A combination of single-cell sequence and omic data analyses to identify the potential biomarkers of Prostate Cancer

Inhalt:
Prostate cancer (PCa) is the most common male malignant tumor. At present, the treatment of PCa includes radical prostatectomy, radiotherapy and hormone therapy, which increases the survival rate of patients. However, there is a lack of adequate treatment for advanced PCa because of the high heterogeneity of this disease. Therefore, it is necessary to determine more sensitive markers for the detection of early PCa and stratify the risk of PCa in order to provide the best treatment for patients. At present, single cell RNA sequencing is very helpful for the study of tumor heterogeneity. Recently, a PCa single cell sequencing data was provided in the Gene Expression Omnibus (GEO) database, we thus integrated single cell sequencing data and omic data analyses to identify the potential biomarkers of PCa. Based on the cluster analysis, differential expression analysis, immune filtration analysis and clinical phenotype analysis, we found that APOD, DCN, FBLN1, CCL3 and MYL9 are potential PCa-related biomarkers, and these results were consisting with previous reports. Our study may help to find the potential treatment strategies for advanced PCa patients.