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

Multifaced roles of the long non-coding RNA DRAIC in cancer progression

Long non-coding RNAs (lncRNA) are functional RNAs, with over 200 nucleotides in length and lacking protein-coding potential. Studies have indicated that lncRNAs are important gene regulators under physiological conditions. Aberrant lncRNA expression is associated with the initiation and progression of various diseases, including cancers. High-throughput transcriptome analyses have revealed thousands of lncRNAs as putative tumor suppressors or promoters in various cancers, but the detailed molecular mechanisms of each lncRNA remain unclear. Downregulated RNA In Cancer, inhibitor of cell invasion and migration (DRAIC) (also known as LOC145837 and RP11-279F6.1) is a lncRNA that inhibits or promotes cancer progression with several modes of action. DRAIC was originally identified as a tumor-suppressive lncRNA in prostate adenocarcinoma. Subsequent studies also revealed that it has an anti-tumor role in glioblastoma, triple-negative breast cancer, and stomach adenocarcinoma. However, DRAIC exhibits oncogenic functions in other malignancies, such as lung adenocarcinoma and esophageal carcinoma, indicating its highly context-dependent effects on cancer progression and clinical outcomes. DRAIC and its associated pathways regulate various biological processes, including proliferation, invasion, metastasis, autophagy, and neuroendocrine function. This review introduces the multifaceted roles of DRAIC, particularly in cancer progression, and discusses its biological significance and clinical implications.

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

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.