An in-silico Approach for Recognition of Long non-coding RNA-Associated Competing Endogenous RNA Axes in Prostate Cancer

Prostate cancer is among the most central sources of cancer-related mortalities. In order to find novel candidates for therapeutic strategies in this kind of cancer, we developed an in-silico method for identification of competing endogenous RNA network.

According to the microarray data analyses between prostate tumor and normal specimens, we attained 1312 differentially expressed (DE)mRNAs, including 778 down-regulated DEmRNAs (such as CXCL13 and BMP5) and 584 up-regulated DEmRNAs (such as OR51E2 and LUZP2), 39 DElncRNAs, including 10 down-regulated DElncRNAs (such as UBXN10-AS1 and FENDRR) and 29 up-regulated DElncRNAs (such as PCA3 and LINC00992) and 10 DEmiRNAs, including 2 down-regulated DEmiRNAs (such as MIR675 and MIR1908) and 8 up-regulated DEmiRNAs (such as MIR6773 and MIR4683).

We constructed the ceRNA network between these transcripts. We also evaluated the related signaling pathways and the significance of these RNAs in prediction of survival of patients with prostate cancer.

This study provides novel candidates for construction of specific treatment routes for prostate cancer.