Transmission/disequilibrium tests of androgen receptor and glutathione S-transferase pi variants in prostate cancer families

p53 Gene Codon 72 Polymorphism but Not Tumor Necrosis Factor-α Gene Is Associated with Prostate Cancer

OBJECTIVE

A polymorphism of gene p53 codon 72 is associated with various cancer formations. Tumor necrosis factor-alpha (TNF-alpha) one of the cytokines secreted by macrophages in response to inflammation and is also related to cancer formation. We aimed to evaluate the association between prostate cancer and the polymorphisms of the TNF-alpha gene promoter -308 and p53 gene codon 72.

PATIENTS AND METHODS

In our study, a normal control group of 126 healthy people and 96 patients with prostate cancer were examined. The polymorphism (G/A) of TNF-alpha gene was detected by polymerase chain reaction (PCR)-based restriction analysis (Nco I endonuclease) and the polymorphism of p53 gene was detected by two PCRs (one for proline and one for arginine form).

RESULTS

There was a significant difference in the distribution of codon 72 polymorphism the p53 gene between prostate cancer patients and the normal controls (p < 0.001). The proline form of p53 gene codon 72 was significantly higher than the arginine form, with an odds ratio of 2.606 (95% CI = 1.052-6.455). This difference was also revealed in the tumor staging (p = 0.035) as the proline form was significantly higher in the metastasis group of prostate cancer. There were no statistical differences in the distribution of -308 polymorphism of the TNF-alpha gene between cancer patients and the control subjects (p = 1.0).

CONCLUSION

Prostate cancer appears to be associated with the p53 gene codon 72 polymorphisms, but not with the TNF-alpha gene. The proline form of p53 gene codon 72 might be a more significant risk factor for the development of metastasis than the arginine form.

How Strong Is the Association Between CAG and GGN Repeat Length Polymorphisms in the Androgen Receptor Gene and Prostate Cancer Risk?

Objective: Although narrative reviews have suggested an association between (CAG)n and (GGN)n polymorphisms in the AR gene and prostate cancer, it has never been quantified systematically. The purpose of this meta-analysis was to provide relative and absolute quantitative summary estimates with sufficient power.

Method: Publications were identified through database searches for epidemiologic studies published until February 2004. For each study, mean differences in repeat length between cases and controls were calculated as well as continuous odds ratios (OR) per one CAG or GGN repeat decrement and discrete ORs to compare prostate cancer risk in men with short CAG repeats (≤21 repeats) versus long CAG repeats (&gt;21 repeats) and short GGN repeats (≤16 repeats) versus long GGN repeats (&gt;16 repeats). The study-specific estimates were combined by random effects metaregression analyses.

Results: Nineteen case-control studies were included in this review comprising a total of 4,274 cases and 5,275 controls. Prostate cancer cases had on average 0.26 fewer CAG repeats and 0.09 fewer GGN repeats than controls. The continuous ORs of prostate cancer per one repeat decrement were 1.02 and 1.01 for CAG and GGN repeats, respectively. The summary discrete OR (95% confidence interval) were 1.19 (1.07-1.31) and 1.31 (1.06-1.61) for CAG and GGN repeat polymorphisms, respectively.

Conclusion: Although the presence of shorter repeats seemed to be modestly associated with prostate cancer risk, the absolute difference in number of repeats between cases and controls is &lt;1 repeat. We question whether such a small difference is enough to yield measurable biological impact in prostate carcinogenesis.

Detection of Prostate Cancer and Predicting Progression

Carcinoma of the prostate is the second leading cause of male cancer-related death in the United States. Better indicators of prostate cancer presence and progression are needed to avoid unnecessary treatment, predict disease course, and develop more effective therapy. Numerous molecular markers have been described in human serum, urine, seminal fluid, and histological specimens that exhibit varying capacities to detect prostate cancer and predict disease course. However, to date, few of these markers have been adequately validated for clinical use. The purpose of this review is to examine the current status of these markers in prostate cancer and to assess the diagnostic potential for future markers from identified genes and molecules that display loss, mutation, or alteration in expression between tumor and normal prostate tissues. In this review we cite 91 molecular markers that display some level of correlation with prostate cancer presence, disease progression, cancer recurrence, prediction of response to therapy, and/or disease-free survival. We suggest criteria to consider when selecting a marker for further development as a clinical tool and discuss five examples of markers (chromogranin A, glutathione S-transferase pi 1, prostate stem cell antigen, prostate-specific membrane antigen, and telomerase reverse transcriptase) that fulfill some of these criteria. Finally, we discuss how to conduct evaluations of candidate prostate cancer markers and some of the issues involved in the validation process.

Human prostate cancer risk factors

Prostate cancer has the highest prevalence of any nonskin cancer in the human body, with similar likelihood of neoplastic foci found within the prostates of men around the world regardless of diet, occupation, lifestyle, or other factors. Essentially all men with circulating androgens will develop microscopic prostate cancer if they live long enough. This review is a contemporary and comprehensive, literature-based analysis of the putative risk factors for human prostate cancer, and the results were presented at a multidisciplinary consensus conference held in Crystal City, Virginia, in the fall of 2002. The objectives were to evaluate known environmental factors and mechanisms of prostatic carcinogenesis and to identify existing data gaps and future research needs. The review is divided into four sections, including 1) epidemiology (endogenous factors [family history, hormones, race, aging and oxidative stress] and exogenous factors [diet, environmental agents, occupation and other factors, including lifestyle factors]); 2) animal and cell culture models for prediction of human risk (rodent models, transgenic models, mouse reconstitution models, severe combined immunodeficiency syndrome mouse models, canine models, xenograft models, and cell culture models); 3) biomarkers in prostate cancer, most of which have been tested only as predictive factors for patient outcome after treatment rather than as risk factors; and 4) genotoxic and nongenotoxic mechanisms of carcinogenesis. The authors conclude that most of the data regarding risk relies, of necessity, on epidemiologic studies, but animal and cell culture models offer promise in confirming some important findings. The current understanding of biomarkers of disease and risk factors is limited. An understanding of the risk factors for prostate cancer has practical importance for public health research and policy, genetic and nutritional education and chemoprevention, and prevention strategies.

Osteocalcin gene HindIII C/T polymorphism is a biomarker for prostate cancer and responsiveness to hormone therapy

OBJECTIVES

Osteocalcin is a vitamin-K dependent protein which is related to the metabolism of bone and calcium. The formation or progression of prostate cancer is presumed to be associated with the osteocalcin gene. The most frequently seen polymorphism is HindIII which is located at the promoter region. HindIII is therefore a possible genetic marker in the search for the association between prostate cancer and normal control subjects.

METHODS

In our study, a normal control group of 132 healthy people and 96 patients with prostate cancer were examined. The polymorphism was seen following polymerase chain reaction (PCR) based restriction analysis.

RESULTS

The result revealed significant differences between normal individuals and cancer patients (p=0.034) and the distribution of the "CC" homozygote in the control group was higher than that in the patient group. No statistical differences were found in clinical staging and grading. The 54 patients who received hormone therapy were further categorized into response and non-response groups, statistical differences between these two groups were revealed (p=0.007, Fisher's exact test).

CONCLUSIONS

Based on our results, we conclude that the HindIII polymorphism of the osteocalcin gene is a suitable genetic marker of prostate cancer which can be used in the prediction of the outcome of patients who receive hormone therapy.