title: Follicle-stimulating hormone and the pituitary-testicular-prostate axis at the time of initial diagnosis of prostate cancer and subsequent cluster selection of the patient population undergoing standard radical prostatectomy.
abstract: OBJECTIVE
  A preceding exploratory analysis has shown that follicle-stimulating hormone (FSH) was significantly correlated to and predicted by prostate-specific antigen (PSA) in a prostate cancer population. The aim of the study was to evaluate FSH physiopathology along the pituitary-testicular-prostate (PTP) axis at the time of initial diagnosis of prostate cancer in an operated population clustered according to the FSH/PSA ratio.
  
  
  METHODS
  The study included 93 patients who underwent standard radical prostatectomy. Age, percentages of positive cores at transrectal ultrasound scan biopsy (TRUSB) (P+), biopsy Gleason score (bGS), pathology Gleason score (pGS), luteinizing hormone (LH), FSH, prolactin hormone (PRL), total testosterone (TT), free testosterone (FT), estradiol (ESR) and PSA were the continuous variables. Category variables were pT and biopsy/pathology Gleason pattern I/II (b/pGPI/II). The population was clustered according to the FSH/PSA ratio which was computed from empirical data and then ranked for clustering the population as groups A (range 0.13 ≤ FSH/PSA ≤ 0.20), B (range 0.20 < FSH/PSA ≤ 0.50), C (range 0.50 < FSH/PSA ≤ 0.75), D (range 0.75 < FSH/PSA ≤ 1.00), E (range 1.00 < FSH/PSA ≤ 1.25), F (range 1.25 < FSH/PSA ≤ 2.00), G (range 2.00 < FSH/PSA ≤ 2.25), H (range 2.25 < FSH/PSA ≤ 6.40) and I (range 6.40 < FSH/ PSA ≤ 19.40). The model was assessed by simple linear regression analysis and differences between the groups were investigated by analysis of variance (ANOVA) for continuous variables and by contingency tables for category variables.
  
  
  RESULTS
  FSH was significantly correlated to and predicted by PSA in groups A (p = 0.04), B (p < 0.0001), C (p < 0.0001), D (p < 0.0001), E (p < 0.0001), F (p < 0.0001), G (p < 0.0001), H (p = 0.0001) and I (p = 0.001). Also, clusters (A-I) differed significantly for mean values of FSH (p < 0.0001), LH (p < 0.0001), TT (p = 0.04), PSA (p < 0.0001), bGS (p = 0.005), pGS (p = 0.01) and PSA/FT ratio (p < 0.0001); moreover, the nine groups showed significant different frequency distributions of pGPI (p = 0.02), pGPII (p = 0.0002) and bGPI (p = 0.04).
  
  
  CONCLUSIONS
  The ranking FSH/PSA ratio significantly clustered, along the PTP axis, an operated population diagnosed with prostate cancer. Also, the ranking FSH/PSA ratio selected prostate cancer clusters expressing different levels of hormonal disorder along the PTP axis and prognostic potential with different risks of progression. As a theory, in the current advancing world, the ranking FSH/PSA model might be considered as an interesting and effective tool for prostate cancer study as well as individualized, risk-adapted approaches of the disease. However, confirmatory studies are needed.
authors.affiliation: Department of Urology, University Integrated Hospitals, Civil Major Hospital, Verona, Italy. drporcaro @ yahoo.com
authors.forename: Antonio B
authors.initials: AB
authors.lastname: Porcaro
authors.forename: Filippo
authors.initials: F
authors.lastname: Migliorini
authors.forename: Aldo
authors.initials: A
authors.lastname: Petrozziello
authors.forename: Teodoro
authors.initials: T
authors.lastname: Sava
authors.forename: Mario
authors.initials: M
authors.lastname: Romano
authors.forename: Beatrice
authors.initials: B
authors.lastname: Caruso
authors.forename: Claudio
authors.initials: C
authors.lastname: Cocco
authors.forename: Claudio
authors.initials: C
authors.lastname: Ghimenton
authors.forename: Stefano
authors.initials: S
authors.lastname: Zecchinini Antoniolli
authors.forename: Vincenzo
authors.initials: V
authors.lastname: Lacola
authors.forename: Emanuele
authors.initials: E
authors.lastname: Rubilotta
authors.forename: Carmelo
authors.initials: C
authors.lastname: Monaco
authors.forename: Luigi
authors.initials: L
authors.lastname: Comunale
mesh_terms: D000368:Aged; D000704:Analysis of Variance; D016000:Cluster Analysis; D003661:Decision Support Techniques; D004958:Estradiol / Q000097:blood; D043373:Follicle Stimulating Hormone, Human / Q000097:blood*; D006801:Humans; D061705:Image-Guided Biopsy; D007610:Kallikreins / Q000097:blood; D016014:Linear Models; D007986:Luteinizing Hormone / Q000097:blood; D008297:Male; D008875:Middle Aged; D060787:Neoplasm Grading; D018579:Patient Selection; D010902:Pituitary Gland / Q000378:metabolism* / Q000503:physiopathology; D011237:Predictive Value of Tests; D011388:Prolactin / Q000097:blood; D011467:Prostate / Q000378:metabolism* / Q000503:physiopathology; D017430:Prostate-Specific Antigen / Q000097:blood; D011468:Prostatectomy; D011471:Prostatic Neoplasms / Q000097:blood / Q000175:diagnosis* / Q000503:physiopathology / Q000601:surgery*; D012189:Retrospective Studies; D013737:Testis / Q000378:metabolism* / Q000503:physiopathology; D013739:Testosterone / Q000097:blood
chemical_list: D043373:Follicle Stimulating Hormone, Human; D013739:Testosterone; D004958:Estradiol; D011388:Prolactin; D007986:Luteinizing Hormone; C549870:KLK3 protein, human; D007610:Kallikreins; D017430:Prostate-Specific Antigen