The goal of this study is to reveal the hub genes and molecular mechanisms of the coronavirus disease 2019 (COVID-19) acute respiratory distress syndrome (ARDS) based on the genome-wide RNA sequencing dataset. The RNA sequencing dataset of COVID-19 ARDS was obtained from GSE163426. A total of 270 differentially expressed genes (DEGs) were identified between COVID-19 ARDS and control group patients. Functional enrichment analysis of DEGs suggests that these DEGs may be involved in the following biological processes: response to cytokine, G-protein coupled receptor activity, ionotropic glutamate receptor signalling pathway and G-protein coupled receptor signalling pathway. By using the weighted correlation network analysis approach to analyse these DEGs, 10 hub DEGs that may play an important role in COVID-19 ARDS were identified. A total of 67 potential COVID-19 ARDS targetted drugs were identified by a complement map analysis. Immune cell infiltration analysis revealed that the levels of T cells CD4 naive, T cells follicular helper, macrophages M1 and eosinophils in COVID-19 ARDS patients were significantly different from those in control group patients. In conclusion, this study identified 10 COVID-19 ARDS-related hub DEGs and numerous potential molecular mechanisms through a comprehensive analysis of the RNA sequencing dataset and also revealed the difference in immune cell infiltration of COVID-19 ARDS. K E Y W O R D S acute respiratory distress syndrome, coronavirus disease 2019, GSE163426, hub genes, molecular mechanism 1 | INTRODUCTION Coronavirus disease 2019 (COVID-19), named by the World Health Organization, refers to pneumonia caused by the 2019 novel coronavirus infection. The International Committee on Classification of Viruses also named this new type of coronavirus 'SARS-CoV-2' (Severe Acute Respiratory Syndrome Coronavirus 2) [1]. At present, there are more than 100 million patients diagnosed with COVID-19 worldwide, and more than 3 million patients have died. There are still 30 million patients with COVID-19. The clinical manifestations of COVID-19-infected pneumonia patients include fever, fatigue, and dry cough, which are its main manifestations [2].Upper respiratory symptoms such as nasal congestion and runny nose are rare, and hypoxia is present [3]. About half of the patients have difficulty breathing more than a week later. In severe cases, it leads to acute respiratory distress syndrome (ARDS), septic shock, metabolic acidosis, and coagulation dysfunction [4,5]. ARDS is also the main cause of death for COVID-19 patients. However, it is still unclear which people are more likely to develop ARDS, and which are the related factors in patients who develop ARDS that lead to death. Therefore, we urgently need a better understanding of COVID-19 ARDS.Wangsheng Deng and Jiaxing Zeng contributed equally to this work.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original ...

Background This study aimed to get a deeper insight into new osteosarcoma (OS) signature based on bone morphogenetic proteins (BMPs)-related genes and to confirm the prognostic pattern to speculate on the overall survival among OS patients. Methods Firstly, pathway analyses using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were managed to search for possible prognostic mechanisms attached to the OS-specific differentially expressed BMPs-related genes (DEBRGs). Secondly, univariate and multivariate Cox analysis was executed to filter the prognostic DEBRGs and establish the polygenic model for risk prediction in OS patients with the least absolute shrinkage and selection operator (LASSO) regression analysis. The receiver operating characteristic (ROC) curve weighed the model’s accuracy. Thirdly, the GEO database (GSE21257) was operated for independent validation. The nomogram was initiated using multivariable Cox regression. Immune infiltration of the OS sample was calculated. Finally, the three discovered hallmark genes’ mRNA and protein expressions were verified. Results A total of 46 DEBRGs were found in the OS and control samples, and three prognostic DEBRGs (DLX2, TERT, and EVX1) were screened under the LASSO regression analyses. Multivariate and univariate Cox regression analysis were devised to forge the OS risk model. Both the TARGET training and validation sets indicated that the prognostic biomarker-based risk score model performed well based on ROC curves. In high- and low-risk groups, immune cells, including memory B, activated mast, resting mast, plasma, and activated memory CD4 + T cells, and the immune, stromal, and ESTIMATE scores showed significant differences. The nomogram that predicts survival was established with good performance according to clinical features of OS patients and risk scores. Finally, the expression of three crucial BMP-related genes in OS cell lines was investigated using quantitative real-time polymerase chain reaction (qRT-PCR) and western blotting (WB). Conclusion The new BMP-related prognostic signature linked to OS can be a new tool to identify biomarkers to detect the disease early and a potential candidate to better treat OS in the future.