“…In addition, tumors were demarcated into three phenotypes based on the spatial distribution of CD8 + T cells, including the inflamed, the excluded, and the deserted subtypes. 42 , 43 The inflamed subtype is considered to be immuno-hot, and excluded and deserted subtypes are considered to be immuno-cold. 44 …”
“…In addition, tumors were demarcated into three phenotypes based on the spatial distribution of CD8 + T cells, including the inflamed, the excluded, and the deserted subtypes. 42 , 43 The inflamed subtype is considered to be immuno-hot, and excluded and deserted subtypes are considered to be immuno-cold. 44 …”
“… ### R packages used in this step # Gene expression profile with genes as the row names and samples as the column names expr = readRDS('expr.rds') # Tumor immune microenvironment (TME) features, taking chemokine genes for example features = readRDS('chemokine.rds') # gene vector # calculation the correlation between candiate gene SECTM1 and chemokine genes correlation = data.frame(do.call(rbind,lapply(intersect(features,rownames(expr)),function(x) { tmp = cor.test(as.numeric(expr['SECTM1',]),as.numeric(expr[x,]),method='pearson') return(c(x,tmp$estimate,tmp$p.value)) }))) colnames(correlation) = c('feature','correlation','pvalue') # plot anno_col = data.frame(SECTM1=as.numeric(expr['SECTM1',]),Sample=colnames(expr)) %>% arrange(SECTM1) %>% column_to_rownames('Sample') mat = expr[intersect(features,rownames(expr)),intersect(rownames(anno_col),colnames(expr))] pheatmap(mat, show_rownames=T, cluster_cols=F, cluster_rows=F, annotation_col=anno_col,scale='row') Note: The features of the tumor immune microenvironment include immunomodulators, the activities of the cancer immunity cycle, infiltration levels of TIICs, and the expression of inhibitory immune checkpoints, tumor purity, the detail information could be found in our previous studies. 17 , 18 Figure 1 Predictive value and immunological correlations of SECTM1 in the PRJEB23709 cohort Reproduced with permission from iScience (Mei et al. 1 ).…”
Section: Step-by-step Methods Detailsmentioning
confidence: 99%
“…Note: The features of the tumor immune microenvironment include immunomodulators, the activities of the cancer immunity cycle, infiltration levels of TIICs, and the expression of inhibitory immune checkpoints, tumor purity, the detail information could be found in our previous studies. 17 , 18 …”
Section: Step-by-step Methods Detailsmentioning
confidence: 99%
“… ### R packages used in this step # gene expression profile with genes as the row names and samples as the column names expr = readRDS('expr_pancancer.rds') # data holding the tumor type of samples type = readRDS('type_pancancer.rds') # tumor immune microenvironment (TME) features featurePath = c('chemokine.rds','receptor.rds','MHC.rds','immunoinhibitor.rds','immunostimulator.rds') features = data.frame(do.call(rbind,lapply(featurePath,function(x){return(data.frame(feature=readRDS(x),type=gsub('\\..∗','',x)))}))) # calculation the correlation between candidate gene SECTM1 and chemokine genes in each cancer type correlation = data.frame(do.call(cbind,lapply(sort(as.character(unique(type$tumor_type))),function(tumor_type){ expr = expr[,intersect(rownames(type)[which(type$tumor_type==tumor_type)],colnames(expr))] return(data.frame(correlation=sapply(intersect(features$feature,rownames(expr)),function(x){return(cor.test(as.numeric(expr['SECTM1',]),as.numeric(expr[x,]),method='pearson')$estimate) }), row.names=intersect(features$feature,rownames(expr)))) }))) colnames(correlation) = sort(as.character(unique(type$tumor_type))) anno_col = features %>% column_to_rownames('feature') pheatmap(correlation, show_rownames=T, show_colnames=T, cluster_cols=F, cluster_rows=F,annotation_col=anno_col) Note: The features of the tumor immune microenvironment include immunomodulators, the activities of the cancer immunity cycle, infiltration levels of TIICs, and the expression of inhibitory immune checkpoints, tumor purity, the detail information could be found in our previous studies. 17 , 18 Alternatives: Sangerbox, 20 a user-interactive online tool, could provide R code-free pan-cancer analysis. Correlations between candidate and MSI gene expression.…”
“…In addition, tumors with different phenotype have distinct therapeutic responses. To be specific, hot tumors, featured by T-cell inflammation, showed a favorite therapeutic response to immunotherapy, while cold tumors are resistant to many treatments [9][10][11][12]. Thus, it is crucial to investigate the alteration of the TME to guide the personalized immunotherapy.…”
Non-small cell lung cancer (NSCLC) is the most common histological type of lung cancer. With the in-depth exploration of cell death manners, numerous studies found that anoikis is an important mechanism that associated with treatment. Therefore, we aimed to explore the prognostic value and treatment guidance of anoikis in NSCLC patients. In the current study, we first constructed a prognostic model based on the anoikisrelated genes based on bulk RNA-sequencing and single-cell RNA-sequencing (scRNA-seq) dataset. Then, immuno-correlations of anoikis-related risk scores (ARGRS) were analyzed. In addition, HMGA1, a risky gene in ARGRS, was further explored to define its expression and immuno-correlation. Results showed that patients with higher ARGRS had worse clinical outcomes. Moreover, the five genes in the prognostic model were all highly expressed on tumor cells. Moreover, further analysis found that the ARGRS was negatively correlated with ImmuneScore, but positively with tumor purity. Besides, patients in the ARGRS-high group had lower levels of immunological characteristics, such as the immune-related signaling pathways and subpopulations. Additionally, in the immunotherapy cohorts, patients with the ARGRS-high phenotype were more resistant to immunotherapy and tended to not achieve remission after treatment. Last, HMGA1 was chosen as the representative biomarker, and analysis of the in-house cohort showed that HMGA1 was highly expressed in tumor tissues and correlated with decreased T cell infiltration. To sum up, ARGRS was correlated with a desert tumor microenvironment and identified immune-cold tumors, which can be a novel biomarker for the recognition of immunological characteristics and an immunotherapeutic response in NSCLC.
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