TY  - JOUR
A1  - Molochnikov, Leonid
A1  - Rabey, Jose M.
A1  - Dobronevsky, Evgenya
A1  - Bonuccelli, Ubaldo
A1  - Ceravolo, Roberto
A1  - Frosini, Daniela
A1  - Grünblatt, Edna
A1  - Riederer, Peter
A1  - Jacob, Christian
A1  - Aharon-Peretz, Judith
A1  - Bashenko, Yulia
A1  - Youdim, Moussa B. H.
A1  - Mandel, Silvia A.
T1  - A molecular signature in blood identifies early Parkinson's disease
JF  - Molecular Neurodegeneration
N2  - Background: The search for biomarkers in Parkinson's disease (PD) is crucial to identify the disease early and monitor the effectiveness of neuroprotective therapies. We aim to assess whether a gene signature could be detected in blood from early/mild PD patients that could support the diagnosis of early PD, focusing on genes found particularly altered in the substantia nigra of sporadic PD. 
Results: The transcriptional expression of seven selected genes was examined in blood samples from 62 early stage PD patients and 64 healthy age-matched controls. Stepwise multivariate logistic regression analysis identified five genes as optimal predictors of PD: p19 S-phase kinase-associated protein 1A (odds ratio [OR] 0.73; 95% confidence interval [CI] 0.60-0.90), huntingtin interacting protein-2 (OR 1.32; CI 1.08-1.61), aldehyde dehydrogenase family 1 subfamily A1 (OR 0.86; 95% CI 0.75-0.99), 19 S proteasomal protein PSMC4 (OR 0.73; 95% CI 0.60-0.89) and heat shock 70-kDa protein 8 (OR 1.39; 95% CI 1.14-1.70). At a 0.5 cut-off the gene panel yielded a sensitivity and specificity in detecting PD of 90.3 and 89.1 respectively and the area under the receiving operating curve (ROC AUC) was 0.96. The performance of the five-gene classifier on the de novo PD individuals alone composing the early PD cohort (n = 38), resulted in a similar ROC with an AUC of 0.95, indicating the stability of the model and also, that patient medication had no significant effect on the predictive probability (PP) of the classifier for PD risk. The predictive ability of the model was validated in an independent cohort of 30 patients at advanced stage of PD, classifying correctly all cases as PD (100% sensitivity). Notably, the nominal average value of the PP for PD (0.95 (SD = 0.09)) in this cohort was higher than that of the early PD group (0.83 (SD = 0.22)), suggesting a potential for the model to assess disease severity. Lastly, the gene panel fully discriminated between PD and Alzheimer's disease (n = 29). 
Conclusions: The findings provide evidence on the ability of a five-gene panel to diagnose early/mild PD, with a possible diagnostic value for detection of asymptomatic PD before overt expression of the disorder.
KW  - cerebrospina
KW  - magnetic-resonance-spectroscopy
KW  - protein
KW  - biomarkers
KW  - E3 ubiquitin ligase
KW  - SCF
KW  - SKP1
KW  - heat shock protein Hsc-70
KW  - early diagnosis
KW  - fluid
KW  - alpha-synuclein
KW  - dehydrogenases
KW  - Alzheimer's disease
KW  - sporadic Parkinson's disease
KW  - blood biomarker
KW  - CSF
KW  - multiple system atrophy
KW  - clinical diagnosis
KW  - substantia nigra
KW  - gene expression
Y1  - 2012
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-134508
VL  - 7
IS  - 26
ER  - 
TY  - JOUR
A1  - Kunz, Meik
A1  - Wolf, Beat
A1  - Schulze, Harald
A1  - Atlan, David
A1  - Walles, Thorsten
A1  - Walles, Heike
A1  - Dandekar, Thomas
T1  - Non-Coding RNAs in Lung Cancer: Contribution of Bioinformatics Analysis to the Development of Non-Invasive Diagnostic Tools
JF  - Genes
N2  - Lung cancer is currently the leading cause of cancer related mortality due to late diagnosis and limited treatment intervention. Non-coding RNAs are not translated into proteins and have emerged as fundamental regulators of gene expression. Recent studies reported that microRNAs and long non-coding RNAs are involved in lung cancer development and progression. Moreover, they appear as new promising non-invasive biomarkers for early lung cancer diagnosis. Here, we highlight their potential as biomarker in lung cancer and present how bioinformatics can contribute to the development of non-invasive diagnostic tools. For this, we discuss several bioinformatics algorithms and software tools for a comprehensive understanding and functional characterization of microRNAs and long non-coding RNAs.
KW  - lung cancer
KW  - non-invasive biomarkers
KW  - miRNAs
KW  - lncRNAs
KW  - bioinformatics
KW  - early diagnosis
KW  - algorithm
Y1  - 2016
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-147990
VL  - 8
IS  - 1
ER  -