ABSTRACT
N6-methyladenosine methylation (m6A) is a common type of epigenetic alteration that prominently affects the prognosis of tumor patients. However, it is unknown how the m6A regulator affects the tumor microenvironment (TME) cell infiltration in adrenocortical carcinoma (ACC) and how it affects the prognosis of ACC patients yet. The m6A alteration patterns of 112 ACC patients were evaluated, furthermore, the association with immune infiltration cell features was investigated. The unsupervised clustering method was applied to typify the m6A alteration patterns of ACC patients. The principal component analysis (PCA) technique was taken to create the m6A score to assess the alteration pattern in specific malignancies. We found two independent patterns of m6A alteration in ACC patients. The TME cell infiltration features were significantly in accordance with phenotypes of tumor immune-inflamed and immune desert in both patterns. The m6Ascore also served as an independent predictive factor in ACC patients. The somatic copy number variation (CNV) and patients prognosis can be predicted by m6A alteration patterns. Moreover, the ACC patients with high m6A scores had better overall survival (OS) and higher efficiency in immune checkpoint blockade therapy. Our work demonstrated the significance of m6A alteration to the ACC patients immunotherapy. The individual m6A alteration patterns analysis might contribute to ACC patients prognosis prediction and immunotherapy choice.
Subject(s)
Adrenal Cortex Neoplasms , Adrenocortical Carcinoma , Humans , Adenosine , Adrenal Cortex Neoplasms/genetics , Adrenal Cortex Neoplasms/therapy , Adrenocortical Carcinoma/genetics , Adrenocortical Carcinoma/therapy , DNA Copy Number Variations , Methylation , Tumor Microenvironment/geneticsABSTRACT
Background: The coronavirus 2019 (COVID-19) disease, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus led to a global pandemic. HCQ and FPV were used early in the pandemic as a treatment modality for COVID-19. Various studies evaluated the HCQ and FPV effectiveness, based on the mortality endpoint and showed conflicting results. We hypothesize that analyzing the difference in the LOS as a significant endpoint would be of a major interest, especially for healthcare providers, to prevent a lengthy hospitalization and disease progression. Methods: This is a retrospective observational study, conducted via a medical chart review of COVD-19 patients who were admitted between April 2020 and March 2021 with a moderate to severe illness. The LOS endpoint was tested using the paired Wilcoxon signed-rank (WSR) model. Prior to using the WSR model, the balance between the HCQ and FPV groups, the propensity score matching, the LOS distribution, and the normality assumptions were tested. Two sensitivity statistical analyses were conducted to confirm the results (stratified log-rank test and U Welch test after transforming the LOS by the squared root values). Results: A total of 200 patients were included for the analysis: 83 patients in the HCQ group and 117 patients in the FPV group. Thirty-seven patients were matched in each group. The LOS data was positively skewed and violated the normality (Shapiro−Wilk p < 0.001) and had an unequal variance (Levene's test, p = 0.019). The WSR test showed no statistical significance in the LOS endpoint, with a median of −0.75 days (95% confidence interval: −4.0 to 2.5, p = 0.629), in favor of the HCQ group (four days), in comparison to seven days of the FPV group. The WSR findings were further confirmed with the stratified log rank test (p = 740) and the U Welch test (p = 391). Conclusions: The study concluded that the HCQ and FPV treatments have a comparable effectiveness in terms of the LOS in the moderate to severe COVID-19 patients. This study highlights the importance of analyzing the LOS as a relevant endpoint, in order to prevent the costs of a lengthy hospitalization and disease progression. The current study also emphasizes the importance of applying the appropriate statistical testing when dealing with two-sample paired data and analyzing non-parametric data such as the LOS.
