lncRNA Expression Research Articles

A combination of conserved and stage-specific lncRNA biomarkers to detect lung adenocarcinoma progression

Lung adenocarcinoma is highly heterogeneous at the molecular level between different stages; therefore, understanding molecular mechanisms contributing to such heterogeneity is needed. In addition, multiple stages of progression are critical factors for lung adenocarcinoma treatment. However, previous studies showed that cancer progression is associated with altered lncRNA expression, highlighting the tissue-specific and developmental stage-specific nature of lncRNAs in various diseases. Therefore, a study using an integrated network approach to explore the role of lncRNA in carcinogenesis was done using expression profiles revealing stage-specific and conserved lncRNA biomarkers in lung adenocarcinoma. We constructed ceRNA networks for each stage of lung adenocarcinoma and analysed them using network topology, differential co-expression network, protein–protein interaction network, functional enrichment, survival analysis, genomic analysis and deep learning to identify potential lncRNA biomarkers. The co-expression networks of healthy and three successive stages of lung adenocarcinoma have shown different network properties. One conserved and four stage-specific lncRNAs are identified as genome regulatory biomarkers. These lncRNAs can successfully identify lung adenocarcinoma and different stages of progression using deep learning. In addition, we identified five mRNAs, four miRNAs and twelve novel carcinogenic interactions associated with the progression of lung adenocarcinoma. These lncRNA biomarkers will provide a novel perspective into the underlying mechanism of adenocarcinoma progression and may be further helpful in early diagnosis, treatment and prognosis of this deadly disease.

Characterization of N6-methyladenosine long non-coding RNAs in sporadic congenital cataract and age-related cataract.

To characterize the N6-methyladenosine (m6A) modification patterns in long non-coding RNAs (lncRNAs) in sporadic congenital cataract (CC) and age-related cataract (ARC). Anterior capsule of the lens were collected from patients with CC and ARC. Methylated RNA immunoprecipitation with next-generation sequencing and RNA sequencing were performed to identify m6A-tagged lncRNAs and lncRNAs expression. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses and Gene Ontology annotation were used to predict potential functions of the m6A-lncRNAs. Large amount of m6A peaks within lncRNA were identified for both CC and ARC, while the level was much higher in ARC (49 870 peaks) than that in CC (18 688 peaks), yet those difference between ARC in younger age group (ARC-1) and ARC in elder age group (ARC-2) was quite slight. A total of 1305 hypermethylated and 1178 hypomethylated lncRNAs, as well as 182 differential expressed lncRNAs were exhibited in ARC compared with CC. On the other hand, 5893 hypermethylated and 5213 hypomethylated lncRNAs, as well as 155 significantly altered lncRNA were identified in ARC-2 compared with ARC-1. Altered lncRNAs in ARC were mainly associated with the organization and biogenesis of intracellular organelles, as well as nucleotide excision repair. Our results for the first time present an overview of the m6A methylomes of lncRNA in CC and ARC, providing a solid basis and uncovering a new insight to reveal the potential pathogenic mechanism of CC and ARC.

Altered expression of Csnk1a1p in Autism Spectrum Disorder in Iranian population: case-control study

Over the past decade, substantial scientific evidence has showed that long non-coding RNAs (lncRNAs) are extensively expressed and play a crucial role in gene modulation through a diverse range of transcriptional, and post-transcriptional mechanisms. Recent discoveries have emphasized the involvement of lncRNAs in maintaining cellular homeostasis and neurogenesis in the brain. Accumulating reports identified dysregulated lncRNAs associated with psychiatric disorders, including autism. In this study, we examined the expression levels of DISC2, Linc00945, Foxg1-as1, Csnk1a1p, and Evf2 lncRNAs in blood samples from 21 clinically diagnosed autistic patients based on the Diagnostic and Statistical Manual of Mental Disorders criteria-5th edition (DSM-5), compared to age, sex, and ethnically-matched 25 healthy individuals. RNA extraction and cDNA synthesis were performed, followed by real-time PCR for quantification of lncRNAs expression levels. Receiver operating characteristic (ROC) curve analysis was used to evaluate biomarker potential. Additionally, we investigated the correlation between gene expression levels and autism comorbidities. Our results showed a significant decrease in Csnk1a1p expression in patients with autism spectrum disorder (ASD) compared to healthy children (P value = 0.0008). ROC curve analysis indicated that Csnk1a1p expression levels could effectively discriminate patients from healthy controls (AUC = 0.837, P value = 0.000284). No significant differences were observed between Csnk1a1p expression levels and comorbidity with ADHD or intellectual disability (p-value > 0.05). Based on these findings, Csnk1a1p may play a significant role in autistic patients and could serve as a potential biomarker for diagnostic and predictive purposes, as well as a therapeutic target.

Abstract A020: LncTransformer: Identifying novel prognostic long-noncoding RNA biomarkers for pancreatic cancer using deep learning

Abstract Pancreatic cancer remains a formidable challenge due to high mortality rates and limited therapeutic options due to late detection of the cancer. Recent literature has focused on investigating the potential of long noncoding RNAs (lncRNAs) as biomarkers in several cancers. To identify significantly altered expression of lncRNA in pancreatic cancer, I collected information from the Cancer Genome Atlas (TCGA) and extracted RNA- sequencing (RNA-seq) transcriptomic profiles of pancreatic carcinomas. Out of 60,660 gene transcripts shared between 151 pancreatic cancer patients, 38 lncRNAs were identified to be significantly differentially expressed. To find the lncRNAs related to metastatic progression of pancreatic cancer, different machine learning algorithms, including logistic regression (LR), support vector machine (SVM), random forest classifier (RFC), and a custom autoencoder model were trained to identify potential prognostic biomarkers. The custom deep learning model predicted metastatic prognostic biomarkers with a 92% accuracy, with the second-best accuracies coming from the SVM and RFC models, having an accuracy of 76%. Using the deep learning model, the following novel lncRNA and gene biomarkers were identified: LINC01300, SERPINB13, AC010789.1, TMPRSS15, DUSP5- DT, AL513128.3, MIR205HG, LINC00486, RF00019, LINC01115, and AC133530.1. Further investigating these results, specifically the identified lncRNA, the LINC01300 was identified to be potentially circulating in the blood. If further analysis proves this hypothesis correct then LINC01300 along with other identified genes could serve as biomarkers in a liquid biopsy. The identified biomarkers would be especially helpful in rural areas with lower accessibility to healthcare as it would allow for an easier, more efficient, and cost-effective method of pancreatic cancer detection. Based on these findings, further investigations of this gene panel in vitro and in vivo will be conducted, as they could be targeted for improved outcomes in pancreatic cancer patients and assist in the diagnosis of metastatic progression based on RNA-seq data of primary pancreatic tumors or the development of a liquid biopsy. Citation Format: Shivali Singh. LncTransformer: Identifying novel prognostic long-noncoding RNA biomarkers for pancreatic cancer using deep learning [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: RNAs as Drivers, Targets, and Therapeutics in Cancer; 2024 Nov 14-17; Bellevue, Washington. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(11_Suppl):Abstract nr A020.

Abstract A067: lncTransformer: Identifying novel prognostic biomarkers for pancreatic cancer using deep learning models

Abstract Pancreatic cancer remains a formidable challenge due to high mortality rates and limited therapeutic options due to late detection of the cancer. Recent literature has focused on investigating the potential of long noncoding RNAs (lncRNAs) as biomarkers in several cancers. To identify significantly altered expression of lncRNA in pancreatic cancer, I collected information from the Cancer Genome Atlas (TCGA) and extracted RNA-sequencing (RNA-seq) transcriptomic profiles of pancreatic carcinomas. Out of 60,660 gene transcripts shared between 151 pancreatic cancer patients, 38 lncRNAs were identified to be significantly differentially expressed. To find the lncRNAs related to metastatic progression of pancreatic cancer, different machine learning algorithms, including logistic regression (LR), support vector machine (SVM), random forest classifier (RFC), and a custom autoencoder model were trained to identify potential prognostic biomarkers. The custom deep learning model predicted metastatic prognostic biomarkers with a 92% accuracy, with the second-best accuracies coming from the SVM and RFC models, having an accuracy of 76%. Using the deep learning model, the following novel lncRNA and gene biomarkers were identified: LINC01300, SERPINB13, AC010789.1, TMPRSS15, DUSP5-DT, AL513128.3, MIR205HG, LINC00486, RF00019, LINC01115, and AC133530.1. Further investigating these results, specifically the identified lncRNA, the LINC01300 was identified to be potentially circulating in the blood. If further analysis proves this hypothesis correct then LINC01300 along with other identified genes could serve as a biomarker panel for a liquid biopsy. The identified biomarkers would be especially helpful in rural areas with lower accessibility to healthcare as it would allow for an easier, more efficient, and cost-effective method of pancreatic cancer detection. Based on these findings, further investigations of this gene panel are being conducted, in vitro and in vivo, to create a novel liquid biopsy to assist in the prognostic diagnosis of metastatic progression of pancreatic cancer. Citation Format: Shivali Singh. lncTransformer: Identifying novel prognostic biomarkers for pancreatic cancer using deep learning models [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A067.

Exploring lncRNA expression in follicular fluid exosomes of patients with obesity and polycystic ovary syndrome based on high-throughput sequencing technology

BackgroundInfertility is a reproductive health problem that attracts worldwide attention. Polycystic ovary syndrome (PCOS) is a major cause of female infertility and patients with obesity and PCOS are particularly common in clinical practice. Long non-coding RNA (lncRNAs) are a functional core in cells that regulate gene expression, transcription, and chromatin modification processes, and participate in epigenetics, cell cycle, and cell differentiation. LncRNAs are assumed to play a role in the occurrence and development of PCOS; however, their specific mechanism of action remains to be elucidated.MethodsHigh-throughput sequencing technology has been used to sequence and analyze lncRNAs in exosomes from the follicular fluid of patients with obesity and PCOS and those who underwent assisted reproductive therapy owing to male factors. Specific expression profiles of patients with obesity and PCOS were obtained and functional information analysis combined with a literature review were performed to screen for differentially expressed lncRNAs, which were validated using real-time reverse transcription quantitative polymerase chain reaction (RT-qPCR).ResultsHigh-throughput sequencing analysis revealed that compared to normal patients with male infertility, patients with obesity and PCOS had a total of 20 lncRNAs with significant expression differences in follicular fluid exosomes. Among them, 17 lncRNAs were upregulated and three were downregulated. Functional analysis showed that differentially expressed genes were mainly enriched in “cell metabolism,” “cell adhesion,” and other aspects: related gene pathways mainly involved Huntington’s disease, Parkinson’s disease, spliceosomes, non-alcoholic fatty liver disease, and ribosomes. Verification of differentially expressed lncRNAs revealed that the expression of lncRNAs TPT1-AS1, PTOV1-AS1, PTPRG-AS1, and SNHG14 in follicular fluid exosomes was consistent with the sequencing results.ConclusionA preliminary differential expression profile of lncRNAs in exosomes of patients with obesity and PCOS was established by transcriptomic analysis of these individuals. Our bioinformatics analysis results may be applicable to further study of the impact mechanism involving obesity and PCOS. These differentially expressed lncRNAs maybe served as potential biomarkers for in-depth studies of the occurrence, development on Follicle quality and function for patients with PCOS in the future.

Identification of lncRNA in circulating exosomes as potential biomarkers for MCI among the elderly

BackgroundThe abnormal expression of lncRNA in elderly patients with mild cognitive impairment (MCI), and the ability of exosomes to stably carry non-coding RNAs provide a reliable physiological basis for exosomal lncRNA in plasma as a biomarker of MCI. MethodsThis case-control study enrolled 155 patients with MCI and 155 healthy controls from a community-based population aged≥60 years. The expression profiles of lncRNA and mRNA in plasma exosomes were analyzed and validated using high-throughput RNA sequencing and qRT-PCR. Pathway enrichment analysis were performed on differentially expressed transcripts to screen for target lncRNAs and genes. Multivariate logistic regression models were used to construct clinical predictive models. The receiver operating characteristic curve was used to analyze the predictive value, with an 184-sample external database validated. Results132 lncRNAs and 459 mRNAs were significantly changed in plasma exosomes of MCI patients compared to healthy controls. LINC001380, ENST00000484033, and ENST00000531087 were screened as candidate exo-lncRNAs for predicting MCI. In logistic regression models, odds ratios and 95%CI for target exo-IncRNAs in MCI patients compared to healthy controls were 1.15(1.03–1.28) for LINC001380, 1.21(1.10–1.34) for ENST00000484033, and 1.23(1.08–1.40) for ENST00000531087, respectively. ROC curve analysis showed that the AUC of the combined predicted probability of target lncRNAs was 70.0 %(64.1 %–76.0 %). In the external database, the AUC for the target genes ATP2A2 and PSEN1 was 69.5 %(61.8 %–77.15 %). ConclusionThis study provided evidence for the specific expression of plasma exosomal lncRNAs in MCI and its possible biological mechanism. The combined detection of the expression levels of lncRNA-LINC001380, lncRNA-ENST00000484033, and lncRNA-ENST00000531087 in plasma exosomes may provide early diagnosis and prevention of cognitive impairment.

Interplay between lncRNAs and the PI3K/AKT signaling pathway in the progression of digestive system neoplasms (Review).

Long non‑coding RNA (lncRNA) is a class of non‑coding RNA molecules located in the cytoplasm or nucleus, which can regulate chromosome structure and function by interacting with DNA, RNA, proteins and other molecules; binding to mRNA bases in a complementary manner, affecting the splicing, stabilization, translation and degradation of mRNA; acting as competing endogenous RNA competitively binds to microRNAs to regulate gene expression and participate in the regulation of various vital activities of the body. The PI3K/AKT signalling pathway plays a key role in numerous biological and cellular processes, such as cell proliferation, invasion, migration and angiogenesis. It has been found that the lncRNA/PI3K/AKT axis regulates the expression of cancer‑related genes and thus tumour progression. The abnormal regulation of lncRNA expression in the lncRNA/PI3K/AKT axis is clearly associated with clinicopathological features and plays an important role in regulating biological functions. In the present review, the expression and biological functions of PI3K/AKT‑related lncRNAs both in vitro and in vivo over recent years, were comprehensively summarized and analyzed. Their correlation with clinicopathological features was also evaluated, with the objective of furnishing a solid theoretical foundation for clinical diagnosis and the monitoring of efficacy in digestive system neoplasms. The present review aimed to provide a comprehensive overview of the expression and biological functions of PI3K/AKT‑related lncRNAs in digestive system neoplasms and to assess their correlation with clinicopathological features. This endeavor seeks to establish a solid theoretical foundation for the clinical diagnosis and efficacy monitoring of digestive system tumors.

Integrating multiomics sequencing analyses uncover the key mechanisms related to oxidative stress, mitochondria, and immune cells in keloid

BackgroundThis study aimed to investigate the key molecular mechanisms underlying keloid pathogenesis by integrating oxidative stress, mitochondria, and immune cells. MethodsTranscriptome sequencing (mRNA, lncRNA, and circRNA expression data), proteomic sequencing, and small RNA sequencing analyses of lesional and non-lesional skin of patients with keloids and healthy control (normal) skin were conducted. By integrating mRNA and publicly available gene expression data (GSE158395), differentially expressed genes related to oxidative stress and mitochondrial function in keloids were identified. Hub genes were identified using various bioinformatics analyses such as immune infiltration analysis, weighted gene co-expression network analysis, machine learning, and expression validation using proteomics sequencing data. Moreover, a competing endogenous RNA (ceRNA) network of hub genes was constructed by combining miRNA, lncRNA, and circRNA expression data. Five hub genes were identified: MGST1, DHCR24, ALDH3A2, ADH1B, and FKBP5. ResultsThese hub genes had a high diagnostic value for keloids, with an AUC value > 0.8 each. In addition, five hub genes were associated with the infiltration of multiple immune cells. The immune cells with the strongest positive and negative correlations with hub genes were M0 and M1 macrophages. A ceRNA network was constructed, and several ceRNAs, such as AC005062.1/miR-134-5p/FKBP5 and BASP1-AS1/miR-503-5p/ADH1B, were identified. These five hub genes may contribute to keloid pathogenesis. ConclusionThese genes and their related ceRNAs may serve as diagnostic biomarkers and therapeutic targets for keloids.

Functional Roles of Long Non-coding RNAs on Stem Cell-related Pathways in Glioblastoma

: Long non-coding RNAs (lncRNAs), characterized by their length exceeding 200 nucleotides and lack of protein-coding capacity, are intricately associated with a wide array of cellular processes, encompassing cell invasion, differentiation, proliferation, migration, apoptosis, and regeneration. Perturbations in lncRNA expression have been observed in numerous diseases and have emerged as pivotal players in the pathogenesis of diverse tumor types. Glioblastoma, a highly malignant primary tumor of the central nervous system (CNS), remains a formidable challenge even with the advent of novel therapeutic interventions, as primary glioblastomas invariably exhibit therapy resistance and aggressive behavior. Glioblastomas can arise from progenitor cells or neuroglial stem cells, revealing profound cellular heterogeneity, notably in the form of glioblastoma stem cells (GSCs) possessing stem-like properties. Glioblastomas comprise neural precursors that harbor essential characteristics of neural stem cells (NSCs). Several signaling pathways have been implicated in the regulation of self-renewal in both cancer cells and stem cells. In addition to their involvement in therapy resistance and survival of glioblastoma, lncRNAs are implicated in the modulation of GSC behaviors through diverse pathways and the intricate regulation of various genes and proteins. This review aims to comprehensively discuss the interplay between lncRNAs, their associated pathways, and GSCs, shedding light on their potential implications in glioblastoma.

Long non-coding RNAs as prognostic biomarkers in non-muscle invasive bladder cancer: A systematic review.

Traditional prognostic tools for non-muscle invasive bladder cancer (NMIBC) often overestimate progression and recurrence risks, underscoring the need for more precise biomarkers. While long non-coding ribonucleic acids (lncRNAs) have been reviewed in bladder cancer, no review has focused on NMIBC. The aim of this study was to address this gap by investigating the role of lncRNAs in predicting NMIBC survival and progression. A systematic review was conducted using PubMed, Scopus, and Cochrane databases as of July 31, 2024. Prognostic studies investigating the association between lncRNA expression and survival outcomes, such as cancer-specific survival, disease-free survival, recurrence-free survival, or overall survival, using Kaplan-Meier curves or hazard ratios, were included. A total of three studies were analyzed, involving 279 NMIBC patients and focusing on three lncRNAs: urothelial cancer associated 1 (UCA1), growth arrest-specific 5 (GAS5), and up-regulated in non-muscle invasive bladder cancer (UNMIBC). Increased UCA1 expression was strongly associated with poor disease-free survival (hazard ratio (HR): 1.974; 95%CI: 1.061-3.673; p = 0.032) and progression-free survival (HR: 3.476; 95%CI: 1.187-10.18; p = 0.023). Reduced GAS5 expression was significantly associated with poor disease-free survival (HR: 2.659; 95%CI: 1.348-5.576; p = 0.005) and progression-free survival (HR: 6.628; 95%CI: 1.494-29.40; p = 0.013). Higher level of UNMIBC was strongly associated with poor recurrence-free survival (HR: 2.362; 95%CI: 1.504-4.837; p = 0.007). In conclusion, lncRNAs have potential as prognostic biomarkers in NMIBC, with UCA1 and UNMIBC overexpression and GAS5 underexpression being significant in predicting disease recurrence and progression, highlighting the clinical relevance of monitoring these lncRNAs to improve prognosis and guide treatment decisions.

Up-regulation of HOXA-AS2 and MEG3 long non-coding RNAs acts as a potential peripheral biomarker for bipolar disorder.

Bipolar disorder (BD) is a psychiatric condition that is frequently misdiagnosed and linked to inadequate treatment. Long non-coding RNAs (lncRNAs) have lately gained recognition as crucial genetic elements and are now regarded as regulatory mechanisms in the neurological system. Our objective was to measure the quantities of HOXA-AS2 and MEG3 ncRNA transcripts. HOXA-AS2 and MEG3 ncRNA levels were checked in the peripheral blood of 50 type I BD and 50 control samples by real-time PCR. Furthermore, we conducted ROC curve analysis and correlation analysis to examine the association between gene expression and specific clinical characteristics in instances with BD. Additionally, a computational study was performed to investigate the binding sites of miRNAs on the HOXA-AS2 and MEG3 lncRNAs. BD subjects showed a significant increase in the expression of HOXA-AS2 and MEG3 compared to controls. The lncRNAs HOXA-AS2 and MEG3 have an area under the ROC curve (AUC) values of 0.70 and 0.71, respectively. There was a significant correlation between the expression levels of ncRNAs HOXA-AS2 and MEG3 in the peripheral blood of patients with BD and occupation scores. The data presented indicate a potential correlation between the expression of HOXA-AS2 and MEG3 lncRNAs with an elevated risk of BD. Furthermore, these lncRNAs may be linked to several molecular pathways. Our findings indicate that the amounts of lncRNAs HOXA-AS2 and MEG3 in transcripts might be a promising potential biomarker for patients with BD.

Effect of the B chromosome-located long non-coding RNAs on gene expression in maize

Using artificial chromosomes in maize breeding allows for site-specific integration of multigene stacks, effectively overcoming the limitations of conventional transgenic approaches. The maize B chromosome, which is dispensable and highly heterochromatic, has minimal impact on phenotypes at low copy numbers, making it a promising platform for engineering artificial chromosomes. However, recent studies have demonstrated that the maize B chromosome can impact gene expression and recombination on the A chromosome. Understanding the genic characteristics of the B chromosomes and their impact on gene expression is essential for their application in artificial chromosome construction. Despite advancements in elucidating how the B chromosome affects A chromosome expression, the role of long non-coding RNAs (lncRNAs) in this context remains unclear. In this study, we analyzed the RNA-seq data from leaf tissue of plants with 0 to 7 B chromosomes, identifying a total of 1614 lncRNAs, including 1516 A chromosome-located and 98 B chromosome-located lncRNAs, 72 of which are specific to the B chromosome. While A-located lncRNAs show greater dependence on the mere presence of the B chromosome, the expression of B-located lncRNAs is significantly affected by the number of B chromosomes present. Regulatory networks constructed in this study suggest that B-located lncRNAs may drive the differential expression of A chromosome-located transcription factors and genes associated with circadian rhythm regulation, indicating their regulatory role in A chromosome gene expression.

A lncRNA Transcriptome Analysis of Phycocyanin-Treated Non-Small Cell Lung Cancer A549 Cell Lines

Phycocyanin is a type of marine food additive with multiple biological properties, including anticancer activity, but its underlying antineoplastic mechanism in non-small cell lung cancer (NSCLC) remains unclear. To investigate the underlying regulatory mechanism of phycocyanin in NSCLC, a lncRNA microarray analysis was performed using a phycocyanin-treated A549 cell model. The classification and expression of lncRNAs were determined. The profiles of differentially expressed lncRNAs were generated and analyzed using Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses. The results showed that 193 lncRNAs were upregulated and 116 lncRNAs were downregulated in the phycocyanin-treated group compared with the control group, and qRT‒PCR analysis confirmed the expression of selected lncRNAs. Bioinformatic analysis indicated that the differentially expressed lncRNAs and their target genes were enriched in the extracellular region, epithelium development, NOD-like receptor pathway, Notch signaling, and apoptosis process. In addition, coexpression network analysis identified 2,238 lncRNA‒mRNA, lncRNA‒lncRNA, and mRNA‒mRNA pairs. In particular, 72 etc. differentially expressed lncRNA target genes were discovered in the interaction network, which provides insights into the potential mechanism of phycocyanin in A549 cells. Moreover, cell phenotype experiments showed that downregulating the expression of lncRNA ENST00000538717, a lncRNA that is downregulated after phycocyanin treatment, could significantly inhibit the migration and viability of A549 and H460 cells. In conclusion, this study lays a theoretical and potential foundation for NSCLC treatment and advances our understanding of the regulatory mechanisms of phycocyanin.

Transcriptomic study reveals alteration in the expression of long non-coding RNAs (lncRNAs) during reversal of HIV-1 latency in monocytic cell line.

The presence of latent HIV reservoirs continues to be the biggest obstacle to achieving an HIV cure. Thus, long non-coding RNAs (lncRNAs) may serve as the preferred targets for HIV latency reversal. The goal of the study was to identify prospective lncRNAs for subsequent in vitro molecular and functional characterization. RNA-sequencing was performed in latently HIV-infected monocytic cell line (U1) under stimulated and unstimulated condition using Illumina-HiSeqX platform, followed by its validation using qRT-PCR assay. Gene ontology (GO), KEGG pathway, and co-expression analyses were performed to identify the enriched biological processes and pathways in U1 cells post-stimulation with the latency reversal agent SAHA. A total of 3,576 and 1,467 significantly altered lncRNAs and protein-coding genes respectively, were identified in SAHA-stimulated U1 cells compared to unstimulated ones. The GO and KEGG pathway analyses of the differentially expressed protein-coding genes showed the enrichment of diverse biological processes and pathways respectively, in SAHA-stimulated U1 cells. Co-expression analysis between lncRNAs and protein-coding gene pairs, helped predict potential pathways with which these lncRNAs are associated. Further in vitro validation in HIV-infected monocytes showed that the expression of the top two candidate lncRNAs, LINC01231 and LINC00560, are specific to HIV infection. Transcriptome analysis revealed changes in the expression of numerous lncRNAs and protein-coding genes following stimulation with SAHA. Co-expression analysis identified candidate lncRNAs and their associated biological pathways. However, additional in vitro experimental exploration using gene knockdown strategies is needed to ascertain the specific role of LINC01231 and LINC00560 lncRNAs in latently infected monocytes.

Identification of fibrosis-associated lncRNAs in diabetic cardiomyopathy patients.

Our study endeavours to ascertain the plasma-derived long noncoding ribonucleic acids (lncRNA) and messenger RNA (mRNA) expression profiles through gene microarray analysis, aiming to elucidate their potential biological roles in the development and progression of diabetic cardiomyopathy (DCM), particularly with respect to myocardial fibrosis. We conducted gene chip experiments to discern differences in lncRNA and mRNA expression profiles between diabetic cardiomyopathy and type 2 diabetes mellitus (T2DM). Differentially expressed mRNAs were subjected to functional enrichment analysis, thereby enabling the identification of key genes. Subsequently, we established an interaction network connecting lncRNAs with mRNAs. To validate myocardial fibrosis-related mRNAs, we further developed a rat model of diabetic cardiomyopathy. We identified 688 differentially expressed lncRNAs and 341 differentially expressed mRNAs, which were primarily enriched in creatine metabolism, small guanosine triphosphate hydrolase (GTPase)-mediated signal transduction, and fatty acid degradation processes. Our analyses revealed 8 core genes (SMD11, DRG1, RPS26, EIF2S1, UBE3A, CEBPZ, NUP153, and EMD) associated with diabetic cardiomyopathy. An investigation into the lncRNA-mRNA coexpression network underscored 4 lncRNAs (lnc-NEK10-3, lnc-KDM4A-2, lnc-PCYOX1-3, and lnc-CDCP2-1) as significantly linked to differentially expressed fibrosis-associated mRNAs. The expression levels of transmembrane protein 173 (TMEM173) and toll-like receptor 7 (TLR7) were found to be significantly higher in DCM compared to normal controls, whereas cathepsin L1 (CTSL) and forkhead box O3 (FOXO3) displayed significantly lower expression levels relative to those of normal controls. Our study disclosed a subset of lncRNAs and mRNAs that are implicated in diabetic cardiomyopathy and myocardial fibrosis, thereby presenting themselves as promising biomarkers and therapeutic targets for the management of both diabetic cardiomyopathy and myocardial fibrosis.

Analysis of lncRNAs and Their Regulatory Network in Skeletal Muscle Development of the Yangtze River Delta White Goat.

lncRNA (long non-coding RNA) has been confirmed to be associated with growth, development, cell proliferation, and other biological processes. This study explored the potential role and dynamic change process of lncRNAs and related ceRNA (competitive endogenous RNA) networks in skeletal muscle development of the Yangtze River Delta White (YDW) goat, and to analyze the differences in muscle fiber characteristics and meat quality levels of goats at different growth stages. In this study, we compared the expression profiles of lncRNAs in the M. Longissimus dorsi of the YDW goats at different stages of growth and development by RNA sequencing. The results revealed that, in terms of muscle fiber characteristics, muscle fiber diameter and muscle fiber area were significantly larger in 6-month-old and 10-month-old goats than those in 2-month-old goats (p < 0.01). In terms of muscle quality, a* and b* values of 6-month-old goats were significantly higher than those of 2-month-old goats (p < 0.01). Additionally, the a*, b*, and L* values of 6-month-old goats were significantly higher than those of 10-month-old goats (p < 0.01). The pH at 45 min post-mortem (pH45min) in 10-month-old goats was significantly higher than that in 2-month-old goats (p = 0.006). However, the pH at 24 h post-mortem (pH24h) in 10-month-old goats was significantly lower than that in both 2-month-old and 6-month-old goats (p < 0.01). Shear force increased gradually with age (p < 0.05), while there was no significant difference in drip loss among the different age groups (p > 0.05). Among the identified lncRNA expression profiles, a total of 3073 lncRNAs were found, including 2676 known lncRNAs and 397 novel lncRNAs. Of these, 110, 93, and 99 lncRNAs were specifically expressed in 2-month-old, 6-month-old, and 10-month-old goats, respectively. The lncRNA target gene enrichment analysis showed that they were mainly involved in actin binding, the actin cytoskeleton, the myocardin complex, as well as the AMPK, FoxO, and GnRH signaling pathways. When constructing the lncRNA-miRNA-mRNA ceRNA network, it was found that the ceRNA networks centered on chi-miR-758 and chi-miR-127-5p were involved in muscle development across all three periods, suggesting that they may play an important role in goat muscle growth and development.

The value of lncRNAs as a biomarker for the pregnancy outcomes of gestational diabetes: a meta-analysis.

Long non-coding RNAs (lncRNAs) are tissue-specific and highly stable and may serve as early molecular markers of adverse pregnancy outcomes in patients with gestational diabetes mellitus (GDM). This study aimed to investigate the relationship between aberrant expression of lncRNAs and adverse pregnancy outcomes in GDM patients. We searched electronic databases: Embase, Medline, EBSCO, PubMed, CNKI, and Wanfang databases to find relevant articles. Included studies quality was assessed using the QUADAS-2 quality assessment tool. Prognostic value of lncRNAs were pooled using risk ratio (RR) with 95% confidence interval (95%CI). The heterogeneity was analyzed using the Cochran Q test and I2 statistical test, and subgroup analyses were performed to explore the sources of heterogeneity. Begg's test funnel plots were analyzed for publication bias. The review included nine studies. Overall, the review found that altered expression of lncRNAs was associated with adverse pregnancy outcomes of GDM patients (RR: 1.616, 95%CI: 1.308-1.996), and was a risk factor for adverse pregnancy outcomes both of Infants (RR: 1.68, 95% CI: 1.30-2.17) and maternal (RR: 4.14, 95% CI: 1.90-9.03) in patients with GDM. The diagnostic procedure, diagnostic criteria, expression trend of lncRNAs, and adverse pregnancy outcomes were the sources of heterogeneity. Aberrant expression of lncRNAs was strongly associated with the adverse pregnancy outcomes in GDM.

Coronary artery disease ameliorates extracellular vesicle lncRNA PUNISHER regulates angiogenic response and endothelial cells function via NFkB-dependent mechanism

Abstract Introduction Augmenting evidence indicates that long noncoding RNAs (lncRNAs) are playing a crucial role in diverse cellular/pathological processes. Intercellular transfer of extracellular vesicles (EVs) transmitted lncRNA regulates vascular health and diseases. However, whether lncRNA expression in EVs is regulated in patients with coronary artery disease, is unknown. A PCR-based lncRNA array analysis revealed that EV-PUNISHER was significantly upregulated in patients with CAD (n=221) compared to healthy subjects. Our data showed that PUNISHER is the most upregulated lncRNA in CAD in accordance with other highly expressed lncRNAs (FGF14-AS2, HYMAI, Gas5, Malat1, etc.). SiRNA-mediated silencing in ECs revealed that depletion of PUNISHER suppresses the migration and proliferation of ECs. Depletion of PUNISHER decreases cell survival by reducing cell viability and proliferation through increased apoptosis and cytotoxicity. To investigate EC function in PUNISHER depleted cells, sprouting and tube formation assay revealed that PUNISHER is an important mediator EC functions, which acts as an anti-angiogenic factor. In vitro atherosclerotic stimuli (OxLDL, TNF-α, IL-6) increased PUNISHER expression in EV/EC in a dose-dependent way. Microarray analysis identified a series of pro- and anti-angiogenic genes as well genes directly involved in cell viability that are differentially regulated. By using lncRNA-FISH and vesicle degradation assays, we showed that PUNISHER is incorporated into EMVs, augmented recipient EC function in vitro upon transfer via EMVs. To examine whether EMV-PUNISHER promotes the EC function, different in vitro functional experiments with ECs (tube formation, angiogenic sprouting, migration, proliferation, apoptosis, etc.) confirmed that PUNISHER is an important regulator. MS analysis has revealed EMV contains numerous proteins, including RNA binding proteins such as hnRNPU, hnRNPK, hnRNPA2/B1, etc. By using RNA-pulldown and RNA immunoprecipitation, we confirmed that PUNISHER interacts with hnRNPU, which facilitates packaging into EMV prior to transfer to recipient EC. The interaction acts as an important positive regulator of cell viability and survival of recipient cells, identified using functional assay (migration, viability, proliferation, and angiogenesis). Transcription factor array has shown that PUNISHER regulates NFkB to control cellular viability and apoptosis. A murine re-endothelialization model after injection of 1×107 EVs or bulk ncRNAs (1, 5, 10 nM) revealed that EV-PUNISHER promotes reendothelialization. Ex vivo internalization/uptake of PKH-67-labeled EVs revealed that EVs are detectable in the cross-section of EV-perfused carotid artery. Our study unveiled for the first time that EV-incorporated PUNISHER exerts its function in EC which might be beneficial in cardiovascular pathologies. Our data indicate that PUNISHER can be targeted to develop targeted therapeutics.

Serum exosomal long non-coding RNA H19 as a theragnostic target for atrial fibrillation

Abstract Background/Introduction Exosomes are membrane vesicles of 30- to 150-nm secreted by most cell types that can mediate intercellular communication by transmitting various forms of RNAs. Long non-coding RNAs (lncRNAs, ≥ 200 nucleotides length) have been reported to play important roles in the pathophysiological processes that promote the development and progression of many diseases. Interestingly, recent studies have shown that exosomal lncRNAs exhibit different expression profiles in various diseases and are being extensively studied in the medical field as an ideal target for the diagnosis and treatment. However, there is still little known about their role in atrial fibrillation (AF). Purpose The aim of this study was to identify a novel theragnostic target exosomal lncRNA for AF. Methods First, serum exosomes from controls and patients with persistent AF were isolated and characterized by transmission electron microscopy, nanoparticle tracking analysis, and western blot. Next, serum exosomal lncRNA expression profiles were explored using RNA-sequencing analysis. In addition, the expression levels of candidate exosomal lncRNAs were confirmed using qRT-PCR (n = 50 per group). Finally, AF-related pathophysiological mechanisms of exosomal lncRNA were investigated in angiotensin II (Ang II)-treated human induced pluripotent stem cell-derived atrial cardiomyocytes (iPSC-atrial CMs). Results After RNA-sequencing analysis, we identified 27 differentially expressed lncRNAs (i.e., 4 upregulated and 23 downregulated lncRNAs with a |fold change| ≥ 2 and p &amp;lt; 0.05) in serum exosomes from patients with persistent AF compared with the controls. In fact, qRT-PCR reveled that exosomal lncRNA H19 was consistently downregulated in the serum of patients with persistent AF compared with the controls (p &amp;lt; 0.01). In addition, exosomal lncRNA H19 exhibited significant diagnostic validity for AF. Notably, we confirmed that exosomal lncRNA H19 was involved in AF-related pathophysiological mechanisms. In Ang II-treated iPSC-atrial CMs, inhibition of lncRNA H19 significantly aggravated Ang II-induced increases in levels of hypertrophic markers (ANP, BNP and β-MHC), cell surface area and inflammation (p &amp;lt; 0.05). Mechanistically, we found that loss of lncRNA H19 weakens the inhibition of its direct target microRNAs, miR-141-3p and miR-200a-3p, leading to downregulation of their target PTEN, thereby promoting the atrial remodeling and the consequent AF. Conclusion In conclusion, serum-derived exosomal lncRNA H19 could serve as a potential target for the diagnosis and treatment of AF.