Diagnosis of prostate cancer using differentially expressed genes in stroma - PubMed (original) (raw)

. 2011 Apr 1;71(7):2476-87.

doi: 10.1158/0008-5472.CAN-10-2585.

Yipeng Wang, Anne Sawyers, Huazhen Yao, Farahnaz Rahmatpanah, Xiao-Qin Xia, Qiang Xu, Rebecca Pio, Tolga Turan, James A Koziol, Steve Goodison, Philip Carpenter, Jessica Wang-Rodriguez, Anne Simoneau, Frank Meyskens, Manuel Sutton, Waldemar Lernhardt, Thomas Beach, Joseph Monforte, Michael McClelland, Dan Mercola

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Diagnosis of prostate cancer using differentially expressed genes in stroma

Zhenyu Jia et al. Cancer Res. 2011.

Abstract

More than one million prostate biopsies are performed in the United States every year. A failure to find cancer is not definitive in a significant percentage of patients due to the presence of equivocal structures or continuing clinical suspicion. We have identified gene expression changes in stroma that can detect tumor nearby. We compared gene expression profiles of 13 biopsies containing stroma near tumor and 15 biopsies from volunteers without prostate cancer. About 3,800 significant expression changes were found and thereafter filtered using independent expression profiles to eliminate possible age-related genes and genes expressed at detectable levels in tumor cells. A stroma-specific classifier for nearby tumor was constructed on the basis of 114 candidate genes and tested on 364 independent samples including 243 tumor-bearing samples and 121 nontumor samples (normal biopsies, normal autopsies, remote stroma, as well as stroma within a few millimeters of tumor). The classifier predicted the tumor status of patients using tumor-free samples with an average accuracy of 97% (sensitivity = 98% and specificity = 88%) whereas classifiers trained with sets of 100 randomly generated genes had no diagnostic value. These results indicate that the prostate cancer microenvironment exhibits reproducible changes useful for categorizing the presence of tumor in patients when a prostate sample is derived from near the tumor but does not contain any recognizable tumor.

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Figures

Figure 1

Figure 1

Histogram of tumor percentage for Datasets 1 – 4. The tumor percentage data of (a) and (b) were provided by SPECS pathologists, while the tumor percentage data of (c) and (d) were estimated by CellPred program (29). The stars in (a) mark the tumor percentages of the misclassified tumor-bearing cases in Dataset 1, which CellPred indicates may actually be non-tumor samples.

Figure 2

Figure 2

Plot of the Principal Component Analysis of training cases using the 131 probe-set Diagnostic Classifier.

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