## 3 Results
**(G)** Heatmap of the differential expression of SHBG, HSD17B2, SOX2, ABCG2, OCT3/4, ALDH1A1, KLF4, SRD5A1, ABCC2, and GAPDH in the 22 Rv1, DU145, LNCaPAP, MDA-PCa 2b, NCI-H660, PC-3, and VCaP prostate cancer cell lines from the NIH GDC Cancer Cell Line Encyclopedia. **(H)** Heatmap of the differential pharmacological effect of Docetaxel on HSD17B2, HSD17B3, SHBG, or SRD5A1 expression in the NCI-60 Human Tumor Cell Lines. Red box shows PCa cell lines. **(I)** Dot and line plots showing the correlation between SRD5A1 knockout by CRISPR and SRD5A1, SHBG, KLF4, POU5F1/OCT4, ABCC2, or ALDH1A1 expression in the GDSC-MGH–Sanger cohort. GAPDH is loading control. GDSC, the genomics of drug sensitivity in cancer project; MGH, Massachusetts General Hospital; Sanger—the Wellcome Sanger Institute (UK). %, percentage; WT, wild type; OE, overexpression; **, \(p<0.01\);**, \(p<0.001\). TCGA PRAD cohort (n = 497) were ably stratified into cluster 1 (nonrecurrent, therapy responsive) and cluster 2 (recurrent, therapy nonresponsive) by the testosterone tetrad and age at diagnosis, while PSA value exhibited no apparent differentiating potential (Figure 6B,C). Patient stratification based on t-distributed stochastic neighbor embedding (t-SNE)-aided dimensionality reduction of the TCGA PRAD dataset showed that combining the testosterone tetrad and age at diagnosis fairly differentiates the nonrecurrent, therapy responsive cases from the recurrent, therapy nonresponsive cases; however, it leaves -22% of the cohort pooling away from their supposedly designated cluster (Figure 6D). Our profiling of these prediction variables using the generalized linear model (\(X^{2}\) = 14.71, \(p\) = 0.012) showed that age at diagnosis exhibited the smallest effect, and may be excluded (Prob \(>X^{2}\) = 0.535) (Figure 6E, _upper;_ also see Supplementary Table S2). More so, receiver operating curve (ROC) analyses showed that the testosterone tetrad very well predicts \"new tumor event after initial treatment\" (Training: n = 407; AUC\({}_{\text{CR}}\) 0.671; AUC\({}_{\text{PR}}\) 0.696; AUC\({}_{\text{PD}}\) 0.767; AUC\({}_{\text{SD}}\) 0.665; Generalized R\({}^{2}\) = 0.156; -Loglikelihood = 160.88 vs. Validation: n = 44; AUC\({}_{\text{CR}}\) 0.698; AUC\({}_{\text{PR}}\) 0.878; AUC\({}_{\text{PD}}\) 0.950; AUC\({}_{\text{SD}}\) 0.566; Generalized R\({}^{2}\) = 0.305; -Loglikelihood = 16.32), and \"follow-up treatment success\" (Training: n = 385; AUC 0.722; Generalized R\({}^{2}\) = 0.126; -Loglikelihood = 233.35 vs. Validation: n = 42; AUC 0.844; Generalized R\({}^{2}\) = 0.178; -Loglikelihood = 22.81) in the TCGA PRAD cohort. Using the artificial neural network (ANN) model, an unsupervised machine learning algorithm, we showed that the differential expression of HSD17B2, HSD17B3, SHBG, and SRD5A1 may concertedly predict \"new tumor event after initial treatment\" and \"follow-up treatment success\" (Figure 6F), especially as the putative prediction of the testosterone tetrad was validated using the GSE40272 PCa dataset (n = 153), with the HSD17B2\({}^{\text{lo}}\)HSD17B3\({}^{\text{hi}}\)SHBG\({}^{\text{lo}}\)SRD5A1\({}^{\text{hi}}\) group exhibiting significantly worse recurrence-free survival compared with their HSD17B2\({}^{\text{hi}}\)HSD17B3\({}^{\text{lo}}\)SHBG\({}^{\text{hi}}\)SRD5A1\({}^{\text{lo}}\) counterparts (concordance index = 77.9%, log-rank equal curves \(p\) = 0.0008, R\({}^{2}\) = 0.31/0.86, risk groups HR = 6.96 (95% CI: 1.9-25.47), \(p\) = 0.003) (Figure 6G).