Comprehensive analysis of DRAIC and TP53TG1 in breast cancer luminal subtypes through the construction of lncRNAs regulatory model

Motalebzadeh, Jamshid; Eskandari, Elaheh

doi:10.1007/s12282-022-01385-7

Cited by 4 publications

(4 citation statements)

References 37 publications

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“…LINC00493 and TP53TG1 are enriched and co-released in extracellular vesicles from colorectal cancer cells (45). Previous studies have highlighted the ambivalent oncogenic or tumor suppressor activity of TP53TG1 in luminal breast cancer (46, 47).…”

Section: Resultsmentioning

confidence: 99%

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Identification of interpretable clusters and associated signatures in breast cancer single cell data: a topic modeling approach

Malagoli

Valle

Barillot

et al. 2022

Preprint

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Single-cell RNA sequencing is a powerful tool to explore cancer heterogeneity. However, the expression of lncRNAs in single cells is still to be studied extensively and methods to deal with the sparsity of this type of data are lacking. Here, we propose a topic modeling approach to investigate the transcriptional heterogeneity of luminal and triple negative breast cancer cells using patient-derived xenograft models of acquired resistance to chemotherapy and targeted therapy. We show that using an integrative clustering that combines the information coming from mRNAs and lncRNAs treated as disjoint omic layers greatly improves the accuracy of cell classification. Topics associated with specific breast cancer subpopulations show a clear enrichment for pathways involved in subtyping and progression of breast cancer and to sets of lncRNA encoded in the open chromatin regions of breast cancer cell lines. We identified lncRNAs strongly associated with cell clusters already well known in the literature, such as MALAT1 and NEAT1, and highlighted some others that may be clinically relevant.

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Section: Resultsmentioning

confidence: 99%

“…Previous studies have highlighted the ambivalent oncogenic or tumor suppressor activity of TP53TG1 in luminal breast cancer (46,47).…”

Section: Lncrnas Associated To the Topic 19 Are Specifically Expresse...mentioning

confidence: 99%

Identification of interpretable clusters and associated signatures in breast cancer single cell data: a topic modeling approach

Malagoli

Valle

Barillot

et al. 2022

Preprint

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“…On the other hand, our investigation revealed TP53TG1 to be a safeguarding element. Previous investigations have demonstrated a significant association between TP53TG1 and lymph node infiltration in breast cancer patients, suggesting its potential oncogenic role in the official cavity subtype of breast cancer [39], possibly due to its characteristic expression in a specific subtype. Overall, our model based on these eight DALs holds promise for early detection and prognostic prediction in BRCA patients, highlighting the potential clinical significance of these lncRNAs in the management of breast cancer.…”

Section: Discussionmentioning

confidence: 97%

Precision prognostication in breast cancer: unveiling a long non-coding RNA-based model linked to disulfidptosis for tailored immunotherapeutic strategies

Jiang,

Zhang,

Jiang

et al. 2024

Aging

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“…LINC00493 and TP53TG1 are enriched and co-released in extracellular vesicles from colorectal cancer cells [48]. Previous studies have highlighted the ambivalent oncogenic or tumor suppressor activity of TP53TG1 in luminal breast cancer [49,58].…”

Section: Discussionmentioning

confidence: 99%

Identification of Interpretable Clusters and Associated Signatures in Breast Cancer Single-Cell Data: A Topic Modeling Approach

Malagoli,

Valle,

Barillot

et al. 2024

Cancers

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Topic modeling is a popular technique in machine learning and natural language processing, where a corpus of text documents is classified into themes or topics using word frequency analysis. This approach has proven successful in various biological data analysis applications, such as predicting cancer subtypes with high accuracy and identifying genes, enhancers, and stable cell types simultaneously from sparse single-cell epigenomics data. The advantage of using a topic model is that it not only serves as a clustering algorithm, but it can also explain clustering results by providing word probability distributions over topics. Our study proposes a novel topic modeling approach for clustering single cells and detecting topics (gene signatures) in single-cell datasets that measure multiple omics simultaneously. We applied this approach to examine the transcriptional heterogeneity of luminal and triple-negative breast cancer cells using patient-derived xenograft models with acquired resistance to chemotherapy and targeted therapy. Through this approach, we identified protein-coding genes and long non-coding RNAs (lncRNAs) that group thousands of cells into biologically similar clusters, accurately distinguishing drug-sensitive and -resistant breast cancer types. In comparison to standard state-of-the-art clustering analyses, our approach offers an optimal partitioning of genes into topics and cells into clusters simultaneously, producing easily interpretable clustering outcomes. Additionally, we demonstrate that an integrative clustering approach, which combines the information from mRNAs and lncRNAs treated as disjoint omics layers, enhances the accuracy of cell classification.

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Comprehensive analysis of DRAIC and TP53TG1 in breast cancer luminal subtypes through the construction of lncRNAs regulatory model

Cited by 4 publications

References 37 publications

Identification of interpretable clusters and associated signatures in breast cancer single cell data: a topic modeling approach

Identification of interpretable clusters and associated signatures in breast cancer single cell data: a topic modeling approach

Precision prognostication in breast cancer: unveiling a long non-coding RNA-based model linked to disulfidptosis for tailored immunotherapeutic strategies

Identification of Interpretable Clusters and Associated Signatures in Breast Cancer Single-Cell Data: A Topic Modeling Approach

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