Analysis of the immune landscape in virus-induced cancers using a novel integrative mechanism discovery approach.

BACKGROUND: The mechanisms of carcinogenesis from viral infections are extraordinarily complex and not well understood. Traditional methods of analyzing RNA-sequencing data may not be sufficient for unraveling complicated interactions between viruses and host cells. Using RNA and DNA-sequencing data from The Cancer Genome Atlas (TCGA), we aim to explore whether virus-induced tumors exhibit similar immune-associated (IA) dysregulations using a new algorithm we developed that focuses on the most important biological mechanisms involved in virus-induced cancers. Differential expression, survival correlation, and clinical variable correlations were used to identify the most clinically relevant IA genes dysregulated in 5 virus-induced cancers (HPV-induced head and neck squamous cell carcinoma, HPV-induced cervical cancer, EBV-induced stomach cancer, HBV-induced liver cancer, and HCV-induced liver cancer) after which a mechanistic approach was adopted to identify pathways implicated in IA gene dysregulation. RESULTS: Our results revealed that IA dysregulations vary with the cancer type and the virus type, but cytokine signaling pathways are dysregulated in all virus-induced cancers. Furthermore, we also found that important similarities exist between all 5 virus-induced cancers in dysregulated clinically relevant oncogenic signatures and IA pathways. Finally, we also discovered potential mechanisms for genomic alterations to induce IA gene dysregulations using our algorithm. CONCLUSIONS: Our study offers a new approach to mechanism identification through integrating functional annotations and large-scale sequencing data, which may be invaluable to the discovery of new immunotherapy targets for virus-induced cancers.

Enhancer RNA Profiling in Smoking and HPV Associated HNSCC Reveals Associations to Key Oncogenes.

Smoking and HPV infection are known causes for the vast majority of head and neck squamous cell carcinomas (HNSCC) due to their likelihood of causing gene dysregulation and genomic alterations. Enhancer RNAs (eRNAs) are non-coding RNAs that are known to increase nearby and target gene expression, and activity that has been suggested to be affected by genetic and epigenetic alterations. Here we sought to identify the effects of smoking and HPV status on eRNA expression in HNSCC tumors. We focused on four patient cohorts including smoking/HPV+, smoking/HPV-, non-smoking/HPV+, and non-smoking/HPV- patients. We used TCGA RNA-seq data from cancer tumors and adjacent normal tissue, extracted eRNA read counts, and correlated these to survival, clinical variables, immune infiltration, cancer pathways, and genomic alterations. We found a large number of differentially expressed eRNA in each patient cohort. We also found several dysregulated eRNA correlated to patient survival, clinical variables, immune pathways, and genomic alterations. Additionally, we were able to find dysregulated eRNA nearby seven key HNSCC-related oncogenes. For example, we found eRNA chr14:103272042-103272430 (eRNA-24036), which is located close to the TRAF3 gene to be differentially expressed and correlated with the pathologic N stage and immune cell populations. Using a separate validation dataset, we performed differential expression and immune infiltration analysis to validate our results from the TCGA data. Our findings may explain the association between eRNA expression, enhancer activity, and nearby gene dysregulation.

COVID-19 Severity Potentially Modulated by Cardiovascular-Disease-Associated Immune Dysregulation.

Patients with underlying cardiovascular conditions are particularly vulnerable to severe COVID-19. In this project, we aimed to characterize similarities in dysregulated immune pathways between COVID-19 patients and patients with cardiomyopathy, venous thromboembolism (VTE), or coronary artery disease (CAD). We hypothesized that these similarly dysregulated pathways may be critical to how cardiovascular diseases (CVDs) exacerbate COVID-19. To evaluate immune dysregulation in different diseases, we used four separate datasets, including RNA-sequencing data from human left ventricular cardiac muscle samples of patients with dilated or ischemic cardiomyopathy and healthy controls; RNA-sequencing data of whole blood samples from patients with single or recurrent event VTE and healthy controls; RNA-sequencing data of human peripheral blood mononuclear cells (PBMCs) from patients with and without obstructive CAD; and RNA-sequencing data of platelets from COVID-19 subjects and healthy controls. We found similar immune dysregulation profiles between patients with CVDs and COVID-19 patients. Interestingly, cardiomyopathy patients display the most similar immune landscape to COVID-19 patients. Additionally, COVID-19 patients experience greater upregulation of cytokine- and inflammasome-related genes than patients with CVDs. In all, patients with CVDs have a significant overlap of cytokine- and inflammasome-related gene expression profiles with that of COVID-19 patients, possibly explaining their greater vulnerability to severe COVID-19.

Using machine learning of clinical data to diagnose COVID-19: a systematic review and meta-analysis.

BACKGROUND: The recent Coronavirus Disease 2019 (COVID-19) pandemic has placed severe stress on healthcare systems worldwide, which is amplified by the critical shortage of COVID-19 tests. METHODS: In this study, we propose to generate a more accurate diagnosis model of COVID-19 based on patient symptoms and routine test results by applying machine learning to reanalyzing COVID-19 data from 151 published studies. We aim to investigate correlations between clinical variables, cluster COVID-19 patients into subtypes, and generate a computational classification model for discriminating between COVID-19 patients and influenza patients based on clinical variables alone. RESULTS: We discovered several novel associations between clinical variables, including correlations between being male and having higher levels of serum lymphocytes and neutrophils. We found that COVID-19 patients could be clustered into subtypes based on serum levels of immune cells, gender, and reported symptoms. Finally, we trained an XGBoost model to achieve a sensitivity of 92.5% and a specificity of 97.9% in discriminating COVID-19 patients from influenza patients. CONCLUSIONS: We demonstrated that computational methods trained on large clinical datasets could yield ever more accurate COVID-19 diagnostic models to mitigate the impact of lack of testing. We also presented previously unknown COVID-19 clinical variable correlations and clinical subgroups.

Comparative Analysis of Age- and Gender-Associated Microbiome in Lung Adenocarcinoma and Lung Squamous Cell Carcinoma.

The intra-tumor microbiota has been increasingly implicated in cancer pathogenesis. In this study, we aimed to examine the microbiome in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) and determine its compositional differences with relation to age and gender. After grouping 497 LUAD and 433 LUSC patients by age and gender and removing potential contaminants, we identified differentially abundant microbes in each patient cohort vs. adjacent normal samples. We then correlated dysregulated microbes with patient survival rates, immune infiltration, immune and cancer pathways, and genomic alterations. We found that most age and gender cohorts in both LUAD and LUSC contained unique, significantly dysregulated microbes. For example, LUAD-associated Escherichia coli str. K-12 substr. W3110 was dysregulated in older female and male patients and correlated with both patient survival and genomic alterations. For LUSC, the most prominent bacterial species that we identified was Pseudomonas putida str. KT2440, which was uniquely associated with young LUSC male patients and immune infiltration. In conclusion, we found differentially abundant microbes implicated with age and gender that are also associated with genomic alterations and immune dysregulations. Further investigation should be conducted to determine the relationship between gender and age-associated microbes and the pathogenesis of lung cancer.

Smoking-Mediated Upregulation of the Androgen Pathway Leads to Increased SARS-CoV-2 Susceptibility.

The COVID-19 pandemic is marked by a wide range of clinical disease courses, ranging from asymptomatic to deadly. There have been many studies seeking to explore the correlations between COVID-19 clinical outcomes and various clinical variables, including age, sex, race, underlying medical problems, and social habits. In particular, the relationship between smoking and COVID-19 outcome is controversial, with multiple conflicting reports in the current literature. In this study, we aim to analyze how smoking may affect the SARS-CoV-2 infection rate. We analyzed sequencing data from lung and oral epithelial samples obtained from The Cancer Genome Atlas (TCGA). We found that the receptor and transmembrane protease necessary for SARS-CoV-2 entry into host cells, ACE2 and TMPRSS2, respectively, were upregulated in smoking samples from both lung and oral epithelial tissue. We then explored the mechanistic hypothesis that smoking may upregulate ACE2 expression through the upregulation of the androgen pathway. ACE2 and TMPRSS2 upregulation were both correlated to androgen pathway enrichment and the specific upregulation of central pathway regulatory genes. These data provide a potential model for the increased susceptibility of smoking patients to COVID-19 and encourage further exploration into the androgen and tobacco upregulation of ACE2 to understand the potential clinical ramifications.