Identification of novel biomarkers of prostate cancer through integrated analysis

Pu Zhang; Bei Qian; Zijian Liu; Decai Wang; Fang Lv; Yifei Xing; Yajun Xiao

doi:10.21037/tau-21-401

Identification of novel biomarkers of prostate cancer through integrated analysis

Transl Androl Urol. 2021 Aug;10(8):3239-3254. doi: 10.21037/tau-21-401.

Authors

Pu Zhang¹, Bei Qian², Zijian Liu³, Decai Wang⁴, Fang Lv¹, Yifei Xing¹, Yajun Xiao¹

Affiliations

¹ Department of Urology Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
² Department of Thyroid and Breast Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
³ Department of Head and Neck Oncology and Department of Radiation Oncology, Cancer Center and State Key Laboratory of Biotherapy, West China Hospital, Sichuan University, Chengdu, China.
⁴ Department of Emergency Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Abstract

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.

Keywords: Biomarkers; bioinformatics; immune infiltration; prognosis; prostate cancer (PCa).