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

Ludwig, Nicole; Fehlmann, Tobias; Kern, Fabian; Gogol, M.; Maetzler, Walter; Deutscher, Stephanie; Gurlit, Simone; Schulte, Claudia; Thaler, Anna‐Katharina von; Deuschle, Christian; Metzger, Florian; Berg, Daniela; Suenkel, Ulrike; Keller, Verena; Backes, Christina; Lenhof, Hans‐Peter; Meese, Eckart

doi:10.1016/j.gpb.2019.09.004

Cited by 71 publications

(45 citation statements)

References 39 publications

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“…miR-129-2-3p, here identified as a common mutated miR, has previously been identified as a regulator in human cancer development and progression [106]. It has been identified as a diagnostic and prognostic biomarker for renal cell carcinoma [107] and a suppressor of serous ovarian cancer [104].…”

Section: Mir1468-5pmentioning

confidence: 99%

The Prediction of miRNAs in SARS-CoV-2 Genomes: hsa-miR Databases Identify 7 Key miRs Linked to Host Responses and Virus Pathogenicity-Related KEGG Pathways Significant for Comorbidities

Arısan

Dart

Grant

et al. 2020

Viruses

163

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Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) is a member of the betacoronavirus family, which causes COVID-19 disease. SARS-CoV-2 pathogenicity in humans leads to increased mortality rates due to alterations of significant pathways, including some resulting in exacerbated inflammatory responses linked to the “cytokine storm” and extensive lung pathology, as well as being linked to a number of comorbidities. Our current study compared five SARS-CoV-2 sequences from different geographical regions to those from SARS, MERS and two cold viruses, OC43 and 229E, to identify the presence of miR-like sequences. We identified seven key miRs, which highlight considerable differences between the SARS-CoV-2 sequences, compared with the other viruses. The level of conservation between the five SARS-CoV-2 sequences was identical but poor compared with the other sequences, with SARS showing the highest degree of conservation. This decrease in similarity could result in reduced levels of transcriptional control, as well as a change in the physiological effect of the virus and associated host-pathogen responses. MERS and the milder symptom viruses showed greater differences and even significant sequence gaps. This divergence away from the SARS-CoV-2 sequences broadly mirrors the phylogenetic relationships obtained from the whole-genome alignments. Therefore, patterns of mutation, occurring during sequence divergence from the longer established human viruses to the more recent ones, may have led to the emergence of sequence motifs that can be related directly to the pathogenicity of SARS-CoV-2. Importantly, we identified 7 key-microRNAs (miRs 8066, 5197, 3611, 3934-3p, 1307-3p, 3691-3p, 1468-5p) with significant links to KEGG pathways linked to viral pathogenicity and host responses. According to Bioproject data (PRJNA615032), SARS-CoV-2 mediated transcriptomic alterations were similar to the target pathways of the selected 7 miRs identified in our study. This mechanism could have considerable significance in determining the symptom spectrum of future potential pandemics. KEGG pathway analysis revealed a number of critical pathways linked to the seven identified miRs that may provide insight into the interplay between the virus and comorbidities. Based on our reported findings, miRNAs may constitute potential and effective therapeutic approaches in COVID-19 and its pathological consequences.

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Section: Mir1468-5pmentioning

confidence: 99%

The Prediction of miRNAs in SARS-CoV-2 Genomes: hsa-miR Databases Identify 7 Key miRs Linked to Host Responses and Virus Pathogenicity-Related KEGG Pathways Significant for Comorbidities

Arısan

Dart

Grant

et al. 2020

Viruses

163

View full text Add to dashboard Cite

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“…Finally, we identi ed the lncRNAs co-expressed with autophagy genes as autophagy-related lncRNAs for subsequent analysis. |Correlation coe cient | ≥ 3 and p-value < 0.001 were used as the screening criteria [12][13].…”

Section: Co-expression Analysis Of Autophagy Genesmentioning

confidence: 99%

Bioinformatics analysis of autophagy-related lncRNAs in esophageal carcinoma

Ding

Fan

2020

Preprint

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Background: Esophageal carcinoma (ESCA) is a malignant tumor with high invasiveness and mortality. Autophagy has multiple roles in the development of cancer; however, there are limited data on autophagy genes associated with long non-coding RNAs (lncRNAs) in ESCA. The purpose of this study was to screen potential diagnostic and prognostic molecules, and to identify gene co-expression networks associated with autophagy in ESCA. Methods: We downloaded transcriptome expression profiles from The Cancer Genome Atlas and autophagy-related gene data from the Human Autophagy Database, and analyzed the co-expression of mRNAs and lncRNAs. In addition, the diagnostic and prognostic value of autophagy-related lncRNAs was analyzed by multivariate Cox regression. Furthermore, Kyoto Encyclopedia of Genes and Genomes analysis was carried out for high-risk patients, and enriched pathways were analyzed by gene set enrichment analysis. Results: The results showed that genes of high-risk patients were enriched in protein export and spliceosome. Based on Cox stepwise regression and survival analysis, we identified seven autophagy-related lncRNAs with prognostic and diagnostic value, with the potential to be used as a combination to predict the prognosis of patients with ESCA. Finally, a co-expression network related to autophagy was constructed. Conclusion: These results suggest that autophagy-related lncRNAs and the spliceosome play important parts in the pathogenesis of ESCA. Our findings provide new insight into the molecular mechanism of ESCA and suggest a new method for improving its treatment.

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“…To demonstrate the functionality of miRSwitch we performed two case studies; a human liquid biopsy biomarker study and a consideration of arm switches in human compared to mouse tissues. Previously, we obtained sncRNAseq data from blood of Alzheimer's disease patients and controls, and validated the data using RT-qPCR [32,33,34]. For 70 samples, reads were mapped and miRNA counts for miRBase v21 entries were quantified using miRDeep2.…”

Section: Case Studiesmentioning

confidence: 99%

miRSwitch: Detecting microRNA arm shift and switch events

Kern

Amand

Senatorov

et al. 2020

Preprint

Self Cite

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AbstractArm selection, the preferential expression of a 3′ or 5′ mature microRNA (miRNA), is a highly dynamic and tissue-specific process. Time-dependent expression shifts or switches between the arms are also relevant for human diseases. We present miRSwitch, a web server to facilitate the analysis and interpretation of arm selection events. Our species-independent tool evaluates pre-processed small non-coding RNA sequencing (sncRNA-seq) data, i.e. expression matrices or output files from miRNA quantification tools (miRDeep2, miRMaster, sRNAbench). miRSwitch highlights potential changes in the distribution of mature miRNAs from the same precursor. Group comparisons from one or several user-provided annotations (e.g. disease states) are possible. Results can be dynamically adjusted by choosing from a continuous range of highly specific to very sensitive parameters. Users can compare potential arm shifts in the provided data to a human reference map of pre-computed arm shift frequencies. We created this map from 46 tissues and 30,521 samples. As case studies we present novel arm shift information in a Alzheimer’s disease biomarker data set and from a comparison of tissues in Homo sapiens and Mus musculus. In summary, miRSwitch offers a broad range of customised arm switch analyses along with comprehensive visualisations, and is freely available at: https://www.ccb.uni-saarland.de/mirswitch/.

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

Cited by 71 publications

References 39 publications

The Prediction of miRNAs in SARS-CoV-2 Genomes: hsa-miR Databases Identify 7 Key miRs Linked to Host Responses and Virus Pathogenicity-Related KEGG Pathways Significant for Comorbidities

The Prediction of miRNAs in SARS-CoV-2 Genomes: hsa-miR Databases Identify 7 Key miRs Linked to Host Responses and Virus Pathogenicity-Related KEGG Pathways Significant for Comorbidities

Bioinformatics analysis of autophagy-related lncRNAs in esophageal carcinoma

miRSwitch: Detecting microRNA arm shift and switch events

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