Protein Corona Gold Nanoparticles Fingerprinting Reveals a Profile of Blood Coagulation Proteins in the Serum of HER2-Overexpressing Breast Cancer Patients

Breast cancer (BC) is a molecularly heterogeneous disease that encompasses five major molecular subtypes (luminal A (LA), luminal B HER2 negative (LB-), luminal B HER2 positive (LB+), HER2 positive (HER2+) and triple negative breast cancer (TNBC)). BC treatment mainly depends on the identification of the specific subtype. Despite the correct identification, therapies could fail in some patients. Thus, further insights into the genetic and molecular status of the different BC subtypes could be very useful to improve the response of BC patients to the range of available therapies. In this way, we used gold nanoparticles (AuNPs, 12.96 ± 0.72 nm) as a scavenging tool in combination with Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH-MS) to quantitatively analyze the serum proteome alterations in the different breast cancer intrinsic subtypes. The differentially regulated proteins specific of each subtype were further analyzed with the bioinformatic tools STRING and PANTHER to identify the major molecular function, biological processes, cellular origin, protein class and biological pathways altered due to the heterogeneity in proteome of the different BC subtypes. Importantly, a profile of blood coagulation proteins was identified in the serum of HER2-overexpressing BC patients.     

Long Non-Coding RNAs as Master Regulators in Cardiovascular Diseases

Cardiovascular disease is the leading cause of death in the United States, accounting for nearly one in every seven deaths. Over the last decade, various targeted therapeutics have been introduced, but there has been no corresponding improvement in patient survival. Since the mortality rate of cardiovascular disease has not been significantly decreased, efforts have been made to understand the link between heart disease and novel therapeutic targets such as non-coding RNAs. Among multiple non-coding RNAs, long non-coding RNA (lncRNA) has emerged as a novel therapeutic in cardiovascular medicine. LncRNAs are endogenous RNAs that contain over 200 nucleotides and regulate gene expression. Recent studies suggest critical roles of lncRNAs in modulating the initiation and progression of cardiovascular diseases. For example, aberrant lncRNA expression has been associated with the pathogenesis of ischemic heart failure. In this article, we present a synopsis of recent discoveries that link the roles and molecular interactions of lncRNAs to cardiovascular diseases. Moreover, we describe the prevalence of circulating lncRNAs and assess their potential utilities as biomarkers for diagnosis and prognosis of heart disease.     

Long Non-Coding RNAs as Functional Codes for Oral Cancer: Translational Potential,Progress and Promises

Oral cancer is one of the leading malignant tumors worldwide. Despite the advent of multidisciplinary approaches, the overall prognosis of patients with oral cancer is poor, mainly due to late diagnosis. There is an urgent need to develop valid biomarkers for early detection and effective therapies. Long non-coding RNAs (lncRNAs) are recognized as key elements of gene regulation, with pivotal roles in various physiological and pathological processes, including cancer. Over the past few years, an exponentially growing number of lncRNAs have been identified and linked to tumorigenesis and prognosis outcomes in oral cancer, illustrating their emerging roles in oral cancer progression and the associated signaling pathways. Herein, we aim to summarize the most recent advances made concerning oral cancer-associated lncRNA, and their expression, involvement, and potential clinical impact, reported to date, with a specific focus on the lncRNA-mediated molecular regulation in oncogenic signaling cascades and oral malignant progression, while exploring their potential, and challenges, for clinical applications as biomarkers or therapeutic targets for oral cancer.     

Understanding the Functions of Long Non-Coding RNAs through Their Higher-Order Structures

Although thousands of long non-coding RNAs (lncRNAs) have been discovered in eukaryotes, very few molecular mechanisms have been characterized due to an insufficient understanding of lncRNA structure. Therefore, investigations of lncRNA structure and subsequent elucidation of the regulatory mechanisms are urgently needed. However, since lncRNA are high molecular weight molecules, which makes their crystallization difficult, obtaining information about their structure is extremely challenging, and the structures of only several lncRNAs have been determined so far. Here, we review the structure–function relationships of the widely studied lncRNAs found in the animal and plant kingdoms, focusing on the principles and applications of both in vitro and in vivo technologies for the study of RNA structures, including dimethyl sulfate-sequencing (DMS-seq), selective 2′-hydroxyl acylation analyzed by primer extension-sequencing (SHAPE-seq), parallel analysis of RNA structure (PARS), and fragmentation sequencing (FragSeq). The aim of this review is to provide a better understanding of lncRNA biological functions by studying them at the structural level.     

Role of Long Noncoding RNAs ZlMSTRG.11348 and UeMSTRG.02678 in Temperature-Dependent Culm Swelling in Zizania latifolia

Temperature influences the physiological processes and ecology of both hosts and endophytes; however, it remains unclear how long noncoding RNAs (lncRNAs) modulate the consequences of temperature-dependent changes in host–pathogen interactions. To explore the role of lncRNAs in culm gall formation induced by the smut fungus Ustilago esculenta in Zizania latifolia, we employed RNA sequencing to identify lncRNAs and their potential cis-targets in Z. latifolia and U. esculenta under different temperatures. In Z. latifolia and U. esculenta, we identified 3194 and 173 lncRNAs as well as 126 and four potential target genes for differentially expressed lncRNAs, respectively. Further function and expression analysis revealed that lncRNA ZlMSTRG.11348 regulates amino acid metabolism in Z. latifolia and lncRNA UeMSTRG.02678 regulates amino acid transport in U. esculenta. The plant defence response was also found to be regulated by lncRNAs and suppressed in Z. latifolia infected with U. esculenta grown at 25 °C, which may result from the expression of effector genes in U. esculenta. Moreover, in Z. latifolia infected with U. esculenta, the expression of genes related to phytohormones was altered under different temperatures. Our results demonstrate that lncRNAs are important components of the regulatory networks in plant-microbe-environment interactions, and may play a part in regulating culm swelling in Z. latifolia plants.     

Machine Learning Reduced Gene/Non-Coding RNA Features That Classify Schizophrenia Patients Accurately and Highlight Insightful Gene Clusters

RNA-seq has been a powerful method to detect the differentially expressed genes/long non-coding RNAs (lncRNAs) in schizophrenia (SCZ) patients; however, due to overfitting problems differentially expressed targets (DETs) cannot be used properly as biomarkers. This study used machine learning to reduce gene/non-coding RNA features. Dorsolateral prefrontal cortex (dlpfc) RNA-seq data from 254 individuals was obtained from the CommonMind consortium. The average predictive accuracy for SCZ patients was 67% based on coding genes, and 96% based on long non-coding RNAs (lncRNAs). Machine learning is a powerful algorithm to reduce functional biomarkers in SCZ patients. The lncRNAs capture the characteristics of SCZ tissue more accurately than mRNA as the former regulate every level of gene expression, not limited to mRNA levels.