After the outbreak of the severe acute respiratory syndrome (SARS) in the world in 2003, human coronaviruses (HCoVs) have been reported as pathogens that cause severe symptoms in respiratory tract infections. Recently, a new emerged HCoV isolated from the respiratory epithelium of unexplained pneumonia patients in the Wuhan seafood market caused a major disease outbreak and has been named the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This virus causes acute lung symptoms, leading to a condition that has been named as “coronavirus disease 2019” (COVID-19). The emergence of SARS-CoV-2 and of SARS-CoV caused widespread fear and concern and has threatened global health security. There are some similarities and differences in the epidemiology and clinical features between these two viruses and diseases that are caused by these viruses. The goal of this work is to systematically review and compare between SARS-CoV and SARS-CoV-2 in the context of their virus incubation, originations, diagnosis and treatment methods, genomic and proteomic sequences, and pathogenic mechanisms.

Deep learning has been applied to many areas in health care, including imaging diagnosis, digital pathology, prediction of hospital admission, drug design, classification of cancer and stromal cells, doctor assistance, etc. Cancer prognosis is to estimate the fate of cancer, probabilities of cancer recurrence and progression, and to provide survival estimation to the patients. The accuracy of cancer prognosis prediction will greatly benefit clinical management of cancer patients. The improvement of biomedical translational research and the application of advanced statistical analysis and machine learning methods are the driving forces to improve cancer prognosis prediction. Recent years, there is a significant increase of computational power and rapid advancement in the technology of artificial intelligence, particularly in deep learning. In addition, the cost reduction in large scale next-generation sequencing, and the availability of such data through open source databases (e.g., TCGA and GEO databases) offer us opportunities to possibly build more powerful and accurate models to predict cancer prognosis more accurately. In this review, we reviewed the most recent published works that used deep learning to build models for cancer prognosis prediction. Deep learning has been suggested to be a more generic model, requires less data engineering, and achieves more accurate prediction when working with large amounts of data. The application of deep learning in cancer prognosis has been shown to be equivalent or better than current approaches, such as Cox-PH. With the burst of multi-omics data, including genomics data, transcriptomics data and clinical information in cancer studies, we believe that deep learning would potentially improve cancer prognosis.

Keratin 8 (KRT8), a type II basic intermediate filament (IF) protein, is essential for the development and metastasis of various cancers. In this study, by analyzing RNA-seq data from the Cancer Genome Atlas (TCGA)-lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), we have determined the expression profile of KRT8, and assessed its prognostic significance and the possible mechanism underlying the dysregulation. Our results showed that KRT8 mRNA expression was significantly up-regulated in both LUAD and LUSC tissues compared with normal lung tissues. The high KRT8 expression group for LUAD patients significantly reduced overall survival (OS) and recurrence-free survival (RFS). Univariate and multivariate analysis revealed that KRT8 expression was an independent prognostic indicator for poor OS and RFS in LUAD patients. However, KRT8 expression had no prognostic value in terms of OS and RFS for LUSC. By exploring DNA copy number alterations (CNAs) of the KRT8 gene in LUAD, we found that DNA low copy gain (+1 and +2) was associated with elevated KRT8 mRNA expression. From the above findings, we have deduced that KRT8 is aberrantly expressed in LUAD tissues and that its expression might independently predict poor OS and RFS for LUAD patients, but not for LUSC patients.

Lysine succinylation (Ksuc) is a novel protein post-translational modification (PTM) wherein a succinyl group modifies a lysine residue. Ksuc leads to significant chemical and structural changes to the modified protein. Recent studies have shown that Ksuc might play an important role in organism physiology and some pathophysiological processes, such as tumorigenesis and metabolic diseases. To provide an understanding of the molecular mechanism and functions of Ksuc in different organisms, we reviewed the current literature about Ksuc, mainly summarizing the research advances in eukaryotes and prokaryotes based on both traditional study methods and site prediction tools. We also discussed inhibitors or activators associated with Ksuc that may contribute to proteomic studies and could be useful in future clinical practice. A deeper understanding of Ksuc may shed new light on life science at the protein level and could lead to novel therapeutic strategies for various diseases.

Aim: To develop a free and quick analysis online tool that allows users to easily investigate the prognostic potencies of interesting genes in kidney renal clear cell carcinoma (KIRC). Patients & methods: A total of 629 KIRC cases with gene expression profiling data and clinical follow-up information are collected from public Gene Expression Omnibus and The Cancer Genome Atlas databases. Results: One web application called Online consensus Survival analysis for KIRC (OSkirc) that can be used for exploring the prognostic implications of interesting genes in KIRC was constructed. By OSkirc, users could simply input the gene symbol to receive the Kaplan-Meier survival plot with hazard ratio and log-rank p-value. Conclusion: OSkirc is extremely valuable for basic and translational researchers to screen and validate the prognostic potencies of genes for KIRC, publicly accessible at http://bioinfo.henu.edu.cn/KIRC/KIRCList.jsp.