Immune and Stroma Related Genes in Breast Cancer: A Comprehensive Analysis of Tumor Microenvironment Based on the Cancer Genome Atlas (TCGA) Database

Xu, Ming; Yu, Li; Li, Wenhui; Zhao, Qiu; Zhang, Qiulei; Le, Kehao; Huang, Ziwei; Yi, Pengfei

doi:10.3389/fmed.2020.00064

Cited by 59 publications

(46 citation statements)

References 84 publications

(76 reference statements)

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“…Its high expression was observed to be associated with favorable overall survival of patients with oral cancers ( Hirohata et al, 2018 ). P2RY13 encodes a protein belongs to the family of G-protein coupled receptors, moreover, its high expression demonstrated significantly higher overall survival rates in patients with breast cancer ( Xu et al, 2020 ) and lung adenocarcinoma ( Fan et al, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Identification of Tumor Microenvironment-Related Prognostic Genes in Sarcoma

et al. 2021

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AimImmune cells that infiltrate the tumor microenvironment (TME) are associated with cancer prognosis. The aim of the current study was to identify TME related gene signatures related to the prognosis of sarcoma (SARC) by using the data from The Cancer Genome Atlas (TCGA).MethodsImmune and stromal scores were calculated by estimation of stromal and immune cells in malignant tumor tissues using expression data algorithms. The least absolute shrinkage and selection operator (lasso) based cox model was then used to select hub survival genes. A risk score model and nomogram were used to predict the overall survival of patients with SARC.ResultsWe selected 255 patients with SARC for our analysis. The Kaplan–Meier method found that higher immune (p = 0.0018) or stromal scores (p = 0.0022) were associated with better prognosis of SARC. The estimated levels of CD4+ (p = 0.0012) and CD8+ T cells (p = 0.017) via the tumor immune estimation resource were higher in patients with SARC with better overall survival. We identified 393 upregulated genes and 108 downregulated genes (p < 0.05, fold change >4) intersecting between the immune and stromal scores based on differentially expressed gene (DEG) analysis. The univariate Cox analysis of each intersecting DEG and subsequent lasso-based Cox model identified 11 hub survival genes (MYOC, NNAT, MEDAG, TNFSF14, MYH11, NRXN1, P2RY13, CXCR3, IGLV3-25, IGHV1-46, and IGLV2-8). Then, a hub survival gene-based risk score gene signature was constructed; higher risk scores predicted worse SARC prognosis (p < 0.0001). A nomogram including the risk scores, immune/stromal scores and clinical factors showed a good prediction value for SARC overall survival (C-index = 0.716). Finally, connectivity mapping analysis identified that the histone deacetylase inhibitors trichostatin A and vorinostat might have the potential to reverse the harmful TME for patients with SARC.ConclusionThe current study provided new indications for the association between the TME and SARC. Lists of TME related survival genes and potential therapeutic drugs were identified for SARC.

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Section: Discussionmentioning

confidence: 99%

Identification of Tumor Microenvironment-Related Prognostic Genes in Sarcoma

et al. 2021

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“…Firstly, we observed the statistically significant difference between two age groups with regard to patient outcomes in the IntCl1, possibly owing to immunerelated genes. Indeed, many previous studies were of interest to the interaction between the immune system and age factor in BRCA (Fuentes et al, 2017;Li et al, 2020;Xu et al, 2020). Especially, Li et al (2020) indicated that there was a positive correlation of aging with i) the lower median immune/stromal scores and ii) the lower OS of BRCA patients.…”

Section: Prognostic Factor Identificationmentioning

confidence: 99%

Multi-Omics Analysis Detects Novel Prognostic Subgroups of Breast Cancer

Nguyen¹,

Nguyen

et al. 2020

Front. Genet.

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The unprecedented proliferation of recent large-scale and multi-omics databases of cancers has given us many new insights into genomic and epigenomic deregulation in cancer discovery in general. However, we wonder whether or not there exists a systematic connection between copy number aberrations (CNA) and methylation (MET)? If so, what is the role of this connection in breast cancer (BRCA) tumorigenesis and progression? At the same time, the PAM50 intrinsic subtypes of BRCA have gained the most attention from BRCA experts. However, this classification system manifests its weaknesses including low accuracy as well as a possible lack of association with biological phenotypes, and even further investigations on their clinical utility were still needed. In this study, we performed an integrative analysis of three-omics profiles, CNA, MET, and mRNA expression, in two BRCA patient cohorts (one for discovery and another for validation)-to elucidate those complicated relationships. To this purpose, we first established a set of CNAcor and METcor genes, which had CNA and MET levels significantly correlated (and anti-correlated) with their corresponding expression levels, respectively. Next, to revisit the current classification of BRCA, we performed single and integrated clustering analyses using our clustering method PINSPlus. We then discovered two biologically distinct subgroups that could be an improved and refined classification system for breast cancer patients, which can be validated by a third-party data. Further studies were then performed and realized each-subgroup-specific genes and different interactions between each of the two identified subgroups with the age factor. These findings can show promise as diagnostic and prognostic values in BRCA, and a potential alternative to the PAM50 intrinsic subtypes in the future.

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“…At present, approximately 25 malignant tumor types have been evaluated by ESTIMATE using expression data across different platforms (12). Furthermore, stromal or immune scores have been analyzed in clinical data to predict tumor progression (13), treatment (14), and prognosis (15). However, these investigations involve only one cancer and therefore cannot compare the differences and explore common characteristics among many kinds of cancers.…”

Section: Introductionmentioning

confidence: 99%

Identification of Immune-Related Prognostic Biomarkers Based on the Tumor Microenvironment in 20 Malignant Tumor Types With Poor Prognosis

et al. 2020

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Cancer, especially malignant tumors with poor prognosis, has become a major hazard to human life and health. The tumor microenvironment is gaining increasing attention from researchers, as it offers a new focus for tumor diagnosis, therapy, and prognosis. The numbers of immune and stromal cells, which are major components of the tumor microenvironment, could be determined from RNA-seq data with the Estimation of STromal and Immune cells in Malignant Tumors using Expression data (ESTIMATE) algorithm. To explore the effects of immune and stromal cells on tumor prognosis, we analyzed associations between overall survival and immune/stromal scores for 20 malignant tumor types based on The Cancer Genome Atlas (TCGA) data. For six of the 20 tumor types, we observed statistically significant associations. Furthermore, to better explain the predictive ability of these scores, differentially expressed genes (DEGs) were identified in groups of cases with high or low immune or stromal scores for each of these six malignant tumor types. In addition, a list of immune-related genes was screened to identify prognostic predictors for one or more tumor types. Thus, multi-database joint analysis can provide a new approach to the assessment of tumor prognosis and allow the identification of related genes that may be new biomarkers for tumor metastasis and prognosis.

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Immune and Stroma Related Genes in Breast Cancer: A Comprehensive Analysis of Tumor Microenvironment Based on the Cancer Genome Atlas (TCGA) Database

Cited by 59 publications

References 84 publications

Identification of Tumor Microenvironment-Related Prognostic Genes in Sarcoma

Identification of Tumor Microenvironment-Related Prognostic Genes in Sarcoma

Multi-Omics Analysis Detects Novel Prognostic Subgroups of Breast Cancer

Identification of Immune-Related Prognostic Biomarkers Based on the Tumor Microenvironment in 20 Malignant Tumor Types With Poor Prognosis

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