstrated in EAs (p < 0.01) but not in AAs; (2) PHG and its racial disparity are differentiated across ages and the groups defined by patient outcomes; (3) For patients with cancers in the same grade category, i.e. the high or low grade, the survival stratification between races is not significant in most geographical areas; and (4) The genes differentially expressed between AAs' and EAs' tumors of the same grade category are relatively rare. The perception that prostate tumors are more lethal in AAs than in EAs is reasonable regarding AAs' higher PHG, while high grade alone could not imply aggressiveness. However, this perception is questionable when the comparison is focused on cases within the same grade category. Supporting observations for this conclusion hold a remarkable implication for erasing racial disparity in PCa. That is, "Equal grade, equal outcomes" is not only a verifiable hypothesis but also an achievable public health goal.
Keyphrases
- prostate cancer
- high grade
- african american
- low grade
- gene expression
- public health
- data analysis
- end stage renal disease
- cardiovascular events
- radical prostatectomy
- ejection fraction
- newly diagnosed
- risk factors
- chronic kidney disease
- magnetic resonance imaging
- prognostic factors
- cardiovascular disease
- electronic health record
- magnetic resonance
- computed tomography
- machine learning
- type diabetes
- peritoneal dialysis
- skeletal muscle
- coronary artery disease
- adipose tissue
- patient reported outcomes
- transcription factor
- deep learning
- big data
- lymph node metastasis
- young adults
- insulin resistance
- papillary thyroid