Guoshuai Cai

declared the coronavirus disease (COVID-19) outbreak a pandemic.As of May 28, 2020, laboratories had confirmed 5,701,337 COVID-19 cases, and 357,668 deaths had been reported in 216 countries, areas, or territories (1).COVID-19 is caused by a new type of pathogenic coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is phylogenetically similar to SARS-CoV, with approximately 80% identity between the genomes (2).SARS viruses affect the respiratory tract and cause an acute respiratory response through the same cell-entry receptor, ACE2 (angiotensin-converting enzyme 2), which is the only experimentally confirmed SARS-CoV-2 receptor.SARS-CoV-2 infection also uses activation of the spike proteins found on the surface of the virus for cellular entry.The best candidates for priming spike proteins are two host cell enzymes called Furin and TMPRSS2 (2).In the current severe global emergency, to enable effective prevention and care, it is imperative to identify potential risk factors, such as cigarette smoking, which is a substantial risk factor for various important bacterial and viral infections.Some of the results of this study were previously published in preprint form (medRxiv, https://www.medrxiv.org/content/10.1101/2020.02.05.20020107v3). MethodsWe evaluated a comprehensive set of transcriptomic data sets to investigate the associations of smoking with ACE2, FURIN, and TMPRSS2 gene expression in lung tissues.Two data sets were generated using normal lung tissues from patients with lung adenocarcinoma: a Caucasian RNA-sequencing (RNA-seq) data set from The Cancer Genome Atlas (n = 48) (3) and an Asian RNA-seq data set from the Gene Expression Omnibus (GSE40419, n = 74) (4).We included three polyethnic microarray data sets of gene expression in healthy small airway epithelium samples (GSE63127, n = 230 [5]; GSE19667, n = 116 [6]; and GSE5058, n = 24 [7]) and large airway epithelium samples (GSE7895, n = 104 [8]).In addition, we analyzed three microarray data sets of samples derived from healthy subjects and patients with chronic obstructive pulmonary disease (COPD), including small airway epithelium samples from current smokers (from GSE5058, n = 26 [7]),

The prognosis of advanced oral squamous cell carcinoma (OSCC) patients remains dismal, and a better understanding of the underlying mechanisms is critical for identifying effective targets with therapeutic potential to improve the survival of patients with OSCC. This study aims to clarify the clinical and biological significance of metastasis-associated long non-coding RNA, metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) in OSCC. We found that MALAT1 is overexpressed in OSCC tissues compared to normal oral mucosa by real-time PCR. MALAT1 served as a new prognostic factor in OSCC patients. When knockdown by small interfering RNA (siRNA) in OSCC cell lines TSCCA and Tca8113, MALAT1 was shown to be required for maintaining epithelial-mesenchymal transition (EMT) mediated cell migration and invasion. Western blot and immunofluorescence staining showed that MALAT1 knockdown significantly suppressed N-cadherin and Vimentin expression but induced E-cadherin expression in vitro. Meanwhile, both nucleus and cytoplasm levels of β-catenin and NF-κB were attenuated, while elevated MALAT1 level triggered the expression of β-catenin and NF-κB. More importantly, targeting MALAT1 inhibited TSCCA cell-induced xenograft tumor growth in vivo. Therefore, these findings provide mechanistic insight into the role of MALAT1 in regulating OSCC metastasis, suggesting that MALAT1 is an important prognostic factor and therapeutic target for OSCC.

Abstract In current severe global emergency situation of 2019-nCov outbreak, it is imperative to identify vulnerable and susceptible groups for effective protection and care. Recently, studies found that 2019-nCov and SARS-nCov share the same receptor, ACE2. In this study, we analyzed five large-scale bulk transcriptomic datasets of normal lung tissue and two single-cell transcriptomic datasets to investigate the disparities related to race, age, gender and smoking status in ACE2 gene expression and its distribution among cell types. We didn’t find significant disparities in ACE2 gene expression between racial groups (Asian vs Caucasian), age groups (>60 vs <60) or gender groups (male vs female). However, we observed significantly higher ACE2 gene expression in former smoker’s lung compared to non-smoker’s lung. Also, we found higher ACE2 gene expression in Asian current smokers compared to non-smokers but not in Caucasian current smokers, which may indicate an existence of gene-smoking interaction. In addition, we found that ACE2 gene is expressed in specific cell types related to smoking history and location. In bronchial epithelium, ACE2 is actively expressed in goblet cells of current smokers and club cells of non-smokers. In alveoli, ACE2 is actively expressed in remodelled AT2 cells of former smokers. Together, this study indicates that smokers especially former smokers may be more susceptible to 2019-nCov and have infection paths different with non-smokers. Thus, smoking history may provide valuable information in identifying susceptible population and standardizing treatment regimen.

Motivation: Molecular subtypes of cancers and autoimmune disease, defined by transcriptomic profiling, have provided insight into disease pathogenesis, molecular heterogeneity and therapeutic responses. However, technical biases inherent to different gene expression profiling platforms present a unique problem when analyzing data generated from different studies. Currently, there is a lack of effective methods designed to eliminate platform-based bias. We present a method to normalize and classify RNA-seq data using machine learning classifiers trained on DNA microarray data and molecular subtypes in two datasets: breast invasive carcinoma (BRCA) and colorectal cancer (CRC). Results: Multiple analyses show that feature specific quantile normalization (FSQN) successfully removes platform-based bias from RNA-seq data, regardless of feature scaling or machine learning algorithm. We achieve up to 98% accuracy for BRCA data and 97% accuracy for CRC data in assigning molecular subtypes to RNA-seq data normalized using FSQN and a support vector machine trained exclusively on DNA microarray data. We find that maximum accuracy was achieved when normalizing RNA-seq datasets that contain at least 25 samples. FSQN allows comparison of RNA-seq data to existing DNA microarray datasets. Using these techniques, we can successfully leverage information from existing gene expression data in new analyses despite different platforms used for gene expression profiling. Availability and implementation: FSQN has been submitted as an R package to CRAN. All code used for this study is available on Github (https://github.com/jenniferfranks/FSQN). Contact: michael.l.whitfield@dartmouth.edu. Supplementary information: Supplementary data are available at Bioinformatics online.