Angiotensin-converting enzyme 2 identifies immuno-hot tumors suggesting angiotensin-(1–7) as a sensitizer for chemotherapy and immunotherapy in breast cancer
Abstract:Background
Angiotensin-converting enzyme 2 (ACE2) is known as a tumor suppressor and lowly expressed in most cancers. The expression pattern and role of ACE2 in breast cancer (BC) have not been deeply elucidated.
Methods
A systematic pan-cancer analysis was conducted to assess the expression pattern and immunological role of ACE2 based on RNA-sequencing (RNA-seq) data downloaded from The Cancer Genome Atlas (TCGA). The correlation of ACE2 expressio… Show more
“…Given of the important role of immunological status of TME in immunotherapeutic response [ 9 , 10 , 52 ], we further investigated the responses of the ARGRS-high and low groups in immunotherapy cohorts. Approximately 87.5% of patients in the ARGRS-high group did not achieve remission after immunotherapy, and were more likely to recurrence or progress, suggesting the immunotherapeutic resistance of these patients.…”
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.
“…Given of the important role of immunological status of TME in immunotherapeutic response [ 9 , 10 , 52 ], we further investigated the responses of the ARGRS-high and low groups in immunotherapy cohorts. Approximately 87.5% of patients in the ARGRS-high group did not achieve remission after immunotherapy, and were more likely to recurrence or progress, suggesting the immunotherapeutic resistance of these patients.…”
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.
“…Up to date, a large number of studies have uncovered multiple genes can serve as biomarkers for immunotherapy response in BrCa or other cancer types, such as CCDC69 ( Yi et al, 2022 ), TIMM8A ( Zhang et al, 2022b ), and ACE2 ( Mei et al, 2022 ). Most of these studies are lacking real-world cohort validation, which is also the main shortcoming of our current research.…”
Breast cancer (BrCa) is a heterogeneous disease, which leads to unsatisfactory prognosis in females worldwide. Previous studies have proved that tumor immune microenvironment (TIME) plays crucial roles in oncogenesis, progression, and therapeutic resistance in Breast cancer. However, biomarkers related to TIME features have not been fully discovered. Proteasome activator complex subunit 2 (PSME2) is a member of proteasome activator subunit gene family, which is critical to protein degradation mediated by the proteasome. In the current research, we comprehensively analyzed the expression and immuno-correlations of Proteasome activator complex subunit 2 in Breast cancer. Proteasome activator complex subunit 2 was significantly upregulated in tumor tissues but associated with well prognosis. In addition, Proteasome activator complex subunit 2 was overexpressed in HER2-positive Breast cancer but not related to other clinicopathological features. Interestingly, Proteasome activator complex subunit 2 was positively related to immune-related processes and identified immuno-hot TIME in Breast cancer. Specifically, Proteasome activator complex subunit 2 was positively correlated with immunomodulators, tumor-infiltrating immune cells (TIICs), immune checkpoints, and tumor mutation burden (TMB) levels. Moreover, the positive correlation between Proteasome activator complex subunit 2 and PD-L1 expression was confirmed in a tissue microarray (TMA) cohort. Furthermore, in an immunotherapy cohort of Breast cancer, patients with pathological complete response (pCR) expressed higher Proteasome activator complex subunit 2 compared with those with non-pathological complete response. In conclusion, Proteasome activator complex subunit 2 is upregulated in tumor tissues and correlated with the immuno-hot tumor immune microenvironment, which can be a novel biomarker for the recognition of tumor immune microenvironment features and immunotherapeutic response in Breast cancer.
“… ### 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.…”
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