Genome-wide MicroRNA Expression Profiles in COPD: Early Predictors for Cancer Development

Chronic obstructive pulmonary disease （COPD） significantly increases the risk of developing cancer. Biomarker studies frequently follow a case-control set-up in which patients diagnosed with a disease are compared to controls. Longitudinal cohort studies such as the COPD-centered German COPD and SYstemic consequences-COmorbidities NETwork （COSYCONET） study provide the patient and biomaterial base for discovering predictive molecular markers. We asked whether microRNA （miRNA） profiles in blood collected from COPD patients prior to a tumor diagnosis could support an early diagnosis of tumor development independent of the tumor type. From 2741 participants of COSYCONET diagnosed with COPD, we selected 534 individuals including 33 patients who developed cancer during the follow-up period of 54 months and 501 patients who did not develop cancer, but had similar age, gender and smoking history. Genome-wide miRNA profiles were generated and evaluated using machine learning techniques. For patients developing cancer we identified nine miRNAs with significantly decreased abundance （two-tailed unpaired t-test adjusted for multiple testing P 〈 0.05）, including members of the miR-320 family. The identified miRNAs regulate different cancer-related pathways including the MAPK pathway （P - 2.3 × 10 -5）. We also observed the impact of confounding factors on the generated miRNA profiles, underlining the value of our matched analysis. For selected miRNAs, qRT-PCR analysis was applied to validate the results. In conclusion, we identified several miRNAs in blood of COPD patients, which could serve as candidates for biomarkers to help identify COPD patients at risk of developing cancer.

Machine Learning to Detect Alzheimer’s Disease from Circulating Non-coding RNAs

Blood-borne small non-coding(snc RNAs)are among the prominent candidates for blood-based diagnostic tests.Often,high-throughput approaches are applied to discover biomarker signatures.These have to be validated in larger cohorts and evaluated by adequate statistical learning approaches.Previously,we published high-throughput sequencing based microRNA(miRNA)signatures in Alzheimer’s disease(AD)patients in the United States(US)and Germany.Here,we determined abundance levels of 21 known circulating miRNAs in 465 individuals encompassing AD patients and controls by RT-qPCR.We computed models to assess the relation between miRNA expression and phenotypes,gender,age,or disease severity(Mini-Mental State Examination;MMSE).Of the 21 miRNAs,expression levels of 20 miRNAs were consistently de-regulated in the US and German cohorts.18 miRNAs were significantly correlated with neurodegeneration(Benjamini-Hochberg adjusted P<0.05)with highest significance for miR-532-5 p(BenjaminiHochberg adjusted P=4.8×10^-30).Machine learning models reached an area under the curve(AUC)value of 87.6%in differentiating AD patients from controls.Further,ten miRNAs were significantly correlated with MMSE,in particular miR-26a/26b-5p(adjusted P=0.0002).Interestingly,the miRNAs with lower abundance in AD were enriched in monocytes and T-helper cells,while those up-regulated in AD were enriched in serum,exosomes,cytotoxic t-cells,and B-cells.Our study represents the next important step in translational research for a miRNA-based AD test.

EDISON-WMW: Exact Dynamic Programing Solution of the Wilcoxon–Mann–Whitney Test

In many research disciplines, hypothesis tests are applied to evaluate whether findings are statistically significant or could be explained by chance. The Wilcoxon-Mann-Whitney （WMW） test is among the most popular hypothesis tests in medicine and life science to analyze if two groups of samples are equally distributed. This nonparametric statistical homogeneity test is commonly applied in molecular diagnosis. Generally, the solution of the WMW test takes a high combinatorial effort for large sample cohorts containing a significant number of ties. Hence, P value is frequently approximated by a normal distribution. We developed EDISON-WMW, a new approach to calcu- late the exact permutation of the two-tailed unpaired WMW test without any corrections required and allowing for ties. The method relies on dynamic programing to solve the combinatorial problem of the WMW test efficiently. Beyond a straightforward implementation of the algorithm, we pre- sented different optimization strategies and developed a parallel solution. Using our program, the exact P value for large cohorts containing more than 1000 samples with ties can be calculated within minutes. We demonstrate the performance of this novel approach on randomly-generated data, benchmark it against 13 other commonly-applied approaches and moreover evaluate molec- ular biomarkers for lung carcinoma and chronic obstructive pulmonary disease （COPD）. We foundthat approximated P values were generally higher than the exact solution provided by EDISON- WMW. Importantly, the algorithm can also be applied to high-throughput omics datasets, where hundreds or thousands of features are included. To provide easy access to the multi-threaded version of EDISON-WMW, a web-based solution of our algorithm is freely available at http：// www.ccb.uni-saarland.de/software/wtest/.