Dataset Information

Exploration the Significance of a Novel Immune-Related Gene Signature in Prognosis and Immune Microenvironment of Breast Cancer.

ABSTRACT: This study was designed to identify an immune-related gene signature (IRGS) associated with breast cancer (BC) patient outcomes. Transcriptomic data from 1411 BC patients in the TCGA and GEO databases were used to identify differentially expressed immune-related genes (DEIGs) when comparing BC tumor and normal tissue samples. We were able to construct a 27-gene IRGS that was able to effectively separate BC patients into high- and low-risk groups that corresponded to significant differences in overall and recurrence-free survival (OS and RFS, respectively). Besides, the relevance of this signature to immune response and immune cell infiltration of BC tumors was evaluated. These high- and low-risk BC patients were found to exhibit significantly different immune responses and functional enrichment. We also identified patients in the high-risk group exhibited significantly reduced immune cell infiltration of tumors relative to low-risk patients. Together, the results of this analysis offer a novel overview of the immune microenvironment within BC tumors and highlight key immunological genes associated with patient survival outcomes.

SUBMITTER: Zhao Y

PROVIDER: S-EPMC7396707 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Exploration the Significance of a Novel Immune-Related Gene Signature in Prognosis and Immune Microenvironment of Breast Cancer.

Zhao Yajie Y Pu Chunrui C Liu Zhenzhen Z

Frontiers in oncology 20200724

This study was designed to identify an immune-related gene signature (IRGS) associated with breast cancer (BC) patient outcomes. Transcriptomic data from 1411 BC patients in the TCGA and GEO databases were used to identify differentially expressed immune-related genes (DEIGs) when comparing BC tumor and normal tissue samples. We were able to construct a 27-gene IRGS that was able to effectively separate BC patients into high- and low-risk groups that corresponded to significant differences in ov ...[more]

PMID: 32850356

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Project description:BackgroundThis study targeted at developing a robust, prognostic signature based on super-enhancer-related genes (SERGs) to reveal survival prognosis and immune microenvironment of breast cancer.MethodsRNA-sequencing data of breast cancer were retrieved from The Cancer Genome Atlas (TCGA), 1069 patients of which were randomly assigned into training or testing set in 1:1 ratio. SERGs were downloaded from Super-Enhancer Database (SEdb). After which, a SERGs signature was established based on the training set, with its prognostic value further validated in the testing set. Subsequently, we identified the potential function enrichment and tumor immune infiltration of the model. Moreover, in vitro experiments were completed to further explore the biological functions of ZIC2 gene (one of the risk genes in the prognostic model) in breast cancer.ResultsA risk score system of prognostic value was constructed with 6 SERGs (ZIC2, NFE2, FOXJ1, KLF15, POU3F2 and SPIB) to find patients in high-risk group with significantly worse prognosis in both training and testing sets. In addition, a multivariate regression was established via integrating the 6 genes with age and N stage, indicating well performance by calibration, time-dependent receiver operating characteristic (ROC) analysis and decision curve analysis (DCA). Further analysis demonstrated that tumor-associated pathological processes and pathways were significantly enriched in the high-risk group. In general, the novel SERGs signature could be applied to screen breast cancer with immunosuppressive microenvironment for the risk score was negatively correlated with ESTIMATE score, tumor-infiltration lymphocytes (such as CD4 + and CD8 + T cell), immune checkpoints and chemotactic factors. Furthermore, down-regulation of ZIC2 gene expression inhibited the cell viability, cellular migration and cell cycle of breast cancer cells.ConclusionsThe novel SERGs signature could predict the prognosis of breast cancer; and SERGs might serve as potential therapeutic targets for breast cancer.

Project description:Background: Lactate, an intermediate product of glycolysis, has become an essential regulator of tumor maintenance, development, and metastasis. Lactate can drive tumors by changing the microenvironment of tumor cells. Because of lactate's important role in cancer, we aim to find a novel prognostic signature based on lactate metabolism-related genes (LMRGs) of breast cancer (BC). Methods: RNA-sequencing data and clinical information of BC were enrolled from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. We obtained LMRGs from the Molecular Signature Database v7.4 and articles, and then we compared candidate genes with TCGA data to get differential genes. Univariate analysis and most minor absolute shrinkage and selector operator (LASSO) Cox regression were employed to filter prognostic genes. A novel lactate metabolism-related risk signature was constructed using a multivariate Cox regression analysis. The signature was validated by time-dependent ROC curve analyses and Kaplan-Meier analyses in TCGA and GEO cohorts. Then, we further investigated in depth the function of the model's immune microenvironment. Results: We constructed a 3-LMRG-based risk signature. Kaplan-Meier curves confirmed that high-risk score subgroups had a worse prognosis in TCGA and GEO cohorts. Then a nomogram to predict the probability of survival for BC was constructed. We also performed Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway function analysis. The function analysis showed that the lactate metabolism-related signature was significantly related to immune response. A significant correlation was observed between prognostic LMRGs and tumor mutation burden, checkpoints, and immune cell infiltration. An mRNA-miRNA network was built to identify an miR-203a-3p/LDHD/LYRM7 regulatory axis in BC. Conclusion: In conclusion, we constructed a novel 3-LMRG signature and nomogram that can be used to predict the prognosis of BC patients. In addition, the signature is closely related to the immune microenvironment, which may provide new insight into future anticancer therapies.

Project description:BackgroundFerroptosis, an iron-dependent form of programmed cell death, significantly impacts cancer, yet its link to prostate cancer (PCa) prognosis remains underexplored. This study aims to develop and validate a ferroptosis-related gene signature to predict PCa prognosis and immune microenvironment differences, potentially identifying therapeutic targets.MethodsRNA-sequencing data of 478 PCa patients and corresponding clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. We investigated the disease-free survival (DFS) rates of the high- and low-risk groups using the Kaplan-Meier method. Functional differences between the high- and low-risk groups were investigated by a gene set enrichment analysis (GSEA), and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. The link between ferroptosis risk score and immune status was examined using CIBERSORT. The expression levels of core prognostic genes in benign prostatic hyperplasia (BPH) and PCa were verified using quantitative real-time polymerase chain reaction (qRT-PCR), Western blot, and immunohistochemistry (IHC).ResultsA novel ferroptosis-related prognostic gene signature was established and tested in the Gene Expression Omnibus (GEO) database based on univariate and multivariate Cox regression analyses. Patients with PCa were classified into high- and low-risk groups based on this ferroptosis signature. Patients in the high-risk group had worse outcomes than those in the low-risk group. The predictive accuracy of the model was demonstrated by a receiver operating characteristic (ROC) analysis. An additional enrichment analysis of TCGA cohort revealed the immune-related pathways were significantly upregulated in the high-risk group, with areas under the curve (AUCs) of 0.85 at 1 year, 0.82 at 3 years, and 0.76 at 5 years. In the GEO cohort, the AUCs reached 0.69 at 1 year, 0.74 at 3 years, and 0.75 at 5 years. An additional enrichment analysis indicated a significant upregulation of cytokine-related pathways, immune receptor activity, and other immune-related pathways in the high-risk group. Furthermore, the analysis revealed that the proportions of mast cells and plasma cells were significantly lower in the high-risk group compared to the low-risk group of PCa patients. Conversely, the proportion of regulatory T cells (Tregs) was significantly higher in the high-risk group than in the low-risk group. According to the qRT-PCR, Western blot, and IHC results, DRD4, SRC, AKR1C2, and AIFM2 expression was significantly higher in PCa than BPH. We also showed that the ferrostatin 1-treated LNCaP cells had higher expression levels of DRD4, SRC, and AKR1C2.ConclusionsA prognostic signature of eight ferroptosis-related genes (FRGs) that may accurately predict PCa patient outcomes was constructed and validated. FRGs may contribute to anti-tumor immunity and serve as therapeutic targets in PCa.

Project description:Breast cancer (BC) is the most diagnosed cancer in women. Cuproptosis is new regulated cell death, distinct from known death mechanisms and dependent on copper and mitochondrial respiration. However, the comprehensive relationship between cuproptosis and BC is still blank until now. In the present study, we acquired 13 cuproptosis-related regulators (CRRs) from the previous research and downloaded the RNA sequencing data of TCGA-BRCA from the UCSC XENA database. The 13 CRRs were all differently expressed between BC and normal samples. Using consensus clustering based on the five prognostic CRRs, BC patients were classified into two cuproptosis-clusters (C1 and C2). C2 had a significant survival advantage and higher immune infiltration levels than C1. According to the Cox and LASSO regression analyses, a novel cuproptosis-related prognostic signature was developed to predict the prognosis of BC effectively. The high- and low-risk groups were divided based on the risk scores. Kaplan-Meier survival analysis indicated that the high-risk group had shorter overall survival (OS) than the low-risk group in the training, test and entire cohorts. GSEA indicated that the immune-related pathways were significantly enriched in the low-risk group. According to the CIBERSORT and ESTIMATE analyses, patients in the high-risk group had higher infiltrating levels of antitumor lymphocyte cell subpopulations and higher immune score than the low-risk group. The typical immune checkpoints were all elevated in the high-risk group. Furthermore, the high-risk group showed a better immunotherapy response than the low-risk group based on the Tumor Immune Dysfunction and Exclusion (TIDE) and Immunophenoscore (IPS). In conclusion, we identified two cuproptosis-clusters with different prognoses using consensus clustering in BC. We also developed a cuproptosis-related prognostic signature and nomogram, which could indicate the outcome, the tumor immune microenvironment, as well as the response to immunotherapy.

Project description:Background: Ferroptosis, a regulated cell death which is driven by the iron-dependent peroxidation of lipids, plays an important role in cancer. However, studies about ferroptosis-related Long non-coding RNAs (lncRNAs) in breast cancer (BC) are limited. Besides, the prognostic role of ferroptosis-related lncRNAs and their relationship to immune microenvironment in breast cancer remain unclear. This study aimed to explore the potential prognostic value of ferroptosis-related lncRNAs and their relationship to immune microenvironment in breast cancer. Methods: RNA-sequencing data of female breast cancer patients were downloaded from TCGA database. 937 patients were randomly separated into training or validation cohort in 2:1 ratio. Ferroptosis-related lncRNAs were screened by Pearson correlation analysis with 239 reported ferroptosis-related genes. A ferroptosis-related lncRNAs signature was constructed with univariate and multivariate Cox regression analyses in the training cohort, and its prognostic value was further tested in the validation cohort. Results: An 8-ferroptosis-related-lncRNAs signature was developed by multivariate Cox regression analysis to divide patients into two risk groups. Patients in the high-risk group had worse prognosis than patients in the low-risk group. Multivariate Cox regression analysis showed the risk score was an independent prognostic indicator. Receiver operating characteristic curve (ROC) analysis proved the predictive accuracy of the signature. The area under time-dependent ROC curve (AUC) reached 0.853 at 1 year, 0.802 at 2 years, 0.740 at 5 years in the training cohort and 0.791 at 1 year, 0.778 at 2 years, 0.722 at 5 years in the validation cohort. Further analysis demonstrated that immune-related pathways were significantly enriched in the high-risk group. Analysis of the immune cell infiltration landscape showed that breast cancer in the high-risk group tended be immunologically "cold". Conclusion: We identified a novel ferroptosis-related lncRNA signature which could precisely predict the prognosis of breast cancer patients. Ferroptosis-related lncRNAs may have a potential role in the process of anti-tumor immunity and serve as therapeutic targets for breast cancer.

Dataset Information

Exploration the Significance of a Novel Immune-Related Gene Signature in Prognosis and Immune Microenvironment of Breast Cancer.

Publications

Exploration the Significance of a Novel Immune-Related Gene Signature in Prognosis and Immune Microenvironment of Breast Cancer.

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