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RNA-seq analysis identified hormone-related genes associated with prognosis of triple negative breast cancer.

ABSTRACT: Triple negative breast cancer (TNBC) is an aggressive subtype of breast cancer that currently lacks effective biomarkers and therapeutic targets required to investigate the diagnosis and treatment of TNBC. Here we performed a comprehensive differential analysis of 165 TNBC samples by integrating RNA-seq data of breast tumor tissues and adjacent normal tissues from both our cohort and The Cancer Genome Atlas (TCGA). Pathway enrichment analysis was conducted to evaluate the biological function of TNBC-specific expressed genes. Further multivariate Cox proportional hazard regression was performed to evaluate the effect of these genes on TNBC prognosis. In this report, we identified a total of 148 TNBC-specific expressed genes that were primarily enriched in mammary gland morphogenesis and hormone levels related pathways, suggesting that mammary gland morphogenesis might play a unique role in TNBC patients differing from other breast cancer types. Further survival analysis revealed that nine genes ( FSIP1, ADCY5, FSD1, HMSD, CMTM5, AFF3, CYP2A7, ATP1A2, and C11orf86) were significantly associated with the prognosis of TNBC patients, while three of them ( ADCY5, CYP2A7, and ATP1A2) were involved in the hormone-related pathways. These findings indicated the vital role of the hormone-related genes in TNBC tumorigenesis and may provide some independent prognostic markers as well as novel therapeutic targets for TNBC.

SUBMITTER: Chen F

PROVIDER: S-EPMC7183303 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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RNA-seq analysis identified hormone-related genes associated with prognosis of triple negative breast cancer.

Chen Fei F Li Yuancheng Y Qin Na N Wang Fengliang F Du Jiangbo J Wang Cheng C Du Fangzhi F Jiang Tao T Jiang Yue Y Dai Juncheng J Hu Zhibin Z Lu Cheng C Shen Hongbing H

Journal of biomedical research 20200101 2

Triple negative breast cancer (TNBC) is an aggressive subtype of breast cancer that currently lacks effective biomarkers and therapeutic targets required to investigate the diagnosis and treatment of TNBC. Here we performed a comprehensive differential analysis of 165 TNBC samples by integrating RNA-seq data of breast tumor tissues and adjacent normal tissues from both our cohort and The Cancer Genome Atlas (TCGA). Pathway enrichment analysis was conducted to evaluate the biological function of ...[more]

PMID: 32305967

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Project description:Triple-negative breast cancer (TNBC) is associated with poor prognosis and invalid therapeutical response to immunotherapy due to biological heterogeneity. There is an urgent need to screen for reliable indices, especially immunotherapy-associated biomarkers that can predict patient outcomes. Pyroptosis, as an inflammation-induced type of programmed cell death, is shown to create a tumor-suppressive environment and improve the chemotherapeutic response in multiple tumors. However, the specific therapeutic effect of pyroptosis in TNBC remains unclear. In this study, we present a consensus clustering by pyroptosis-related signatures of 119 patients with TNBC into two subtypes (clusterA and clusterB) with distinct immunological and prognostic characteristics. First, clusterB, associated with better outcomes, was characterized by a significantly higher pyroptosis-related signature expression, tumor microenvironment prognostic score, and upregulation of immunotherapy checkpoints. A total of 262 differentially expressed genes between the subtypes were further identified and the Ps-score was built using LASSO and COX regression analyses. The external GEO data set demonstrated that cohorts with low Ps-scores consistently had higher expression of pyroptosis-related signatures, immunocyte infiltration levels, and better prognosis. In addition, external immunotherapy and chemotherapy cohorts validated that patients with lower Ps-scores exhibited significant therapeutic response and clinical benefit. Combined with other clinical characteristics, we successfully constructed a nomogram to effectively predict the survival rate of patients with TNBC. Finally, using the scRNA-seq data sets, we validated the landscape of cellular subtypes of TNBC and successfully constructed an miRNA-Ps-score gene interaction network. These findings indicated that the systematic assessment of tumor pyroptosis and identification of Ps-scores has potential clinical implications and facilitates tailoring optimal immunotherapeutic strategies for TNBC.

Project description:Breast cancer is the most commonly diagnosed cancer in the world, with triple-negative breast cancer (TNBC) making up 12% of these diagnoses. TNBC tumours are highly heterogeneous in both inter-tumour and intra-tumour gene expression profiles, where they form subclonal populations of varying levels of aggressiveness. These aspects make it difficult to study and treat TNBC, requiring further research into tumour heterogeneity as well as potential therapeutic targets and biomarkers. Recently, it was discovered that the majority of the transcribed genome comprises non-coding RNAs, in particular long non-coding RNAs (lncRNAs). LncRNAs are transcripts of >200 nucleotides in length that do not encode a protein. They have been characterised as regulatory molecules and their expression can be associated with a malignant phenotype. We set out to explore TNBC tumour heterogeneity in vivo at a single cell level to investigate whether lncRNA expression varies across different cells within the tumour, even if cells are coming from the same cell line, and whether lncRNA expression is sufficient to define cellular subpopulations. We applied single-cell expression profiling due to its ability to capture expression signals of lncRNAs expressed in small subpopulations of cells. Overall, we observed most lncRNAs to be expressed at low, but detectable levels in TNBC xenografts, with a median of 25 lncRNAs detected per cell. LncRNA expression alone was insufficient to define a subpopulation of cells, and lncRNAs showed highly heterogeneous expression patterns, including ubiquitous expression, subpopulation-specific expression, and a hybrid pattern of lncRNAs expressed in several, but not all subpopulations. These findings reinforce that transcriptionally defined tumour cell subpopulations can be identified in cell-line derived xenografts, and uses single-cell RNA-seq (scRNA-seq) to detect and characterise lncRNA expression across these subpopulations in xenografted tumours. Future studies will aim to investigate the spatial distribution of lncRNAs within xenografts and patient tissues, and study the potential of subclone-specific lncRNAs as new therapeutic targets and/or biomarkers.

Project description:IntroductionEpithelial-mesenchymal transition (EMT) is an important biological process in tumor invasion and metastasis, and thus a potential indicator of the progression and drug resistance of breast cancer. This study comprehensively analyzed EMT-related genes in triple-negative breast cancer (TNBC) to develop an EMT-related prognostic gene signature.MethodsWith the application of The Cancer Genome Atlas (TCGA) database, Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), and the Genotype-Tissue Expression (GTEx) database, we identified EMT-related signature genes (EMGs) by Cox univariate regression and LASSO regression analysis. Risk scores were calculated and used to divide patients with TNBC into high-risk group and low-risk groups by the median value. Kaplan-Meier (K-M) and receiver operating characteristic (ROC) curve analyses were applied for model validation. Independent prognostic predictors were used to develop nomograms. Then, we assessed the risk model in terms of the immune microenvironment, genetic alteration and DNA methylation effects on prognosis, the probability of response to immunotherapy and chemotherapy, and small molecule drugs predicted by The Connectivity Map (Cmap) database.ResultsThirteen EMT-related genes with independent prognostic value were identified and used to stratify the patients with TNBC into high- and low-risk groups. The survival analysis revealed that patients in the high-risk group had significantly poorer overall survival than patients in the low-risk group. Populations of immune cells, including CD4 memory resting T cells, CD4 memory activated T cells, and activated dendritic cells, significantly differed between the high- and low-risk groups. Moreover, some therapeutic drugs to which the high-risk group might show sensitivity were identified.ConclusionsOur research identified the significant impact of EMGs on prognosis in TNBC, providing new strategies for personalizing TNBC treatment and improving clinical outcomes.

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RNA-seq analysis identified hormone-related genes associated with prognosis of triple negative breast cancer.

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RNA-seq analysis identified hormone-related genes associated with prognosis of triple negative breast cancer.

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