scholarly journals LINC01106 drives colorectal cancer growth and stemness through a positive feedback loop to regulate the Gli family factors

2020 ◽  
Vol 11 (10) ◽  
Author(s):  
Kun Guo ◽  
Wenbin Gong ◽  
Qin Wang ◽  
Guosheng Gu ◽  
Tao Zheng ◽  
...  
Keyword(s):  
Feedback Loop ◽  
Colon Adenocarcinoma ◽  
Family Factors ◽  
Transcription Activator ◽  
Normal Colon ◽  
Protein Coding ◽  
Non Coding Rnas ◽  
High Level ◽  
Ucsc Database

Abstract Long non-coding RNAs (lncRNAs) are essential contributors to the progression of various human cancers. Long intergenic non-protein coding RNA 1106 is a member of lncRNAs family. Until now, the specific role of LINC01106 in CRC remains undefined. The aim the current study was to unveil the functions of LINC01106 and explore its potential molecular mechanism in CRC. Based on the data of online database GEPIA, we determined that LINC01106 was expressed at a high level in colon adenocarcinoma (COAD) tissues compared to normal colon tissues. More importantly, high level of LINC01106 had negative correlation with the overall survival of COAD patients. Additionally, we also determined the low level of LINC01106 in normal colon tissues based on UCSC database. Through qRT-PCR, we identified that LINC01106 was highly expressed in CRC tissues compared to adjacent normal ones. Similarly, we detected the expression of LINC01106 and confirmed that LINC01106 was expressed higher in CRC cells than that in normal cells. Subsequently, LINC01106 was mainly distributed in the cytoplasm. LINC01106 induced the proliferation, migration, and stem-like phenotype of CRC cells. Mechanistically, cytoplasmic LINC01106 positively modulated Gli4 in CRC cells by serving as a miR-449b-5p sponge. Furthermore, nuclear LINC01106 could activate the transcription of Gli1 and Gli2 through recruiting FUS to Gli1 and Gli2 promoters. Mechanism of investigation unveiled that Gli2 was a transcription activator of LINC01106. In conclusion, Gli2-induced upregulation of LINC01106 aggravates CRC progression through upregulating Gli2, Gli2, and Gli4.

scholarly journals Non-Coding RNAs as Prognostic Markers for Endometrial Cancer

2021 ◽  
Vol 22 (6) ◽  
pp. 3151 ◽  
Author(s):  
Roberto Piergentili ◽  
Simona Zaami ◽  
Anna Franca Cavaliere ◽  
Fabrizio Signore ◽  
Giovanni Scambia ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Prognostic Markers ◽  
Classification Systems ◽  
Prognostic Role ◽  
Protein Coding ◽  
Pathological Characteristics ◽  
Epigenetic Events ◽  
Cancer Types ◽  
Non Coding Rnas

Endometrial cancer (EC) has been classified over the years, for prognostic and therapeutic purposes. In recent years, classification systems have been emerging not only based on EC clinical and pathological characteristics but also on its genetic and epigenetic features. Noncoding RNAs (ncRNAs) are emerging as promising markers in several cancer types, including EC, for which their prognostic value is currently under investigation and will likely integrate the present prognostic tools based on protein coding genes. This review aims to underline the importance of the genetic and epigenetic events in the EC tumorigenesis, by expounding upon the prognostic role of ncRNAs.

scholarly journals Functions and Roles of Long-Non-Coding RNAs in Human Nasopharyngeal Carcinoma

2018 ◽  
Vol 45 (3) ◽  
pp. 1191-1204 ◽  
Author(s):  
JingJing Wu ◽  
Swei Sunny Hann
Keyword(s):  
Nasopharyngeal Carcinoma ◽  
Functional Characterization ◽  
Clinical Information ◽  
Stem Cell Differentiation ◽  
Cancer Prognosis ◽  
Protein Coding ◽  
Rna Molecules ◽  
Gene Therapies ◽  
Non Coding Rnas

Nasopharyngeal carcinoma (NPC) is one of the most common cancers originating in the nasopharynx and occurring at high frequency in South-eastern Asia and North Africa. Long non-coding RNAs (lncRNAs) are a class of non-protein-coding RNA molecules and key regulators of developmental, physiological, and pathological processes in humans. Emerging studies have shown that lncRNAs play critical roles in tumorgenicity and cancer prognosis. With the development of deep sequencing analyses, an extensive amount of functional lncRNAs have been discovered in nasopharyngeal carcinoma tissues and cell lines. However, the roles and mechanisms of aberrantly expressed lncRNAs in the pathogenesis of NPC are not fully understood. In this review, we briefly illustrate the concept, identification, functional characterization, and summarize recent advancements of biological functions of lncRNAs with heterogeneous mechanistic characterization and their involvement in NPC. Then, we describe individual lncRNAs that have been associated with tumorgenesis, growth, invasion, cancer stem cell differentiation, metastasis, drug resistance and discuss the strategies of their therapeutic manipulation in NPC. We also review the emerging insights into the role of lncRNAs and their potential as biomarkers and therapeutic targets for novel treatment paradigms. Finally, we highlight the up-to-date of clinical information involving lncRNAs and future directions in the linking lncRNAs to potential gene therapies, and how modifications of lncRNAs can be exploited for prevention and treatment of NPC.

scholarly journals lncRNA_Mdeep: An Alignment-Free Predictor for Distinguishing Long Non-Coding RNAs from Protein-Coding Transcripts by Multimodal Deep Learning

2020 ◽  
Vol 21 (15) ◽  
pp. 5222 ◽  
Author(s):  
Xiao-Nan Fan ◽  
Shao-Wu Zhang ◽  
Song-Yao Zhang ◽  
Jin-Jie Ni
Keyword(s):  
Deep Learning ◽  
Prediction Accuracy ◽  
Protein Coding ◽  
Learning Framework ◽  
Alignment Free ◽  
Independent Test ◽  
Non Coding Rnas ◽  
High Level ◽  
Fold Cross Validation ◽  
Human Complex

Long non-coding RNAs (lncRNAs) play crucial roles in diverse biological processes and human complex diseases. Distinguishing lncRNAs from protein-coding transcripts is a fundamental step for analyzing the lncRNA functional mechanism. However, the experimental identification of lncRNAs is expensive and time-consuming. In this study, we presented an alignment-free multimodal deep learning framework (namely lncRNA_Mdeep) to distinguish lncRNAs from protein-coding transcripts. LncRNA_Mdeep incorporated three different input modalities, then a multimodal deep learning framework was built for learning the high-level abstract representations and predicting the probability whether a transcript was lncRNA or not. LncRNA_Mdeep achieved 98.73% prediction accuracy in a 10-fold cross-validation test on humans. Compared with other eight state-of-the-art methods, lncRNA_Mdeep showed 93.12% prediction accuracy independent test on humans, which was 0.94%~15.41% higher than that of other eight methods. In addition, the results on 11 cross-species datasets showed that lncRNA_Mdeep was a powerful predictor for predicting lncRNAs.

scholarly journals LncRNA LINC01518 induced by GATA3 promotes cell proliferation, migration and invasion via miR-206/PRKACB in neuroblastoma

2021 ◽  
Author(s):  
Chunying Zhang ◽  
Lin Yang ◽  
Ge Zhao ◽  
Jiaxiang Wang ◽  
Juntao Pan ◽  
...  
Keyword(s):  
Cell Lines ◽  
Migration And Invasion ◽  
Cell Phenotype ◽  
Competing Endogenous Rna ◽  
Protein Coding ◽  
Solid Malignancy ◽  
Active Transcription ◽  
Non Coding Rnas ◽  
Regulate Protein

Neuroblastoma (NBL) exists as the most common solid malignancy which predominantly occurs in children. Long non-coding RNAs (lncRNAs) have been widely confirmed to exert functions in modulating the pathogenesis of diverse diseases. Nevertheless, whether the putative function of long intergenic non-protein coding RNA 1518 (LINC01518) in NBL has not been elucidated yet. In this study, RT-qPCR was used for determining LINC01518 expression and LINC01518 was found to be notably overexpressed in NBL tissues and cell lines compared with normal nerve tissues and cell lines. Functional experiments and mechanism assays were respectively done for the investigation into cell phenotype and for the exploration of correlation among genes. LINC01518 silencing was discovered to repress cell malignant phenotype. We observed that GATA binding protein 3 (GATA3) was an active transcription factor of LINC01518. Besides, LINC01518 functioned as a competing endogenous RNA (ceRNA), which sequestered microRNA-206 (miR-206) to up-regulate protein kinase cAMP-activated catalytic subunit beta (PRKACB). Afterwards, rescue assays validated the oncogenic role of GATA3/LINC01518/miR-206/PRKACB axis in NBL. To be summarized, our research determined that LINC01518 might be used as a putative molecular marker for NBL diagnosis and treatment.

scholarly journals Regulation of Adaptive Tumor Immunity by Non-Coding RNAs

Cancers ◽  
2021 ◽  
Vol 13 (22) ◽  
pp. 5651
Author(s):  
Eleftheria Papaioannou ◽  
María del Pilar González-Molina ◽  
Ana M. Prieto-Muñoz ◽  
Laura Gámez-Reche ◽  
Alicia González-Martín
Keyword(s):  
Immune Responses ◽  
Cancer Immunotherapy ◽  
Tumor Immunity ◽  
Immune Cell ◽  
Protein Coding ◽  
Protein Coding Genes ◽  
Therapeutic Value ◽  
Non Coding Rnas ◽  
And Function

Cancer immunology research has mainly focused on the role of protein-coding genes in regulating immune responses to tumors. However, despite more than 70% of the human genome is transcribed, less than 2% encodes proteins. Many non-coding RNAs (ncRNAs), including microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), have been identified as critical regulators of immune cell development and function, suggesting that they might play important roles in orchestrating immune responses against tumors. In this review, we summarize the scientific advances on the role of ncRNAs in regulating adaptive tumor immunity, and discuss their potential therapeutic value in the context of cancer immunotherapy.

scholarly journals Non-coding RNAs in the Pathogenesis of Multiple Sclerosis

2021 ◽  
Vol 12 ◽  
Author(s):  
Aadil Yousuf ◽  
Abrar Qurashi
Keyword(s):  
Multiple Sclerosis ◽  
Axonal Loss ◽  
Biological Processes ◽  
Protein Coding ◽  
The Past ◽  
The Central Nervous System ◽  
Non Coding Rnas ◽  
Genetic And Environmental Factors ◽  
Ms Pathogenesis

Multiple sclerosis (MS) is an early onset chronic neurological condition in adults characterized by inflammation, demyelination, gliosis, and axonal loss in the central nervous system. The pathological cause of MS is complex and includes both genetic and environmental factors. Non-protein-coding RNAs (ncRNAs), specifically miRNAs and lncRNAs, are important regulators of various biological processes. Over the past decade, many studies have investigated both miRNAs and lncRNAs in patients with MS. Since then, insightful knowledge has been gained in this field. Here, we review the role of miRNAs and lncRNAs in MS pathogenesis and discuss their implications for diagnosis and treatment.

scholarly journals lncRNA_Mdeep: an alignment-free predictor for long non-coding RNAs identification by multimodal deep learning

2020 ◽  
Author(s):  
Xiao-Nan Fan ◽  
Shao-Wu Zhang ◽  
Song-Yao Zhang ◽  
Jin-Jie Ni
Keyword(s):  
Deep Learning ◽  
Prediction Accuracy ◽  
Protein Coding ◽  
Learning Framework ◽  
Alignment Free ◽  
Independent Test ◽  
Non Coding Rnas ◽  
High Level ◽  
Fold Cross Validation ◽  
Human Complex

Abstract Background: Long non-coding RNAs (lncRNAs) play crucial roles in diverse biological processes and human complex diseases. Distinguishing lncRNAs from protein-coding transcripts is a fundamental step for analyzing lncRNA functional mechanism. However, the experimental identification of lncRNAs is expensive and time-consuming. Results: In this study, we present an alignment-free multimodal deep learning framework (namely lncRNA_Mdeep) to distinguish lncRNAs from protein-coding transcripts. LncRNA_Mdeep incorporates three different input modalities (i.e. OFH modality, k-mer modality, and sequence modality), then a multimodal deep learning framework is built for learning the high-level abstract representations and predicting the probability whether a transcript is lncRNA or not. Conclusions: LncRNA_Mdeep achieves 98.73% prediction accuracy in 10-fold cross-validation test on human. Compared with other eight state-of-the-art methods, lncRNA_Mdeep shows 93.12% prediction accuracy independent test on human, which is 0.94%~15.41% higher than that of other eight methods. In addition, the results on 11 cross-species datasets show that lncRNA_Mdeep is a powerful predictor for identifying lncRNAs. The source code can be downloaded from https://github.com/NWPU-903PR/lncRNA_Mdeep.

MicroRNAs and other non-coding RNAs in adipose tissue and obesity: emerging roles as biomarkers and therapeutic targets

2019 ◽  
Vol 133 (1) ◽  
pp. 23-40 ◽  
Author(s):  
Silvia Lorente-Cebrián ◽  
Pedro González-Muniesa ◽  
Fermín I. Milagro ◽  
J. Alfredo Martínez
Keyword(s):  
Adipose Tissue ◽  
Current Knowledge ◽  
Metabolic Effects ◽  
Protein Coding ◽  
Non Coding Rnas ◽  
Potential Impact ◽  
Dietary Strategies ◽  
Potential Use

AbstractObesity is a metabolic condition usually accompanied by insulin resistance (IR), type 2 diabetes (T2D), and dyslipidaemia, which is characterised by excessive fat accumulation and related to white adipose tissue (WAT) dysfunction. Enlargement of WAT is associated with a transcriptional alteration of coding and non-coding RNAs (ncRNAs). For many years, big efforts have focused on understanding protein-coding RNAs and their involvement in the regulation of adipocyte physiology and subsequent role in obesity. However, diverse findings have suggested that a dysfunctional adipocyte phenotype in obesity might be also dependent on specific alterations in the expression pattern of ncRNAs, such as miRNAs. The aim of this review is to update current knowledge on the physiological roles of miRNAs and other ncRNAs in adipose tissue function and their potential impact on obesity. Therefore, we examined their regulatory role on specific WAT features: adipogenesis, adipokine secretion, inflammation, glucose metabolism, lipolysis, lipogenesis, hypoxia and WAT browning. MiRNAs can be released to body fluids and can be transported (free or inside microvesicles) to other organs, where they might trigger metabolic effects in distant tissues, thus opening new possibilities to a potential use of miRNAs as biomarkers for diagnosis, prognosis, and personalisation of obesity treatment. Understanding the role of miRNAs also opens the possibility of using these molecules on individualised dietary strategies for precision weight management. MiRNAs should be envisaged as a future therapeutic approach given that miRNA levels could be modulated by synthetic molecules (f.i. miRNA mimics and inhibitors) and/or specific nutrients or bioactive compounds.

scholarly journals Evolutionary Patterns of Non-Coding RNA in Cardiovascular Biology

2019 ◽  
Vol 5 (1) ◽  
pp. 15 ◽  
Author(s):  
Shrey Gandhi ◽  
Frank Ruehle ◽  
Monika Stoll
Keyword(s):  
Vascular System ◽  
Functional Characterization ◽  
Evolutionary Patterns ◽  
Protein Coding ◽  
Rna Transcripts ◽  
Non Coding Rna ◽  
High Prevalence ◽  
Cardiovascular Biology ◽  
Non Coding Rnas

Cardiovascular diseases (CVDs) affect the heart and the vascular system with a high prevalence and place a huge burden on society as well as the healthcare system. These complex diseases are often the result of multiple genetic and environmental risk factors and pose a great challenge to understanding their etiology and consequences. With the advent of next generation sequencing, many non-coding RNA transcripts, especially long non-coding RNAs (lncRNAs), have been linked to the pathogenesis of CVD. Despite increasing evidence, the proper functional characterization of most of these molecules is still lacking. The exploration of conservation of sequences across related species has been used to functionally annotate protein coding genes. In contrast, the rapid evolutionary turnover and weak sequence conservation of lncRNAs make it difficult to characterize functional homologs for these sequences. Recent studies have tried to explore other dimensions of interspecies conservation to elucidate the functional role of these novel transcripts. In this review, we summarize various methodologies adopted to explore the evolutionary conservation of cardiovascular non-coding RNAs at sequence, secondary structure, syntenic, and expression level.

scholarly journals Deciphering the Role of the Non-Coding Genome in Regulating Gene-Diet Interactions

Nutrients ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1831
Author(s):  
Pui-Pik Law ◽  
Michelle Holland
Keyword(s):  
Protein Translation ◽  
Nutritional Intervention ◽  
Transcriptional Level ◽  
Nutritional Regulation ◽  
Protein Coding ◽  
Protein Encoding ◽  
Mammalian Genomes ◽  
Non Coding Rnas ◽  
Number Of Classes

Protein encoding genes constitute a small fraction of mammalian genomes. In addition to the protein coding genes, there are other functional units within the genome that are transcribed, but not translated into protein, the so called non-coding RNAs. There are many types of non-coding RNAs that have been identified and shown to have important roles in regulating gene expression either at the transcriptional or post-transcriptional level. A number of recent studies have highlighted that dietary manipulation in mammals can influence the expression or function of a number of classes of non-coding RNAs that contribute to the protein translation machinery. The identification of protein translation as a common target for nutritional regulation underscores the need to investigate how this may mechanistically contribute to phenotypes and diseases that are modified by nutritional intervention. Finally, we describe the state of the art and the application of emerging ‘-omics’ technologies to address the regulation of protein translation in response to diet.

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