Multiple sclerosis and related disorders · 2022
Identification of potential key genes and immune infiltration in Multiple sclerosis.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Liu Yang, Zhou Yinglian, Yue Hui, Dou Haitong, Rang Xinming, Wang Xin, Xu Chaohan, Fu Jin
Аннотация
BACKGROUND: Multiple sclerosis (MS) is an extremely serious autoimmune disease of the nervous system. Extensive evidence indicated that immune system activation plays a crucial role in the development of MS. However, the exact mechanism of MS is still not well understood. Our objective was to identify potential key genes of Multiple sclerosis (MS) via bioinformatic analysis and apply CIBERSORT algorithms to calculate the proportion of infiltrating immune cells.
METHODS: The differentially expressed genes (DEGs) were analyzed from two public datasets, which included 99 MS, 45 controls and 133 MS, 79 controls. Then the common DEGs were obtained (p < 0.05). LASSO regression analysis was performed on common DEGs of GSE17048. The receiver operating characteristic (ROC) curves were created. The key genes were screened based on area under the receiver operating characteristic curve (AUC). CIBERSORT algorithms were used to explore the immune infiltration in MS.
RESULTS: 516 common DEGs were screened from two public datasets. And then 54 signature genes were obtained by constructing LASSO model. MS4A6A, CACNA1I, C9orf46, EIF4EBP2, SERTAD2, TGFBR2 and RAB34 with the largest AUC values were selected as the key genes. Neutrophils, Monocytes, resting memory CD4+ T cells, CD8+ T cells and resting NK cells accounted for a large proportion of infiltrating immune cells in MS.
CONCLUSION: MS4A6A, CACNA1I, C9orf46, EIF4EBP2, SERTAD2, TGFBR2 and RAB34 may be closely related pathogenesis of MS, and may represent new candidate biomarkers. In addition, immune cell infiltration may also play an important role in the progression of MS.
METHODS: The differentially expressed genes (DEGs) were analyzed from two public datasets, which included 99 MS, 45 controls and 133 MS, 79 controls. Then the common DEGs were obtained (p < 0.05). LASSO regression analysis was performed on common DEGs of GSE17048. The receiver operating characteristic (ROC) curves were created. The key genes were screened based on area under the receiver operating characteristic curve (AUC). CIBERSORT algorithms were used to explore the immune infiltration in MS.
RESULTS: 516 common DEGs were screened from two public datasets. And then 54 signature genes were obtained by constructing LASSO model. MS4A6A, CACNA1I, C9orf46, EIF4EBP2, SERTAD2, TGFBR2 and RAB34 with the largest AUC values were selected as the key genes. Neutrophils, Monocytes, resting memory CD4+ T cells, CD8+ T cells and resting NK cells accounted for a large proportion of infiltrating immune cells in MS.
CONCLUSION: MS4A6A, CACNA1I, C9orf46, EIF4EBP2, SERTAD2, TGFBR2 and RAB34 may be closely related pathogenesis of MS, and may represent new candidate biomarkers. In addition, immune cell infiltration may also play an important role in the progression of MS.