Transcriptome-Based Network Analysis Unveils Eight Immune-Related Genes as Molecular Signatures in the Immunomodulatory Subtype of Triple-Negative Breast Cancer

Background. Triple-negative breast cancer (TNBC) is usually poorly differentiated, highly invasive, susceptible to distant metastasis, and less responsive to endocrine and targeted therapy. However, immunotherapy is a promising treatment for TNBC patients recently. Methods. The prognostic value of immune-related genes (IRGs) was explored by using RNA sequencing and microarray data of 123 and 107 TNBC patients from TCGA and GEO databases, respectively. Results. In TCGA database, GO and KEGG pathway analysis of 119 differential IRGs indicated that they actively participate in the interaction of cytokines and receptors. A nomogram model constructed by the prognosis-related CCL25, IL29, TDGF3, GPR44, and GREM2 in the IRGs could personalize and visualize the 1-, 2-, 3-, 4-, and 5-year overall survival (OS) of TNBC patients. Moreover, TNBC patients could be defined as low-risk ( risk score < 194 ) or high-risk ( risk score ≥ 194 ) cohorts based on the risk score derived from the nomogram model. The results could be validated by the GSE58812 dataset. Furthermore, the risk score was an independent risk factor for TNBC patients ( HR = 1.019 , 95% CI 1.012-1.027, p < 0.001 ) and was positively related to stage ( p = 0.017 ). Interestingly, the risk score could reflect the infiltration of B cells, CD4+ T cells, CD8+ T cells, dendritic cells, and neutrophils. Conclusion. These findings provided a reference for personalized OS prediction in TNBC patients and might be potential immune biomarkers for designing novel therapy.

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Immune Classification and Immune Landscape Analysis of Triple-Negative Breast Cancer

Frontiers in Genetics ◽

10.3389/fgene.2021.710534 ◽

2021 ◽

Vol 12 ◽

Author(s):

Shaojun Hu ◽

Xiusheng Qu ◽

Yu Jiao ◽

Jiahui Hu ◽

Bo Wang

Keyword(s):

Breast Cancer ◽

Triple Negative Breast Cancer ◽

Drug Sensitivity ◽

Triple Negative ◽

Cox Regression ◽

Landscape Analysis ◽

Immune Gene ◽

Immune Genes ◽

New Perspective ◽

Immune Related Genes

Background: To classify triple-negative breast cancer (TNBC) immunotyping using the public database, analyze the differences between subtypes in terms of clinical characteristics and explore the role and clinical significance of immune subtypes in TNBC immunotherapy.Methods: We downloaded TNBC data from the cBioPortal and GEO databases. The immune genes were grouped to obtain immune gene modules and annotate their biological functions. Log-rank tests and Cox regression were used to evaluate the prognosis of immune subtypes (IS). Drug sensitivity analysis was also performed for the differences among immune subtypes in immunotherapy and chemotherapy. In addition, dimension reduction analysis based on graph learning was utilized to reveal the internal structure of the immune system and visualize the distribution of patients.Results: Significant differences in prognosis were observed between subtypes (IS1, IS2, and IS3), with the best in IS3 and the worst in IS1. The sensitivity of IS3 to immunotherapy and chemotherapy was better than the other two subtypes. In addition, Immune landscape analysis found the intra-class heterogeneity of immune subtypes and further classified IS3 subtypes (IS3A and IS3B). Immune-related genes were divided into seven functional modules (The turquoise module has the worst prognosis). Five hub genes (RASSF5, CD8A, ICOS, IRF8, and CD247) were screened out as the final characteristic genes related to poor prognosis by low expression.Conclusions: The immune subtypes of TNBC were significantly different in prognosis, gene mutation, immune infiltration, drug sensitivity, and heterogeneity. We validated the independent role of immune subtypes in tumor progression and immunotherapy for TNBC. This study provides a new perspective for personalized immunotherapy and the prognosis evaluation of TNBC patients in the future.

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Weighted correlation network analysis of triple‑negative breast cancer progression: Identifying specific modules and hub genes based on the GEO and TCGA database

Oncology Letters ◽

10.3892/ol.2019.10407 ◽

2019 ◽

Author(s):

Lei Lan ◽

Bin Xu ◽

Qu Chen ◽

Jingting Jiang ◽

Yueping Shen

Keyword(s):

Breast Cancer ◽

Network Analysis ◽

Cancer Progression ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Breast Cancer Progression ◽

Correlation Network ◽

Hub Genes ◽

Weighted Correlation

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Novel key genes in triple‐negative breast cancer identified by weighted gene co‐expression network analysis

Journal of Cellular Biochemistry ◽

10.1002/jcb.28948 ◽

2019 ◽

Vol 120 (10) ◽

pp. 16900-16912 ◽

Cited By ~ 6

Author(s):

Jian Chen ◽

Xiaojun Qian ◽

Yifu He ◽

Xinghua Han ◽

Yueyin Pan

Keyword(s):

Breast Cancer ◽

Network Analysis ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Key Genes

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Abstract LB-225: RNA molecular signatures as predictive biomarkers of response to monotherapy pembrolizumab in patients with metastatic triple-negative breast cancer: KEYNOTE-086

10.1158/1538-7445.sabcs18-lb-225 ◽

2019 ◽

Author(s):

Sherene Loi ◽

Peter Schmid ◽

Javier Cortés ◽

David W. Cescon ◽

Eric P. Winer ◽

...

Keyword(s):

Breast Cancer ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Predictive Biomarkers ◽

Molecular Signatures

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Identification of Metastasis-Associated Genes in Triple-Negative Breast Cancer Using Weighted Gene Co-expression Network Analysis

Evolutionary Bioinformatics ◽

10.1177/1176934320954868 ◽

2020 ◽

Vol 16 ◽

pp. 117693432095486

Author(s):

Wenting Xie ◽

Zhongshi Du ◽

Yijie Chen ◽

Naxiang Liu ◽

Zhaoming Zhong ◽

...

Keyword(s):

Breast Cancer ◽

Network Analysis ◽

Candidate Genes ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Characteristic Curve ◽

Enrichment Analysis ◽

The Cancer Genome Atlas ◽

Functional Enrichment ◽

Diagnosis And Prognosis

Triple-negative breast cancer (TNBC) is the most aggressive and fatal sub-type of breast cancer. This study aimed to identify metastasis-associated genes that could serve as biomarkers for TNBC diagnosis and prognosis. RNA-seq data and clinical information on TNBC from the Cancer Genome Atlas were used to conduct analyses. Expression data were used to establish co-expression modules using average linkage hierarchical clustering. We used weighted gene co-expression network analysis to explore the associations between gene sets and clinical features and to identify metastasis-associated candidate biomarkers. The K-M plotter website was used to explore the association between the expression of candidate biomarkers and patient survival. In addition, receiver operating characteristic curve analysis was used to illustrate the diagnostic performance of candidate genes. The pale turquoise module was significantly associated with the occurrence of metastasis. In this module, 64 genes were identified, and its functional enrichment analysis revealed that they were mainly associated with transcriptional misregulation in cancer, microRNAs in cancer, and negative regulation of angiogenesis. Further, 4 genes, IGSF10, RUNX1T1, XIST, and TSHZ2, which were negatively associated with relapse-free survival and have seldom been reported before in TNBC, were selected. In addition, the mRNA expression levels of the 4 candidate genes were significantly lower in TNBC tumor tissues compared with healthy tissues. Based on the K-M plotter, these 4 genes were correlated with poor prognosis of TNBC. The area under the curve of IGSF10, RUNX1T1, TSHZ2, and XIST was 0.918, 0.957, 0.977, and 0.749. These findings provide new insight into TNBC metastasis. IGSF10, RUNX1T1, TSHZ2, and XIST could be used as candidate biomarkers for the diagnosis and prognosis of TNBC metastasis.

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