Soaring cases of coronavirus disease (COVID-19) are pummeling the global health system. Overwhelmed health facilities have endeavored to mitigate the pandemic, but mortality of COVID-19 continues to increase. Here, we present a mortality risk prediction model for COVID-19 (MRPMC) that uses patients’ clinical data on admission to stratify patients by mortality risk, which enables prediction of physiological deterioration and death up to 20 days in advance. This ensemble model is built using four machine learning methods including Logistic Regression, Support Vector Machine, Gradient Boosted Decision Tree, and Neural Network. We validate MRPMC in an internal validation cohort and two external validation cohorts, where it achieves an AUC of 0.9621 (95% CI: 0.9464–0.9778), 0.9760 (0.9613–0.9906), and 0.9246 (0.8763–0.9729), respectively. This model enables expeditious and accurate mortality risk stratification of patients with COVID-19, and potentially facilitates more responsive health systems that are conducive to high risk COVID-19 patients.

No abstract

No abstract

Treatment of ovarian cancer (OC) remains the biggest challenge among gynecological malignancies. Immune checkpoint blockade therapy is promising in many cancers but shows low response rates in OC because of its heterogeneity. Although the biological and molecular heterogeneity of OC has been extensively investigated, heterogeneity of immune microenvironment remains elusive. We have collected the expression profiles of 3071 OC patients from 22 publicly available datasets. CIBERSORT was applied to infer the infiltration fraction of 22 immune cells among 2086 patients with CIBERSORT P < .05. We then explored the heterogeneity landscape of immune microenvironment in OC at three levels (immune infiltration, prognostic relevance of immune infiltration, immune checkpoint expression patterns). Multivariable Cox regression model was used to investigate the associations between survival risk and immune infiltration. Constructed immune risk score stratified patients with significantly different survival risk (HR: 1.47, 95% CI: 1.31-1.66, P < .0001). The immune infiltration landscape, prognostic relevance of immune cells, and expression patterns of 79 immune checkpoints exhibited remarkable clinicopathological heterogeneity. For instance, M1 macrophages were significantly associated with better outcomes among patients with high-grade, late-stage, type-II OC (HR: 0.77-0.83), and worse outcomes among patients with type-I OC (HR: 1.78); M2 macrophages were significantly associated with worse outcomes among patients with high-grade, type-II OC (HR: 1.14-1.17); Neutrophils were significantly associated with worse outcomes among patients with high-grade, late-stage, type-I OC (HR: 1.14-1.73). The heterogeneous landscape of immune microenvironment presented in this study provided new insights into prognostic prediction and tailored immunotherapy of OC.

Genetics data visualization plays an important role in the sharing of knowledge from cancer genome research. Many types of visualization are widely used, most of which are static and require sufficient coding experience to create. Here, we present Oviz-Bio, a web-based platform that provides interactive and real-time visualizations of cancer genomics data. Researchers can interactively explore visual outputs and export high-quality diagrams. Oviz-Bio supports a diverse range of visualizations on common cancer mutation types, including annotation and signatures of small scale mutations, haplotype view and focal clusters of copy number variations, split-reads alignment and heatmap view of structural variations, transcript junction of fusion genes and genomic hotspot of oncovirus integrations. Furthermore, Oviz-Bio allows landscape view to investigate multi-layered data in samples cohort. All Oviz-Bio visual applications are freely available at https://bio.oviz.org/.

Sorafenib is the first-line drug used in the treatment of liver cancer; however, drug resistance seriously limits the clinical response to sorafenib. The present study investigated the molecular mechanisms of sorafenib resistance in liver cancer cells. The data indicated that forkhead box M1 (FoxM1) was significantly overexpressed in sorafenib-resistant cells, at the mRNA and protein levels. Knockdown of FoxM1 rendered drug-tolerant cells sensitive to sorafenib. Furthermore, FoxM1 was upregulated at the transcriptional level. Overexpression of c-jun was associated with the upregulation of FoxM1. The results of a reporter gene assay, electrophoretic mobility shift assay and chromatin immunoprecipitation assay demonstrated that there is an activator protein-1 (AP1) binding site in the promoter of FoxM1, located at-608 to-618. Knockdown of c-jun significantly decreased the levels of FoxM1, accompanied by enhanced cell sensitivity to sorafenib. Furthermore, the activation of AKT contributed to the upregulation of c-jun and FoxM1. Inhibition of AKT using BEZ-235 markedly suppressed the upregulation of c-jun and FoxM1, and increased the sensitivity of drug-resistant cells to sorafenib in vitro and in vivo. The data indicated that the activation of the AKT/AP1/FoxM1 signaling axis is an important determinant of sorafenib tolerance.

Background Copy number variation is crucial in deciphering the mechanism and cure of complex disorders and cancers. The recent advancement of scDNA sequencing technology sheds light upon addressing intratumor heterogeneity, detecting rare subclones, and reconstructing tumor evolution lineages at single-cell resolution. Nevertheless, the current circular binary segmentation based approach proves to fail to efficiently and effectively identify copy number shifts on some exceptional trails. Results Here, we propose SCYN, a CNV segmentation method powered with dynamic programming. SCYN resolves the precise segmentation on in silico dataset. Then we verified SCYN manifested accurate copy number inferring on triple negative breast cancer scDNA data, with array comparative genomic hybridization results of purified bulk samples as ground truth validation. We tested SCYN on two datasets of the newly emerged 10x Genomics CNV solution. SCYN successfully recognizes gastric cancer cells from 1% and 10% spike-ins 10x datasets. Moreover, SCYN is about 150 times faster than state of the art tool when dealing with the datasets of approximately 2000 cells. Conclusions SCYN robustly and efficiently detects segmentations and infers copy number profiles on single cell DNA sequencing data. It serves to reveal the tumor intra-heterogeneity. The source code of SCYN can be accessed in https://github.com/xikanfeng2/SCYN.

Autophagy is closely related to the growth and drug resistance of cancer cells, and autophagy related 4B (ATG4B) performs a crucial role in the process of autophagy. The long non-coding RNA (lncRNA) colorectal neoplasia differentially expressed (CRNDE) promotes the progression of hepatocellular carcinoma (HCC), but it is unclear whether the tumor-promoting effect of CRNDE is associated with the regulation of ATG4B and autophagy. Herein, we for the first time demonstrated that CRNDE triggered autophagy via upregulating ATG4B in HCC cells. Mechanistically, CRNDE enhanced the stability of ATG4B mRNA by sequestrating miR-543, leading to the elevation of ATG4B and autophagy in HCC cells. Moreover, sorafenib induced CRNDE and ATG4B as well as autophagy in HCC cells. Knockdown of CRNDE sensitized HCC cells to sorafenib in vitro and in vivo. Collectively, these results reveal that CRNDE drives ATG4B-mediated autophagy, which attenuates the sensitivity of sorafenib in HCC cells, suggesting that the pathway CRNDE/ATG4B/autophagy may be a novel target to develop sensitizing measures of sorafenib in HCC treatment.