Identification of key genes and novel immune infiltration-associated biomarkers of sepsis

Sepsis is the major cause of mortality in the intensive care unit. The aim of this study was to identify the key prognostic biomarkers of abnormal expression and immune infiltration in sepsis. In this study, a total of 36 differentially expressed genes were identified to be mainly involved in a number of immune-related Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways. The hub genes (MMP9 and C3AR1) were significantly related to the prognosis of sepsis patients. The immune infiltration analysis indicated a significant difference in the relative cell content of naive B cells, follicular Th cells, activated NK cells, eosinophils, neutrophils and monocytes between sepsis and normal controls. Weighted gene co-expression network analysis and a de-convolution algorithm that quantifies the cellular composition of immune cells were used to analyse the sepsis expression data from the Gene Expression Omnibus database and to identify modules related to differential immune cells. CEBPB is the key immune-related gene that may be involved in sepsis. Gene set enrichment analysis revealed that CEBPB is involved in the processes of T cell selection, B cell–mediated immunity, NK cell activation and pathways of T cells, B cells and NK cells. Therefore, CEBPB may play a key role in the biological and immunological processes of sepsis.     

SPP1在头颈部鳞状细胞癌的免疫浸润及临床相关性分析

探讨分泌型磷蛋白1 (Secreted Phosphoprotein 1,SPP1)在头颈部鳞状细胞癌(Head and neck squamous cell carcinoma, HNSC)中与免疫浸润及临床的相关性,明确SPP1在HNSC预后和个体化治疗中的潜在价值。使用癌症基因组图谱(The Cancer Genome Atlas,TCGA)HNSC数据分析SPP1表达。使用来自TCGA的临床生存数据评估SPP1的临床预后价值。使用R语言的clusterProfiler 包进行SPP1相关的富集分析。使用R语言的CIBERSORT函数评估22种肿瘤浸润免疫细胞在HNSC中的浸润情况,分析肿瘤浸润免疫细胞与SPP1表达之间的关联。差异表达分析发现SPP1在HNSC中高表达(P<0.001),临床相关性分析发现SPP1表达与T分期(P=0.001)、临床分期(P=0.013)相关,SPP1高表达患者的总生存期明显短于低表达患者(P=0.020 4)。基因富集分析发现SPP1在HNSC中与免疫学功能及免疫相关通路有关联。肿瘤浸润免疫细胞分析发现在高SPP1表达组中,M2巨噬细胞(P=0.001 1)、未活化树突状细胞(P=0.005 5)、活化肥大细胞(P=0.048 8)浸润比例增加,而活化记忆性CD4+ T淋巴细胞(P<0.001)、浆细胞(P=0.026 6)、未活化肥大细胞(P=0.038 6)浸润比例减少。研究表明 SPP1在HNSC中充当致癌基因,并与患者的临床结果相关,SPP1在肿瘤免疫微环境中起重要作用,可能成为HNSC中有价值的预后生物标志物及免疫治疗生物靶标。     

Difference of immune cell infiltration between stable and unstable carotid artery atherosclerosis

Atherosclerotic plaque instability contributes to ischaemic stroke and myocardial infarction. This study is to compare the abundance and difference of immune cell subtypes within unstable atherosclerotic tissues. CIBERSORT was used to speculate the proportions of 22 immune cell types based on a microarray of atherosclerotic carotid artery samples. R software was utilized to illustrate the bar plot, heat map and vioplot. The immune cell landscape in atherosclerosis was diverse, dominated by M2 macrophages, M0 macrophages, resting CD4 memory T cells and CD8 T cells. There was a significant difference in resting CD4 memory T cells (p = 0.032), T cells follicular helper (p = 0.033), M0 (p = 0.047) and M2 macrophages (p = 0.012) between stable and unstable atherosclerotic plaques. Compared with stable atherosclerotic plaques, unstable atherosclerotic plaques had a higher percentage of M2 macrophages. Moreover, correlation analysis indicated that the percentage of naïve CD4 T cells was strongly correlated with that of gamma delta T cells (r = 0.93, p < 0.001), while memory B cells were correlated with plasma cells (r = 0.85, p < 0.001). In summary, our study explored the abundance and difference of specific immune cell subgroups at unstable plaques, which would aid new immunotherapies for atherosclerosis.     

Identification of immune-based prostate cancer subtypes using mRNA expression

Immune infiltration in Prostate Cancer (PCa) was reported to be strongly associated with clinical outcomes. However, previous research could not elucidate the diversity of different immune cell types that contribute to the functioning of the immune response system. In the present study, the CIBERSORT method was employed to evaluate the relative proportions of immune cell profiling in PCa samples, adjacent tumor samples and normal samples. Three types of molecular classification were identified in tumor samples using the ‘CancerSubtypes’ package of the R software. Each subtype had specific molecular and clinical characteristics. In addition, functional enrichment was analyzed in each subtype. The submap and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms were also used to predict clinical response to the immune checkpoint blockade. Moreover, the Genomics of Drug Sensitivity in Cancer (GDSC) database was employed to screen for potential chemotherapeutic targets for the treatment of PCa. The results showed that Cluster I was associated with advanced PCa and was more likely to respond to immunotherapy. The findings demonstrated that differences in immune responses may be important drivers of PCa progression and response to treatment. Therefore, this comprehensive assessment of the 22 immune cell types in the PCa Tumor Environment (TEM) provides insights on the mechanisms of tumor response to immunotherapy and may help clinicians explore the development of new drugs.