Gene Expression Profiling Predicts Survival in Conventional Renal Cell Carcinoma

Department of Radiation Sciences, Umeå University, Umeå, Västerbotten, Sweden
PLoS Medicine (Impact Factor: 14.43). 01/2006; 3(1):e13. DOI: 10.1371/journal.pmed.0030013
Source: PubMed


Conventional renal cell carcinoma (cRCC) accounts for most of the deaths due to kidney cancer. Tumor stage, grade, and patient performance status are used currently to predict survival after surgery. Our goal was to identify gene expression features, using comprehensive gene expression profiling, that correlate with survival.
Gene expression profiles were determined in 177 primary cRCCs using DNA microarrays. Unsupervised hierarchical clustering analysis segregated cRCC into five gene expression subgroups. Expression subgroup was correlated with survival in long-term follow-up and was independent of grade, stage, and performance status. The tumors were then divided evenly into training and test sets that were balanced for grade, stage, performance status, and length of follow-up. A semisupervised learning algorithm (supervised principal components analysis) was applied to identify transcripts whose expression was associated with survival in the training set, and the performance of this gene expression-based survival predictor was assessed using the test set. With this method, we identified 259 genes that accurately predicted disease-specific survival among patients in the independent validation group (p < 0.001). In multivariate analysis, the gene expression predictor was a strong predictor of survival independent of tumor stage, grade, and performance status (p < 0.001).
cRCC displays molecular heterogeneity and can be separated into gene expression subgroups that correlate with survival after surgery. We have identified a set of 259 genes that predict survival after surgery independent of clinical prognostic factors.

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    • "Dicerl has been found to be expressed at low level in nasopharyngeal carcinoma tissues no matter whether at the gene or at the protein levels, and it could also be a novel prognostic biomarker [9]. As for KIRC, several studies have been performed to detect gene expression signatures which may provide diagnostic and prognostic information [10] [11] [12]. Ge et al. have identified miRNA signature including 22 miRNAs as an independent novel predictor of patient outcomes [13]. "
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    ABSTRACT: Kidney renal clear cell carcinoma (KIRC) is one of the most common cancers with high mortality all over the world. Many studies have proposed that genes could be used to predict prognosis in KIRC. In this study, RNA expression data from next-generation sequencing and clinical information of 523 patients downloaded from The Cancer Genome Atlas (TCGA) dataset were analyzed in order to identify the relationship between gene expression level and the prognosis of KIRC patients. A set of five genes that significantly associated with overall survival time was identified and a model containing these five genes was constructed by Cox regression analysis. By Kaplan-Meier and Receiver Operating Characteristic (ROC) analysis, we confirmed that the model had good sensitivity and specificity. In summary, expression of the five-gene model is associated with the prognosis outcomes of KIRC patients, and it may have an important clinical significance.
    Computational and Mathematical Methods in Medicine 01/2015; 2015(2):1-7. DOI:10.1155/2015/842784 · 0.77 Impact Factor
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    • "gene expression signatures validated: the aggressive subgroup defined by Kosari [31] had worse CSS than the nonaggressive subgroup (HR: 2.85; p < 0.001); the Zhao [33] poor prognosis cluster 2 had worse CSS than cluster 1 (HR: 5.26; p < 0.001). The aggressive subgroup defined by Lane et al. [32] showed worse CSS than the indolent subgroup (HR: 4.21; p < 0.001); the Brannon [10] poor prognosis ccB subgroup (HR: 4.90; p < 0.001) had worse CSS than the ccA subgroup. "
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    ABSTRACT: Background Candidate biomarkers have been identified for clear cell renal cell carcinoma (ccRCC) patients, but most have not been validated. Objective To validate published ccRCC prognostic biomarkers in an independent patient cohort and to assess intratumour heterogeneity (ITH) of the most promising markers to guide biomarker optimisation. Design, setting, and participants Cancer-specific survival (CSS) for each of 28 identified genetic or transcriptomic biomarkers was assessed in 350 ccRCC patients. ITH was interrogated in a multiregion biopsy data set of 10 ccRCCs. Outcome measurements and statistical analysis Biomarker association with CSS was analysed by univariate and multivariate analyses. Results and limitations A total of 17 of 28 biomarkers (TP53 mutations; amplifications of chromosomes 8q, 12, 20q11.21q13.32, and 20 and deletions of 4p, 9p, 9p21.3p24.1, and 22q; low EDNRB and TSPAN7 expression and six gene expression signatures) were validated as predictors of poor CSS in univariate analysis. Tumour stage and the ccB expression signature were the only independent predictors in multivariate analysis. ITH of the ccB signature was identified in 8 of 10 tumours. Several genetic alterations that were significant in univariate analysis were enriched, and chromosomal instability indices were increased in samples expressing the ccB signature. The study may be underpowered to validate low-prevalence biomarkers. Conclusions The ccB signature was the only independent prognostic biomarker. Enrichment of multiple poor prognosis genetic alterations in ccB samples indicated that several events may be required to establish this aggressive phenotype, catalysed in some tumours by chromosomal instability. Multiregion assessment may improve the precision of this biomarker.
    European Urology 07/2014; 66(5). DOI:10.1016/j.eururo.2014.06.053 · 13.94 Impact Factor
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    • "In addition, if a training data set is used for a double purpose (i.e., to construct the compound covariate and then to test it), the resulting over-fitting means the p-value is not reliable when testing the regression parameter. Therefore, we use a 2-fold method (e.g., [13,14]), splitting all observations in the training cohort into two parts, one part for assigning gene weights, and another part for testing the regression parameter through a partial likelihood score test. The remainder of this paper is organized as follows: We outline creation of the compound score using a random covariate approach. "
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    ABSTRACT: Background Dealing with high dimensional markers, such as gene expression data obtained using microarray chip technology or genomics studies, is a key challenge because the numbers of features greatly exceeds the number of biological samples. After selecting biologically relevant genes, how to summarize the expression of selected genes and then further build predicted model is an important issue in medical applications. One intuitive method of addressing this challenge assigns different weights to different features, subsequently combining this information into a single score, named the compound covariate. Investigators commonly employ this score to assess whether an association exists between the compound covariate and clinical outcomes adjusted for baseline covariates. However, we found that some clinical papers concerned with such analysis report bias p-values based on flawed compound covariate in their training data set. Results We correct this flaw in the analysis and we also propose treating the compound score as a random covariate, to achieve more appropriate results and significantly improve study power for survival outcomes. With this proposed method, we thoroughly assess the performance of two commonly used estimated gene weights through simulation studies. When the sample size is 100, and censoring rates are 50%, 30%, and 10%, power is increased by 10.6%, 3.5%, and 0.4%, respectively, by treating the compound score as a random covariate rather than a fixed covariate. Finally, we assess our proposed method using two publicly available microarray data sets. Conclusion In this article, we correct this flaw in the analysis and the propose method, treating the compound score as a random covariate, can achieve more appropriate results and improve study power for survival outcomes.
    BMC Systems Biology 12/2012; 6(3). DOI:10.1186/1752-0509-6-S3-S11 · 2.44 Impact Factor
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