title: Serum lipid profiles: novel biomarkers predicting advanced prostate cancer in patients receiving radical prostatectomy.
abstract: This study aimed to evaluate the role of serum lipid profiles as novel biomarkers in predicting pathological characteristics of prostate cancer (PCa). We retrospectively analyzed 322 consecutive patients with clinically localized PCa receiving radical prostatectomy (RP) and extended pelvic lymphadenectomy. Unconditional logistic regression was used to estimate the prostatectomy Gleason score (pGS), pathological stage and lymph node involvement (LNI) in RP specimens. Preoperative prostate-specific antigen (PSA) levels, biopsy GS (bGS), and preoperative tumor, node, metastasis staging were used as basic variables to predict postoperative pathological characteristics. Preoperative serum lipid profiles were introduced as potential predictors. A receiver operating characteristic (ROC) curve was used to determine predictive efficacy. Significant differences in pathological characteristics were observed among patients with normal and abnormal total cholesterol (TC), triglyceride (TG), and low-density lipoprotein (LDL) levels, with the exception of pGS in the TG group. Multivariable regression analysis revealed that the odds ratio for high levels of TC for LNI compared with normal TC levels was 6.386 (95% confidence interval [CI] 1.510-27.010), 3.270 (95% CI: 1.470-7.278) for high levels of TG for pT3-4 disease, and 2.670 (95% CI: 1.134-6.287) for high levels of LDL for pGS. The area under the ROC curve of the models with dyslipidemia was larger than that in models without dyslipidemia, in predicting pathological characteristics. Abnormal TC, TG, and LDL levels are significantly associated with postoperative pathological status in PCa patients. Together with preoperative PSA levels, bGS, and clinical stage, dyslipidemia is more accurate in predicting pathological characteristics.
authors.forename: Gui-Ming
authors.initials: GM
authors.lastname: Zhang
authors.forename: Xiao-Jian
authors.initials: XJ
authors.lastname: Qin
authors.forename: Hai-Liang
authors.initials: HL
authors.lastname: Zhang
authors.forename: Wen-Jun
authors.initials: WJ
authors.lastname: Xiao
authors.forename: Yao
authors.initials: Y
authors.lastname: Zhu
authors.forename: Cheng-Yuan
authors.initials: CY
authors.lastname: Gu
authors.forename: Bo
authors.initials: B
authors.lastname: Dai
authors.forename: Guo-Hai
authors.initials: GH
authors.lastname: Shi
authors.affiliation: Department of Urology, Fudan University Shanghai Cancer Center, Shanghai, China.
authors.forename: Ding-Wei
authors.initials: DW
authors.lastname: Ye
mesh_terms: D000368:Aged; D014408:Biomarkers, Tumor / Q000097:blood*; D001706:Biopsy; D018450:Disease Progression; D006801:Humans; D008055:Lipids / Q000097:blood*; D008197:Lymph Node Excision; D008297:Male; D008875:Middle Aged; D009367:Neoplasm Staging; D011237:Predictive Value of Tests; D011379:Prognosis; D017430:Prostate-Specific Antigen / Q000097:blood; D011468:Prostatectomy; D011471:Prostatic Neoplasms / Q000175:diagnosis* / Q000473:pathology / Q000601:surgery*; D012044:Regression Analysis; D012189:Retrospective Studies; D012720:Severity of Illness Index
chemical_list: D014408:Biomarkers, Tumor; D008055:Lipids; D017430:Prostate-Specific Antigen