Molecular Classification of Breast Cancer Utilizing Long Non-Coding RNA (lncRNA) Transcriptomes Identifies Novel Diagnostic lncRNA Panel for Triple-Negative Breast Cancer

Abstract

Breast cancer remains the world’s most prevalent cancer, responsible for around 685,000 deaths globally despite international research efforts and advances in clinical management. While estrogen receptor positive (ER+), progesterone receptor positive (PR+), and human epidermal growth factor receptor positive (HER2+) subtypes are easily classified and can be targeted, there remains no direct diagnostic test for triple-negative breast cancer (TNBC), except for the lack of receptors expression. The identification of long non-coding RNAs (lncRNAs) and the roles they play in cancer progression has recently proven to be beneficial. In the current study, we utilize RNA sequencing data to identify lncRNA-based biomarkers associated with TNBC, ER+ subtypes, and normal breast tissue. The Marker Finder algorithm identified the lncRNA transcript panel most associated with each molecular subtype and the receiver operating characteristic (ROC) analysis was used to validate the diagnostic potential (area under the curve (AUC) of ≥8.0 and p value < 0.0001). Focusing on TNBC, findings from the discovery cohort were validated in an additional two cohorts, identifying 13 common lncRNA transcripts enriched in TNBC. Binary regression analysis identified a four lncRNA transcript signature (ENST00000425820.1, ENST00000448208.5, ENST00000521666.1, and ENST00000650510.1) with the highest diagnostic power for TNBC. The ENST00000671612.1 lncRNA transcript correlated with worse refractory free survival (RFS). Our data provides a step towards finding a novel diagnostic lncRNA-based panel for TNBC with potential therapeutic implications.

Single-Cell Transcriptome Analysis Revealed Heterogeneity and Identified Novel Therapeutic Targets for Breast Cancer Subtypes

“…Transcriptomic data from a cohort of 360 TNBC patients were retrieved from the Sequence Read Archive (SRA) database ( ; accessed on 27 March 2023) using the SRA toolkit v2.9.2 [ 12 ]. Paired-end RNA-seq FASTQ files were subsequently pseudo-aligned to the GENCODE release (v33) using KALLISTO 0.4.2.1, as described before [ 13 , 14 ]. Log2 transformed expression data were then used for expression analysis of different TNBC molecule subtypes (Basal-Like Immunosuppressed (BLIS), Immunomodulatory (IM), Luminal Androgen Receptor (LAR), and Mesenchymal (MES)).…”

LncRNA-Based Classification of Triple Negative Breast Cancer Revealed Inherent Tumor Heterogeneity and Vulnerabilities

“…However, such a classification does not provide a full explanation of TNBC heterogeneity and variable response to therapy. We recently employed lncRNA transcriptomes to classify breast cancer, taking into consideration their molecular subtypes, which revealed distinct lncRNA profiles associated with each molecular subtype [ 11 , 14 ]. However, whether lncRNA transcriptome can be used to classify TNBC and better resolve tumor heterogeneity has not yet been explored.…”

“…While the bulk of research on the molecular classification of BC has focused on protein-coding mRNAs, the use of lncRNA-based classification is gaining momentum, given their tissue-specific expression patterns [ 10 ]. In fact, a comprehensive classification of breast cancer subtypes was recently performed employing their lncRNA transcriptional portraits [ 11 ]. However, currently there is a lack of knowledge on the utilization of lncRNA-based classification to reveal the heterogeneity of TNBC and their clinical implications.…”

Advancing Cancer Therapy Predictions with Patient-Derived Organoid Models of Metastatic Breast Cancer

“…BC is known to be classified into specific subtypes based on histological characteristics and receptor status (ERα, PR, and HER2) [30]. The most common BC subtypes are invasive ductal carcinoma (IDC) of no special type (NST) and invasive lobular carcinoma (ILC) [31].…”