Identification of angiotensin-converting enzyme 2 (ACE2) protein as the potential biomarker in SARS-CoV-2 infection-related lung cancer using computational analyses.

COVID-19 is a pandemic that began to spread worldwide caused by SARS-CoV-2. Lung cancer patients are more susceptible to SARS-CoV-2 infection. The SARS-CoV-2 enters into the host by the ACE2 receptor. Thus, ACE2 is the key to understand the mechanism of SARS-CoV-2 infection. However, the lack of knowledge about the biomarker of COVID-19 warrants the development of ACE2 biomarkers. The analysis of ACE2 expression in lung cancer was performed using The Cancer Genome Atlas (TCGA). Therefore, we investigated the prognosis, clinical characteristics, and mutational analysis of lung cancer. We also analyzed the shared proteins between the COVID-19 and lung cancer, protein-protein interactions, gene-miRNAs, gene-transcription factors (TFs), and the signaling pathway. Finally, we compared the mRNA expression of ACE2 and its co-expressed proteins using the TCGA. The up-regulation of ACE2 in lung adenocarcinoma (LUAD) and lung squamous carcinoma (LUSC) was found irrespective of gender and age. We found the low survival rate in high expression of ACE2 in lung cancer patients and 16 mutational positions. The functional assessment of targeted 12,671, 3107, and 29 positive genes were found in COVID-19 disease, LUAD, and LUSC, respectively. Then, we identified eight common genes that interact with 20 genes, 219 miRNAs, and 16 TFs. The common genes performed the mRNA expression in lung cancer, which proved the ACE2 is the best potential biomarker compared to co-expressed genes. This study uncovers the relationship between COVID-19 disease and lung cancer. We identified ACE2 and also its co-expressed proteins are the potential biomarker and therapy as the current COVID-19 disease and lung cancer.

Mandatory preoperative COVID-19 testing for cancer patients-Is it justified?

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 has caused substantial disruptions in routine clinical care. Emerging data show that surgery in coronavirus disease (COVID)-positive cases can be associated with worsening of clinical outcomes and increased postoperative mortality. Hence, preoperative COVID-19 testing for all patients before elective surgery was implemented in our institution. MATERIALS AND METHODS: Two hundred and sixty-two asymptomatic cancer patients were preoperatively tested for COVID-19 using reverse-transcription polymerase chain reaction technique with nasopharyngeal and oropharyngeal swabbing. All negative patients were operated within 72 hours, and positive patients were quarantined for a minimum 14 days before re-swabbing. RESULTS: In our cohort, 21 of 262 (8.0%) asymptomatic preoperative patients, who were otherwise fit for surgery, tested positive. After adequate quarantine and a negative follow-up test report, 12 of 21 (57%) had an operation. No major postoperative morbidity due to COVID-19 was noted during the immediate postoperative period before discharge from the hospital. CONCLUSION: Routine preoperative COVID-19 testing was successful in identifying asymptomatic viral carriers. There was no incidence of symptomatic COVID-19 disease in the postoperative period, and there was no incidence of morbidity attributable to COVID-19. These data suggested a beneficial role for mandatory preoperative COVID-19 testing.

Assessing and Reassessing the Association of Comorbidities and Coinfections in COVID-19 Patients.

Coronavirus disease 2019 (COVID-19) has posed an enormous global health and economic burden. To date, 324 million confirmed cases and over 5.5 million deaths have been reported. Several studies have reported comorbidities and coinfections associated with complicated and serious COVID-19 infections. Data from retrospective, prospective, case series, and case reports from various geographical locations were assessed, which included ~ 2300 COVID-19 patients with varying comorbidities and coinfection. We report that Enterobacterales with Staphylococcus aureus was the most while Mycoplasma pneumoniae was the least prevalent coinfection in COVID-19 patients with a comorbidity. In this order, hypertension, diabetes, cardiovascular disease, and pulmonary disease were the prevalent comorbidities observed in COVID-19 patients. There was a statistically significant difference in the prevalent comorbidities observed in patients coinfected with Staphylococcus aureus and COVID-19 and a statistically non-significant difference in the prevalent comorbidities in patients coinfected with Mycoplasma pneumoniae and COVID-19 as compared to similar infections in non-COVID-19 coinfection. We report a significant difference in the prevalent comorbidities recorded in COVID-19 patients with varying coinfections and varying geographic study regions. Our study provides informative data on the prevalence of comorbidities and coinfections in COVID-19 patients to aid in evidence-based patient management and care.

The Cancer Research Database (CRDB): Integrated Platform to Gain Statistical Insight Into the Correlation Between Cancer and COVID-19.

BACKGROUND: The advancement of cancer research has been facilitated through freely available cancer literature, databases, and tools. The age of genomics and big data has given rise to the need for cooperation and data sharing in order to make efficient use of this new information in the COVID-19 pandemic. Although there are many databases for cancer research, their access is not easy owing to different ways of processing and managing the data. There is an absence of a unified platform to manage all of them in a transparent and more comprehensible way. OBJECTIVE: In this study, an improved integrated cancer research database and platform is provided to facilitate a deeper statistical insight into the correlation between cancer and the COVID-19 pandemic, unifying the collection of almost all previous published cancer databases and defining a model web database for cancer research, and scoring databases on the basis of the variety types of cancer, sample size, completeness of omics results, and user interface. METHODS: Databases examined and integrated include the Data Portal database, Genomic database, Proteomic database, Expression database, Gene database, and Mutation database; and it is expected that this launch will sort, save, advance the understanding and encourage the use of these resources in the cancer research environment. RESULTS: To make it easy to search valuable information, 85 cancer databases are provided in the form of a table, and a database of databases named the Cancer Research Database (CRDB) has been built and presented herein. Furthermore, the CRDB has been herein equipped with unique navigation tools in order to be explored by three methods; that is, any single database can be browsed by typing the name in the given search bar, while all categories can be browsed by clicking on the name of the category or image expression icon, thus serving as a facility that could provide all the category databases on a single click. CONCLUSIONS: The computational platform (PHP, HTML, CSS, and MySQL) used to build CRDB for the cancer scientific community can be freely investigated and browsed on the internet and is planned to be updated in a timely manner. In addition, based on the proposed platform, the status and diagnoses statistics of cancer during the COVID-19 pandemic have been thoroughly investigated herein using CRDB, thus providing an easy-to-manage, understandable framework that mines knowledge for future researchers.

IntegralVac: A Machine Learning-Based Comprehensive Multivalent Epitope Vaccine Design Method.

In the growing field of vaccine design for COVID and cancer research, it is essential to predict accurate peptide binding affinity and immunogenicity. We developed a comprehensive machine learning method, 'IntegralVac,' by integrating three existing deep learning tools: DeepVacPred, MHCSeqNet, and HemoPI. IntegralVac makes predictions for single and multivalent cancer and COVID-19 epitopes without manually selecting epitope prediction possibilities. We performed several rounds of optimization before integration, then re-trained IntegralVac for multiple datasets. We validated the IntegralVac with 4500 human cancer MHC I peptides obtained from the Immune Epitope Database (IEDB) and with cancer and COVID epitopes previously selected in our laboratory. The other data referenced from existing deep learning tools served as a positive control to ensure successful prediction was possible. As evidenced by increased accuracy and AUC, IntegralVac improved the prediction rate of top-ranked epitopes. We also examined the compatibility between other servers' clinical checkpoint filters and IntegralVac. This was to ensure that the other servers had a means for predicting additional checkpoint filters that we wanted to implement in IntegralVac. The clinical checkpoint filters, including allergenicity, antigenicity, and toxicity, were used as additional predictors to improve IntegralVac's prediction accuracy. We generated immunogenicity scores by cross-comparing sequence inputs with each other and determining the overlap between each individual peptide sequence. The IntegralVac increased the immunogenicity prediction accuracy to 90.1% AUC and the binding affinity accuracy to 95.4% compared to the control NetMHCPan server. The IntegralVac opens new avenues for future in silico methods, by building upon established models for continued prediction accuracy improvement.

COVID-19 vaccines for patients with cancer: benefits likely outweigh risks.

Less than a year since the start of the COVID-19 pandemic, ten vaccines against SARS-CoV-2 have been approved for at least limited use, with over sixty others in clinical trials. This swift achievement has generated excitement and arrives at a time of great need, as the number of COVID-19 cases worldwide continues to rapidly increase. Two vaccines are currently approved for full use, both built on mRNA and lipid nanotechnology platforms, a success story of mRNA technology 20 years in the making. For patients with cancer, questions arise around the safety and efficacy of these vaccines in the setting of immune alterations engendered by their malignancy and/or therapies. We summarize the current data on leading COVID-19 vaccine candidates and vaccination of patients undergoing immunomodulatory cancer treatments. Most current cancer therapeutics should not prevent the generation of protective immunity. We call for more research in this area and recommend that the majority of patients with cancer receive COVID vaccinations when possible.