Dataset Information

An EMT-Related Gene Signature to Predict the Prognosis of Triple-Negative Breast Cancer.

ABSTRACT:

Introduction

Epithelial-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.

Methods

With 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.

Results

Thirteen 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.

Conclusions

Our research identified the significant impact of EMGs on prognosis in TNBC, providing new strategies for personalizing TNBC treatment and improving clinical outcomes.

SUBMITTER: Zhang B

PROVIDER: S-EPMC10499992 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Publications

An EMT-Related Gene Signature to Predict the Prognosis of Triple-Negative Breast Cancer.

Zhang Bo B Zhao Rong R Wang Qi Q Zhang Ya-Jing YJ Yang Liu L Yuan Zhou-Jun ZJ Yang Jun J Wang Qian-Jun QJ Yao Liang L

Advances in therapy 20230718 10

<h4>Introduction</h4>Epithelial-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.<h4>Methods</h4>With the application of The Cancer Genome Atlas (TCGA) database, Molecular Taxonomy of Breast Cancer International Conso ...[more]

PMID: 37462865

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Project description:Background Triple-negative breast cancer (TNBC) is a common carcinoma in women, and the prognosis of TNBC is the worst. Using data from The Cancer Genome Atlas (TCGA) database, we analyzed the functional roles of cytokine-related genes in TNBC. Methods The clinical and transcriptome data of TNBC patients were downloaded from TCGA database. A systematical analyses of the data from TCGA database were conducted to screen the prognostic genes and identify the main cytokine-related pathways related to TNBC. Results We identified 499 prognostic genes in TNBC patients from TCGA database and the cytokine-related pathways closely related to TNBC. TCGA-TNBC patients were divided into the high-risk cluster (C1) group and the low-risk cluster (C2) group based on the cytokine-related genes. The C1 group patients exhibited tumor metastasis and an advanced tumor stage. The functional analysis revealed that the upregulated differentially expressed genes (DEGs) in the C1 group were mainly associated with the extracellular matrix (ECM)-receptor interaction, stem cell proliferation, focal adhesion, and cyclic adenosine monophosphate (cAMP) signaling pathway, while the downregulated DEGs in the C1 group were mainly associated with cytokine and cytokine receptors, T-helper 17 (Th17) cell differentiation, and primary immunodeficiency. The immune activity of C1 group was lower than that of C2 group, and the identified half-maximal inhibitory concentration scores of 3 chemotherapy drugs (i.e., doxorubicin, methotrexate, and paclitaxel) were lower in C2 group than C1 group. More importantly, we constructed a novel prognostic signature and identified the following 8 genes: CCL25, CXCL13, IL12RB2, IL21, TNFRSF13C, TNFRSF8, CCL7 and GDF5. Conclusion The status of the cytokine-related pathway was closely related to tumor classification and immune activity in the TNBC patients. The gene signature of the cytokine-related genes showed an good performance in predicting the prognosis of TNBC patients, and could predict the prognosis of TNBC patients.

Project description:BackgroundRecently, researchers have classified highly heterogeneous triple negative breast cancer (TNBC) into different subtypes from different perspectives and investigated the characteristics of different subtypes to pursue individualized treatment. With the increase of immunotherapy and its preliminary application in TNBC treatment, the value of immune-related strategies in the treatment of TNBC has been initially reflected. Based thereon, this study plans to classify and further explore TNBC from the perspective of immune cell infiltration.MethodThe fractions of immune cells of TNBC patients were assessed by six immune component analysis methods in The Cancer Genome Atlas (TCGA) database. Hub genes significantly related to poor prognosis were verified by weighted gene co-expression network analysis (WGCNA) analysis, Lasso analysis, and univariate KM analysis. Two cohorts of TNBC patients with complete prognosis information were collected for validation analysis. Finally, the Genomics of Drug Sensitivity in Cancer (GDSC) database was adopted to ascertain the sensitivity differences of different populations based on hub-gene grouping to different chemotherapy drugs.ResultsFive hub genes (CD79A, CXCL13, IGLL5, LHFPL2, and PLEKHF1) of the key co-expression gene module could divide TNBC patients into two groups (Cluster A and Cluster B) based on consistency cluster analysis. The patients with Cluster A were responsible for significantly worse prognosis than the patients with Cluster B (P = 0.023). In addition, another classification method, PCoA, and two other datasets (GSE103091 and GSE76124), were used to obtain consistent results with previous findings, which verified the stability of the classification method and dataset in this study. The grouping criteria based on the previous results were developed and the accuracy of the cut-off values was validated. A prognosis model of TNBC patients was then constructed based on the grouping results of five hub genes and N staging as prognostic factors. The results of ROC and decision curve analyses showed that this model had high prediction accuracy and patients could benefit therefrom. Finally, GDSC database analysis proved that patients in Cluster A were more sensitive to Vinorelbine. Separate analysis of the sensitivity of patients in Cluster A to Gemcitabine and Vinorelbine showed that the patients in Cluster A exhibited higher sensitivity to Vinorelbine. We hypothesized that these five genes were related to gemcitabine resistance and they could serve as biomarkers for clinical drug decision-making after anthracene resistance and taxane resistance in patients with advanced TNBC.ConclusionThis study found five hub prognostic genes associated with macrophages, and a prognostic model was established to predict the survival of TNBC patients. Finally, these five genes were related to gemcitabine resistance in TNBC patients.

Project description:Background: There is a lack of biomarkers for predicting the efficacy of immunotherapy in triple-negative breast cancer (TNBC). Hence, we constructed an immune risk score (IRS) model to predict the prognosis of patients with TNBC and evaluate those who are sensitive to immunotherapy. Methods: The ribonucleic acid (RNA) sequencing data, mutation data, and clinical information of TNBC patients were obtained from The Cancer Genome Atlas database. Data of immune-related genes were obtained from the Import and InnateDB databases. The IRS model was constructed using univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses, and the predictive ability of the prognostic model was evaluated. Further external validation was performed using the Gene Expression Omnibus (GEO) databases GSE58812 and GSE135565. Data on the clinical characteristics, immune landscape, and immune checkpoint inhibitors used in different risk groups were analyzed. Finally, the drug sensitivity of the patients in the high- and low-risk groups was predicted. Results: The prognostic risk score model comprised six genes: HSPA6, LCN1, ARTN, IL36G, BCL2A1, and CASP12. The area under the curve values at 1 year, 3 years, and 5 years were 0.835, 0.852, and 0.843, respectively, indicating that the model has a good potential for predicting the long-term survival of TNBC patients, which is consistent with the results of the GEO cohort. Compared with the high-risk group, the low-risk group had a better prognosis; more abundant immune-activated cell infiltrates, such as CD8+ T cells and CD4 memory-activated T cells, and a higher enrichment of immune-related signaling pathways, such as the cytokine receptor interaction, nucleotide oligomerization domain-like receptor signal pathway, T-cell receptor signal pathway, and B-cell receptor signaling pathway, were observed. In addition, the immune checkpoint encoding genes, such as CD274, CTLA4, PDCD1, and PDCD1LG2 were highly expressed in the low-risk group, which showed that this group was more likely to benefit from immunotherapy. Conclusion: A new IRS gene feature was established to predict the patients' prognosis and guide immunotherapy. Moreover, it was revealed that several potential therapeutic drugs can be used in high-risk patients who are unresponsive to immunotherapy.

Dataset Information

An EMT-Related Gene Signature to Predict the Prognosis of Triple-Negative Breast Cancer.

Introduction

Methods

Results

Conclusions

Publications

An EMT-Related Gene Signature to Predict the Prognosis of Triple-Negative Breast Cancer.

Similar Datasets

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