The SARS-CoV-2 host cell membrane fusion protein TMPRSS2 is a tumor suppressor and its downregulation correlates with increased antitumor immunity and immunotherapy response in lung adenocarcinoma

BackgroundTMPRSS2 is a host cell membrane fusion protein for SARS-CoV-2 invading human host cells. It also has an association with cancer, particularly prostate cancer. However, its association with lung cancer remains insufficiently explored. Thus, an in-depth investigation into the association between TMPRSS2 and lung cancer is significant, considering that lung cancer is the leading cause of cancer death and that the lungs are the primary organ SARS-CoV-2 attacks. MethodsUsing five lung adenocarcinoma (LUAD) genomics datasets, we explored associations between TMPRSS2 expression and immune signatures, cancer-associated pathways, tumor progression phenotypes, and clinical prognosis in LUAD by the bioinformatics approach. Furthermore, we validated the findings from the bioinformatics analysis by performing in vitro experiments with the human LUAD cell line A549 and in vivo experiments with mouse tumor models. We also validated our findings in LUAD patients from Jiangsu Cancer Hospital, China. ResultsTMPRSS2 expression levels were negatively correlated with the enrichment levels of CD8+ T and NK cells and immune cytolytic activity in LUAD, which represent antitumor immune signatures. Meanwhile, TMPRSS2 expression levels were negatively correlated with the enrichment levels of CD4+ regulatory T cells and myeloid-derived suppressor cells and PD-L1 expression levels in LUAD, which represent antitumor immunosuppressive signatures. However, TMPRSS2 expression levels showed a significant positive correlation with the ratios of immune-stimulatory/immune-inhibitory signatures (CD8+ T cells/PD-L1) in LUAD. It indicated that TMPRSS2 levels had a stronger negative correlation with immune-inhibitory signatures than with immune-stimulatory signatures. TMPRSS2 downregulation correlated with elevated activities of many oncogenic pathways in LUAD, including cell cycle, mismatch repair, p53, and extracellular matrix (ECM) signaling. TMPRSS2 downregulation correlated with increased proliferation, stemness, genomic instability, tumor advancement, and worse survival in LUAD. In vitro and in vivo experiments validated the association of TMPRSS2 deficiency with increased tumor cell proliferation and invasion and antitumor immunity in LUAD. Moreover, in vivo experiments demonstrated that TMPRSS2-knockdown tumors were more sensitive to BMS-1, an inhibitor of PD-1/PD-L1. ConclusionsTMPRSS2 is a tumor suppressor, while its downregulation is a positive biomarker of immunotherapy in LUAD. Our data provide a connection between lung cancer and pneumonia caused by SARS-CoV-2 infection.

Identification of COVID-19 Subtypes Based on Immunogenomic Profiling

Although previous studies have shown that the host immune response is crucial in determining clinical outcomes in COVID-19 patients, the association between host immune signatures and COVID-19 patient outcomes remains unclear. Based on the enrichment levels of 11 immune signatures (eight immune-inciting and three immune-inhibiting signatures) in leukocytes of 100 COVID-19 patients, we identified three COVID-19 subtypes: Im-C1, Im-C2, and Im-C3, by clustering analysis. Im-C1 had the lowest immune-inciting signatures and high immune-inhibiting signatures. Im-C2 had medium immune-inciting signatures and high immune-inhibiting signatures. Im-C3 had the highest immune-inciting signatures while the lowest immune-inhibiting signatures. Im-C3 and Im-C1 displayed the best and worst clinical outcomes, respectively, suggesting that antiviral immune responses alleviated the severity of COVID-19 patients. We further demonstrated that the adaptive immune response had a stronger impact on COVID-19 outcomes than the innate immune response. The patients in Im-C3 were younger than those in Im-C1, indicating that younger persons have stronger antiviral immune responses than older persons. Nevertheless, we did not observe a significant association between sex and immune responses in COVID-19 patients. In addition, we found that the type II IFN response signature was an adverse prognostic factor for COVID-19. Our identification of COVID-19 immune subtypes has potential clinical implications for the management of COVID-19 patients.

Evaluation of the current therapeutic approaches for COVID-19: a meta-analysis

Background and rationaleLimited data on the efficacy and safety of currently applied COVID-19 therapeutics and their impact on COVID-19 outcomes have raised additional concern. Aim and MethodsWe estimated the impact of the current treatments on the efficacy and safety of COVID-19 by a meta-analysis. The comprehensive search included studies reporting clinical features and treatment strategies published from January 21, 2020, to May 15, 2020. ResultsWe included 52 studies that involved 13,966 COVID-19 patients. We found that the most prevalent treatments were antivirals (proportion: 0.74, 95% CI1: [0.65, 0.83]) and antibiotics (proportion: 0.73, 95% CI: [0.62, 0.83]). The COVID-19 severity increased among patients taking glucocorticoids (risk ratio (RR)2 = 1.71, 95% CI: [1.06, 2.76]) or immunoglobulins (RR = 3.83, 95% CI: [1.27, 11.53]), and renal replacement therapy (RRT) and glucocorticoids increased the length of ICU stay (RRT3: RR = 11.89, 95% CI: [3.26, 43.39]; glucocorticoids: RR = 3.10, 95% CI: [1.52, 6.29]). The COVID-19 severity and mortality increased among patients taking tocilizumab (severity: F = 25.53, P = 0.02; mortality: F4 = 19.37, P = 0.02). The most effective treatment was the combination of arbidol with lopinavir/ritonavir compared with placebo (mean difference = 0.5, 95% CI [-0.60, 1.66]), and the safest combination was remdesivir and lopinavir/ritonavir (RR = 0.78, 95% CI [0.32, 1.91]). Conclusionglucocorticoids, immunoglobulins, RRT, and tocilizumab might worsen COVID-19 outcomes, and themost effective and safest treatment strategy for COVID-19 is the combination of different antivirals.

Identifying novel factors associated with COVID-19 transmission and fatality using the machine learning approach

The COVID-19 virus has infected millions of people and resulted in hundreds of thousands of deaths worldwide. By using the logistic regression model, we identified novel critical factors associated with COVID19 cases, death, and case fatality rates in 154 countries and in the 50 U.S. states. Among numerous factors associated with COVID-19 risk, we found that the unitary state system was counter-intuitively positively associated with increased COVID-19 cases and deaths. Blood type B was a protective factor for COVID-19 risk, while blood type A was a risk factor. The prevalence of HIV, influenza and pneumonia, and chronic lower respiratory diseases was associated with reduced COVID-19 risk. Obesity and the condition of unimproved water sources were associated with increased COVID-19 risk. Other factors included temperature, humidity, social distancing, smoking, and vitamin D intake. Our comprehensive identification of the factors affecting COVID-19 transmission and fatality may provide new insights into the COVID-19 pandemic and advise effective strategies for preventing and migrating COVID-19 spread.

Predicting the epidemic trend of COVID-19 in China and across the world using the machine learning approach

BackgroundAlthough COVID-19 has been well controlled in China, it is rapidly spreading outside the country and may have catastrophic results globally without implementation of necessary mitigation measures. Because the COVID-19 outbreak has made comprehensive and profound impacts on the world, an accurate prediction of its epidemic trend is significant. Although many studies have predicted the COVID-19 epidemic trend, most have used early-stage data and focused on Chinese cases. MethodsWe first built models to predict daily numbers of cumulative confirmed cases (CCCs), new cases (NCs), and death cases (DCs) of COVID-19 in China based on data from January 20, 2020, to March 1, 2020. Based on these models, we built models to predict the epidemic trend across the world (outside China). We also built models to predict the epidemic trend in Italy, Spain, Germany, France, UK, and USA where COVID-19 is rapidly spreading. ResultsThe COVID-19 outbreak will have peaked on February 22, 2020, in China and will peak on May 22, 2020, across the world. It will be basically under control in early April 2020 in China and late August 2020 across the world. The total number of COVID-19 cases will reach around 89,000 in China and 6,126,000 across the world during the epidemic. Around 4,000 and 290,000 people will die of COVID-19 in China and across the world, respectively. The COVID-19 outbreak will have peaked recently in Italy and will peak in Spain, Germany, France, UK, and USA within two weeks. ConclusionThe COVID-19 outbreak is controllable in the foreseeable future if comprehensive and stringent control measures are taken.

Discovery of molecular associations among aging, stem cells, and cancer based on gene expression profiling / 癌症

The emergence of a huge volume of "omics" data enables a computational approach to the investigation of the biology of cancer. The cancer informatics approach is a useful supplement to the traditional experimental approach. I reviewed several reports that used a bioinformatics approach to analyze the associations among aging, stem cells, and cancer by microarray gene expression profiling. The high expression of aging- or human embryonic stem cell-related molecules in cancer suggests that certain important mechanisms are commonly underlying aging, stem cells, and cancer. These mechanisms are involved in cell cycle regulation, metabolic process, DNA damage response, apoptosis, p53 signaling pathway, immune/inflammatory response, and other processes, suggesting that cancer is a developmental and evolutional disease that is strongly related to aging. Moreover, these mechanisms demonstrate that the initiation, proliferation, and metastasis of cancer are associated with the deregulation of stem cells. These findings provide insights into the biology of cancer. Certainly, the findings that are obtained by the informatics approach should be justified by experimental validation. This review also noted that next-generation sequencing data provide enriched sources for cancer informatics study.

Expression and significance of hMSH2 protein in laryngeal squamous cell carcinoma / 临床耳鼻咽喉头颈外科杂志

OBJECTIVE@#To study the expression and significance of hMSH2 protein in laryngeal squamous cell carcinoma.@*METHOD@#The expression of hMSH2 protein were detected by immunohistochemistry SP method in 51 cases of laryngeal squamous cell carcinoma, the control group included 30 cases of atypical hyperplasia tissue of vocal fold and 16 cases of normal laryngeal tissue.@*RESULT@#The expression rates of hMSH2 in laryngeal squamous cell carcinoma, atypical hyperplasia tissue of vocal fold and normal laryngeal tissue were 58.8%, 73.3%, 87.5% respectively. There was significant difference among them (P 0.05), but the expression was related with metastasis of lymph node and differentiation level (P < 0.05).@*CONCLUSION@#The deletion of hMSH2 maybe participate the early occurrence of laryngeal carcinoma; hMSH2 protein maybe delay and suppress oncogenesis and development of laryngeal carcinoma.

Gray matter volume differences in deficit and nondeficit schizophrenia:a voxel-based morphometric study / 中华行为医学与脑科学杂志

Objective To examine the differences in the structure of brain white matter among deficit schizophrenia, nondeficit schizophrenia and healthy controls by using voxel-based morphometry (VBM). Methods Ten deficit schizophrenic patients, eleven nondeficit patients and fifteen healthy comparison subjects participated in the study. All the subjects were scanned by GE Twin Speed 1.5T MRI system. Whole brain, voxel-wise analyses of regional white matter volume were conducted by the VBM toolbox on the Matlab7.6 and SPM5. t -test was then used for the comparison between groups. Results Compared to the healthy controls, nondeficit schizophrenic patients significantly decreased the density of gray matter in the frontal, parietal, temporal, occipital lobe and basal ganglia , while the deficit patients showed the characteristically broad and significant decreasion in the frontal lobe, including left medial frontal gyrus, bilateral inferior frontal gyrus, left middle frontal gyrus, and left orbital gyrus (Cluster ≥ 30 mm3, P<0.01). Moreover, deficit patients showed the decreasion in the temporal cortex and the limbic lobe (right insula). Relative to the nondeficit schizophrenic patients, deficit patients had significant regional gray matter decreases in the left medial frontal gyrus, bilateral inferior frontal gyrus, right precentral gyrus, and right superior temporal gyrus (Cluster ≥ 30 mm3, P<0.01). Conclusion Structural heterogeneity in schizophrenia may relate to specific patterns of gray matter density reductions in deficit and nondeficit patient. However the two subtype of schizophremia patients share a common prefrontal-temperal pattern of structural brain alterations.