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.