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See detailCommon diseases alter the physiological age-related blood microRNA profile.
Fehlmann, Tobias; Lehallier, Benoit; Schaum, Nicholas et al

in Nature communications (2020), 11(1), 5958

Aging is a key risk factor for chronic diseases of the elderly. MicroRNAs regulate post-transcriptional gene silencing through base-pair binding on their target mRNAs. We identified nonlinear changes in ... [more ▼]

Aging is a key risk factor for chronic diseases of the elderly. MicroRNAs regulate post-transcriptional gene silencing through base-pair binding on their target mRNAs. We identified nonlinear changes in age-related microRNAs by analyzing whole blood from 1334 healthy individuals. We observed a larger influence of the age as compared to the sex and provide evidence for a shift to the 5' mature form of miRNAs in healthy aging. The addition of 3059 diseased patients uncovered pan-disease and disease-specific alterations in aging profiles. Disease biomarker sets for all diseases were different between young and old patients. Computational deconvolution of whole-blood miRNAs into blood cell types suggests that cell intrinsic gene expression changes may impart greater significance than cell abundance changes to the whole blood miRNA profile. Altogether, these data provide a foundation for understanding the relationship between healthy aging and disease, and for the development of age-specific disease biomarkers. [less ▲]

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See detailDeep sncRNA-seq of the PPMI cohort to study Parkinson’s disease progression
Kern, Fabian; Fehlmann, Tobias; Violich, Ivo et al

E-print/Working paper (2020)

Coding and non-coding RNAs have diagnostic and prognostic importance in Parkinson’s diseases (PD). We studied circulating small non-coding RNAs (sncRNAs) in 7, 003 samples from two longitudinal PD cohorts ... [more ▼]

Coding and non-coding RNAs have diagnostic and prognostic importance in Parkinson’s diseases (PD). We studied circulating small non-coding RNAs (sncRNAs) in 7, 003 samples from two longitudinal PD cohorts (Parkinson’s Progression Marker Initiative (PPMI) and Luxembourg Parkinson’s Study (NCER-PD)) and modelled their influence on the transcriptome. First, we sequenced sncRNAs in 5, 450 blood samples of 1, 614 individuals in PPMI. The majority of 323 billion reads (59 million reads per sample) mapped to miRNAs. Other covered RNA classes include piRNAs, rRNAs, snoRNAs, tRNAs, scaRNAs, and snRNAs. De-regulated miRNAs were associated with the disease and disease progression and occur in two distinct waves in the third and seventh decade of live. Originating mostly from a characteristic set of immune cells they resemble a systemic inflammation response and mitochondrial dysfunction, two hallmarks of PD. By profiling 1, 553 samples from 1, 024 individuals in the NCER-PD cohort using an independent technology, we validate relevant findings from the sequencing study. Finally, network analysis of sncRNAs and transcriptome sequencing of the original cohort identified regulatory modules emerging in progressing PD patients.Competing Interest StatementThe authors have declared no competing interest. [less ▲]

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See detailEvaluating the Use of Circulating MicroRNA Profiles for Lung Cancer Detection in Symptomatic Patients
Fehlmann, Tobias; Kahraman, Mustafa; Backes, Christina et al

in JAMA Oncology (2020)

Importance The overall low survival rate of patients with lung cancer calls for improved detection tools to enable better treatment options and improved patient outcomes. Multivariable molecular ... [more ▼]

Importance The overall low survival rate of patients with lung cancer calls for improved detection tools to enable better treatment options and improved patient outcomes. Multivariable molecular signatures, such as blood-borne microRNA (miRNA) signatures, may have high rates of sensitivity and specificity but require additional studies with large cohorts and standardized measurements to confirm the generalizability of miRNA signatures. Objective To investigate the use of blood-borne miRNAs as potential circulating markers for detecting lung cancer in an extended cohort of symptomatic patients and control participants. Design, Setting, and Participants This multicenter, cohort study included patients from case-control and cohort studies (TREND and COSYCONET) with 3102 patients being enrolled by convenience sampling between March 3, 2009, and March 19, 2018. For the cohort study TREND, population sampling was performed. Clinical diagnoses were obtained for 3046 patients (606 patients with non–small cell and small cell lung cancer, 593 patients with nontumor lung diseases, 883 patients with diseases not affecting the lung, and 964 unaffected control participants). No samples were removed because of experimental issues. The collected data were analyzed between April 2018 and November 2019. Main Outcomes and Measures Sensitivity and specificity of liquid biopsy using miRNA signatures for detection of lung cancer. Results A total of 3102 patients with a mean (SD) age of 61.1 (16.2) years were enrolled. Data on the sex of the participants were available for 2856 participants; 1727 (60.5%) were men. Genome-wide miRNA profiles of blood samples from 3046 individuals were evaluated by machine-learning methods. Three classification scenarios were investigated by splitting the samples equally into training and validation sets. First, a 15-miRNA signature from the training set was used to distinguish patients diagnosed with lung cancer from all other individuals in the validation set with an accuracy of 91.4% (95% CI, 91.0%-91.9%), a sensitivity of 82.8% (95% CI, 81.5%-84.1%), and a specificity of 93.5% (95% CI, 93.2%-93.8%). Second, a 14-miRNA signature from the training set was used to distinguish patients with lung cancer from patients with nontumor lung diseases in the validation set with an accuracy of 92.5% (95% CI, 92.1%-92.9%), sensitivity of 96.4% (95% CI, 95.9%-96.9%), and specificity of 88.6% (95% CI, 88.1%-89.2%). Third, a 14-miRNA signature from the training set was used to distinguish patients with early-stage lung cancer from all individuals without lung cancer in the validation set with an accuracy of 95.9% (95% CI, 95.7%-96.2%), sensitivity of 76.3% (95% CI, 74.5%-78.0%), and specificity of 97.5% (95% CI, 97.2%-97.7%). Conclusions and Relevance The findings of the study suggest that the identified patterns of miRNAs may be used as a component of a minimally invasive lung cancer test, complementing imaging, sputum cytology, and biopsy tests. [less ▲]

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