Identification of predictors based on drug targets highlights accurate treatment of goserelin in breast and prostate cancer

Abstract

Goserelin is an effective alternative to surgery or estrogen therapy in prostate cancer palliation, and possibly to ovariectomy in premenopausal breast cancer. However, not all users of goserelin can benefit from it, or some patients are not sensitive to goserelin. The advent of network pharmacology has highlighted the need for accurate treatment and predictive biomarkers. In this study, we successfully to identify 76 potential targets related to the compound of goserelin through network pharmacology approach. We also identified 18 DEGs in breast cancer tissues and 5 DEGs in cells, and 6 DEGs in prostate cancer tissues and 9 DEGs in cells. CRABP2 is the common DEG both in breast and prostate cancer. The risk prediction models constructed with potential prognostic targets of goserelin can successfully predict the prognosis in breast and prostate cancer, especially for very young breast cancer patients. Moreover, seven subgroups in breast cancer and six subgroups in prostate cancer were respectively identified based on consensus clustering using potential prognostic targets of goserelin that significantly influenced survival. The expression of representative genes including CORO1A and ANXA5 in breast and DPP4 in prostate showed strong correlations with clinic-pathological factors. Taken together, the novel signature can facilitate identification of new biomarkers which sensitive to goserelin, increase the using accuracy of goserelin and clarify the classification of disease molecular subtypes in breast and prostate cancer.