Many of these pipelines are mainly data-driven and enable clustering and supervised machine learning techniques to find essential patterns of features contributing to the identification of, for example, proteins that are associated with NDDs , or to reveal cross-talk patterns in multi-omics data . According to the necessity of approaching AG-014699 (Rucaparib) complex diseases with the use of multiple omics-layers, data-driven methods and large amounts of data, we combined the data of three omics layers from databases and literature mining of more than 1 million subjects and 177 studies to show the shared genes between the four analyzed NDDs and extract the pathways and processes in which they are overrepresented. To classify the gained information in this study, it is crucial to keep in mind that the transcriptomic and proteomic data were gathered from various tissues, partly different severities of diseases and using different methods. of regulation throughout all transcriptomic studies for this set of 139 genes, with the closest relation regarding this common gene set seen between AD and HD. GO-Term and pathway analysis of the proteomic overlap led to biological processes (BPs), related to protein folding and humoral immune response. Taken together, we could confirm the existence of many relations between Alzheimers disease, Parkinsons disease, Huntingtons disease, and amyotrophic lateral sclerosis on transcriptomic and proteomic levels by analyzing the pathways and GO-Terms arising in these intersections. The significance of the connection and the striking relation of the results to processes leading to neurodegeneration between the transcriptomic and proteomic data for all four analyzed neurodegenerative diseases showed that exploring many studies simultaneously, including multiple omics-layers of different neurodegenerative diseases simultaneously, holds new relevant insights that do not emerge from analyzing these data separately. Furthermore, the results shed light on processes like the humoral immune response that have previously been described only for certain diseases. Our data therefore suggest human patients with neurodegenerative diseases should be addressed as complex biological systems by integrating multiple underlying data sources. and (TDP-43) genes [59,63]. Cellular aggregates, including FUS, SOD1, TDP-43, OPTN, UBQLN2, and the translational product of intronic repeats in the gene are found both in the sporadic and the familial form . The described overlap of phenotypic traits TGFB2 of the NDD suggests common pathogenic mechanisms underlying distinct NDDs. Compared to studying individual AG-014699 (Rucaparib) diseases separately, identifying and analyzing the common dysfunctional proteins and dysregulated diseases pathways might elucidate fundamental insights into their pathogenic process . It was previously shown that there is nearly no overlap between AD, PD, and ALS on genomic data and some shared pathways for AD, PD, ALS, and HD in transcriptomic data , but proteomic data and the latest entries in the databases have not been considered. Besides looking for overlapping genes between the different NDDs or omics layers, we also analyzed whether this number is sufficiently high to claim a significant relationship between NDDs or omics layers. An overview of the methodologic procedure is given in Figure 2. By investigating 177 studies in total, this meta-study was able to detect stable signals that arise mainly in late-stage NDDs across tissues, methods and omics layers, which could help unravel patterns across neurodegenerative diseases. Such findings could contribute to a better understanding of the underlying neurodegeneration process and might also have pharmacological relevance for various neurodegenerative diseases. Open in a separate window Figure 2 Workflow Overview: Data acquisition was performed using the genome-wide association studies (GWAS) Catalog for genomic data, the European Bioinformatics Institute (EMBL-EBI) Expression Atlas and the Gene Expression Omnibus database for transcriptomic data, and literature research in PubMed and Google Scholar for proteomic data. After filtering these raw data tables and applying some data transformation, the processed AG-014699 (Rucaparib) data were used for the data analysis. For every omics layer, the intersections of all four analyzed NDDs were visualized as Venn diagrams. Common transcriptional patterns were AG-014699 (Rucaparib) searched with a hierarchical clustering approach and visualized as a heatmap showing the mean transcriptional direction of regulation per gene, and a dendrogram showing the clustering results. Finally, each set of genes after the intersections was used for the Kyoto Encyclopedia for Genes and Genomes (KEGG) pathway and GO-Term analyses. 2. Materials and Methods 2.1. Data Acquisition/Literature Research 2.1.1. Genome The genome-wide association studies (GWAS) Catalog data for Alzheimers disease (AD), Parkinsons disease (PD), amyotrophic lateral sclerosis (ALS) and Huntingtons disease (HD) were downloaded on 28 April 2020. The GWAS Catalog contains single nucleotide polymorphism (SNP) data of GWAS studies for SNPs showing a statistical significance of SNP-trait and em PDGFRB /em . NF- and RelA form a dimer with a transactivating domain that binds to specific DNA sequences as transcription factor controlling genes that are involved in immune and inflammatory responses and control of cell proliferation and apoptosis . Misregulation of NF- can lead to cancer , neurodegenerative , autoimmune.