Background: Renal cell carcinoma (RCC) is divided into three major histopathologic groups—clear cell (ccRCC), papillary (pRCC) and chromophobe RCC (chRCC). We performed a comprehensive re-analysis of publicly available RCC datasets from the TCGA (The Cancer Genome Atlas) database, thereby combining samples from all three subgroups, for an exploratory transcriptome profiling of RCC subgroups.Materials and Methods: We used FPKM (fragments per kilobase per million) files derived from the ccRCC, pRCC and chRCC cohorts of the TCGA database, representing transcriptomic data of 891 patients. Using principal component analysis, we visualized datasets as t-SNE plot for cluster detection. Clusters were characterized by machine learning, resulting gene signatures were validated by correlation analyses in the TCGA dataset and three external datasets (ICGC RECA-EU, CPTAC-3-Kidney, and GSE157256).Results: Many RCC samples co-clustered according to histopathology. However, a substantial number of samples clustered independently from histopathologic origin (mixed subgroup)—demonstrating divergence between histopathology and transcriptomic data. Further analyses of mixed subgroup via machine learning revealed a predominant mitochondrial gene signature—a trait previously known for chRCC—across all histopathologic subgroups. Additionally, ccRCC samples from mixed subgroup presented an inverse correlation of mitochondrial and angiogenesis-related genes in the TCGA and in three external validation cohorts. Moreover, mixed subgroup affiliation was associated with a highly significant shorter overall survival for patients with ccRCC—and a highly significant longer overall survival for chRCC patients.Conclusions: Pan-RCC clustering according to RNA-sequencing data revealed a distinct histology-independent subgroup characterized by strengthened mitochondrial and weakened angiogenesis-related gene signatures. Moreover, affiliation to mixed subgroup went along with a significantly shorter overall survival for ccRCC and a longer overall survival for chRCC patients. Further research could offer a therapy stratification by specifically addressing the mitochondrial metabolism of such tumors and its microenvironment.

Background: Timely assessment of COVID-19 severity is crucial for the rapid provision of appropriate treatments. Definitive criteria for the early identification of severe COVID-19 cases that require intensive care unit admission are lacking.Methods: This was a single-center, retrospective case-control study of 95 consecutive adults admitted to the intensive care unit (cases) or a medical ward (controls) for laboratory-confirmed COVID-19. Clinical data were collected and changes in laboratory test results were calculated between presentation at the emergency department and admission. Univariate and multivariable logistic regression was performed to calculate odds ratios for intensive care unit admission according to changes in laboratory variables.Results: Of the 95 adults with COVID-19, 25 were admitted to intensive care and 70 to a medical ward after a median 6 h stay in the emergency department. During this interval, neutrophil counts increased in cases and decreased in controls (median, 934 vs. −295 × 106/L; P = 0.006), while lymphocyte counts decreased in cases and increased in controls (median, −184 vs. 109 × 106/L; P < 0.001). In cases, the neutrophil-to-lymphocyte ratio increased 6-fold and the urea-to-creatinine ratio increased 20-fold during the emergency department stay, but these ratios did not change in controls (P < 0.001 for both comparisons). By multivariable logistic regression, short-term increases in the neutrophil-to-lymphocyte ratio (OR = 1.43; 95% CI, 1.16–1.76) and urea-to-creatinine ratio (OR = 1.72; 95% CI, 1.20–2.66) were independent predictors of intensive care unit admission.Conclusion: Short-time changes in neutrophil-to-lymphocyte ratio and urea-to-creatinine ratio emerged as stand-alone parameters able to identify patients with aggressive disease at an early stage.