Limits...
Integrated genomic analysis identifies subclasses and prognosis signatures of kidney cancer.

Christinat Y, Krek W - Oncotarget (2015)

Bottom Line: Data analysis was based on a novel computational approach that selectively considers patients with extreme expression values of miRNAs to detect survival-associated molecular signatures.Integrated analysis of multidimensional data from the TCGA archive allowed to draw a portrait of distinct molecular subclasses of human ccRCC and to define signatures for prognosticating disease outcome.Together, these results offer new prospects for more accurate stratification and prognostication of ccRCC.

View Article: PubMed Central - PubMed

Affiliation: Institute of Molecular Health Sciences, ETH Zurich, 8093 Zurich, Switzerland.

ABSTRACT

Purpose: To define robust miRNA-based molecular classifiers for human clear cell renal cell carcinoma (ccRCC) subgrouping and prognostication.

Experimental design: Multidimensional data of over 500 clear cell renal cell carcinoma (ccRCC) patients were retrieved from The Cancer Genome Atlas (TCGA) archive. Data analysis was based on a novel computational approach that selectively considers patients with extreme expression values of miRNAs to detect survival-associated molecular signatures.

Results: Our in silico analysis unveiled a novel ccRCC-specific 5-miRNA (miR-10b, miR-21, miR-143, miR-183, and miR-192) signature able, when combined with information from conventional TNM staging and the age of the patient, to prognosticate ccRCC outcome more accurately than known ccRCC miRNA signatures or TNM staging alone. Furthermore, our approach revealed the existence of 6 distinct subgroups of ccRCC characterized by discrete differences in overall survival, tumor stage, and mutational spectra in key ccRCC tumor suppressor genes. It also demonstrated that BAP1 mutations correlate with tumor progression rather than overall survival.

Conclusion: Integrated analysis of multidimensional data from the TCGA archive allowed to draw a portrait of distinct molecular subclasses of human ccRCC and to define signatures for prognosticating disease outcome. Together, these results offer new prospects for more accurate stratification and prognostication of ccRCC.

No MeSH data available.


Related in: MedlinePlus

Identification of miRNAs associated to overall survival (OS) in ccRCCNMF clustering consensus map of the 65 identified miRNAs (training cohort) and Kaplan-Meier plot (validation cohort) for the most significantly OS-associated cluster representative, miR-146b-5p. Identified clusters (color-coded) and representative miRNAs are displayed on the right side of the map. Dotted lines represent the 5% confidence interval.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4496372&req=5

Figure 1: Identification of miRNAs associated to overall survival (OS) in ccRCCNMF clustering consensus map of the 65 identified miRNAs (training cohort) and Kaplan-Meier plot (validation cohort) for the most significantly OS-associated cluster representative, miR-146b-5p. Identified clusters (color-coded) and representative miRNAs are displayed on the right side of the map. Dotted lines represent the 5% confidence interval.

Mentions: To identify miRNAs linked to patient survival, we used TCGA datasets of over 500 ccRCC patients that we split into training and validation cohorts based on the respective miRNA-sequencing technology, yielding 252 patients in the training cohort and 261 in the validation cohort. We then asked whether low or high levels of a given miRNA had a significant correlation with a patient's overall survival. For each miRNA, patients were first separated by expression level quartiles of the given miRNA. Then, the overall survival of the patient group characterized by low expression of the miRNA—below the first quartile—was compared to the survival of the patient group with high expression levels—above the third quartile—through a log-rank statistical test. Using this new methodology, we identified 65 miRNAs that were statistically linked to overall survival (pFDR < 0.1; Supplementary Table S1). Among them, 32 were also significantly associated to overall survival in the validation cohort (p-value of log-rank test < 5%). A clustering procedure revealed the presence of 5 distinct miRNA clusters, which were best represented by miR-21, miR-146b-3p/5p, and miR-155 for cluster 1, miR-1 and miR-143 for cluster 2, miR-10b for cluster 3, mir-194-3p and miR-192-3p/5p for cluster 4, and miR-182, miR-183, and miR-221 for cluster 5 (Fig. 1). Note that although published miRNA signatures overlap with our set of 65 miRNAs, none has miRNAs in more than 3 of the 5 identified clusters.


Integrated genomic analysis identifies subclasses and prognosis signatures of kidney cancer.

Christinat Y, Krek W - Oncotarget (2015)

Identification of miRNAs associated to overall survival (OS) in ccRCCNMF clustering consensus map of the 65 identified miRNAs (training cohort) and Kaplan-Meier plot (validation cohort) for the most significantly OS-associated cluster representative, miR-146b-5p. Identified clusters (color-coded) and representative miRNAs are displayed on the right side of the map. Dotted lines represent the 5% confidence interval.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4496372&req=5

Figure 1: Identification of miRNAs associated to overall survival (OS) in ccRCCNMF clustering consensus map of the 65 identified miRNAs (training cohort) and Kaplan-Meier plot (validation cohort) for the most significantly OS-associated cluster representative, miR-146b-5p. Identified clusters (color-coded) and representative miRNAs are displayed on the right side of the map. Dotted lines represent the 5% confidence interval.
Mentions: To identify miRNAs linked to patient survival, we used TCGA datasets of over 500 ccRCC patients that we split into training and validation cohorts based on the respective miRNA-sequencing technology, yielding 252 patients in the training cohort and 261 in the validation cohort. We then asked whether low or high levels of a given miRNA had a significant correlation with a patient's overall survival. For each miRNA, patients were first separated by expression level quartiles of the given miRNA. Then, the overall survival of the patient group characterized by low expression of the miRNA—below the first quartile—was compared to the survival of the patient group with high expression levels—above the third quartile—through a log-rank statistical test. Using this new methodology, we identified 65 miRNAs that were statistically linked to overall survival (pFDR < 0.1; Supplementary Table S1). Among them, 32 were also significantly associated to overall survival in the validation cohort (p-value of log-rank test < 5%). A clustering procedure revealed the presence of 5 distinct miRNA clusters, which were best represented by miR-21, miR-146b-3p/5p, and miR-155 for cluster 1, miR-1 and miR-143 for cluster 2, miR-10b for cluster 3, mir-194-3p and miR-192-3p/5p for cluster 4, and miR-182, miR-183, and miR-221 for cluster 5 (Fig. 1). Note that although published miRNA signatures overlap with our set of 65 miRNAs, none has miRNAs in more than 3 of the 5 identified clusters.

Bottom Line: Data analysis was based on a novel computational approach that selectively considers patients with extreme expression values of miRNAs to detect survival-associated molecular signatures.Integrated analysis of multidimensional data from the TCGA archive allowed to draw a portrait of distinct molecular subclasses of human ccRCC and to define signatures for prognosticating disease outcome.Together, these results offer new prospects for more accurate stratification and prognostication of ccRCC.

View Article: PubMed Central - PubMed

Affiliation: Institute of Molecular Health Sciences, ETH Zurich, 8093 Zurich, Switzerland.

ABSTRACT

Purpose: To define robust miRNA-based molecular classifiers for human clear cell renal cell carcinoma (ccRCC) subgrouping and prognostication.

Experimental design: Multidimensional data of over 500 clear cell renal cell carcinoma (ccRCC) patients were retrieved from The Cancer Genome Atlas (TCGA) archive. Data analysis was based on a novel computational approach that selectively considers patients with extreme expression values of miRNAs to detect survival-associated molecular signatures.

Results: Our in silico analysis unveiled a novel ccRCC-specific 5-miRNA (miR-10b, miR-21, miR-143, miR-183, and miR-192) signature able, when combined with information from conventional TNM staging and the age of the patient, to prognosticate ccRCC outcome more accurately than known ccRCC miRNA signatures or TNM staging alone. Furthermore, our approach revealed the existence of 6 distinct subgroups of ccRCC characterized by discrete differences in overall survival, tumor stage, and mutational spectra in key ccRCC tumor suppressor genes. It also demonstrated that BAP1 mutations correlate with tumor progression rather than overall survival.

Conclusion: Integrated analysis of multidimensional data from the TCGA archive allowed to draw a portrait of distinct molecular subclasses of human ccRCC and to define signatures for prognosticating disease outcome. Together, these results offer new prospects for more accurate stratification and prognostication of ccRCC.

No MeSH data available.


Related in: MedlinePlus