PLAIDOH: a novel method for functional prediction of long non-coding RNAs identifies cancer-specific LncRNA activities

Integration of transcription regulation and functional genomic data reveals lncRNA SNHG6's role in hematopoietic differentiation and leukemia

BACKGROUND

Long non-coding RNAs (lncRNAs) are pivotal players in cellular processes, and their unique cell-type specific expression patterns render them attractive biomarkers and therapeutic targets. Yet, the functional roles of most lncRNAs remain enigmatic. To address the need to identify new druggable lncRNAs, we developed a comprehensive approach integrating transcription factor binding data with other genetic features to generate a machine learning model, which we have called INFLAMeR (Identifying Novel Functional LncRNAs with Advanced Machine Learning Resources).

METHODS

INFLAMeR was trained on high-throughput CRISPR interference (CRISPRi) screens across seven cell lines, and the algorithm was based on 71 genetic features. To validate the predictions, we selected candidate lncRNAs in the human K562 leukemia cell line and determined the impact of their knockdown (KD) on cell proliferation and chemotherapeutic drug response. We further performed transcriptomic analysis for candidate genes. Based on these findings, we assessed the lncRNA small nucleolar RNA host gene 6 (SNHG6) for its role in myeloid differentiation. Finally, we established a mouse K562 leukemia xenograft model to determine whether SNHG6 KD attenuates tumor growth in vivo.

RESULTS

The INFLAMeR model successfully reconstituted CRISPRi screening data and predicted functional lncRNAs that were previously overlooked. Intensive cell-based and transcriptomic validation of nearly fifty genes in K562 revealed cell type-specific functionality for 85% of the predicted lncRNAs. In this respect, our cell-based and transcriptomic analyses predicted a role for SNHG6 in hematopoiesis and leukemia. Consistent with its predicted role in hematopoietic differentiation, SNHG6 transcription is regulated by hematopoiesis-associated transcription factors. SNHG6 KD reduced the proliferation of leukemia cells and sensitized them to differentiation. Treatment of K562 leukemic cells with hemin and PMA, respectively, demonstrated that SNHG6 inhibits red blood cell differentiation but strongly promotes megakaryocyte differentiation. Using a xenograft mouse model, we demonstrate that SNHG6 KD attenuated tumor growth in vivo.

CONCLUSIONS

Our approach not only improved the identification and characterization of functional lncRNAs through genomic approaches in a cell type-specific manner, but also identified new lncRNAs with roles in hematopoiesis and leukemia. Such approaches can be readily applied to identify novel targets for precision medicine.

Down regulation of Cathepsin W is associated with poor prognosis in pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is associated with a very poor prognosis. Therefore, there has been a focus on identifying new biomarkers for its early diagnosis and the prediction of patient survival. Genome-wide RNA and microRNA sequencing, bioinformatics and Machine Learning approaches to identify differentially expressed genes (DEGs), followed by validation in an additional cohort of PDAC patients has been undertaken. To identify DEGs, genome RNA sequencing and clinical data from pancreatic cancer patients were extracted from The Cancer Genome Atlas Database (TCGA). We used Kaplan-Meier analysis of survival curves was used to assess prognostic biomarkers. Ensemble learning, Random Forest (RF), Max Voting, Adaboost, Gradient boosting machines (GBM), and Extreme Gradient Boosting (XGB) techniques were used, and Gradient boosting machines (GBM) were selected with 100% accuracy for analysis. Moreover, protein-protein interaction (PPI), molecular pathways, concomitant expression of DEGs, and correlations between DEGs and clinical data were analyzed. We have evaluated candidate genes, miRNAs, and a combination of these obtained from machine learning algorithms and survival analysis. The results of Machine learning identified 23 genes with negative regulation, five genes with positive regulation, seven microRNAs with negative regulation, and 20 microRNAs with positive regulation in PDAC. Key genes BMF, FRMD4A, ADAP2, PPP1R17, and CACNG3 had the highest coefficient in the advanced stages of the disease. In addition, the survival analysis showed decreased expression of hsa.miR.642a, hsa.mir.363, CD22, BTNL9, and CTSW and overexpression of hsa.miR.153.1, hsa.miR.539, hsa.miR.412 reduced survival rate. CTSW was identified as a novel genetic marker and this was validated using RT-PCR. Machine learning algorithms may be used to Identify key dysregulated genes/miRNAs involved in the disease pathogenesis can be used to detect patients in earlier stages. Our data also demonstrated the prognostic and diagnostic value of CTSW in PDAC.

The Idiopathic Pulmonary Fibrosis-Associated Single Nucleotide Polymorphism RS35705950 Is Transcribed in a MUC5B Promoter Associated Long Non-Coding RNA (AC061979.1)

LncRNAs are involved in regulatory processes in the human genome, including gene expression. The rs35705950 SNP, previously associated with IPF, overlaps with the recently annotated lncRNA AC061979.1, a 1712 nucleotide transcript located within the MUC5B promoter at chromosome 11p15.5. To document the expression pattern of the transcript, we processed 3.9 TBases of publicly available RNA-SEQ data across 27 independent studies involving lung airway epithelial cells. Epithelial lung cells showed expression of this putative pancRNA. The findings were independently validated in cell lines and primary cells. The rs35705950 is found within a conserved region (from fish to primates) within the expressed sequence indicating functional importance. These results implicate the rs35705950-containing AC061979.1 pancRNA as a novel component of the MUC5B expression control minicircuitry.

Identification and Verification of a Novel MAGI2-AS3/miRNA-374-5p/FOXO1 Network Associated with HBV-Related HCC

BACKGROUND

Hepatocellular carcinoma (HCC) is a very common neoplasm worldwide, and competitive endogenous RNA (ceRNA) plays an important role in the development of HCC. The purpose of this study is to investigate the molecular mechanisms of ceRNAs in HCC.

METHODS

This study detects potential ceRNAs from HCC through whole genome analysis of lncRNA, miRNA and mRNA expression. We then performed high-throughput sequencing of tissues from five hepatitis B related HCC patients to screen ceRNAs and those screened ceRNAs expressions were verified on tissues from an independent group of six patients. Finally, the function of ceRNAs of interest was illustrated in vitro.

RESULT

Functional and pathway analysis of The Cancer Genome Atlas revealed ceRNA networks. The high-throughput sequencing identified 985 upregulated and 1612 downregulated lncRNAs and 887 upregulated and 1116 downregulated mRNAs in HCC patients. Differentially expressed genes were parallel to cancer-associated processes, comprising 18 upregulated and 35 downregulated significantly enriched pathways including alcoholism and viral carcinogenesis. Among them, a potential ceRNA network was detected and verified in six HCC patients. CeRNAs of the lncRNA MAGI2-AS3/miR-374-5p/FOXO1 pathway were significantly dysregulated in HCC, and validation in vitro showed that FOXO1 is positively regulated by MAGI2-AS3 through the induction of miR-374a/b-5p in HCC cells. In addition, the overexpression of FOXO1 is associated with proliferation, migration, and invasion of HCC cells and increases apoptosis of HCC cells. MiR-374a/b-5p caused an opposite effect by directly suppressing FOXO1 in HCC cells.

CONCLUSION

CeRNA networks were found in HCC and aberrantly expressed ceRNAs of lncRNA MAGI2-AS3/miR-374-5p/FOXO1 plays a crucial role in HCC, assisting in diagnosis and providing a method for treatment.

Intronic long noncoding RNA, RICE FLOWERING ASSOCIATED (RIFLA), regulates OsMADS56-mediated flowering in rice

Long noncoding RNAs (lncRNAs) are known to play important roles in several plant processes such as flowering, organ development and stress response. However, studies exploring the diversity and complexity of lncRNAs and their mechanism of action in plants are far fewer that those in animals. Here, we show that an intronic lncRNA in rice (Oryza sativa L.), RICE FLOWERING ASSOCIATED (RIFLA), is required for the inhibition of OsMADS56 expression. RIFLA is produced from the first intron of the OsMADS56 gene. Overexpression of RIFLA in rice repressed OsMADS56 expression but activated the expression of flowering inducers Hd3a and RFT1. Additionally, RIFLA-overexpressing transgenic rice plants flowered earlier than the wild type. Under normal conditions, the transcript level of the rice enhancer of zeste gene OsiEZ1, a homolog of Arabidopsis histone H3K27-specific methyltransferase genes SWINGER (SWN) and CURLY LEAF (CLF), was as low as that of RIFLA, whereas the transcript level of OsMADS56 was relatively high. In the osiez1 mutant, OsMADS56 expression was upregulated, whereas RIFLA expression was downregulated. Additionally, RIFLA formed a complex with OsiEZ1. Together, these results suggest that the floral repressor activity of OsMADS56 is epigenetically regulated by RIFLA and OsiEZ1.

Designing libraries for pooled CRISPR functional screens of long noncoding RNAs

Human and other genomes encode tens of thousands of long noncoding RNAs (lncRNAs), the vast majority of which remain uncharacterised. High-throughput functional screening methods, notably those based on pooled CRISPR-Cas perturbations, promise to unlock the biological significance and biomedical potential of lncRNAs. Such screens are based on libraries of single guide RNAs (sgRNAs) whose design is critical for success. Few off-the-shelf libraries are presently available, and lncRNAs tend to have cell-type-specific expression profiles, meaning that library design remains in the hands of researchers. Here we introduce the topic of pooled CRISPR screens for lncRNAs and guide readers through the three key steps of library design: accurate annotation of transcript structures, curation of optimal candidate sets, and design of sgRNAs. This review is a starting point and reference for researchers seeking to design custom CRISPR screening libraries for lncRNAs.

A systems genomics approach to uncover patient-specific pathogenic pathways and proteins in ulcerative colitis

We describe a precision medicine workflow, the integrated single nucleotide polymorphism network platform (iSNP), designed to determine the mechanisms by which SNPs affect cellular regulatory networks, and how SNP co-occurrences contribute to disease pathogenesis in ulcerative colitis (UC). Using SNP profiles of 378 UC patients we map the regulatory effects of the SNPs to a human signalling network containing protein-protein, miRNA-mRNA and transcription factor binding interactions. With unsupervised clustering algorithms we group these patient-specific networks into four distinct clusters driven by PRKCB, HLA, SNAI1/CEBPB/PTPN1 and VEGFA/XPO5/POLH hubs. The pathway analysis identifies calcium homeostasis, wound healing and cell motility as key processes in UC pathogenesis. Using transcriptomic data from an independent patient cohort, with three complementary validation approaches focusing on the SNP-affected genes, the patient specific modules and affected functions, we confirm the regulatory impact of non-coding SNPs. iSNP identified regulatory effects for disease-associated non-coding SNPs, and by predicting the patient-specific pathogenic processes, we propose a systems-level way to stratify patients.

SGII: Systematic Identification of Essential lncRNAs in Mouse and Human Genome With lncRNA-Protein-Protein Heterogeneous Interaction Network

Long noncoding RNAs (lncRNAs) play important roles in a variety of biological processes. Knocking out or knocking down some lncRNA genes can lead to death or infertility. These lncRNAs are called essential lncRNAs. Identifying the essential lncRNA is of importance for complex disease diagnosis and treatments. However, experimental methods for identifying essential lncRNAs are always costly and time consuming. Therefore, computational methods can be considered as an alternative approach. We propose a method to identify essential lncRNAs by combining network centrality measures and lncRNA sequence information. By constructing a lncRNA-protein-protein interaction network, we measure the essentiality of lncRNAs from their role in the network and their sequence together. We name our method as the systematic gene importance index (SGII). As far as we can tell, this is the first attempt to identify essential lncRNAs by combining sequence and network information together. The results of our method indicated that essential lncRNAs have similar roles in the LPPI network as the essential coding genes in the PPI network. Another encouraging observation is that the network information can significantly boost the predictive performance of sequence-based method. All source code and dataset of SGII have been deposited in a GitHub repository (https://github.com/ninglolo/SGII).

LncRNA functional annotation with improved false discovery rate achieved by disease associations

The long non‐coding RNAs (lncRNAs) play critical roles in various biological processes and are associated with many diseases. Functional annotation of lncRNAs in diseases attracts great attention in understanding their etiology. However, the traditional co-expression-based analysis usually produces a significant number of false positive function assignments. It is thus crucial to develop a new approach to obtain lower false discovery rate for functional annotation of lncRNAs. Here, a novel strategy termed DAnet which combining disease associations with cis-regulatory network between lncRNAs and neighboring protein-coding genes was developed, and the performance of DAnet was systematically compared with that of the traditional differential expression-based approach. Based on a gold standard analysis of the experimentally validated lncRNAs, the proposed strategy was found to perform better in identifying the experimentally validated lncRNAs compared with the other method. Moreover, the majority of biological pathways (40%∼100%) identified by DAnet were reported to be associated with the studied diseases. In sum, the DAnet is expected to be used to identify the function of specific lncRNAs in a particular disease or multiple diseases.

Common Features in lncRNA Annotation and Classification: A Survey

Long non-coding RNAs (lncRNAs) are widely recognized as important regulators of gene expression. Their molecular functions range from miRNA sponging to chromatin-associated mechanisms, leading to effects in disease progression and establishing them as diagnostic and therapeutic targets. Still, only a few representatives of this diverse class of RNAs are well studied, while the vast majority is poorly described beyond the existence of their transcripts. In this review we survey common in silico approaches for lncRNA annotation. We focus on the well-established sets of features used for classification and discuss their specific advantages and weaknesses. While the available tools perform very well for the task of distinguishing coding sequence from other RNAs, we find that current methods are not well suited to distinguish lncRNAs or parts thereof from other non-protein-coding input sequences. We conclude that the distinction of lncRNAs from intronic sequences and untranslated regions of coding mRNAs remains a pressing research gap.

The cell type-specific ER membrane protein UGS148 is not essential in mice

Genomes of higher eukaryotes encode many uncharacterized proteins, and the functions of these proteins cannot be predicted from the primary sequences due to a lack of conserved functional domains. In this study, we focused on a poorly characterized protein UGS148 that is highly expressed in a specialized cell type called tanycytes that line the ventral wall of the third ventricle in the hypothalamus. Immunostaining of UGS148 revealed the fine morphology of tanycytes with highly branched apical ER membranes. Immunoprecipitation revealed that UGS148 associated with mitochondrial ATPase at least in vitro, and ER and mitochondrial signals occasionally overlapped in tanycytes. Mutant mice lacking UGS148 did not exhibit overt phenotypes, suggesting that UGS148 was not essential in mice reared under normal laboratory conditions. We also found that RNA probes that were predicted to uniquely detect UGS148 mRNA cross-reacted with uncharacterized RNAs, highlighting the importance of experimental validation of the specificity of probes during the hybridization-based study of RNA localization.

Long non-coding RNAs and their potential impact on diagnosis, prognosis, and therapy in prostate cancer: racial, ethnic, and geographical considerations

INTRODUCTION

Advances in high-throughput sequencing have greatly advanced our understanding of long non-coding RNAs (lncRNAs) in a relatively short period of time. This has expanded our knowledge of cancer, particularly how lncRNAs drive many important cancer phenotypes via their regulation of gene expression.

AREAS COVERED

Men of African descent are disproportionately affected by PC in terms of incidence, morbidity, and mortality. LncRNAs could serve as biomarkers to differentiate low-risk from high-risk diseases. Additionally, they may represent therapeutic targets for advanced and castrate-resistant cancer. We review current research surrounding lncRNAs and their association with PC. We discuss how lncRNAs can provide new insights and diagnostic biomarkers for African American men. Finally, we review advances in computational approaches that predict the regulatory effects of lncRNAs in cancer.

EXPERT OPINION

PC diagnostic biomarkers that offer high specificity and sensitivity are urgently needed. PC specific lncRNAs are compelling as diagnostic biomarkers owing to their high tissue and tumor specificity and presence in bodily fluids. Recent studies indicate that PCA3 clinical utility might be restricted to men of European descent. Further work is required to develop lncRNA biomarkers tailored for men of African descent.

Obscurin: A multitasking giant in the fight against cancer

Giant obscurins (720-870 kDa), encoded by OBSCN, were originally discovered in striated muscles as cytoskeletal proteins with scaffolding and regulatory roles. Recently though, they have risen to the spotlight as key players in cancer development and progression. Herein, we provide a timely prudent synopsis of the expanse of OBSCN mutations across 16 cancer types. Given the extensive work on OBSCN's role in breast epithelium, we summarize functional studies implicating obscurins as potent tumor suppressors in breast cancer and delve into an in silico analysis of its mutational profile and epigenetic (de)regulation using different dataset platforms and sophisticated computational tools. Lastly, we formally describe the OBSCN-Antisense-RNA-1 gene, which belongs to the long non-coding RNA family and discuss its potential role in modulating OBSCN expression in breast cancer. Collectively, we highlight the escalating involvement of obscurins in cancer biology and outline novel potential mechanisms of OBSCN (de)regulation that warrant further investigation.

Decoding LncRNAs

Non-coding RNAs (ncRNAs) have been considered as unimportant additions to the transcriptome. Yet, in light of numerous studies, it has become clear that ncRNAs play important roles in development, health and disease. Long-ignored, long non-coding RNAs (lncRNAs), ncRNAs made of more than 200 nucleotides have gained attention due to their involvement as drivers or suppressors of a myriad of tumours. The detailed understanding of some of their functions, structures and interactomes has been the result of interdisciplinary efforts, as in many cases, new methods need to be created or adapted to characterise these molecules. Unlike most reviews on lncRNAs, we summarize the achievements on lncRNA studies by taking into consideration the approaches for identification of lncRNA functions, interactomes, and structural arrangements. We also provide information about the recent data on the involvement of lncRNAs in diseases and present applications of these molecules, especially in medicine.

Approaches to Identify and Characterise the Post-Transcriptional Roles of lncRNAs in Cancer

It is becoming increasingly evident that the non-coding genome and transcriptome exert great influence over their coding counterparts through complex molecular interactions. Among non-coding RNAs (ncRNA), long non-coding RNAs (lncRNAs) in particular present increased potential to participate in dysregulation of post-transcriptional processes through both RNA and protein interactions. Since such processes can play key roles in contributing to cancer progression, it is desirable to continue expanding the search for lncRNAs impacting cancer through post-transcriptional mechanisms. The sheer diversity of mechanisms requires diverse resources and methods that have been developed and refined over the past decade. We provide an overview of computational resources as well as proven low-to-high throughput techniques to enable identification and characterisation of lncRNAs in their complex interactive contexts. As more cancer research strategies evolve to explore the non-coding genome and transcriptome, we anticipate this will provide a valuable primer and perspective of how these technologies have matured and will continue to evolve to assist researchers in elucidating post-transcriptional roles of lncRNAs in cancer.

Bioinformatics Approaches for Functional Prediction of Long Noncoding RNAs

There is accumulating evidence that long noncoding RNAs (lncRNAs) play crucial roles in biological processes and diseases. In recent years, computational models have been widely used to predict potential lncRNA-disease relations. In this chapter, we systematically describe various computational algorithms and prediction tools that have been developed to elucidate the roles of lncRNAs in diseases, coding potential/functional characterization, or ascertaining their involvement in critical biological processes as well as provide a comprehensive summary of these applications.

Integrated analysis of lncRNAs and mRNAs reveals key trans-target genes associated with ETEC-F4ac adhesion phenotype in porcine small intestine epithelial cells

BACKGROUND

Long non-coding RNAs (lncRNAs) play crucial roles in gene regulation at the transcriptional and post-transcriptional levels. LncRNAs are belonging to a large class of transcripts with ≥200 nt in length which do not code for proteins, have been widely investigated in various physiological and pathological contexts by high-throughput sequencing techniques and bioinformatics analysis. However, little is known about the regulatory mechanisms by which lncRNAs regulate genes that are associated with Enterotoxigenic Escherichia coli F4 fimbriae (ETEC-F4ac) adhesion phenotype in small intestine epithelial cells of Large White piglets. To address this, we used RNA sequencing to profile lncRNAs and mRNAs of small intestine epithelial cells in Large White piglets differing in their ETEC-F4 adhesion phenotypes and ITGB5 genotypes. Eight male piglets were used in this study and were divided into two groups on the basis of their adhesion phenotype and ITGB5 genotypes, a candidate gene for F4ac receptor. Non-adhesive group (n = 4) with CC genotype and adhesive group (n = 4) with TT genotype.

RESULTS

In total, 78 differentially expressed lncRNAs (DE-lncRNA) and 223 differentially expressed mRNAs (log2 |FC| > 1, P < 0.05) were identified in the comparison of non-adhesive vs. adhesive small intestine epithelial cells. Furthermore, cis- and trans-regulatory target genes of DE-lncRNAs were identified, then interaction networks of lncRNAs and their cis- and trans-target differentially expressed genes (DEGs) were constructed separately. A total of 194 cis-targets were involved in the lncRNAs-cis genes interaction network and 61 trans-targets, were involved in lncRNA-trans gene interaction network that we constructed. We determined that cis-target genes were involved in alcoholism, systemic lupus erythematosus, viral carcinogenesis and malaria. Whereas trans-target DEGs were engaged in three important pathways related to the ETEC-F4 adhesion phenotype namely cGMP-PKG signaling pathway, focal adhesion, and adherens junction. The trans-target DEGs which directly involved in these pathways are KCNMB1 in cGMP-PKG signaling pathway, GRB2 in focal adhesion pathway and ACTN4 in focal adhesion and adherens junction pathways.

CONCLUSION

The findings of the current study provides an insight into biological functions and epigenetic regulatory mechanism of lncRNAs on porcine small intestine epithelial cells adhesion to ETEC-F4-ac and piglets' diarrhea susceptibility/resistance.

Towards a comprehensive pipeline to identify and functionally annotate long noncoding RNA (lncRNA)

Long noncoding RNAs (lncRNAs) are implicated in various genetic diseases and cancer, attributed to their critical role in gene regulation. They are a divergent group of RNAs and are easily differentiated from other types with unique characteristics, functions, and mechanisms of action. In this review, we provide a list of some of the prominent data repositories containing lncRNAs, their interactome, and predicted and validated disease associations. Next, we discuss various wet-lab experiments formulated to obtain the data for these repositories. We also provide a critical review of in silico methods available for the identification purpose and suggest techniques to further improve their performance. The bulk of the methods currently focus on distinguishing lncRNA transcripts from the coding ones. Functional annotation of these transcripts still remains a grey area and more efforts are needed in that space. Finally, we provide details of current progress, discuss impediments, and illustrate a roadmap for developing a generalized computational pipeline for comprehensive annotation of lncRNAs, which is essential to accelerate research in this area.

BCALM (AC099524.1) Is a Human B Lymphocyte-Specific Long Noncoding RNA That Modulates B Cell Receptor-Mediated Calcium Signaling

Of the thousands of long noncoding RNAs (lncRNA) identified in lymphocytes, very few have defined functions. In this study, we report the discovery and functional elucidation of a human B cell-specific lncRNA with high levels of expression in three types of B cell cancer and normal B cells. The AC099524.1 gene is upstream of the gene encoding the B cell-specific phospholipase C γ 2 (PLCG2), a B cell-specific enzyme that stimulates intracellular Ca2+ signaling in response to BCR activation. AC099524.1 (B cell-associated lncRNA modulator of BCR-mediated Ca+ signaling [BCALM]) transcripts are localized in the cytoplasm and, as expected, CRISPR/Cas9 knockout of AC099524.1 did not affect PLCG2 mRNA or protein expression. lncRNA interactome, RNA immunoprecipitation, and coimmunoprecipitation studies identified BCALM-interacting proteins in B cells, including phospholipase D 1 (PLD1), and kinase adaptor proteins AKAP9 (AKAP450) and AKAP13 (AKAP-Lbc). These two AKAP proteins form signaling complexes containing protein kinases A and C, which phosphorylate and activate PLD1 to produce phosphatidic acid (PA). BCR stimulation of BCALM-deficient B cells resulted in decreased PLD1 phosphorylation and increased intracellular Ca+ flux relative to wild-type cells. These results suggest that BCALM promotes negative feedback that downmodulates BCR-mediated Ca+ signaling by promoting phosphorylation of PLD1 by AKAP-associated kinases, enhancing production of PA. PA activates SHP-1, which negatively regulates BCR signaling. We propose the name BCALM for B-Cell Associated LncRNA Modulator of BCR-mediated Ca+ signaling. Our findings suggest a new, to our knowledge, paradigm for lncRNA-mediated modulation of lymphocyte activation and signaling, with implications for B cell immune response and BCR-dependent cancers.

Screening of long non-coding RNAs markers in plasma of children with chronic gastritis

Objective

The study aimed to detect and analyze long non-coding RNAs (lncRNAs) in plasma of children diagnosed with chronic gastritis, and to explore its biological functions and involved signaling pathways.

Methods

The plasma samples were collected from six children that were diagnosed with chronic gastritis by physical examination, gastroscopy, and pathological examination and six healthy children. The plasma samples were assayed for determining the expression profiles of lncRNA based upon the gen chip detection. The specific expression of lcnRNA in plasma of children with chronic gastritis was analyzed and its biological functions were speculated.

Results

Five lncRNAs (RP11-697M17.1, RP11-388M20.9, AFAP1-AS1, BC062758, and XLOC001406) were significantly up-regulated, and five lncRNAs (UNQ697, BX571672.5, CYP4F35P, ANKRD20A5P, and AL832737) were observed to be significantly down-regulated. The lncRNAs RP11-697M17.1, and UNQ697 were detected with the highest up-regulation and down-regulation, respectively. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showed that the up-regulated lncRNAs were significantly enriched in 20 signaling pathways such as phosphoinositide-3-kinase–protein kinase B (PI3K-Akt) pathway, and the down-regulated lncRNAs target genes were significantly enriched in 20 signaling pathways such as the metabolic pathway.

Conclusion

The analysis of the lncRNA expression profiles in plasma of children with chronic gastritis revealed that the lncRNA RP11-697M17.1, and lncRNA UNQ697 may act as plasma markers for predicting chronic gastritis in children.

Probing lncRNA-Protein Interactions: Data Repositories, Models, and Algorithms

Identifying lncRNA-protein interactions (LPIs) is vital to understanding various key biological processes. Wet experiments found a few LPIs, but experimental methods are costly and time-consuming. Therefore, computational methods are increasingly exploited to capture LPI candidates. We introduced relevant data repositories, focused on two types of LPI prediction models: network-based methods and machine learning-based methods. Machine learning-based methods contain matrix factorization-based techniques and ensemble learning-based techniques. To detect the performance of computational methods, we compared parts of LPI prediction models on Leave-One-Out cross-validation (LOOCV) and fivefold cross-validation. The results show that SFPEL-LPI obtained the best performance of AUC. Although computational models have efficiently unraveled some LPI candidates, there are many limitations involved. We discussed future directions to further boost LPI predictive performance.