Background: Coronavirus disease 2019 (COVID-19) has now brought major challenges to public health and economy globally since December 2019, which requires effective treatment and prevention strategies to adapt for the impact of the pandemic. We therefore explored the prognostic factors for patients with COVID-19 and the contribution of immunomodulatory therapy on COVID-19 outcome. Methods: From 1 Feb to 16 March 2020, consecutive cases with COVID-19 were analyzed in the West Campus of Wuhan Union Hospital, a tertiary care center that is designated to care for patients with COVID-19 in Wuhan, China. The observation was based on follow-up until in-hospital death or discharge. Logistic regressions were performed for prognostic factors associated with in-hospital death. Furthermore, a propensity score-matched analysis was done using a multivariable logistic regression model to analyze the contributions of multiple treatments on COVID-19 death. Results: Three hundred and seventeen patients with COVID-19 were enrolled, of whom 269 were discharged and 48 died in hospital. After propensity score-matching based on age, gender, symptoms and comorbidities, multivariable logistic regression was performed with the adjustment of other variables that were significant risk factors in the univariate regression. Treatments with glucocorticoids, immunoglobulin, thymosin, and ammonium glycyrrhizinate were significantly associated with a higher rate of COVID-19 death. Conclusions: For in-hospital patients with COVID-19 of all severity levels, high risk for fatal outcome was observed in those treated with glucocorticoids, immunoglobulin, thymosin, and ammonium glycyrrhizinate. The results of this study do not support immunomodulatory therapy in patients admitted to hospital with COVID-19. Further prospective studies are essential to clarify our findings, especially for non-critically ill patients.

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Alteration in the polybromo‐1 (PBRM1) protein encoding gene PBRM1 is the second most frequent mutation in clear cell renal cell carcinoma (ccRCC). It causes a series of changes in the tumorigenesis, progression, prognosis, and immune response of ccRCC patients. This study explored the PBRM1‐associated immunological features and identified the immune‐related genes (IRGs) linked to PBRM1 mutation using bioinformatics methods. A total of 37 survival IRGs associated with PBRM1 mutation in ccRCC patients were identified. To further explore the role of these IRGs in ccRCC and their association with immune status, eight IRGs with remarkable potential as individual targets were selected. An immune model that was constructed showed good performance in stratifying patients into different subgroups, showing clinical application potential compared to traditional clinical factors. Patients in the high‐risk group were inclined to have more advanced stage and higher grade tumors with node metastasis, distant metastasis, and poorer prognosis. Furthermore, these patients had high percentages of regulatory T cells, follicular helper T cells, and M0 macrophages and exhibited high expression levels of immune checkpoints proteins, such as CTLA‐4, PD‐1, LAG‐3, TIGIT, and CD47. Finally, a nomogram integrating the model and clinical factors for clinical application showed a more robust predictive performance for prognosis. The prediction model associated with PBRM1 mutation status and immunity can serve as a promising tool to stratify patients depending upon their immune status, thus facilitating immunotherapy in the future.