The homogeneity and heterogeneity in somatic mutations, copy number alterations and methylation across different cancer types have been extensively explored. However, the related exploration based on transcriptome data is lacking. In this study we explored gene expression profiles across 33 human cancer types using The Cancer Genome Atlas (TCGA) data. We identified consistently upregulated genes (such as E2F1, EZH2, FOXM1, MYBL2, PLK1, TTK, AURKA/B and BUB1) and consistently downregulated genes (such as SCARA5, MYOM1, NKAPL, PEG3, USP2, SLC5A7 and HMGCLL1) across various cancers. The dysregulation of these genes is likely to be associated with poor clinical outcomes in cancer. The dysregulated pathways commonly in cancers include cell cycle, DNA replication, repair, and recombination, Notch signaling, p53 signaling, Wnt signaling, TGFβ signaling, immune response etc. We also identified genes consistently upregulated or downregulated in highly-advanced cancers compared to lowly-advanced cancers. The highly (low) expressed genes in highly-advanced cancers are likely to have higher (lower) expression levels in cancers than in normal tissue, indicating that common gene expression perturbations drive cancer initiation and cancer progression. In addition, we identified a substantial number of genes exclusively dysregulated in a single cancer type or inconsistently dysregulated in different cancer types, demonstrating the intertumor heterogeneity. More importantly, we found a number of genes commonly dysregulated in various cancers such as PLP1, MYOM1, NKAPL and USP2 which were investigated in few cancer related studies, and thus represent our novel findings. Our study provides comprehensive portraits of transcriptional landscape of human cancers.
The coronavirus disease 2019 (COVID‐19) outbreak caused by severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) began in December 2019 and was basically under control in April 2020 in Wuhan. To explore the impact of intervention measures on the COVID‐19 epidemic, we established susceptible–exposed–infectious–recovered (SEIR) models to predict the epidemic characteristics of COVID‐19 at four different phases (beginning, outbreak, recession, and plateau) from January 1st to March 30th, 2020. We found that the infection rate rapidly grew up to 0.3647 at Phase II from 0.1100 at Phase I and went down to 0.0600 and 0.0006 at Phase III and IV, respectively. The reproduction numbers of COVID‐19 were 10.7843, 13.8144, 1.4815, and 0.0137 at Phase I, II, III, and IV, respectively. These results suggest that intensive interventions, including compulsory home isolation and rapid improvement of medical resources, can effectively reduce the COVID‐19 transmission. Furthermore, the predicted COVID‐19 epidemic trend by our models was close to the actual epidemic trend in Wuhan. Our phase‐based SEIR models demonstrate that intensive intervention measures can effectively control COVID‐19 spread even without specific medicines and vaccines against this disease.
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