Genes showing higher expression in either tumor or metastatic tissues can help in better understanding tumor formation and can serve as biomarkers of progression or as potential therapy targets. Our goal was to establish an integrated database using available transcriptome-level datasets and to create a web platform which enables the mining of this database by comparing normal, tumor and metastatic data across all genes in real time. We utilized data generated by either gene arrays from the Gene Expression Omnibus of the National Center for Biotechnology Information (NCBI-GEO) or RNA-seq from The Cancer Genome Atlas (TCGA), Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and The Genotype-Tissue Expression (GTEx) repositories. The altered expression within different platforms was analyzed separately. Statistical significance was computed using Mann–Whitney or Kruskal–Wallis tests. False Discovery Rate (FDR) was computed using the Benjamini–Hochberg method. The entire database contains 56,938 samples, including 33,520 samples from 3180 gene chip-based studies (453 metastatic, 29,376 tumorous and 3691 normal samples), 11,010 samples from TCGA (394 metastatic, 9886 tumorous and 730 normal), 1193 samples from TARGET (1 metastatic, 1180 tumorous and 12 normal) and 11,215 normal samples from GTEx. The most consistently upregulated genes across multiple tumor types were TOP2A (FC = 7.8), SPP1 (FC = 7.0) and CENPA (FC = 6.03), and the most consistently downregulated gene was ADH1B (FC = 0.15). Validation of differential expression using equally sized training and test sets confirmed the reliability of the database in breast, colon, and lung cancer at an FDR below 10%. The online analysis platform enables unrestricted mining of the database and is accessible at TNMplot.com.

Introduction: Genomic alterations in a viral genome can lead to either better or worse outcome and identifying these mutations is of utmost importance. Here, we correlated protein-level mutations in the SARS-CoV-2 virus to clinical outcome. Methods: Mutations in viral sequences from the GISAID virus repository were evaluated by using “hCoV-19/Wuhan/WIV04/2019” as the reference. Patient outcomes were classified as mild disease, hospitalization and severe disease (death or documented treatment in an intensive-care unit). Chi-square test was applied to examine the association between each mutation and patient outcome. False discovery rate was computed to correct for multiple hypothesis testing and results passing a FDR cutoff of 5% were accepted as significant. Result: Mutations were mapped to amino acid changes for 2,120 non-silent mutations. Mutations correlated to mild outcome were located in the ORF8, NSP6, ORF3a, NSP4, and in the nucleocapsid phosphoprotein N. Mutations associated with inferior outcome were located in the surface (S) glycoprotein, in the RNA dependent RNA polymerase, in the 3’-to5’ exonuclease, in ORF3a, NSP2 and N. Mutations leading to severe outcome with low prevalence were found in the surface (S) glycoprotein and in NSP7. Five out of 17 of the most significant mutations mapped onto a 10 amino acid long phosphorylated stretch of N indicating that in spite of obvious sampling restrictions the approach can find functionally relevant sites in the viral genome. Conclusions: We demonstrate that mutations in the viral genes may have a direct correlation to clinical outcome. Our results help to quickly identify SARS-CoV-2 infections harboring mutations related to severe outcome.