Dataset Information

A Topic Modeling Analysis of TCGA Breast and Lung Cancer Transcriptomic Data.

ABSTRACT: Topic modeling is a widely used technique to extract relevant information from large arrays of data. The problem of finding a topic structure in a dataset was recently recognized to be analogous to the community detection problem in network theory. Leveraging on this analogy, a new class of topic modeling strategies has been introduced to overcome some of the limitations of classical methods. This paper applies these recent ideas to TCGA transcriptomic data on breast and lung cancer. The established cancer subtype organization is well reconstructed in the inferred latent topic structure. Moreover, we identify specific topics that are enriched in genes known to play a role in the corresponding disease and are strongly related to the survival probability of patients. Finally, we show that a simple neural network classifier operating in the low dimensional topic space is able to predict with high accuracy the cancer subtype of a test expression sample.

SUBMITTER: Valle F

PROVIDER: S-EPMC7766023 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

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A Topic Modeling Analysis of TCGA Breast and Lung Cancer Transcriptomic Data.

Valle Filippo F Osella Matteo M Caselle Michele M

Cancers 20201216 12

Topic modeling is a widely used technique to extract relevant information from large arrays of data. The problem of finding a topic structure in a dataset was recently recognized to be analogous to the community detection problem in network theory. Leveraging on this analogy, a new class of topic modeling strategies has been introduced to overcome some of the limitations of classical methods. This paper applies these recent ideas to TCGA transcriptomic data on breast and lung cancer. The establi ...[more]

PMID: 33339347

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Project description:Long non-coding RNAs (lncRNAs) play an important role in many diseases and are involved in the post-transcriptional regulatory network of tumors. The purpose of this study is to mine new lncRNA-mRNA regulatory pairs and analyze the new mechanism of lncRNA involvement in breast cancer progression. Using breast cancer miRNA and mRNA expression profiling from The Cancer Genome Atlas (TCGA), we identified 59 differentially expressed lncRNAs, 88 differentially expressed miRNAs, and 1,465 differentially expressed mRNAs between breast cancer tissue and adjacent normal breast cancer. Whereafter, four candidate lncRNAs (FGF14-AS2, LINC01235, AC055854.1, and AC124798.1) were identified by the Kaplan-Meier (K-M) plotter. Furthermore, we screened the hub lncRNA (LINC01235) through univariate Cox analysis, multivariate Cox analysis, and qPCR validation, which was significantly correlated with breast cancer stage, ER status, and pathological N. Subsequently, 107 LINC01235-related mRNAs were obtained by combining differentially expressed miRNAs, differentially expressed mRNAs, and LINC01235 targeting miRNAs and mRNAs. The protein-protein interaction (PPI) network was established by Cytoscape software, and 53 key genes were screened. Function and pathway enrichment showed that LINC01235-related key genes might be involved in the process of cell differentiation, cell proliferation, and p53 signal pathway. In addition, LINC01235 has been confirmed to regulate the proliferation, migration, and invasion of MCF-7 cells in in vitro experiments. Furthermore, we screened three mRNAs (ESR1, ADRA2A, and DTL) associated with breast cancer drug resistance from key genes. Through RNA interference experiments in vitro and correlation analysis, we found that there was a negative feedback mechanism between LINC01235 and ESR1/ADRA2A. In conclusion, our results suggest that LINC01235-ESR1 and LINC01235-ADRA2A could serve as important co-expression pairs in the progression of breast cancer, and LINC01235 plays a key role as an independent prognostic factor in patients with breast cancer. The findings of this work greatly increase our understanding of the molecular regulatory mechanisms of lncRNA in breast cancer.

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Dataset Information

A Topic Modeling Analysis of TCGA Breast and Lung Cancer Transcriptomic Data.

Publications

A Topic Modeling Analysis of TCGA Breast and Lung Cancer Transcriptomic Data.

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