Uncovering the roles of microRNAs/lncRNAs in characterising breast cancer subtypes and prognosis

Abstract Background Accurate prognosis and identification of cancer subtypes at molecular level are important steps towards effective and personalised treatments of breast cancer. To this end, many computational methods have been developed to use gene (mRNA) expression data for breast cancer subtyping and prognosis. Meanwhile, microRNAs (miRNAs) and long non-coding RNAs (lncRNAs) have been extensively studied in the last 2 decades and their associations with breast cancer subtypes and prognosis have been evidenced. However, it is not clear whether using miRNA and/or lncRNA expression data helps improve the performance of gene expression based subtyping and prognosis methods, and this raises challenges as to how and when to use these data and methods in practice. Results In this paper, we conduct a comparative study of 35 methods, including 12 breast cancer subtyping methods and 23 breast cancer prognosis methods, on a collection of 19 independent breast cancer datasets. We aim to uncover the roles of miRNAs and lncRNAs in breast cancer subtyping and prognosis from the systematic comparison. In addition, we created an R package, CancerSubtypesPrognosis, including all the 35 methods to facilitate the reproducibility of the methods and streamline the evaluation. Conclusions The experimental results show that integrating miRNA expression data helps improve the performance of the mRNA-based cancer subtyping methods. However, miRNA signatures are not as good as mRNA signatures for breast cancer prognosis. In general, lncRNA expression data does not help improve the mRNA-based methods in both cancer subtyping and cancer prognosis. These results suggest that the prognostic roles of miRNA/lncRNA signatures in the improvement of breast cancer prognosis needs to be further verified.

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Identification of key genes unique to the luminal A and basal-like breast cancer subtypes via bioinformatic analysis

10.21203/rs.3.rs-28637/v1 ◽

2020 ◽

Author(s):

Rong Jia ◽

Zhongxian Li ◽

Wei Liang ◽

Yucheng Ji ◽

Yujie Weng ◽

...

Keyword(s):

Breast Cancer ◽

Differentially Expressed Genes ◽

Survival Data ◽

Breast Cancer Subtypes ◽

Differentially Expressed ◽

Expression Data ◽

Luminal A ◽

Cancer Subtypes ◽

Protein Protein Interaction ◽

Basal Like Breast Cancer

Abstract Background Breast cancer subtypes are statistically associated with prognosis. The search for markers of breast tumor heterogeneity and the development of precision medicine for patients are the current focuses of the field. Methods We used a bioinformatic approach to identify key disease-causing genes unique to the luminal A and basal-like subtypes of breast cancer. First, we retrieved gene expression data for luminal A breast cancer, basal-like breast cancer, and normal breast tissue samples from The Cancer Genome Atlas database. The differentially expressed genes unique to the 2 breast cancer subtypes were identified and subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. We constructed protein–protein interaction networks of the differentially expressed genes. Finally, we analyzed the key modules of the networks, which we combined with survival data to identify the unique cancer genes associated with each breast cancer subtype. Results We identified 1,114 differentially expressed genes in luminal A breast cancer and 1,042 differentially expressed genes in basal-like breast cancer, of which the subtypes shared 500. We observed 614 and 542 differentially expressed genes unique to luminal A and basal-like breast cancer, respectively. Through enrichment analyses, protein–protein interaction network analysis, and module mining, we identified 8 key differentially expressed genes unique to each subtype. Analysis of the gene expression data in the context of the survival data revealed that high expression of NMUR1 and NCAM1 in luminal A breast cancer statistically correlated with poor prognosis, whereas the low expression levels of CDC7 , KIF18A , STIL , and CKS2 in basal-like breast cancer statistically correlated with poor prognosis. Conclusions NMUR1 and NCAM1 are novel key disease-causing genes for luminal A breast cancer, and STIL is a novel key disease-causing gene for basal-like breast cancer. These genes are potential targets for clinical treatment.

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A novel single-cell based method for breast cancer prognosis

10.1101/2020.04.26.062794 ◽

2020 ◽

Author(s):

Xiaomei Li ◽

Lin Liu ◽

Greg Goodall ◽

Andreas Schreiber ◽

Taosheng Xu ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Single Cell ◽

Tumor Heterogeneity ◽

Breast Cancer Prognosis ◽

Cancer Prognosis ◽

Biological Processes ◽

Expression Data ◽

Rna Seq ◽

Novel Method

AbstractBreast cancer prognosis is challenging due to the heterogeneity of the disease. Various computational methods using bulk RNA-seq data have been proposed for breast cancer prognosis. However, these methods suffer from limited performances or ambiguous biological relevance, as a result of the neglect of intra-tumor heterogeneity. Recently, single cell RNA-sequencing (scRNA-seq) has emerged for studying tumor heterogeneity at cellular levels. In this paper, we propose a novel method, scPrognosis, to improve breast cancer prognosis with scRNA-seq data. scPrognosis uses the scRNA-seq data of the biological process Epithelial-to-Mesenchymal Transition (EMT). It firstly infers the EMT pseudotime and a dynamic gene co-expression network, then uses an integrative model to select genes important in EMT based on their expression variation and differentiation in different stages of EMT, and their roles in the dynamic gene co-expression network. To validate and apply the selected signatures to breast cancer prognosis, we use them as the features to build a prediction model with bulk RNA-seq data. The experimental results show that scPrognosis outperforms other benchmark breast cancer prognosis methods that use bulk RNA-seq data. Moreover, the dynamic changes in the expression of the selected signature genes in EMT may provide clues to the link between EMT and clinical outcomes of breast cancer. scPrognosis will also be useful when applied to scRNA-seq datasets of different biological processes other than EMT.Author summaryVarious computational methods have been developed for breast cancer prognosis. However, those methods mainly use the gene expression data generated by the bulk RNA sequencing techniques, which average the expression level of a gene across different cell types. As breast cancer is a heterogenous disease, the bulk gene expression may not be the ideal resource for cancer prognosis. In this study, we propose a novel method to improve breast cancer prognosis using scRNA-seq data. The proposed method has been applied to the EMT scRNA-seq dataset for identifying breast cancer signatures for prognosis. In comparison with existing bulk expression data based methods in breast cancer prognosis, our method shows a better performance. Our single-cell-based signatures provide clues to the relation between EMT and clinical outcomes of breast cancer. In addition, the proposed method can also be useful when applied to scRNA-seq datasets of different biological processes other than EMT.

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Identification of key genes unique to the luminal a and basal-like breast cancer subtypes via bioinformatic analysis

World Journal of Surgical Oncology ◽

10.1186/s12957-020-02042-z ◽

2020 ◽

Vol 18 (1) ◽

Author(s):

Rong Jia ◽

Zhongxian Li ◽

Wei Liang ◽

Yucheng Ji ◽

Yujie Weng ◽

...

Keyword(s):

Breast Cancer ◽

Differentially Expressed Genes ◽

Survival Data ◽

Breast Cancer Subtypes ◽

Differentially Expressed ◽

Expression Data ◽

Luminal A ◽

Cancer Subtypes ◽

Protein Protein Interaction ◽

Basal Like Breast Cancer

Abstract Background Breast cancer subtypes are statistically associated with prognosis. The search for markers of breast tumor heterogeneity and the development of precision medicine for patients are the current focuses of the field. Methods We used a bioinformatic approach to identify key disease-causing genes unique to the luminal A and basal-like subtypes of breast cancer. First, we retrieved gene expression data for luminal A breast cancer, basal-like breast cancer, and normal breast tissue samples from The Cancer Genome Atlas database. The differentially expressed genes unique to the 2 breast cancer subtypes were identified and subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. We constructed protein–protein interaction networks of the differentially expressed genes. Finally, we analyzed the key modules of the networks, which we combined with survival data to identify the unique cancer genes associated with each breast cancer subtype. Results We identified 1114 differentially expressed genes in luminal A breast cancer and 1042 differentially expressed genes in basal-like breast cancer, of which the subtypes shared 500. We observed 614 and 542 differentially expressed genes unique to luminal A and basal-like breast cancer, respectively. Through enrichment analyses, protein–protein interaction network analysis, and module mining, we identified 8 key differentially expressed genes unique to each subtype. Analysis of the gene expression data in the context of the survival data revealed that high expression of NMUR1 and NCAM1 in luminal A breast cancer statistically correlated with poor prognosis, whereas the low expression levels of CDC7, KIF18A, STIL, and CKS2 in basal-like breast cancer statistically correlated with poor prognosis. Conclusions NMUR1 and NCAM1 are novel key disease-causing genes for luminal A breast cancer, and STIL is a novel key disease-causing gene for basal-like breast cancer. These genes are potential targets for clinical treatment.

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Breast cancer prognosis by combinatorial analysis of gene expression data

Breast Cancer Research ◽

10.1186/bcr1512 ◽

2006 ◽

Vol 8 (4) ◽

Cited By ~ 44

Author(s):

Gabriela Alexe ◽

Sorin Alexe ◽

David E Axelrod ◽

Tibérius O Bonates ◽

Irina I Lozina ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Gene Expression Data ◽

Breast Cancer Prognosis ◽

Cancer Prognosis ◽

Combinatorial Analysis ◽

Expression Data

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Differences in Breast Cancer Subtypes Among Racial/Ethnic Groups

10.21203/rs.3.rs-734284/v1 ◽

2021 ◽

Author(s):

Surbhi Bansil ◽

Anthony Silva ◽

Corinne Jones ◽

Elena Hidalgo ◽

Ian Pagano ◽

...

Keyword(s):

Breast Cancer ◽

Ethnic Groups ◽

Breast Cancer Subtype ◽

Cancer Prognosis ◽

Breast Cancer Subtypes ◽

Her2 Receptor ◽

Cancer Subtypes ◽

Her2 Breast Cancer ◽

Cancer Subtype ◽

Racial Ethnic

Abstract PurposeDifferences in incidence of breast cancer subtypes among racial/ethnic groups have been evaluated as a contributing factor in the disparities seen in breast cancer prognosis. We evaluated new breast cancer cases in Hawaii to determine if there were subtype differences according to race/ethnicity that may contribute to known disparities.MethodsWe reviewed 4,318 cases of women diagnosed with breast cancer from two large tumor registries between 2013-2020. We evaluated the new breast cancer cases according to age at diagnosis, self-reported race, and breast cancer subtype (ER, PR, and HER2 receptor status).ResultsWe found both premenopausal and postmenopausal Native Hawaiian women were less likely to be diagnosed with triple negative breast cancer (OR=0.33, P=0.009; OR=0.62, P=0.03 respectively). Premenopausal Japanese women were 71% less likely to be diagnosed with triple positive (ER+/PR+/HER2+) breast cancer (OR=0.29, P=0.0003). Postmenopausal Filipino women were 89% more likely to be diagnosed with ER-/PR-/HER2+ breast cancer (OR=1.89, P=0.02). ConclusionsResults of our study support that there are racial/ethnic differences in breast cancer subtypes among our population which may contribute to the differences in outcome seen. Further evaluation of other clinical and pathological features in each breast cancer subtype may inform potential mechanisms for outcome disparities seen among different racial/ethnic groups.

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Identification of key genes unique to the luminal A and basal-like breast cancer subtypes via bioinformatic analysis

10.21203/rs.3.rs-28637/v2 ◽

2020 ◽

Author(s):

Rong Jia ◽

Zhongxian Li ◽

Wei Liang ◽

Yucheng Ji ◽

Yujie Weng ◽

...

Keyword(s):

Breast Cancer ◽

Differentially Expressed Genes ◽

Survival Data ◽

Breast Cancer Subtypes ◽

Differentially Expressed ◽

Expression Data ◽

Luminal A ◽

Cancer Subtypes ◽

Protein Protein Interaction ◽

Basal Like Breast Cancer

Abstract Background: Breast cancer subtypes are statistically associated with prognosis. The search for markers of breast tumor heterogeneity and the development of precision medicine for patients are the current focuses of the field.Methods: We used a bioinformatic approach to identify key disease-causing genes unique to the luminal A and basal-like subtypes of breast cancer. First, we retrieved gene expression data for luminal A breast cancer, basal-like breast cancer, and normal breast tissue samples from The Cancer Genome Atlas database. The differentially expressed genes unique to the 2 breast cancer subtypes were identified and subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. We constructed protein–protein interaction networks of the differentially expressed genes. Finally, we analyzed the key modules of the networks, which we combined with survival data to identify the unique cancer genes associated with each breast cancer subtype.Results: We identified 1,114 differentially expressed genes in luminal A breast cancer and 1,042 differentially expressed genes in basal-like breast cancer, of which the subtypes shared 500. We observed 614 and 542 differentially expressed genes unique to luminal A and basal-like breast cancer, respectively. Through enrichment analyses, protein–protein interaction network analysis, and module mining, we identified 8 key differentially expressed genes unique to each subtype. Analysis of the gene expression data in the context of the survival data revealed that high expression of NMUR1 and NCAM1 in luminal A breast cancer statistically correlated with poor prognosis, whereas the low expression levels of CDC7, KIF18A, STIL, and CKS2 in basal-like breast cancer statistically correlated with poor prognosis.Conclusions: NMUR1 and NCAM1 are novel key disease-causing genes for luminal A breast cancer, and STIL is a novel key disease-causing gene for basal-like breast cancer. These genes are potential targets for clinical treatment.

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c-kit: Identification of co-regulated genes by gene expression profiling and clinical relevance of two breast cancer subtypes with stem cell like features

Journal of Clinical Oncology ◽

10.1200/jco.2006.24.18_suppl.622 ◽

2006 ◽

Vol 24 (18_suppl) ◽

pp. 622-622 ◽

Cited By ~ 5

Author(s):

A. Rody ◽

U. Holtrich ◽

V. Müller ◽

R. Gaetje ◽

R. Diallo ◽

...

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Stem Cell ◽

Gene Expression Profiling ◽

Cancer Patients ◽

Expression Profiling ◽

Breast Cancer Subtypes ◽

Expression Data ◽

Breast Cancer Patients ◽

Cancer Subtypes

622 Background: Expression of the proto-oncogene c-kit has been found in malignant tissue including a subset of breast cancers. c-Kit is also expressed in normal breast tissue and several authors found a loss of c-kit expression in breast carcinoma suggesting it might be involved in the growth control of mammary epithelium. Until now, only a few markers were described to be co-regulated with c-kit. To elucidate the possible role of c-kit in malignant transformation, we analyzed gene expression data of breast cancer patients. Methods: Tumor tissue of n=171 breast cancer patients were analyzed by gene expression profiling using Affymetrix Hg U133 Arrays (22,500 genes) and bioinformatic analyses. Tumor samples with high stromal and low epithelial cell content by gene expression profiling were excluded for further analysis. Validation was performed with n=100 independent samples. Results: A total of 10.5% of the tumors showed strong c-kit expression (2.5 fold above median). A careful dissection of global expression data revealed strong correlations of c-kit with the expression of a large cluster of genes containing several for whom c-kit coexpression was already described (HER1, CK-5/-17, PDGFR) as well as several members of the wnt signalling pathway, providing a possible novel link to mammary epithelial differentiation. Analysis of n=171 breast cancer samples according to this gene set allows the identification of putative “stem cell like” tumors (SCL) characterized by expression of several known stem cell markers. Surprisingly, a tight link of ER status and proliferation is restricted only to these SCL tumors but lost among non-SCL tumors. The clinical implications of our findings will be presented. Conclusions: For the first time these data bring together the description of two breast cancer subtypes identified by gene expression profiling with the actual stem cell model of the development of breast cancer. No significant financial relationships to disclose.

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DNA methylation profiles capturing breast cancer heterogeneity

BMC Genomics ◽

10.1186/s12864-019-6142-y ◽

2019 ◽

Vol 20 (1) ◽

Author(s):

Xiao Chen ◽

Jianying Zhang ◽

Xiaofeng Dai

Keyword(s):

Breast Cancer ◽

Dna Methylation ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Gene Expression Regulation ◽

Breast Cancer Subtype ◽

Breast Cancer Prognosis ◽

Cancer Prognosis ◽

Cancer Subtypes ◽

Cancer Heterogeneity

Abstract Background As one of the most described epigenetic marks in human cancers, DNA methylation plays essential roles in gene expression regulation and has been implicated in the prognosis and therapeutics of many cancers. We are motivated in this study to explore DNA methylation profiles capturing breast cancer heterogeneity to improve breast cancer prognosis at the epigenetic level. Results Through comparisons on differentially methylated CpG sites among breast cancer subtypes followed by a sequential validation and functional studies using computational approaches, we propose 313 CpG, corresponding to 191 genes, whose methylation pattern identifies the triple negative breast cancer subtype, and report cell migration as represented by extracellular matrix organization and cell proliferation as mediated via MAPK and Wnt signalings are the primary factors driving breast cancer subtyping. Conclusions Our study offers novel CpGs and gene methylation patterns with translational potential on triple negative breast cancer prognosis, as well as fresh insights from the epigenetic level on breast cancer heterogeneity.

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A mixture model to detect edges in sparse co-expression graphs with an application for comparing breast cancer subtypes

PLoS ONE ◽

10.1371/journal.pone.0246945 ◽

2021 ◽

Vol 16 (2) ◽

pp. e0246945

Author(s):

Haim Bar ◽

Seojin Bang

Keyword(s):

Breast Cancer ◽

Mixture Model ◽

Network Structure ◽

Gene Network ◽

Correlation Coefficients ◽

R Package ◽

Breast Cancer Subtypes ◽

Luminal A ◽

Component Mixture ◽

Cancer Subtypes

We develop a method to recover a gene network’s structure from co-expression data, measured in terms of normalized Pearson’s correlation coefficients between gene pairs. We treat these co-expression measurements as weights in the complete graph in which nodes correspond to genes. To decide which edges exist in the gene network, we fit a three-component mixture model such that the observed weights of ‘null edges’ follow a normal distribution with mean 0, and the non-null edges follow a mixture of two lognormal distributions, one for positively- and one for negatively-correlated pairs. We show that this so-called L2 N mixture model outperforms other methods in terms of power to detect edges, and it allows to control the false discovery rate. Importantly, our method makes no assumptions about the true network structure. We demonstrate our method, which is implemented in an R package called edgefinder, using a large dataset consisting of expression values of 12,750 genes obtained from 1,616 women. We infer the gene network structure by cancer subtype, and find insightful subtype characteristics. For example, we find thirteen pathways which are enriched in each of the cancer groups but not in the Normal group, with two of the pathways associated with autoimmune diseases and two other with graft rejection. We also find specific characteristics of different breast cancer subtypes. For example, the Luminal A network includes a single, highly connected cluster of genes, which is enriched in the human diseases category, and in the Her2 subtype network we find a distinct, and highly interconnected cluster which is uniquely enriched in drug metabolism pathways.

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LncRNA RP11-19E11 is an E2F1 target required for proliferation and survival of basal breast cancer

npj Breast Cancer ◽

10.1038/s41523-019-0144-4 ◽

2020 ◽

Vol 6 (1) ◽

Cited By ~ 3

Author(s):

A. Giro-Perafita ◽

L. Luo ◽

A. Khodadadi-Jamayran ◽

M. Thompson ◽

B. Akgol Oksuz ◽

...

Keyword(s):

Breast Cancer ◽

Embryonic Stem ◽

Gene Set Enrichment Analysis ◽

Breast Cancer Subtypes ◽

Breast Cancers ◽

Primary Tumors ◽

Cancer Subtypes ◽

Lncrna Expression ◽

Basal Breast Cancer

AbstractLong non-coding RNAs (lncRNAs) play key roles in the regulation of breast cancer initiation and progression. LncRNAs are differentially expressed in breast cancer subtypes. Basal-like breast cancers are generally poorly differentiated tumors, are enriched in embryonic stem cell signatures, lack expression of estrogen receptor, progesterone receptor, and HER2 (triple-negative breast cancer), and show activation of proliferation-associated factors. We hypothesized that lncRNAs are key regulators of basal breast cancers. Using The Cancer Genome Atlas, we identified lncRNAs that are overexpressed in basal tumors compared to other breast cancer subtypes and expressed in at least 10% of patients. Remarkably, we identified lncRNAs whose expression correlated with patient prognosis. We then evaluated the function of a subset of lncRNA candidates in the oncogenic process in vitro. Here, we report the identification and characterization of the chromatin-associated lncRNA, RP11-19E11.1, which is upregulated in 40% of basal primary breast cancers. Gene set enrichment analysis in primary tumors and in cell lines uncovered a correlation between RP11-19E11.1 expression level and the E2F oncogenic pathway. We show that this lncRNA is chromatin-associated and an E2F1 target, and its expression is necessary for cancer cell proliferation and survival. Finally, we used lncRNA expression levels as a tool for drug discovery in vitro, identifying protein kinase C (PKC) as a potential therapeutic target for a subset of basal-like breast cancers. Our findings suggest that lncRNA overexpression is clinically relevant. Understanding deregulated lncRNA expression in basal-like breast cancer may lead to potential prognostic and therapeutic applications.

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