TY  - JOUR
A1  - Kotlyar, Mischa J.
A1  - Krebs, Markus
A1  - Solimando, Antonio Giovanni
A1  - Marquardt, André
A1  - Burger, Maximilian
A1  - Kübler, Hubert
A1  - Bargou, Ralf
A1  - Kneitz, Susanne
A1  - Otto, Wolfgang
A1  - Breyer, Johannes
A1  - Vergho, Daniel C.
A1  - Kneitz, Burkhard
A1  - Kalogirou, Charis
T1  - Critical evaluation of a microRNA-based risk classifier predicting cancer-specific survival in renal cell carcinoma with tumor thrombus of the inferior vena cava
JF  - Cancers
N2  - (1) Background: Clear cell renal cell carcinoma extending into the inferior vena cava (ccRCC\(^{IVC}\)) represents a clinical high-risk setting. However, there is substantial heterogeneity within this patient subgroup regarding survival outcomes. Previously, members of our group developed a microRNA(miR)-based risk classifier — containing miR-21-5p, miR-126-3p and miR-221-3p expression — which significantly predicted the cancer-specific survival (CSS) of ccRCC\(^{IVC}\) patients. (2) Methods: Examining a single-center cohort of tumor tissue from n = 56 patients with ccRCC\(^{IVC}\), we measured the expression levels of miR-21, miR-126, and miR-221 using qRT-PCR. The prognostic impact of clinicopathological parameters and miR expression were investigated via single-variable and multivariable Cox regression. Referring to the previously established risk classifier, we performed Kaplan–Meier analyses for single miR expression levels and the combined risk classifier. Cut-off values and weights within the risk classifier were taken from the previous study. (3) Results: miR-21 and miR-126 expression were significantly associated with lymphonodal status at the time of surgery, the development of metastasis during follow-up, and cancer-related death. In Kaplan–Meier analyses, miR-21 and miR-126 significantly impacted CSS in our cohort. Moreover, applying the miR-based risk classifier significantly stratified ccRCC\(^{IVC}\) according to CSS. (4) Conclusions: In our retrospective analysis, we successfully validated the miR-based risk classifier within an independent ccRCC\(^{IVC}\) cohort.
KW  - kidney cancer
KW  - RCC
KW  - venous infiltration
KW  - biomarker
KW  - miR
KW  - risk stratification
Y1  - 2023
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-311040
SN  - 2072-6694
VL  - 15
IS  - 7
ER  - 
TY  - JOUR
A1  - Stojanović, Stevan D.
A1  - Fuchs, Maximilian
A1  - Fiedler, Jan
A1  - Xiao, Ke
A1  - Meinecke, Anna
A1  - Just, Annette
A1  - Pich, Andreas
A1  - Thum, Thomas
A1  - Kunz, Meik
T1  - Comprehensive bioinformatics identifies key microRNA players in ATG7-deficient lung fibroblasts
JF  - International Journal of Molecular Sciences
N2  - Background: Deficient autophagy has been recently implicated as a driver of pulmonary fibrosis, yet bioinformatics approaches to study this cellular process are lacking. Autophagy-related 5 and 7 (ATG5/ATG7) are critical elements of macro-autophagy. However, an alternative ATG5/ATG7-independent macro-autophagy pathway was recently discovered, its regulation being unknown. Using a bioinformatics proteome profiling analysis of ATG7-deficient human fibroblasts, we aimed to identify key microRNA (miR) regulators in autophagy. Method: We have generated ATG7-knockout MRC-5 fibroblasts and performed mass spectrometry to generate a large-scale proteomics dataset. We further quantified the interactions between various proteins combining bioinformatics molecular network reconstruction and functional enrichment analysis. The predicted key regulatory miRs were validated via quantitative polymerase chain reaction. Results: The functional enrichment analysis of the 26 deregulated proteins showed decreased cellular trafficking, increased mitophagy and senescence as the major overarching processes in ATG7-deficient lung fibroblasts. The 26 proteins reconstitute a protein interactome of 46 nodes and miR-regulated interactome of 834 nodes. The miR network shows three functional cluster modules around miR-16-5p, miR-17-5p and let-7a-5p related to multiple deregulated proteins. Confirming these results in a biological setting, serially passaged wild-type and autophagy-deficient fibroblasts displayed senescence-dependent expression profiles of miR-16-5p and miR-17-5p. Conclusions: We have developed a bioinformatics proteome profiling approach that successfully identifies biologically relevant miR regulators from a proteomics dataset of the ATG-7-deficient milieu in lung fibroblasts, and thus may be used to elucidate key molecular players in complex fibrotic pathological processes. The approach is not limited to a specific cell-type and disease, thus highlighting its high relevance in proteome and non-coding RNA research.
KW  - bioinformatics
KW  - miR
KW  - proteomics
KW  - functional network analysis
KW  - senescence
KW  - lung fibrosis
KW  - autophagy
Y1  - 2020
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-285181
SN  - 1422-0067
VL  - 21
IS  - 11
ER  -