SLC45A2 and prostate cancer: To examine whether combination of different modalities will improve the prediction model, blood LSR, Nomogram, Gleason’s grade and the status of 8 fusion transcripts (TRMT11-GRIK2, SLC45A2-AMACR, MTOR-TP53BP1, LRRC59-FLJ60017, TMEM135 –CCDC67, KDM4-AC011523.2, MAN2A1-FER and CCNH-C5orf30)[23] in the prostate cancer samples were combined through linear discriminant analysis (LDA) to train the prediction model in the training set.