Identification of novel biomarkers of prostate cancer through integrated analysis

Background: The current methods adopted to screen for prostate cancer (PCa) can sometimes be misleading and inaccurate. Moreover, for advanced stages of PCa, the current effect of treatment is not satisfactory for some patients. Accordingly, we aimed to identify new biomarkers for the diagnosis and prognosis of PCa. Methods: A series of bioinformatic tools were utilized to search for potential new biomarkers of PCa and analyze their functions, expression, clinical relevance, prognostic value, and underlying mechanisms. Results: Although ASPN was overexpressed in PCa, EDN3, PENK, MEIS2, IGF1, and CXCL12 were downregulated. The univariate Cox regression analysis showed that abnormally high expression of ASPN and low expression of other genes predicted worse prognosis. Moreover, the multivariate Cox regression analysis showed that ASPN, PENK, and MEIS2 were independently associated with the overall survival (OS) of patients, whereas other markers were not. The outcomes of gene ontology and gene set enrichment analysis showed that the expression levels of these genes might be associated with cell proliferation and infiltration of immune cells in PCa. Conclusions: We demonstrated that ASPN, EDN3, PENK, MEIS2, IGF1, and CXCL12 are possibly novel diagnostic indicators for PCa, whereas ASPN, PENK, and MEIS2 show appealing potential to predict the prognosis of this disease.