Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first reported in Wuhan, Hubei Province, China and has subsequently spread to other regions of China and 37 countries, including the United States, Japan, Australia, and France.¹ SARS-CoV-2, which belongs to a unique clade of the sarbecovirus subgenus of the Orthocoronavirinae subfamily, was identified as the pathogen of coronavirus disease 2019 (COVID-19) in January 2020.²

Background: It is of great urgency to explore useful prognostic markers for patients with clear cell renal cell carcinoma (ccRCC). Prognostic models based on ferroptosis-related gene (FRG) in ccRCC is poorly reported for now. Methods: Comprehensive analysis of 22 FRGs were performed in 629 ccRCC samples from two independent patient cohorts. We carried out least absolute shrinkage and selection operator analysis to screen out prognosis-related FRGs and constructed prognosis model for patients with ccRCC. Weighted gene co-expression network analysis was also carried out for potential functional enrichment analysis. Results: Based on the TCGA cohort, a total of 11 prognosis-associated FRGs were selected for the construction of the prognosis model. Significantly differential overall survival (hazard ratio = 3.61, 95% CI: 2.68–4.87, p < 0.0001) was observed between patients with high and low FRG score in the TCGA cohort, which was further verified in the CPTAC cohort with hazard ratio value of 5.13 (95% CI: 1.65–15.90, p = 0.019). Subgroup survival analysis revealed that our FRG score could significantly distinguish patients with high survival risk among different tumor stages and different tumor grades. Functional enrichment analysis illustrated that the process of cell cycle, including cell cycle-mitotic pathway, cytokinesis pathway and nuclear division pathway, might be involved in the regulation of ccRCC through ferroptosis. Conclusions: We developed and verified a FRG signature for the prognosis prediction of patients with ccRCC, which could act as a risk factor and help to update the tumor staging system when integrated with clinicopathological characteristics. Cell cycle-related pathways might be involved in the regulation of ccRCC through ferroptosis.

Traditional histopathology performed by pathologists by the naked eye is insufficient for accurate and efficient diagnosis of bladder cancer (BCa). We collected 643 H&E‐stained BCa images from Shanghai General Hospital and The Cancer Genome Atlas (TCGA). We constructed and cross‐verified automatic diagnosis and prognosis models by performing a machine learning algorithm based on pathomics data. Our study indicated that high diagnostic efficiency of the machine learning‐based diagnosis model was observed in patients with BCa, with area under the curve (AUC) values of 96.3%, 89.2%, and 94.1% in the training cohort, test cohort, and external validation cohort, respectively. Our diagnosis model also performed well in distinguishing patients with BCa from patients with glandular cystitis, with an AUC value of 93.4% in the General cohort. Significant differences were found in overall survival in TCGA cohort (hazard ratio (HR) = 2.09, 95% confidence interval (CI): 1.56‐2.81, P < .0001) and the General cohort (HR = 5.32, 95% CI: 2.95‐9.59, P < .0001) comparing patients with BCa of high risk vs low risk stratified by risk score, which was proved to be an independent prognostic factor for BCa. The integration nomogram based on our risk score and clinicopathologic characters displayed higher prediction accuracy than current tumor stage/grade systems, with AUC values of 77.7%, 83.8%, and 81.3% for 1‐, 3‐, and 5‐y overall survival prediction of patients with BCa. However, prospective studies are still needed for further verifications.

Currently, we are on a global pandemic of Coronavirus disease-2019 (COVID-19) which causes fever, dry cough, fatigue and acute respiratory distress syndrome (ARDS) that may ultimately lead to the death of the infected. Current researches on COVID-19 continue to highlight the necessity for further understanding the virus-host synergies. In this study, we have highlighted the key cytokines induced by coronavirus infections. We have demonstrated that genes coding interleukins (Il-1α, Il-1β, Il-6, Il-10), chemokine (Ccl2, Ccl3, Ccl5, Ccl10), and interferon (Ifn-α2, Ifn-β1, Ifn2) upsurge significantly which in line with the elevated infiltration of T cells, NK cells and monocytes in SARS-Cov treated group at 24 hours. Also, interleukins (IL-6, IL-23α, IL-10, IL-7, IL-1α, IL-1β) and interferon (IFN-α2, IFN2, IFN-γ) have increased dramatically in MERS-Cov at 24 hours. A similar cytokine profile showed the cytokine storm served a critical role in the infection process. Subsequent investigation of 463 patients with COVID-19 disease revealed the decreased amount of total lymphocytes, CD3+, CD4+, and CD8+ T lymphocytes in the severe type patients which indicated COVID-19 can impose hard blows on human lymphocyte resulting in lethal pneumonia. Thus, taking control of changes in immune factors could be critical in the treatment of COVID-19.

Background: The accumulating evidence confirms that long non-coding RNAs (lncRNAs) play a critical regulatory role in the progression of renal cell carcinoma (RCC). But, the application of lncRNAs in gene therapy remains scarce. Here, we investigated the efficacy of a delivery system by introducing the plasmid-encoding tumor suppressor lncRNA-SLERCC (SLERCC) in RCC cells. Methods: We performed lncRNAs expression profiling in paired cancer and normal tissues through microarray and validated in our clinical data and TCGA dataset. The Plasmid-SLERCC@PDA@MUC12 nanoparticles (PSPM-NPs) were tested in vivo and in vitro, including cellular uptake, entry, CCK-8 assay, tumor growth inhibition, histological assessment, and safety evaluations. Furthermore, experiments with nude mice xenografts model were performed to evaluate the therapeutic effect of PSPM-NPs nanotherapeutic system specific to the SLERCC. Results: We found that the expression of SLERCC was downregulated in RCC tissues, and exogenous upregulation of SLERCC could suppress metastasis of RCC cells. Furthermore, high expression DNMT3A was recruited at the SLERCC promoter, which induced aberrant hypermethylation, eventually leading to downregulation of SLERCC expression in RCC. Mechanistically, SLERCC could directly bind to UPF1 and exert tumor-suppressive effects through the Wnt/β-catenin signaling pathway, thereby inhibiting progression and metastasis in RCC. Subsequently, the PSPM-NPs nanotherapeutic system can effectively inhibit the growth of RCC metastases in vivo. Conclusions: Our findings suggested that SLERCC is a promising therapeutic target and that plasmid-encapsulated nanomaterials targeting transmembrane metastasis markers may open a new avenue for the treatment in RCC.