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.

The identification of high-risk stage II colon cancers is key to the selection of patients who require adjuvant treatment after surgery. Microarray-based multigene-expression signatures derived from stem cells and progenitor cells hold promise, but they are difficult to use in clinical practice. We used a new bioinformatics approach to search for biomarkers of colon epithelial differentiation across gene-expression arrays and then ranked candidate genes according to the availability of clinical-grade diagnostic assays. With the use of subgroup analysis involving independent and retrospective cohorts of patients with stage II or stage III colon cancer, the top candidate gene was tested for its association with disease-free survival and a benefit from adjuvant chemotherapy. The transcription factor CDX2 ranked first in our screening test. A group of 87 of 2115 tumor samples (4.1%) lacked CDX2 expression. In the discovery data set, which included 466 patients, the rate of 5-year disease-free survival was lower among the 32 patients (6.9%) with CDX2-negative colon cancers than among the 434 (93.1%) with CDX2-positive colon cancers (hazard ratio for disease recurrence, 3.44; 95% confidence interval [CI], 1.60 to 7.38; P=0.002). In the validation data set, which included 314 patients, the rate of 5-year disease-free survival was lower among the 38 patients (12.1%) with CDX2 protein–negative colon cancers than among the 276 (87.9%) with CDX2 protein–positive colon cancers (hazard ratio, 2.42; 95% CI, 1.36 to 4.29; P=0.003). In both these groups, these findings were independent of the patient’s age, sex, and tumor stage and grade. Among patients with stage II cancer, the difference in 5-year disease-free survival was significant both in the discovery data set (49% among 15 patients with CDX2-negative tumors vs. 87% among 191 patients with CDX2-positive tumors, P=0.003) and in the validation data set (51% among 15 patients with CDX2-negative tumors vs. 80% among 106 patients with CDX2-positive tumors, P=0.004). In a pooled database of all patient cohorts, the rate of 5-year disease-free survival was higher among 23 patients with stage II CDX2-negative tumors who were treated with adjuvant chemotherapy than among 25 who were not treated with adjuvant chemotherapy (91% vs. 56%, P=0.006). Lack of CDX2 expression identified a subgroup of patients with high-risk stage II colon cancer who appeared to benefit from adjuvant chemotherapy. (Funded by the National Comprehensive Cancer Network, the National Institutes of Health, and others.)

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.

Background: Molecular characterization of tumors has been critical for identifying important genes in cancer biology and for improving tumor classification and diagnosis. Long non-coding RNAs, as a new, relatively unstudied class of transcripts, provide a rich opportunity to identify both functional drivers and cancer-type-specific biomarkers. However, despite the potential importance of long non-coding RNAs to the cancer field, no comprehensive survey of long non-coding RNA expression across various cancers has been reported. Results: We performed a sequencing-based transcriptional survey of both known long non-coding RNAs and novel intergenic transcripts across a panel of 64 archival tumor samples comprising 17 diagnostic subtypes of adenocarcinomas, squamous cell carcinomas and sarcomas. We identified hundreds of transcripts from among the known 1,065 long non-coding RNAs surveyed that showed variability in transcript levels between the tumor types and are therefore potential biomarker candidates. We discovered 1,071 novel intergenic transcribed regions and demonstrate that these show similar patterns of variability between tumor types. We found that many of these differentially expressed cancer transcripts are also expressed in normal tissues. One such novel transcript specifically expressed in breast tissue was further evaluated using RNA in situ hybridization on a panel of breast tumors. It was shown to correlate with low tumor grade and estrogen receptor expression, thereby representing a potentially important new breast cancer biomarker. Conclusions: This study provides the first large survey of long non-coding RNA expression within a panel of solid cancers and also identifies a number of novel transcribed regions differentially expressed across distinct cancer types that represent candidate biomarkers for future research.

14-3-3 proteins are ubiquitously expressed regulators of various cellular functions, including proliferation, metabolism, and differentiation, and altered 14-3-3 expression is associated with development and progression of cancer. We report a transforming 14-3-3 oncoprotein, which we identified through conventional cytogenetics and whole-transcriptome sequencing analysis as a highly recurrent genetic mechanism in a clinically aggressive form of uterine sarcoma: high-grade endometrial stromal sarcoma (ESS). The 14-3-3 oncoprotein results from a t (10;17) genomic rearrangement, leading to fusion between 14-3-3ε (YWHAE) and either of two nearly identical FAM22 family members (FAM22A or FAM22B). Expression of YWHAE–FAM22 fusion oncoproteins was demonstrated by immunoblot in t (10;17)-bearing frozen tumor and cell line samples. YWHAE–FAM22 fusion gene knockdowns were performed with shRNAs and siRNAs targeting various FAM22A exons in an t (10;17)-bearing ESS cell line (ESS1): Fusion protein expression was inhibited, with corresponding reduction in cell growth and migration. YWHAE–FAM22 maintains a structurally and functionally intact 14-3-3ε (YWHAE) protein-binding domain, which is directed to the nucleus by a FAM22 nuclear localization sequence. In contrast to classic ESS, harboring JAZF1 genetic fusions, YWHAE–FAM22 ESS display high-grade histologic features, a distinct gene-expression profile, and a more aggressive clinical course. Fluorescence in situ hybridization analysis demonstrated absolute specificity of YWHAE–FAM22A/B genetic rearrangement for high-grade ESS, with no fusions detected in other uterine and nonuterine mesenchymal tumors (55 tumor types, n = 827). These discoveries reveal diagnostically and therapeutically relevant models for characterizing aberrant 14-3-3 oncogenic functions.

Endometrial stromal sarcoma (ESS) characterized by YWHAE-FAM22 genetic fusion is histologically higher grade and clinically more aggressive than ESS with JAZF1-SUZ12 or equivalent genetic rearrangements, hence it is clinically important to recognize this subset of ESS. To identify diagnostic immunomarkers for this biologically defined ESS subset, we compared gene expression profiles between YWHAE-FAM22ESS and JAZF1-rearranged ESS. These studies showed consistent upregulation of cyclin D1 in YWHAE-FAM22 ESS compared with JAZF1-SUZ12 ESS. Immunohistochemically, the high-grade round cell component of all 12 YWHAE-FAM22 ESS demonstrated diffuse (≥70%) moderate to strong nuclear cyclin D1 staining, and this diffuse positivity was not seen in 34 ESSs with JAZF1 and equivalent genetic rearrangements or in 21 low-grade ESS with no demonstrable genetic rearrangements. In a series of 243 non-ESS pure uterine mesenchymal and mixed epithelial-mesenchymal tumors, only 2 of 8 undifferentiated endometrial sarcomas with nuclear uniformity and 1 of 80 uterine leiomyosarcomas demonstrate diffuse cyclin D1 immunoreactivity. Both cyclin D1-positive undifferentiated endometrial sarcomas showed diffuse strong CD10 staining, which is consistently absent in the high-grade round cell component of YWHAE-FAM22 ESS. The low-grade spindle cell component of YWHAE-FAM22 ESS showed a spatially heterogenous cyclin D1 staining pattern that was weaker and less diffuse overall. Our findings indicate that cyclin D1 is a sensitive and specific diagnostic immunomarker for YWHAE-FAM22 ESS. When evaluating high-grade uterine sarcomas, cyclin D1 can be included in the immunohistochemical panel as an indicator of YWHAE-FAM22 ESS.