Gene expression correlates of clinical prostate cancer behavior.

By Dinesh Singh, P. Febbo, K. Ross, D. Jackson, J. Manola, C. Ladd, Pablo Tamayo, A. Renshaw, A. D'Amico, J. Richie and 5 more. Cancer Cell, 2002

2,634 citations recorded by the source corpus.

Prostate tumors are among the most heterogeneous of cancers, both histologically and clinically. Microarray expression analysis was used to determine whether global biological differences underlie common pathological features of prostate cancer and to identify genes that might anticipate the clinical behavior of this disease. While no expression correlates of age, serum prostate specific antigen (PSA), and measures of local invasion were found, a set of genes was identified that strongly correlated with the state of tumor differentiation as measured by Gleason score. Moreover, a model using gene expression data alone accurately predicted patient outcome following prostatectomy. These results support the notion that the clinical behavior of prostate cancer is linked to underlying gene expression differences that are detectable at the time of diagnosis.

Subfields: Cancer Research, Molecular Biology, Pulmonary and Respiratory Medicine

View Paper: https://www.semanticscholar.org/paper/95b1ddb21d6a0b66176ac3a9489e6e352d4eb697

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Citations in the corpus

Ensemble machine learning on gene expression data for cancer classification. (2003, positive): “Prostate cancer (Singh et al 2002).”
High Dimensional Classification Using Features Annealed Independence Rules. (2007, methodology): “The last example uses the prostate cancer data studied in Singh et al. (2002).”
Learning the coordinate gradients (2011, methodology): “In particular our proposed method achieved competitive feature selection results on the Leukemia dataset [11] and Prostate cancer dataset [29] compared with existing method such as SVM-RFE [12].”
Supervised, Unsupervised, and Semi-Supervised Feature Selection: A Review on Gene Selection (2016, background): “(Maulik and Chakraborty, 2014) [90] Fuzzy preference based rough set (FPRS) & Leukemia [66], [67]; Transductive SVM (TSVM) k-fold CV (k 1⁄4 5); & SRBCT [76]; & DLBCL [62]; & Prostate [69]; & Childhood Leukemia 976 IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, VOL.”
Two-sample tests for high-dimension, strongly spiked eigenvalue models (2016, methodology): “ADDITIONAL SIMULATIONS 23 We used the test procedure by (5.5). We set κ(ni) = (n −1 i log ni) 1/2 in (S2.2). Let τ̃i(j) = τ̂i(j){1 + jκ(ni)} for all i, j. We calculated that (τ̃1(1), τ̃1(2), τ̃1(3)) = (0.407, 0.993, 1.302) and (τ̃2(1), τ̃2(2), τ̃2(3), τ̃2(4)) = (0.579, 0.7, 0.902, 1.307), so that k̂”
Partially supervised feature selection with regularized linear models (2009, background): “The Prostate cancer task discriminates between tumor and non-tumor samples (Singh et al., 2002).”
Robust biomarker identification for cancer diagnosis with ensemble feature selection methods (2010, background): “The normalized expression value f̄ij is defined as follows. f̄ij = fij −mj IQRj/1.35 where fij is the original expression value of gene j from sample i, mj is the median of expression of this gene over all samples and IQRj stands for the gene-specific interquartile range (Tukey, 1977).”
Androgen-Induced Differentiation and Tumorigenicity of Human Prostate Epithelial Cells (2004, methodology): “expressing PrECs permitted us to reliably organize expression profiles taken from benign and malignant prostate samples ( 26 ).”
A Complete Analysis of the l_1, p Group-Lasso (2012, background): “The ﬁrst data set from (Singh et al., 2002) is made up of laser intensity images from microarrays.”
Bayesian Subset Modeling for High-Dimensional Generalized Linear Models (2013, methodology): “Prostate Singh et al. (2002) 102 6033 Tumor/normal tissue (|ξn||D) had a mode at 5. Hans, Dobra, and West (2007) identified the top eight genes ranked in marginal inclusion probabilities: rgs3, dxy155e, atp6v1f, mgc8721, vdac1, gem, wsb1, and prrg1.”