TY  - JOUR
A1  - Vergho, Daniel Claudius
A1  - Kneitz, Susanne
A1  - Kalogirou, Charis
A1  - Burger, Maximilian
A1  - Krebs, Markus
A1  - Rosenwald, Andreas
A1  - Spahn, Martin
A1  - Löser, Andreas
A1  - Kocot, Arkadius
A1  - Riedmiller, Hubertus
A1  - Kneitz, Burkhard
T1  - Impact of miR-21, miR-126 and miR-221 as Prognostic Factors of Clear Cell Renal Cell Carcinoma with Tumor Thrombus of the Inferior Vena Cava
N2  - Clear cell renal cell carcinoma (ccRCC) characterized by a tumor thrombus (TT) extending into the inferior vena cava (IVC) generally indicates poor prognosis. Nevertheless, the risk for tumor recurrence after nephrectomy and thrombectomy varies. An applicable and accurate prediction system to select ccRCC patients with TT of the IVC (ccRCC/TT) at high risk after nephrectomy is urgently needed, but has not been established up to now. To our knowledge, a possible role of microRNAs (miRs) for the development of ccRCC/TT or their impact as prognostic markers in ccRCC/TT has not been explored yet. Therefore, we analyzed the expression of the previously described onco-miRs miR-200c, miR-210, miR-126, miR-221, let-7b, miR-21, miR-143 and miR-141 in a study collective of 74 ccRCC patients. Using the expression profiles of these eight miRs we developed classification systems that accurately differentiate ccRCC from non-cancerous renal tissue and ccRCC/TT from tumors without TT. In the subgroup of 37 ccRCC/TT cases we found that miR-21, miR-126, and miR-221 predicted cancer related death (CRD) accurately and independently from other clinico-pathological features. Furthermore, a combined risk score based on the expression of miR-21, miR-126 and miR-221 was developed and showed high sensitivity and specificity to predict cancer specific survival (CSS) in ccRCC/TT. Using the combined risk score we were able to classify ccRCC/TT patients correctly into high and low risk cases. The risk stratification by the combined risk score (CRS) will benefit from further cohort validation and might have potential for clinical application as a molecular prediction system to identify high- risk ccRCC/TT patients.
KW  - forecasting
KW  - metastasis
KW  - renal cancer
KW  - renal cell carcinoma
KW  - kidneys
KW  - surgical oncology
KW  - surgical and invasive medical procedures
KW  - regression analysis
Y1  - 2014
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-113633
ER  - 
TY  - JOUR
A1  - Marquardt, André
A1  - Kollmannsberger, Philip
A1  - Krebs, Markus
A1  - Argentiero, Antonella
A1  - Knott, Markus
A1  - Solimando, Antonio Giovanni
A1  - Kerscher, Alexander Georg
T1  - Visual clustering of transcriptomic data from primary and metastatic tumors — dependencies and novel pitfalls
JF  - Genes
N2  - Personalized oncology is a rapidly evolving area and offers cancer patients therapy options that are more specific than ever. However, there is still a lack of understanding regarding transcriptomic similarities or differences of metastases and corresponding primary sites. Applying two unsupervised dimension reduction methods (t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP)) on three datasets of metastases (n = 682 samples) with three different data transformations (unprocessed, log10 as well as log10 + 1 transformed values), we visualized potential underlying clusters. Additionally, we analyzed two datasets (n = 616 samples) containing metastases and primary tumors of one entity, to point out potential familiarities. Using these methods, no tight link between the site of resection and cluster formation outcome could be demonstrated, or for datasets consisting of solely metastasis or mixed datasets. Instead, dimension reduction methods and data transformation significantly impacted visual clustering results. Our findings strongly suggest data transformation to be considered as another key element in the interpretation of visual clustering approaches along with initialization and different parameters. Furthermore, the results highlight the need for a more thorough examination of parameters used in the analysis of clusters.
KW  - visual clustering
KW  - t-SNE
KW  - UMAP
KW  - transcriptomic analysis
KW  - cancer
KW  - metastasis
Y1  - 2022
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-281872
SN  - 2073-4425
VL  - 13
IS  - 8
ER  -