A lncRNA landscape in breast cancer reveals a potential role for AC009283.1 in proliferation and apoptosis in HER2-enriched subtype

Abstract

Breast cancer is the most commonly diagnosed neoplasm in women worldwide with a well-recognized heterogeneous pathology, classified into four molecular subtypes: Luminal A, Luminal B, HER2enriched and Basal-like, each one with different biological and clinical characteristics. Long noncoding RNAs (lncRNAs) represent 33% of the human transcriptome and play critical roles in breast carcinogenesis, but most of their functions are still unknown. Therefore, cancer research could benefit from continued exploration into the biology of lncRNAs in this neoplasm. We characterized lncRNA expression portraits in 74 breast tumors belonging to the four molecular subtypes using transcriptome microarrays. To infer the biological role of the deregulated lncRNAs in the molecular subtypes, we performed co-expression analysis of lncRNA-mRNA and gene ontology analysis. We identified 307 deregulated lncRNAs in tumor compared to normal tissue and 354 deregulated lncRNAs among the different molecular subtypes. Through co-expression analysis between lncRNAs and proteincoding genes, along with gene enrichment analysis, we inferred the potential function of the most deregulated lncRNAs in each molecular subtype, and independently validated our results taking advantage of TCGA data. Overexpression of the AC009283.1 was observed in the HER2-enriched subtype and it is localized in an amplification zone at chromosome 17q12, suggesting it to be a potential tumorigenic lncRNA. The functional role of lncRNA AC009283.1 was examined through loss of function assays in vitro and determining its impact on global gene expression. These studies revealed that AC009283.1 regulates genes involved in proliferation, cell cycle and apoptosis in a HER2 cellular model. We further confirmed these findings through ssGSEA and CEMITool analysis in an independent HER2-amplified breast cancer cohort. Our findings suggest a wide range of biological functions for lncRNAs in each breast cancer molecular subtype and provide a basis for their biological and functional study, as was conducted for AC009283.1, showing it to be a potential regulator of proliferation and apoptosis in the HER2-enriched subtype.

Dissecting the Role of N6-Methylandenosine-Related Long Non-coding RNAs Signature in Prognosis and Immune Microenvironment of Breast Cancer

“…Recent bioinformatic research has identified an immune-related lncRNA signature model to predict the risk of 5-year recurrence-free survival in BC (Lai et al, 2020). Other studies have depicted the lncRNA landscape using transcriptome microarrays in BC and identified deregulated lncRNA expression patterns across different molecular subtypes (Cedro-Tanda et al, 2020). However, the overall perspective of m6A regulators involved in the dysregulation of lncRNAs in BC is not fully understood.…”

Integrative Analysis Reveals Potentially Functional N6-Methylandenosine-Related Long Noncoding RNAs in Colon Adenocarcinoma

“…The correlation analysis between the DE-lncRNAs and DE-m6A regulators identified 2,479 m6A-related lncRNAs (Spearman's correlation >0.3 or < −0.3). As the lncRNAs were associated with cancer subtypes (Cedro-Tanda et al, 2020;Arnes et al, 2019), the m6A-related lncRNAs could also clearly stratify the COAD samples into three subtypes. We found that nearly 40% of samples in the subtype 2 had microsatellite instability (MSI), which was significantly higher than in the two other subtypes.…”

LINC00460 Is a Dual Biomarker That Acts as a Predictor for Increased Prognosis in Basal-Like Breast Cancer and Potentially Regulates Immunogenic and Differentiation-Related Genes

“…Using the BRCA cohort previously reported by our group in ( 62 ) (n = 74; Luminal A = 24, Luminal B = 23, HER2 = 14, Basal = 13) in which samples were analyzed using Human Transcriptome Array 2.0 (Affymetrix, Inc, Santa Clara, CA), we were able to identify LINC00460 expression levels across BRCA subtypes. We used the Robust Multi-chip Analysis (RMA) algorithm to minimize the effect of probe-specific affinity differences and to normalize samples ( 63 ).…”