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Validation of proposed prostate cancer biomarkers with gene expression data: a long road to travel.

ABSTRACT: Biomarkers are important for early detection of cancer, prognosis, response prediction, and detection of residual or relapsing disease. Special attention has been given to diagnostic markers for prostate cancer since it is thought that early detection and surgery might reduce prostate cancer-specific mortality. The use of prostate-specific antigen, PSA (KLK3), has been debated on the base of cohort studies that show that its use in preventive screenings only marginally influences mortality from prostate cancer. Many groups have identified alternative or additional markers, among which PCA3, in order to detect early prostate cancer through screening, to distinguish potentially lethal from indolent prostate cancers, and to guide the treatment decision. The large number of markers proposed has led us to the present study in which we analyze these indicators for their diagnostic and prognostic potential using publicly available genomic data. We identified 380 markers from literature analysis on 20,000 articles on prostate cancer markers. The most interesting ones appeared to be claudin 3 (CLDN3) and alpha-methysacyl-CoA racemase highly expressed in prostate cancer and filamin C (FLNC) and keratin 5 with highest expression in normal prostate tissue. None of the markers proposed can compete with PSA for tissue specificity. The indicators proposed generally show a great variability of expression in normal and tumor tissue or are expressed at similar levels in other tissues. Those proposed as prognostic markers distinguish cases with marginally different risk of progression and appear to have a clinically limited use. We used data sets sampling 152 prostate tissues, data sets with 281 prostate cancers analyzed by microarray analysis and a study of integrated genomics on 218 cases to develop a multigene score. A multivariate model that combines several indicators increases the discrimination power but does not add impressively to the information obtained from Gleason scoring. This analysis of 10 years of marker research suggests that diagnostic and prognostic testing is more difficult in prostate cancer than in other neoplasms and that we must continue to search for better candidates.

SUBMITTER: Amaro A

PROVIDER: S-EPMC4113682 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BACKGROUND: Official reports on modal risk have not chosen appropriate numerators and denominators to enable like-for-like comparisons. We report age- and sex-specific deaths and injury rates from equivalent incidents in England by travel mode, distance travelled and time spent travelling. METHODS: Hospital admissions and deaths in England 2007-2009 were obtained for relevant ICD-10 external codes for pedestrians, cyclists, and car/van drivers, by age-group and sex. Distance travelled by age-group, sex and mode in England (National Travel Survey 2007-2009 data) was converted to time spent travelling using mean trip speeds. Fatality rates were compared with age-specific Netherlands data. RESULTS: All-age fatalities per million hours' use (f/mhu) varied over the same factor-of-three range for both sexes (0.15-0.45 f/mhu by mode for men, 0.09-0.31 f/mhu for women). Risks were similar for men aged 21-49 y for all three modes and for female pedestrians and drivers aged 21-69 y. Most at risk were: males 17-20 y (1.3 f/mhu (95% CI 1.2-1.4)) for driving; males 70+ (2.2 f/mhu(1.6-3.0)) for cycling; and females 70+ (0.95 f/mhu (0.86-1.1)) for pedestrians. In general, fatality rates were substantially higher among males than females. Risks per hour for male drivers <30 y were similar or higher than for male cyclists; for males aged 17-20 y, the risk was higher for drivers (33/Bn km (30-36), 1.3 f/mhu (1.2-1.4)) than cyclists (20/Bn km (10-37), 0.24 f/mhu (0.12-0.45)) whether using distance or time. Similar age patterns occurred for cyclists and drivers in the Netherlands. Age-sex patterns for injuries resulting in hospital admission were similar for cyclists and pedestrians but lower for drivers. CONCLUSIONS: When all relevant ICD-10 codes are used, fatalities by time spent travelling vary within similar ranges for walking, cycling and driving. Risks for drivers were highest in youth and fell with age, while for pedestrians and cyclists, risks increased with age. For the young, especially males, cycling is safer than driving.

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Validation of proposed prostate cancer biomarkers with gene expression data: a long road to travel.

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