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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

Performance assessment of several prognosis signatures on the validation cohort(A) Overall survival prediction from pure miRNA signatures. (B) Detailed analysis, stratified by tumor stage, of miRNA signatures that outperform a random 5-miRNA signature by more than a standard deviation. (C) Prognosis performance of miRNA signatures when combined with the patient's age and TNM tumor stage. (D) Same as B but for miRNA signature combined with the patient's age and tumor stage. Error bars represent the standard deviation.
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Figure 2: Performance assessment of several prognosis signatures on the validation cohort(A) Overall survival prediction from pure miRNA signatures. (B) Detailed analysis, stratified by tumor stage, of miRNA signatures that outperform a random 5-miRNA signature by more than a standard deviation. (C) Prognosis performance of miRNA signatures when combined with the patient's age and TNM tumor stage. (D) Same as B but for miRNA signature combined with the patient's age and tumor stage. Error bars represent the standard deviation.

Mentions: When compared to other published miRNA signatures on the validation cohort, our “top miRs” signature outperforms all other, to the exception of Wu et al.'s, in terms of overall survival prediction (Fig. 2A). Of note, it surpassed all other signatures in the training cohort (Supplementary Fig. S1A). However, with respect to tumor stage and metastasis, one observes that our “top miRs” signature is superior to all other signature for early-stage patients and for late-stage patients with metastases, and only slightly inferior to Wu et al.'s for late-stage patients without metastases (Fig. 2B). Interestingly, only four signatures, including ours, lie further away than one standard deviation from a random 5-miRNA signature.


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

Christinat Y, Krek W - Oncotarget (2015)

Performance assessment of several prognosis signatures on the validation cohort(A) Overall survival prediction from pure miRNA signatures. (B) Detailed analysis, stratified by tumor stage, of miRNA signatures that outperform a random 5-miRNA signature by more than a standard deviation. (C) Prognosis performance of miRNA signatures when combined with the patient's age and TNM tumor stage. (D) Same as B but for miRNA signature combined with the patient's age and tumor stage. Error bars represent the standard deviation.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Performance assessment of several prognosis signatures on the validation cohort(A) Overall survival prediction from pure miRNA signatures. (B) Detailed analysis, stratified by tumor stage, of miRNA signatures that outperform a random 5-miRNA signature by more than a standard deviation. (C) Prognosis performance of miRNA signatures when combined with the patient's age and TNM tumor stage. (D) Same as B but for miRNA signature combined with the patient's age and tumor stage. Error bars represent the standard deviation.
Mentions: When compared to other published miRNA signatures on the validation cohort, our “top miRs” signature outperforms all other, to the exception of Wu et al.'s, in terms of overall survival prediction (Fig. 2A). Of note, it surpassed all other signatures in the training cohort (Supplementary Fig. S1A). However, with respect to tumor stage and metastasis, one observes that our “top miRs” signature is superior to all other signature for early-stage patients and for late-stage patients with metastases, and only slightly inferior to Wu et al.'s for late-stage patients without metastases (Fig. 2B). Interestingly, only four signatures, including ours, lie further away than one standard deviation from a random 5-miRNA signature.

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