Identification of Crucial lncRNAs for Luminal A Breast Cancer through RNA Sequencing

Transcriptomics analysis of long non-coding RNAs in smooth muscle cells from patients with peripheral artery disease and diabetes mellitus

Diabetes mellitus (DM) is a significant risk factor for peripheral arterial disease (PAD), and PAD is an independent predictor of cardiovascular disorders (CVDs). Growing evidence suggests that long non-coding RNAs (lncRNAs) significantly contribute to disease development and underlying complications, particularly affecting smooth muscle cells (SMCs). So far, no study has focused on transcriptome analysis of lncRNAs in PAD patients with and without DM. Tissue samples were obtained from our Vascular Biobank. Due to the sample's heterogeneity, expression analysis of lncRNAs in whole tissue detected only ACTA2-AS1 with a 4.9-fold increase in PAD patients with DM. In contrast, transcriptomics of SMCs revealed 28 lncRNAs significantly differentially expressed between PAD with and without DM (FDR < 0.1). Sixteen lncRNAs were of unknown function, six were described in cancer, one connected with macrophages polarisation, and four were associated with CVDs, mainly with SMC function and phenotypic switch (NEAT1, MIR100HG, HIF1A-AS3, and MRI29B2CHG). The enrichment analysis detected additional lncRNAs H19, CARMN, FTX, and MEG3 linked with DM. Our study revealed several lncRNAs in diabetic PAD patients associated with the physiological function of SMCs. These lncRNAs might serve as potential therapeutic targets to improve the function of SMCs within the diseased tissue and, thus, the clinical outcome.

Rapid, Sensitive, and Label-Free Detection of Long Noncoding RNAs in Breast Cancer Tissues by RecJf Exonuclease-Assisted Recombinase Polymerase Amplification

An abnormal expression level of long noncoding RNAs (lncRNAs) is implicated in multiple cancers, and their sensitive and rapid measurement is pivotal for early cancer diagnosis and cancer treatment. The conventional lncRNA assays often suffer from labor-intensive/time-consuming procedures and limited sensitivity. Herein, we report a simple and sensitive fluorescent biosensor for rapid and label-free measurement of lncRNAs based on recombinase polymerase amplification (RPA) without the involvement of thermal cycling and reverse transcription. Target lncRNAs can bind with the 5'-end of the DNA template to create a DNA-lncRNA hybrid, protecting the DNA template from RecJf exonuclease-mediated degradation. Subsequently, the primers hybridize with the intact DNA templates and are extended to generate the dsDNA products with the assistance of polymerase. The resultant dsDNA products may be amplified by exponential recombinase polymerase amplification to produce abundant dsDNAs, generating a distinct fluorescence signal within 10 min. This biosensor achieves a wide dynamic range from 10-17 to 10-9 M and high sensitivity with a detection limit of 1.23 aM. Moreover, it can distinguish the expressions of lncRNA HOTAIR in the tissues of healthy individuals and breast cancer patients, with broad application prospects in lncRNA-related research and early diagnosis of cancers.

An insight into the potential role of LINC00968 in luminal breast cancer: Case-control study and bioinformatics analysis

Background

Luminal A and B subtypes of breast cancer (BC) comprises up to 70% of all BC patients. LncRNAs can affect many biological and pathological processes, and dysregulation of them is related to human cancers. The potential role of lncRNA LINC00968 in luminal BC is still unclear.

Materials and methods

We analyzed the LINC00968 expression across 44 paired luminal BC tissues from the TCGA-BRCA RNA sequencing dataset. Besides, we used the GEPIA2 web server and GENEVESTIGATOR software, as well. Real-Time Quantitative Reverse Transcription PCR (qRT-PCR) assay was performed to confirm the LINC00968 expression in 71 paired luminal BC tissues and two luminal A cell lines (MCF7 and T47D). Moreover, to better understanding the potential role of LINC00968 in luminal BC, computational data analyses including co-expression analysis, functional annotation analysis, and genetic alteration analysis have been done.

Results

The results of data analyses retrieved from BRCA dataset and databases revealed the significant downregulation of LINC00968 in luminal A and B BC. Also, the results of qRT-PCR in luminal BC tissues and cell lines confirmed the earlier data. LINC00968 expression was negatively associated with tumor stage and lymph node metastasis. Additionally, functional annotation analyses revealed that LINC00968 might be involved in vascular development and angiogenesis, extracellular matrix organization, and cell motility and migration. LINC00968 might play role in some cancer-related signaling pathways.

Conclusion

Our study found that downregulation of LINC00968 might promote tumorigenesis, invasion, and metastasis of luminal BC.

Non-Coding RNAs: Foes or Friends for Targeting Tumor Microenvironment

Non-coding RNAs (ncRNAs) are a group of molecules critical for cell development and growth regulation. They are key regulators of important cellular pathways in the tumor microenvironment. To analyze ncRNAs in the tumor microenvironment, the use of RNA sequencing technology has revolutionized the field. The advancement of this technique has broadened our understanding of the molecular biology of cancer, presenting abundant possibilities for the exploration of novel biomarkers for cancer treatment. In this review, we will summarize recent achievements in understanding the complex role of ncRNA in the tumor microenvironment, we will report the latest studies on the tumor microenvironment using RNA sequencing, and we will discuss the potential use of ncRNAs as therapeutics for the treatment of cancer.

MCluster-VAEs: An end-to-end variational deep learning-based clustering method for subtype discovery using multi-omics data

The discovery of cancer subtypes based on unsupervised clustering helps in providing a precise diagnosis, guide treatment, and improve patients' prognoses. Instead of single-omics data, multi-omics data can improve the clustering performance because it obtains a comprehensive landscape for understanding biological systems and mechanisms. However, heterogeneous data from multiple sources raises high complexity and different kinds of noise, which are detrimental to the extraction of clustering information. We propose an end-to-end deep learning based method, called Multi-omics Clustering Variational Autoencoders (MCluster-VAEs), that can extract cluster-friendly representations on multi-omics data. First, a unified network architecture with an attention mechanism was developed for accurately modeling multi-omics data. Then, using a novel objective function built from the Variational Bayes technique, the model was trained to effectively obtain the posterior estimation of the clustering assignments. Compared with 12 other state-of-the-art multi-omics clustering methods, MCluster-VAEs achieved an outstanding performance on benchmark datasets from the TCGA database. On the Pan Cancer dataset, MCluster-VAEs achieved an adjusted Rand index of approximately 0.78 for cancer category recognition, an increase of more than 18% compared with other methods. Furthermore, a survival analysis and clinical parameter enrichment tests conducted on 10 cancer datasets demonstrated that MCluster-VAEs provides comparable and even better results than many common integrative approaches. These results demonstrate that MCluster-VAEs are a powerful new tool for dissecting complex multi-omics relationships and providing new insights for cancer subtype discovery.