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Simple Summary  Breast cancer is the most commonly diagnosed cancer in women today and accounts for thousands of cancer-related deaths each year. While some breast cancer subtypes can be easily diagnosed and targeted for therapy, triple-negative breast cancer, which lacks receptor expression, is the most challenging to diagnose and treat. In this study, we use multiple RNA sequencing data to look specifically at long non-coding RNA (lncRNA) expression portraits at the transcript level and to identify lncRNA-based biomarkers associated with each breast cancer subtype. Receiver operating characteristic (ROC) analysis was used to validate their diagnostic potential, which was validated in two independent cohorts. Several lncRNA transcripts were found to be enriched in TNBC across all validation cohorts. Binary regression analysis identified a four lncRNA transcript signature with the highest diagnostic power for TNBC as potential novel biomarkers for diagnostic and therapeutic intervention. Interestingly, several of the identified lncRNAs were shown to have prognostic potential in TNBC.    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 (ENST425820.1, ENST448208.5, ENST521666.1, and ENST650510.1) with the highest diagnostic power for TNBC. The ENST671612.1 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.

cancers

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