Few long noncoding RNAs (lncRNAs) that act as oncogenic genes in breast cancer have been identified. Oncogenic lncRNAs associated with tumourigenesis and worse survival outcomes were examined and validated in Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), respectively. Then, the potential biological functions and expression regulation of these lncRNAs were studied via bioinformatics and genome data analysis. Moreover, progressive breast cancer subtype-specific lncRNAs were investigated via high-throughput sequencing in our cohort and TCGA validation. To elucidate the mechanisms of the regulation of these lncRNAs, genomic alterations from the TCGA, Broad, Sanger and BCCRC data, as well as epigenetic modifications from GEO data, were then applied and examined to meet this objective. Finally, cell proliferation assays, flow cytometry analyses and TUNEL assays were applied to validate the oncogenic roles of these lncRNAs in vitro. A cluster of oncogenic lncRNAs that was upregulated in breast cancer tissue and was associated with worse survival outcomes was identified. These oncogenic lncRNAs are involved in regulating immune system activation and the TGF-beta and Jak-STAT signalling pathways. Moreover, TINCR, LINC00511, and PPP1R26-AS1 were identified as subtype-specific lncRNAs associated with HER-2, triple-negative and luminal B subtypes of breast cancer, respectively. The up-regulation of these oncogenic lncRNAs is mainly caused by gene amplification in the genome in breast cancer and other solid tumours. Finally, the knockdown of TINCR, DSCAM-AS1 or HOTAIR inhibited breast cancer cell proliferation, increased apoptosis and inhibited cell cycle progression in vitro. These findings enhance the landscape of known oncogenic lncRNAs in breast cancer and provide insights into their roles. This understanding may potentially aid in the comprehensive management of breast cancer.

Background: An increasing number of studies have shown that long noncoding RNAs (lncRNAs) play essential roles in tumor initiation and progression. LncRNAs act as tumor promoters or suppressors by targeting specific genes via epigenetic modifications and competing endogenous RNA (ceRNA) mechanisms. In this study, we explored the function and detailed mechanisms of long intergenic nonprotein coding RNA 673 (LINC00673) in breast cancer progression. Methods: Quantitative real-time PCR (qRT-PCR) was used to examine the expression of LINC00673 in breast cancer tissues and in adjacent normal tissues. Gain-of-function and loss-of function experiments were conducted to investigate the biological functions of LINC00673 in vitro and in vivo. We also explored the potential role of LINC00673 as a therapeutic target using antisense oligonucleotide (ASO) in vivo. RNA sequencing (RNA-seq), dual-luciferase reporter assays, chromatin immunoprecipitation (ChIP) assay, and rescue experiments were performed to uncover the detailed mechanism of LINC00673 in promoting breast cancer progression. Results: In the present study, LINC00673 displayed a trend of remarkably increased expression in breast cancer tissues and was associated with poor prognosis in breast cancer patients. Importantly, LINC00673 depletion inhibited breast cancer cell proliferation by inhibiting the cell cycle and increasing apoptosis. Furthermore, ASO therapy targeting LINC00673 substantially suppressed breast cancer cell proliferation in vivo. Mechanistically, LINC00673 was found to act as a ceRNA by sponging miR-515-5p to regulate MARK4 expression, thus inhibiting the Hippo signaling pathway. Finally, ChIP assay showed that the transcription factor Yin Yang 1 (YY1) could bind to the LINC00673 promoter and increase its transcription in cis. Conclusions: YY1-activated LINC00673 may exert an oncogenic function by acting as a sponge for miR-515-5p to upregulate the MARK4 and then inhibit Hippo signaling pathway, and may serve as a potential therapeutic target.

The biology function of antisense intronic long noncoding RNA (Ai-lncRNA) is still unknown. Meanwhile, cancer patients with paclitaxel resistance have limited therapeutic options in the clinic. However, the potential involvement of Ai-lncRNA in paclitaxel sensitivity remains unclear in human cancer. Whole transcriptome sequencing of 33 breast specimens was performed to identify Ai-lncRNA EGOT. Next, the role of EGOT in regulation of paclitaxel sensitivity was investigated. Moreover, the mechanism of EGOT enhancing autophagy sensitizes paclitaxel cytotoxicity via upregulation of ITPR1 expression by RNA-RNA and RNA-protein interactions was investigated in detail. Furthermore, upstream transcriptional regulation of EGOT expression was also investigated by co-immunoprecipitation and chromatin immunoprecipitation. Finally, clinical breast specimens in our cohort, TCGA and ICGC were applied to validate the role of EGOT in enhancing of paclitaxel sensitivity. EGOT enhances autophagosome accumulation via the up-regulation of ITPR1 expression, thereby sensitizing cells to paclitaxel toxicity. Mechanistically, on one hand, EGOT upregulates ITPR1 levels via formation of a pre-ITPR1/EGOT dsRNA that induces pre-ITPR1 accumulation to increase ITPR1 protein expression in cis. On the other hand, EGOT recruits hnRNPH1 to enhance the alternative splicing of pre-ITPR1 in trans via two binding motifs in EGOT segment 2 (324–645 nucleotides) in exon 1. Moreover, EGOT is transcriptionally regulated by stress conditions. Finally, EGOT expression enhances paclitaxel sensitivity via assessment of cancer specimens. These findings broaden comprehensive understanding of the biology function of Ai-lncRNAs. Proper regulation of EGOT may be a novel synergistic strategy for enhancing paclitaxel sensitivity in cancer therapy.

The inference of cell–cell communication (CCC) is crucial for a better understanding of complex cellular dynamics and regulatory mechanisms in biological systems. However, accurately inferring spatial CCCs at single-cell resolution remains a significant challenge. To address this issue, we present a versatile method, called DeepTalk, to infer spatial CCC at single-cell resolution by integrating single-cell RNA sequencing (scRNA-seq) data and spatial transcriptomics (ST) data. DeepTalk utilizes graph attention network (GAT) to integrate scRNA-seq and ST data, which enables accurate cell-type identification for single-cell ST data and deconvolution for spot-based ST data. Then, DeepTalk can capture the connections among cells at multiple levels using subgraph-based GAT, and further achieve spatially resolved CCC inference at single-cell resolution. DeepTalk achieves excellent performance in discovering meaningful spatial CCCs on multiple cross-platform datasets, which demonstrates its superior ability to dissect cellular behavior within intricate biological processes.