Insulin-like growth factor (IGF)-I, IGF-II and IGF type I receptor (IGFR-I) expression in prostatic cancer

Abstract Despite evidence implicating the insulin-like growth factor (IGF) system in the pathogenesis of prostate cancer, its precise role remains unclear. In this study we investigated the differential expression of IGF-I, IGF-II and their type I receptor (IGFR-I) in the epithelium and stroma of prostate neoplastic tissues. Using immunohistochemistry and in situ hybridization techniques, we analyzed 43 paraffin-embedded prostatic samples and compared prostatic cancer (Pca) with prostatic intraepithelial neoplasia (PIN) and its normal adjacent prostate (NAP) counterpart. We then determined a possible correlation of the immunohistochemical and in situ findings with two known prognostic clinical-pathological indices: Gleason score histological tumor grade and TNM clinical stage. In 22 of the 43 frozen prostatectomy specimens, IGF-I, IGF-II and IGFR-I mRNA expression were also evaluated by semiquantitative RT-PCR. Non neoplastic and neoplastic tissues examined contained detectable amounts of epithelial and stromal IGF-I, IGF-II and IGFR-I protein and mRNA; all levels increased significantly from normal tissue to PIN to Pca. In all three areas examined, IGFR-I expression was invariably higher in epithelium than in stroma, whereas expression of IGF ligands differed. In normal prostatic tissue, IGF-I and IGF-II expression was higher in stroma than in epithelium. Conversely, in Pca tissue, both factors were more strongly expressed in epithelial malignant cells. PIN areas showed IGF-I lowest, and IGF-II and IGFR-I levels highest in the epithelium. IGF-II protein and mRNA reached their highest levels in prostate tumors with high Gleason scores. These findings indicate that the IGF system changes as prostate tissue progresses from a normal to a malignant state. Differential expression of certain IGF system components in Pca may be associated with the malignant phenotype and more aggressive tumor behavior. Hence IGFs could serve to predict the outcome of prostatic cancer.