The lncLocator: a subcellular localization predictor for long non-coding RNAs based on a stacked ensemble classifier

EL-RMLocNet: An explainable LSTM network for RNA-associated multi-compartment localization prediction

Subcellular localization of Ribonucleic Acid (RNA) molecules provide significant insights into the functionality of RNAs and helps to explore their association with various diseases. Predominantly developed single-compartment localization predictors (SCLPs) lack to demystify RNA association with diverse biochemical and pathological processes mainly happen through RNA co-localization in multiple compartments. Limited multi-compartment localization predictors (MCLPs) manage to produce decent performance only for target RNA class of particular sub-type. Further, existing computational approaches have limited practical significance and potential to optimize therapeutics due to the poor degree of model explainability. The paper in hand presents an explainable Long Short-Term Memory (LSTM) network "EL-RMLocNet", predictive performance and interpretability of which are optimized using a novel GeneticSeq2Vec statistical representation learning scheme and attention mechanism for accurate multi-compartment localization prediction of different RNAs solely using raw RNA sequences. GeneticSeq2Vec generates optimized statistical vectors of raw RNA sequences by capturing short and long range relations of nucleotide k-mers. Using sequence vectors generated by GeneticSeq2Vec scheme, Long Short Term Memory layers extract most informative features, weighting of which on the basis of discriminative potential for accurate multi-compartment localization prediction is performed using attention layer. Through reverse engineering, weights of statistical feature space are mapped to nucleotide k-mers patterns to make multi-compartment localization prediction decision making transparent and explainable for different RNA classes and species. Empirical evaluation indicates that EL-RMLocNet outperforms state-of-the-art predictor for subcellular localization prediction of 4 different RNA classes by an average accuracy figure of 8% for Homo Sapiens species and 6% for Mus Musculus species. EL-RMLocNet is freely available as a web server at (https://sds_genetic_analysis.opendfki.de/subcellular_loc/).

Integrative Analysis and Experimental Validation Indicated That SNHG17 Is a Prognostic Marker in Prostate Cancer and a Modulator of the Tumor Microenvironment via a Competitive Endogenous RNA Regulatory Network

The incidence of prostate cancer (PC) is growing rapidly worldwide, and studies uncovering the molecular mechanisms driving the progression and modulating the immune infiltration and antitumor immunity of PC are urgently needed. The long noncoding RNA SNHG family has been recognized as a prognostic marker in cancers and contributes to the progression of multiple cancers, including PC. In this study, we aimed to clarify the prognostic values and underlying mechanisms of SNHGs in promoting the progression and modulating the tumor microenvironment of PC through data mining based on The Cancer Genome Atlas (TCGA) database. We identified that within the SNHG family, SNHG17 was most correlated with the overall survival of PC patients and could act as an independent predictor. Moreover, we constructed a competitive endogenous RNA (ceRNA) network by which SNHG17 promotes progression and potentially inhibits the immune infiltration and immune response of prostate cancer. By interacting with miR-23a-3p/23b-3p/23c, SNHG17 upregulates the expression of UBE2M and OTUB1, which have been demonstrated to play critical roles in the tumorigenesis of human cancers, more importantly promoting cancer cell immunosuppression and resistance to cytotoxic stimulation. Finally, we examined the correlation between SNHG17 expression and the clinical progression of PC patients based on our cohort of 52 PC patients. We also verified the SNHG17/miR-23a/OTUB1 axis in RV-1 and PC-3 cells by dual luciferase and RIP assays, and we further identified that SNHG17 promoted cellular invasive capacity by modulating OTUB1. In summary, the current study conducted a ceRNA-based SNHG17-UBE2M/OTUB1 axis and indicated that SNHG17 might be a novel prognostic factor associated with the progression, immunosuppression, and cytotoxic resistance of PC.

Construction of a Novel MYC-Associated ceRNA Regulatory Network to Identify Prognostic Biomarkers in Colon Adenocarcinoma

Colorectal cancer (CRC) includes colon adenocarcinoma (COAD) and rectal adenocarcinoma (READ). Competitive endogenous RNA (ceRNA) is crucial for cancer pathogenesis. Abnormal expression of MYC is generally associated with a poor colon adenocarcinoma prognosis. The present study aimed to identify a novel MYC-associated ceRNA regulatory network and identify potential prognostic markers associated with COAD. We obtained the transcriptome sequencing profiles of 462 COAD cases from the TCGA database and analyzed differentially expressed genes (DEGs) in MYC high expression (MYChigh) and MYC low expression (Myclow) tumors. We identified an important lncRNA, LINC00114, which effectively predicts overall survival and plays a protective role in COAD. Moreover, the LINC00114/miR-216a-5p axis was identified as a clinical prognostic model. The predicted target genes of the LINC00114/miR-216a-5p axis include uromodulin Like 1 (UMODL1) and oncoprotein induced transcript 3 (OIT3), which are closely related to the survival and prognosis of COAD patients. In summary, we constructed a novel ceRNA regulatory network and identified potential biomarkers for the targeted therapy and prognosis of COAD.

LncRNA and mRNA expression profiles in brown adipose tissue of obesity-prone and obesity-resistant mice

Obesity-prone or obesity-resistant phenotypes can exist in individuals who consume the same diet type. Brown adipose tissue functions to dissipate energy in response to cold exposure or overfeeding. Long noncoding RNAs play important roles in a wide range of biological processes. However, systematic examination of lncRNAs in phenotypically divergent mice has not yet been reported. Here, the lncRNA expression profiles in BAT of HFD-induced C57BL/6J mice were investigated by high-throughput RNA sequencing. Genes that play roles in thermogenesis and related pathways were identified. We found lncRNA (Gm44502) may play a thermogenic role in obesity resistance by interacting with six mRNAs. Our results also indicated that seven differentially expressed lncRNAs (4930528G23Rik, Gm39490, Gm5627, Gm15551, Gm16083, Gm36860, Gm42002) may play roles in reducing heat production in obesity susceptibility by interacting with seven differentially expressed mRNAs. The screened lncRNAs may participate in the pathogenesis of weight regulation and provide insight into obesity therapy.

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First lncRNA profiles in BAT of OR and OP mice via bioinformatic analysis

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Gm44502 may play a thermogenic role by interacting with 6 mRNAs

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7 DElncRNAs may reduce thermogenesis by interacting with 7 DEmRNAs

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Validation of expression changes of candidate genes in BAT by in vivo or in vitro

TACOS: a novel approach for accurate prediction of cell-specific long noncoding RNAs subcellular localization

Long noncoding RNAs (lncRNAs) are primarily regulated by their cellular localization, which is responsible for their molecular functions, including cell cycle regulation and genome rearrangements. Accurately identifying the subcellular location of lncRNAs from sequence information is crucial for a better understanding of their biological functions and mechanisms. In contrast to traditional experimental methods, bioinformatics or computational methods can be applied for the annotation of lncRNA subcellular locations in humans more effectively. In the past, several machine learning-based methods have been developed to identify lncRNA subcellular localization, but relevant work for identifying cell-specific localization of human lncRNA remains limited. In this study, we present the first application of the tree-based stacking approach, TACOS, which allows users to identify the subcellular localization of human lncRNA in 10 different cell types. Specifically, we conducted comprehensive evaluations of six tree-based classifiers with 10 different feature descriptors, using a newly constructed balanced training dataset for each cell type. Subsequently, the strengths of the AdaBoost baseline models were integrated via a stacking approach, with an appropriate tree-based classifier for the final prediction. TACOS displayed consistent performance in both the cross-validation and independent assessments compared with the other two approaches employed in this study. The user-friendly online TACOS web server can be accessed at https://balalab-skku.org/TACOS.

Identification and Validation of lncRNA-SNHG17 in Lung Adenocarcinoma: A Novel Prognostic and Diagnostic Indicator

Background

Lung cancer has the highest death rate among cancers globally. Accumulating evidence has indicated that cancer-related inflammation plays an important role in the initiation and progression of lung cancer. However, the prognosis, immunological role, and associated regulation axis of inflammatory response-related gene (IRRGs) in non-small-cell lung cancer (NSCLC) remains unclear.

Methods

In this study, we perform comprehensive bioinformatics analysis and constructed a prognostic inflammatory response-related gene (IRRGs) and related competing endogenous RNA (ceRNA) network. We also utilized the Pearson’s correlation analysis to determine the correlation between IRRGs expression and tumor mutational burden (TMB), microsatellite instability (MSI), tumor-immune infiltration, and the drug sensitivity in NSCLC. Growth curve and Transwell assay used to verify the function of SNHG17 on NSCLC progression.

Results

First, we found that IRRGs were significantly upregulated in lung cancer, and its high expression was correlated with poor prognosis; high expression of IRRGs was significantly correlated with the tumor stage and poor prognosis in lung cancer patients. Moreover, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment indicated that these IRRGs are mainly involved in the inflammatory and immune response-related signaling pathway in the progression of NSCLC. We utilized 10 prognostic-related genes to construct a prognostic IRRGs model that could predict the overall survival of lung adenocarcinoma (LUAD) patients possessing high specificity and accuracy. Our evidence demonstrated that IRRGs expression was significantly correlated with the TMB, MSI, immune-cell infiltration, and diverse cancer-related drug sensitivity. Finally, we identified the upstream regulatory axis of IRRGs in NSCLC, namely, lncRNA MIR503HG/SNHG17/miR-330-3p/regulatory axis. Finally, knockdown of SNHG17 expression inhibited lung adenocarcinoma (LUAD) cell proliferation and migration. Our findings confirmed that SNHG17 is a novel oncogenic lncRNA and may be a biomarker for the prognosis and diagnosis of LUAD.

Conclusion

DNA hypomethylation/lncRNA MIR503HG/SNHG17/microRNA-330-3p/regulatory axis may be a valuable biomarker for prognosis and is significantly correlated with immune cell infiltration in lung cancer.

Comprehensive Analysis of Potential ceRNA Network and Different Degrees of Immune Cell Infiltration in Acute Respiratory Distress Syndrome

Acute respiratory distress syndrome (ARDS) is a leading cause of death in critically ill patients due to hypoxemic respiratory failure. The specific pathogenesis underlying ARDS has not been fully elucidated. In this study, we constructed a triple regulatory network involving competing endogenous RNA (ceRNA) to investigate the potential mechanism of ARDS and evaluated the immune cell infiltration patterns in ARDS patients. Overall, we downloaded three microarray datasets that included 60 patients with sepsis-induced ARDS and 79 patients with sepsis alone from the public Gene Expression Omnibus (GEO) database and identified differentially expressed genes (DEGs, including 9 DElncRNAs, 9 DEmiRNAs, and 269 DEmRNAs) by R software. The DEGs were subjected to the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) for functional enrichment analysis, and a protein–protein interaction (PPI) network was generated for uncovering interactive relationships among DEmRNAs. Then, a ceRNA network that contained 5 DElncRNAs, 7 DEmiRNAs, and 71 DEmRNAs was established according to the overlapping genes in both DEGs and predicted genes by public databases. Finally, we identified the TUG1/miR-140-5p/NFE2L2 pathway as the hub pathway in the whole network through Cytoscape. In addition, we evaluated the distribution of 22 subtypes of immune cells and recognized three differentially expressed immune cells in patients with sepsis-induced ARDS by “Cell Type Identification by Estimating Relative Subsets of Known RNA Transcripts (CIBERSORT)” algorithm, namely, naive B cells, regulatory T cells, and eosinophils. Correlations between differentially expressed immune cells and hub genes in the ceRNA network were also performed. In conclusion, we demonstrated a new potential regulatory mechanism underlying ARDS (the TUG1/miR-140-5p/NFE2L2 ceRNA regulatory pathway), which may help in further exploring the pathogenesis of ARDS.

Identification of novel biomarkers in prostate cancer diagnosis and prognosis

Prostate cancer (PCa) is a common urinary malignancy. The lack of specific and sensitive biomarkers for the early diagnosis and prognosis of PCa makes it important to seek alternatives. R software was used to analyze the PCa expression profile from data sets in Gene Expression Omnibus. Core differential genes were identified by String and Cytoscape and further validated by Gene Expression Profiling Interactive Analysis (GEPIA) and The Human Protein Atlas (HPA). Gene Ontology analysis was done in the DIVID database and visualization analysis was conducted by Hiplot. Pathway enrichment was analyzed by IPA. To identify potential competitive endogenous RNAs (ceRNA) networks, the experimentally validated microRNA-target interactions database (miRTarBase), The Encyclopedia of RNA Interactomes (StarBase), lncBase, and GEPIA were used. The lncLocator was utilized to perform subcellular localization of long noncoding RNAs (lncRNAs). Both miRTarBase and StarBase were used to find the binding site of mRNAs-miRNAs and miRNAs-lncRNAs. Visualization of the ceRNA network was performed with Cytoscape. Nine genes closely related to the diagnosis and prognosis of PCa were obtained, including four identified biomarkers by HPA, CENPF, TPX2, TK1, and CCNB1, and five novel PCa biomarkers, RRM2, UBE2C, TOP2A, BIRC5, and ZWINT. Pathway analysis indicated that PCa carcinogenesis was highly correlated with liver fibrosis pathways, ILK signaling, and NRF2-mediated oxidative stress response. Two sets of ceRNA networks, BIRC5/hsa-miR-218-5p/NEAT1 and UBE2C/hsa-miR-483-3p/NEAT1 were found to be novel biomarkers for the identification of PCa. The quantitative real-time polymerase chain reaction results verified that UBE2C, BIRC5, and NEAT1 were upregulated and hsa-miR-218-5p and hsa-miR-483-3p were downregulated in human PCa cells compared with normal prostate epithelial cells. The novel identified biomarkers in this study would be valuable for the diagnosis and prognosis of PCa.

The Biological and Molecular Function of LINC00665 in Human Cancers

Long non-coding RNAs (lncRNAs) are more than 200 nucleotides in length and are implicated in the development of human cancers, without protein-coding function. Mounting evidence indicates that cancer initiation and progression are triggered by lncRNA dysregulation. Recently, a growing number of studies have found that LINC00665, a long intergenic non-protein coding RNA, may be associated with various cancers, including gastrointestinal tumors, gynecological tumors, and respiratory neoplasms. LINC00665 was reported to be significantly dysregulated in cancers and has an important clinical association. It participates in cell proliferation, migration, invasion, and apoptosis through different biological pathways. In this review, we summarize the current findings on LINC00665, including its biological roles and molecular mechanisms in various cancers. LINC00665 may be a potential prognostic biomarker and novel therapeutic target for cancers.

Increased LDL receptor by SREBP2 or SREBP2-induced lncRNA LDLR-AS promotes triglyceride accumulation in fish

LDLR, as the uptake receptor of low-density lipoprotein, plays a crucial role in lipid metabolism. However, the detailed mechanism by which LDLR affects hepatic triglyceride (TG) accumulation has rarely been reported. Here, we found that knockdown of LDLR effectively mitigated PA-induced TG accumulation. Further analysis revealed that the expression of LDLR was controlled by SREBP2 directly and indirectly. On one hand, transcription factor SREBP2 activated the transcription of LDLR directly. On the other hand, SREBP2 indirectly regulated LDLR by increasing the transcription of lncRNA LDLR-AS in fish. Mechanism analysis found that LDLR-AS functioned as an RNA scaffold to recruit heterogeneous nuclear ribonucleoprotein R (hnRNPR) to the 5′ UTR region of LDLR mRNA, which stabilized LDLR mRNA at the post-transcription level. In conclusion, our study demonstrates that increased LDLR transcription and mRNA stability is regulated by SREBP2 directly or indirectly, and promotes hepatic TG accumulation by endocytosing LDL in fish.

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PA-mediated LDLR increases triglyceride accumulation via the uptake of LDL in fish

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SREBP2 activated by TNFα promotes LDLR transcription in fish

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LncRNA LDLR-AS increases LDLR mRNA stability by recruiting hnRNPR in fish

Illuminating RNA biology through imaging

RNA processing plays a central role in accurately transmitting genetic information into functional RNA and protein regulators. To fully appreciate the RNA life-cycle, tools to observe RNA with high spatial and temporal resolution are critical. Here we review recent advances in RNA imaging and highlight how they will propel the field of RNA biology. We discuss current trends in RNA imaging and their potential to elucidate unanswered questions in RNA biology.

IDDLncLoc: Subcellular Localization of LncRNAs Based on a Framework for Imbalanced Data Distributions

Long non-coding RNAs play a crucial role in many life processes of cell, such as genetic markers, RNA splicing, signaling, and protein regulation. Considering that identifying lncRNA's localization in the cell through experimental methods is complicated, hard to reproduce, and expensive, we propose a novel method named IDDLncLoc in this paper, which adopts an ensemble model to solve the problem of the subcellular localization. In the proposal model, dinucleotide-based auto-cross covariance features, k-mer nucleotide composition features, and composition, transition, and distribution features are introduced to encode a raw RNA sequence to vector. To screen out reliable features, feature selection through binomial distribution, and recursive feature elimination is employed. Furthermore, strategies of oversampling in mini-batch, random sampling, and stacking ensemble strategies are customized to overcome the problem of data imbalance on the benchmark dataset. Finally, compared with the latest methods, IDDLncLoc achieves an accuracy of 94.96% on the benchmark dataset, which is 2.59% higher than the best method, and the results further demonstrate IDDLncLoc is excellent on the subcellular localization of lncRNA. Besides, a user-friendly web server is available at http://lncloc.club .

Comprehensive Analyses of MELK-Associated ceRNA Networks Reveal a Potential Biomarker for Predicting Poor Prognosis and Immunotherapy Efficacy in Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world with high morbidity and mortality. Identifying specific molecular markers that can predict HCC prognosis is extremely important. MELK has been reported to play key roles in several types of human cancers and predict poor prognosis. This study was aimed to explore the impact of MELK on HCC.

Methods: A pan-cancer analysis of MELK was conducted by The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) data. The prognosis of MELK in various cancers was analyzed in GEPIA. Then, a ceRNA network of MELK was constructed based on the comprehensive consideration of the expression analysis, the correlation analysis, and the survival analysis by R software. The correlation of MELK and immune cell infiltration was analyzed by TIMER and CIBERSORT. Then, the overall survival of differentially expressed immune cells was conducted. The correlation of MELK and immune checkpoints expression was analyzed by GEPIA.

Results: MELK was overexpressed in 14 types of human cancers, and its expression was significantly higher than that in both unmatched and paired normal samples in HCC. Higher MELK expression was correlated with poorer survival and advanced clinical stage, topography (T) stage, and histological grade. The univariate and multivariate Cox regression analyses showed that MELK was an independent risk factor for poor prognosis in HCC. Then, we constructed a ceRNA network consisting of MELK, miR-101-3p, and two lncRNAs (SNHG1 and SNHG6) after evaluating the expression and impact on prognosis in HCC of these RNAs. TIMER and CIBERSORT databases indicated that MELK was correlated with various immune cells including B cells, CD8+ T cells, CD4+ T cells, macrophage, neutrophil, and dendritic cells in HCC. Of them, B cells, CD4+ T cells, macrophage, and neutrophil were related to the prognosis of HCC. In addition, MELK was significantly positively correlated with the immune checkpoint genes.

Conclusions: MELK may be a novel potential biomarker for predicting prognosis and immunotherapy efficacy in patients with HCC. Our study may provide new molecular and therapeutic strategies for the treatment of HCC patients.

Effect of berberine on global modulation of lncRNAs and mRNAs expression profiles in patients with stable coronary heart disease

BACKGROUND

Berberine (BBR) is an isoquinoline alkaloid found in the Berberis species. It was found to have protected effects in cardiovascular diseases. Here, we investigated the effect the regulatory function of long noncoding RNAs (lncRNAs) during the treatment of stable coronary heart disease (CHD) using BBR. We performed microarray analyses to identify differentially expressed (DE) lncRNAs and mRNAs between whole blood samples from 5 patients with stable CHD taking BBR and 5 no BBR volunteers. DE lncRNAs and mRNAs were validated by quantitative real-time PCR.

RESULTS

A total of 1703 DE lncRNAs and 912 DE mRNAs were identified. Kyoto Encyclopedia of Genes and Genomes pathway analysis indicated DE mRNAs might be associated with mammalian target of rapamycin and mitogen-activated protein kinase pathway. These pathways may be involved in the healing process after CHD. To study the relationship between mRNAs encoding transcription factors (DNA damage inducible transcript 3, sal-like protein 4 and estrogen receptor alpha gene) and CHD related de mRNAs, we performed protein and protein interaction analysis on their corresponding proteins. AKT and apoptosis pathway were significant enriched in protein and protein interaction network. BBR may affect downstream apoptosis pathways through DNA damage inducible transcript 3, sal-like protein 4 and estrogen receptor alpha gene. Growth arrest-specific transcript 5 might regulate CHD-related mRNAs through competing endogenous RNA mechanism and may be the downstream target gene regulated by BBR. Verified by the quantitative real-time PCR, we identified 8 DE lncRNAs that may relate to CHD. We performed coding and non-coding co-expression and competing endogenous RNA mechanism analysis of these 8 DE lncRNAs and CHD-related DE mRNA, and predicted their subcellular localization and N6-methyladenosine modification sites.

CONCLUSION

Our research found that BBR may affect mammalian target of rapamycin, mitogen-activated protein kinase, apoptosis pathway and growth arrest-specific transcript 5 in the process of CHD. These pathways may be involved in the healing process after CHD. Our research might provide novel insights for functional research of BBR.

Comprehensive analysis of GSEC/miR-101-3p/SNX16/PAPOLG axis in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most lethal malignancies. A growing number of studies have shown that competitive endogenous RNA (ceRNA) regulatory networks might play important roles during HCC process. The present study aimed to identify a regulatory axis of the ceRNA network associated with the development of HCC. The roles of SNX16 and PAPOLG in HCC were comprehensively analyzed using bioinformatics tools. Subsequently, the “mRNA-miRNA-lncRNA” model was then used to predict the upstream miRNAs and lncRNAs of SNX16 and PAPOLG using the miRNet database, and the miRNAs with low expression and good prognosis in HCC and the lncRNAs with high expression and poor prognosis in HCC were screened by differential expression and survival analysis. Finally, the risk-prognosis models of ceRNA network axes were constructed by univariate and multifactorial Cox proportional risk analysis, and the immune correlations of ceRNA network axes were analyzed using the TIMER and GEPIA database. In this study, the relevant ceRNA network axis GSEC/miR-101-3p/SNX16/PAPOLG with HCC prognosis was constructed, in which GSEC, SNX16, and PAPOLG were highly expressed in HCC with poor prognosis, while miR-101-3p was lowly expressed in HCC with good prognosis. The risk-prognosis model predicted AUC of 0.691, 0.623, and 0.626 for patient survival at 1, 3, and 5 years, respectively. Immuno-infiltration analysis suggested that the GSEC/miR-101-3p/SNX16/PAPOLG axis might affect macrophage polarization. The GSEC/miR-101-3p/SNX16/PAPOLG axis of the ceRNA network axis might be an important factor associated with HCC prognosis and immune infiltration.

Mechanism of miR-340-5p in laryngeal cancer cell proliferation and invasion through the lncRNA NEAT1/MMP11 axis

OBJECTIVES

Laryngeal cancer (LC), with a relatively rare diagnosis, is a primary malignancy originating from laryngeal mucosa. This study investigated the mechanisms of microRNA (miR)- 340-5p in LC cell proliferation and invasion.

METHODS

The expression patterns of miR-340-5p, long non-coding RNA (lncRNA) nuclear paraspeckle assembly transcript 1 (NEAT1), and matrix metallopeptidase 11 (MMP11) in LC cells, tissues, and para-carcinoma tissues, and human bronchial epithelial cells (HBEC) were examined via RT-qPCR. The effects of elevating or silencing miR-340-5p on LC cell proliferation and invasion were examined. The subcellular localization of lncRNA NEAT1 was determined. The binding relations among miR-340-5p, lncRNA NEAT1, and MMP11 were verified. Functional rescue experiments were designed to verify the functions of lncRNA NEAT1 and MMP11 on LC cell proliferation and invasion. Nude-mouse tumor models were established to assess the role of miR-340-5p in LC in vivo.

RESULTS

miR-340-5p was under-expressed in LC, and miR-340-5p overexpression repressed LC cell proliferation and invasion. Mechanically, miR-340-5p decreased lncRNA NEAT1 stability via directly binding to lncRNA NEAT1 and thus declined lncRNA NEAT1 expression in LC cells, while lncRNA NEAT1 accelerated MMP11 transcription via binding to heat shock factor 1 (HSF1). Overexpression of lncRNA NEAT1 or MMP11 reversed the repression of miR-340-5p overexpression on LC cell proliferation and invasion. In vivo, miR-340-5p overexpression repressed the tumor growth.

CONCLUSION

miR-340-5p overexpression reduced lncRNA NEAT1 stability via binding to lncRNA NEAT1, which declined lncRNA NEAT1 expression and reduced the binding of lncRNA NEAT1 to HSF1 to further inhibit MMP11 transcription, thus repressing LC cell proliferation and invasion.

Epithelial-mesenchymal transition-related genes in coronary artery disease

Epithelial-mesenchymal transition (EMT) is critical in the development of coronary artery disease (CAD). However, landscapes of EMT-related genes have not been fully established in CAD. We identified the differentially expressed mRNAs and lncRNAs (DElncRNAs) from the Gene Expression Omnibus database. Pearson’s correlation analysis, the least absolute shrinkage and selection operator regression, and support vector machine reverse feature elimination algorithms were used to screen EMT-related lncRNAs. The cis–trans regulatory networks were constructed based on EMT-related lncRNAs. Quantitative real-time polymerase chain reaction was performed to validate the expression of EMT-related genes in a cohort of six patients with CAD and six healthy controls. We further estimated the infiltration of the immune cells in CAD patients with five algorithms, and the correlation between EMT-related genes and infiltrating immune cells was analyzed. We identified eight EMT-related lncRNAs in CAD. The area under curve value was greater than 0.95. The immune analysis revealed significant CD8 T cells, monocytes, and NK cells in CAD and found that EMT-related lncRNAs were correlated with these immune cell subsets. Moreover, SNAI2, an EMT-TF gene, was found in the trans-regulatory network of EMT-related lncRNAs. Further, we found SNAI2 as a biomarker for the diagnosis of CAD but it also had a close correlation with immune cell subsets in CAD. Eight EMT-related lncRNAs and SNAI2 have important significance in the diagnosis of CAD patients.

Comprehensive Analysis Identifies Ameloblastin-Related Competitive Endogenous RNA as a Prognostic Biomarker for Testicular Germ Cell Tumour

Testicular Germ Cell Tumour (TGCT) is one of the most common tumours in young men. Increasing evidence shows that the extracellular matrix has a key role in the prognosis and metastasis of various human cancers. This study analysed the relationship between the matrix protein ameloblastin (AMBN) and potential biological markers associated with TGCT diagnosis and prognosis. The relationship between AMBN and TGCT prognosis was determined by bioinformatic analysis using the expression profiles of three RNAs (long non-coding RNAs (lncRNAs), microRNAs (miRNAs) and mRNAs) from The Cancer Genome Atlas (TCGA) database, and available clinical information of the corresponding patients. Prediction and validation of competitive endogenous RNA (ceRNA) regulatory networks related to AMBN was performed. AMBN and its associated ceRNA regulatory network were found to be related to the recurrence of TGCT, and LINC02701 may be used as a diagnostic factor in TGCT. Furthermore, we identified PELATON (Plaque Enriched LncRNA In Atherosclerotic And Inflammatory Bowel Macrophage Regulation) as an independent prognostic factor for TGCT progression-free interval.

Insight Into the Long Noncoding RNA and mRNA Coexpression Profile in the Human Blood Transcriptome Upon Leishmania infantum Infection

Visceral leishmaniasis (VL) is a vector-borne infectious disease that can be potentially fatal if left untreated. In Brazil, it is caused by Leishmania infantum parasites. Blood transcriptomics allows us to assess the molecular mechanisms involved in the immunopathological processes of several clinical conditions, namely, parasitic diseases. Here, we performed mRNA sequencing of peripheral blood from patients with visceral leishmaniasis during the active phase of the disease and six months after successful treatment, when the patients were considered clinically cured. To strengthen the study, the RNA-seq data analysis included two other non-diseased groups composed of healthy uninfected volunteers and asymptomatic individuals. We identified thousands of differentially expressed genes between VL patients and non-diseased groups. Overall, pathway analysis corroborated the importance of signaling involving interferons, chemokines, Toll-like receptors and the neutrophil response. Cellular deconvolution of gene expression profiles was able to discriminate cellular subtypes, highlighting the contribution of plasma cells and NK cells in the course of the disease. Beyond the biological processes involved in the immunopathology of VL revealed by the expression of protein coding genes (PCGs), we observed a significant participation of long noncoding RNAs (lncRNAs) in our blood transcriptome dataset. Genome-wide analysis of lncRNAs expression in VL has never been performed. lncRNAs have been considered key regulators of disease progression, mainly in cancers; however, their pattern regulation may also help to understand the complexity and heterogeneity of host immune responses elicited by L. infantum infections in humans. Among our findings, we identified lncRNAs such as IL21-AS1, MIR4435-2HG and LINC01501 and coexpressed lncRNA/mRNA pairs such as CA3-AS1/CA1, GASAL1/IFNG and LINC01127/IL1R1-IL1R2. Thus, for the first time, we present an integrated analysis of PCGs and lncRNAs by exploring the lncRNA–mRNA coexpression profile of VL to provide insights into the regulatory gene network involved in the development of this inflammatory and infectious disease.

NcRNA-Mediated High Expression of HMMR as a Prognostic Biomarker Correlated With Cell Proliferation and Cell Migration in Lung Adenocarcinoma

Background

Hyaluronan-mediated motility receptor (HMMR) plays a pivotal role in cell proliferation in various cancers, including lung cancer. However, its function and biological mechanism in lung adenocarcinoma (LUAD) remain unclear.

Methods

Data on HMMR expression from several public databases were extensively analyzed, including the prognosis of HMMR in the Gene Expression Profiling Interactive Analysis (GEPIA) database. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were analyzed using DAVID and gene set enrichment analysis (GSEA) software. The correlation between HMMR expression and immune cell infiltration was analyzed in the Tumor Immune Estimation Resource (TIMER) database, and the gene and protein networks were examined using the GeneMANIA and STRING databases. Experimentally, the expression of HMMR in LUAD and lung cancer cell lines was determined using immunohistochemistry and quantitative RT-PCR assays. Besides, the function of HMMR on cancer cell proliferation and migration was examined using cell growth curve and colony formation, Transwell, and wound healing assays.

Results

In this study, we found that HMMR was elevated in LUAD and that its high expression was associated with poor clinicopathological features and adverse outcomes in LUAD patients. Furthermore, our results demonstrated that the expression of HMMR was positively correlated with immune cell infiltration and immune modulation. Interestingly, diverse immune cell infiltration affects the prognosis of LUAD. In the functional assay, depletion of HMMR significantly repressed the cancer cell growth and migration of LUAD. Mechanically, we found that that the DNA methylation/TMPO-AS1/let-7b-5p axis mediated the high expression of HMMR in LUAD. Depletion of TMPO-AS1 and overexpression of let-7b-5p could result in the decreased expression of HMMR in LUAD cells. Furthermore, we found that TMPO-AS1 was positively correlated with HMMR, yet negatively correlated with let-7b-5p expression in LUAD.

Conclusions

Our findings elucidated that the DNA methylation/TMPO-AS1/let-7b-5p axis mediated the high expression of HMMR, which may be considered as a biomarker to predict prognosis in LUAD.

Long non-coding RNA PTPRG-AS1/microRNA-124-3p regulates radiosensitivity of nasopharyngeal carcinoma via the LIM Homeobox 2-dependent Notch pathway through competitive endogenous RNA mechanism

Nasopharyngeal carcinoma (NPC) is a malignant tumor in the nasopharyngeal cavity. LncRNA PTPRG-AS1 is essential in NPC radiosensitivity. This study sought to explore the mechanism of PTPRG-AS1 in NPC radiosensitivity by regulating the miR-124-3p/LHX2 axis. First, NPC-related microarray was analyzed to screen differentially expressed lncRNAs. PTPRG-AS1 and miR-124-3p expression patterns in NPC tissues and adjacent tissues of NPC patients and NPC cell lines were detected by RT-qPCR. PTPRG-AS1 was knocked down in CNE2 and 5–8 F cells by transfection. The radiosensitivity, proliferation and apoptosis before and after radiotherapy (0/6 Gy) were detected by cloning formation assay, CCK-8 assay, and flow cytometry. Bioinformatics, Pearson correlation analysis, RNA pull-down, and luciferase reporter assays were performed to explore the regulatory relationship of the lncRNA PTPRG-AS1/miR-124-3/LHX2 axis. The corresponding functions were verified in the complementation test. The levels of LHX2 and Notch pathway-related proteins were detected by Western blot. PTPRG-AS1 was upregulated in NPC cell lines and tissues. PTPRG-AS1 knockdown decreased NPC cell proliferation and promoted radiotherapy-induced apoptosis and cell radiosensitivity. PTPRG-AS1 upregulated LHX2 as a ceRNA of miR-124-3p. miR-124-3p inhibition partially reversed PTPRG-AS1 silencing-induced NPC cell radiosensitivity. miR-124-3p targeted LHX2. LHX2 overexpression attenuated the miR-124-3p overexpression-induced NPC cell radiosensitivity. LHX2 attenuated NPC cell radiosensitivity by activating the Notch pathway. Briefly, lncRNA PTPRG-AS1 reduced NPC cell radiosensitivity by regulating the miR-124-3p/LHX2 axis through the ceRNA mechanism.

Integrated Analysis of lncRNA and mRNA Expression Profiles Indicates Age-Related Changes in Meniscus

Little has been known about the role of long non-coding RNA (lncRNA) involves in change of aged meniscus. Microarray analyses were performed to identify lncRNAs and mRNAs expression profiles of meniscus in young and aging adults and apple bioinformatics methods to analyse their potential roles. The differentially expressed (DE) lncRNAs and mRNAs were confirmed by qRT-PCR. A total of 1608 DE lncRNAs and 1809 DE mRNAs were identified. Functional and pathway enrichment analyses of all DE mRNAs showed that DE mRNAs were mainly involved in the TGF-beta, Wnt, Hippo, PI3K-Akt signaling pathway. The expressions of TNFRSF11B and BMP2 were significantly upregulated in aging group. LASSO logistic regression analysis of the DE lncRNAs revealed four lncRNAs (AC124312.5, HCG11, POC1B-AS1, and AP001011.1) that were associated with meniscus degradation. CNC analysis demonstrated that AP001011 inhibited the expression of TNFRSF11B and AC1243125 upregulated the expression of TNFRSF11B. CeRNA analysis suggested that POC1B-AS1 regulates the expression of BMP2 by sponging miR 130a-3p, miR136-5p, miR 18a-3p, and miR 608. Furthermore, subcellular localization and m⁶A modification sites prediction analysis of these four lncRNAs was performed. These data lay a foundation for extensive studies on the role of lncRNAs in change of aged meniscus.

Systematic Analysis and Validation of the Prognosis, Immunological Role and Biology Function of the Ferroptosis-Related lncRNA GSEC/miRNA-101-3p/CISD1 Axis in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common type of lung cancer, accounting for approximately 85% of pulmonary malignancies. Emerging evidence has demonstrated that ferroptosis plays a central role in both immunities as well as tumor proliferation. However, the clinical significance, immunological function, and upstream modulatory mechanism of ferroptosis-related genes in LUAD remain unclear. Here, we utilized various bioinformatics data to identify differentially expressed (DEGs) and prognosis-related ferroptosis (FRGs) genes in LUAD. Based upon identified DEGs, FRG, and ceRNA modulatory networks were constructed. Pearson’s correlation analysis was used to evaluate the correlation between FRGs and the tumor mutational burden, microsatellite instability, tumor-infiltrating immunity, cellular checkpoint control, and drug sensitivity in LUAD. A loss-of-function analysis was performed to verify the function of CISD1 in LUAD progression. Our findings revealed that certain FRGs (CISD1, ATP5MC3, PGD, SLC7A11, ACSL3, and FANCD2) are significantly upregulated in LUAD and that their elevated expression is associated with both advanced tumor stage and unfavorable prognosis. Furthermore, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment results revealed these FRGs to be primarily involved in ferroptosis and glutathione metabolism in LUAD. We constructed a prognostic FRG-based model capable of accurately predicting LUAD patient overall survival with high specificity. The upstream lncRNA GSEC/miRNA-101-3p regulatory axis involving CISD1, ATP5MC3, and PGD was identified to be relevant in tumor progression. We also found GSEC, CISD1, ATP5MC3, and PGD to be upregulated, with miRNA-101-3p downregulated, in the setting of LUAD. Immunohistochemical analysis revealed CISD1, ATP5MC3, and PGD overexpression in LUAD tissue samples; CISD1 knockdown was noted to significantly inhibit LUAD proliferation and migration. In summary, this study characterizes relevant functional roles of the lncRNA GSEC/miR-101-3p axis in the setting of LUAD and suggests diagnostic and therapeutic biomarkers potentially useful in the clinical management of this illness.

Identifying Protein Subcellular Locations With Embeddings-Based node2loc

Identifying protein subcellular locations is an important topic in protein function prediction. Interacting proteins may share similar locations. Thus, it is imperative to infer protein subcellular locations by taking protein-protein interactions (PPIs)into account. In this study, we present a network embedding-based method, node2loc, to identify protein subcellular locations. node2loc first learns distributed embeddings of proteins in a protein-protein interaction (PPI)network using node2vec. Then the learned embeddings are further fed into a recurrent neural network (RNN). To resolve the severe class imbalance of different subcellular locations, Synthetic Minority Over-sampling Technique (SMOTE)is applied to artificially synthesize proteins for minority classes. node2loc is evaluated on our constructed human benchmark dataset with 16 subcellular locations and yields a Matthews correlation coefficient (MCC)value of 0.800, which is superior to baseline methods. In addition, node2loc yields a better performance on a Yeast benchmark dataset with 17 locations. The results demonstrate that the learned representations from a PPI network have certain discriminative ability for classifying protein subcellular locations. However, node2loc is a transductive method, it only works for proteins connected in a PPI network, and it needs to be retrained for new proteins. In addition, the PPI network needs be annotated to some extent with location information. node2loc is freely available at https://github.com/xypan1232/node2loc.

Pseudogene AK4P1 promotes pancreatic ductal adenocarcinoma progression through relieving miR-375-mediated YAP1 degradation

Pseudogenes have been reported to play oncogenic or tumor-suppressive roles in cancer progression. However, the molecular mechanism of most pseudogenes in pancreatic ductal adenocarcinoma (PDAC) remains unknown. Herein, we characterized a novel pseudogene-miRNA-mRNA network associated with PDAC progression using bioinformatics analysis. After screening by dreamBase and GEPIA, 12 up-regulated and 7 down-regulated differentially expressed pseudogenes (DEPs) were identified. According to survival analysis, only elevated AK4P1 indicated a poor prognosis for PDAC patients. Moreover, we found that AK4 acts as a cognate gene of AK4P1 and also predicts worse survival for PDAC patients. Furthermore, 32 miRNAs were predicted to bind to AK4P1 by starBase, among which miR-375 was identified as the most potential binding miRNA of AK4P1. A total of 477 potential target genes of miR-375 were obtained by miRNet, in which 49 hub genes with node degree ≥ 20 were identified by STRING. Subsequent analysis for hub genes demonstrated that YAP1 may be a functional downstream target of AK4P1. To confirmed the above findings, microarray, and qRT-PCR assay revealed that YAP1 was dramatically upregulated in both PDAC cells and tissues. Functional experiments showed that knockdown of YAP1 significantly suppressed PDAC cells growth, increased apoptosis, and decreased the ability of invasion. In conclusion, amplification of AK4P1 may fuel the onset and development of PDAC by targeting YAP1 through competitively binding to miR-375, and serve as a promising biomarker and therapeutic target for PDAC.

LncRNA NCK1-AS1 Promotes Malignant Cellular Phenotypes of Laryngeal Squamous Cell Carcinoma via miR-137/NCK1 Axis

Increasing evidence demonstrates that many long noncoding RNAs (lncRNAs) are implicated with the development of laryngeal squamous cell carcinoma (LSCC). As shown by bioinformatics analysis, lncRNA non-catalytic region of tyrosine kinase adaptor protein 1-antisense 1 (NCK1-AS1) is upregulated in tissues of head and neck squamous cell carcinoma. The study aimed to explore the role and mechanism of NCK1-AS1 in LSCC. NCK1-AS1 expression in LSCC cells was evaluated by reverse transcription qPCR. The viability, proliferation, invasion, migration, and apoptosis of LSCC cells with indicated transfection were evaluated by CCK-8 assays, Ethynyl deoxyuridine incorporation assays, Transwell assays, wound healing assays, and TUNEL assays, respectively. Subcellular fractionation assays were performed to evaluate the cellular distribution of NCK1-AS1 and NCK1. NCK1 protein level in LSCC cells with indicated transfection was quantified by western blotting. The binding relation between miR-137 and NCK1-AS1 (or NCK1) were determined using RNA immunoprecipitation assays and luciferase reporter assays. NCK1-AS1 was highly expressed in LSCC cell lines. NCK1-AS1 depletion suppressed LSCC cell viability, proliferation, invasion, and migration while enhancing cell apoptosis. NCK1, an adjacent gene of NCK1-AS1, is also highly expressed in LSCC cells and was positively regulated by NCK1-AS1. Moreover, NCK1-AS1 interact with miR-137 to upregulate NCK1 expression. NCK1 was the downstream target of miR-137 and was negatively correlated to miR-137. In addition, overexpressed NCK1 reversed the suppressive impact of NCK1-AS1 depletion on malignant behaviors of LSCC cells. NCK1-AS1 contributes to LSCC cellular behaviors by upregulating NCK1 via interaction with miR-137.

Comprehensive Analyses of the Immunological and Prognostic Roles of an IQGAP3AR/let-7c-5p/IQGAP3 Axis in Different Types of Human Cancer

IQ motif containing GTPase-activating protein 3 (IQGAP3) is a member of the Rho family of guanosine-5′-triphosphatases (GTPases). IQGAP3 plays a crucial part in the development and progression of several types of cancer. However, the prognostic, upstream-regulatory, and immunological roles of IQGAP3 in human cancer types are not known. We found that IQGAP3 expression was increased in different types of human cancer. The high expression of IQGAP3 was correlated with tumor stage, lymph node metastasis, and a poor prognosis in diverse types of human cancer. The DNA methylation of IQGAP3 was highly and negatively correlated with IQGAP3 expression in diverse cancer types. High DNA methylation in IQGAP3 was correlated with better overall survival in human cancer types. High mRNA expression of IQGAP3 was associated with tumor mutational burden, microsatellite instability, immune cell infiltration, and immune modulators. Analyses of signaling pathway enrichment showed that IQGAP3 was involved in the cell cycle. IQGAP3 expression was associated with sensitivity to a wide array of drugs in cancer cells lines. We revealed that polypyrimidine tract–binding protein 1 (PTBP1) and an IQGAP3-associated lncRNA (IQGAP3AR)/let-7c-5p axis were potential regulations for IQGAP3 expression. We provided the first evidence to show that an IQGAP3AR/let-7c-5p/IQGAP3 axis has indispensable roles in the progression and immune response in different types of human cancer.

MiRLoc: predicting miRNA subcellular localization by incorporating miRNA-mRNA interactions and mRNA subcellular localization

Subcellular localization of microRNAs (miRNAs) is an important reflection of their biological functions. Considering the spatio-temporal specificity of miRNA subcellular localization, experimental detection techniques are expensive and time-consuming, which strongly motivates an efficient and economical computational method to predict miRNA subcellular localization. In this paper, we describe a computational framework, MiRLoc, to predict the subcellular localization of miRNAs. In contrast to existing methods, MiRLoc uses the functional similarity between miRNAs instead of sequence features and incorporates information about the subcellular localization of the corresponding target mRNAs. The results show that miRNA functional similarity data can be effectively used to predict miRNA subcellular localization, and that inclusion of subcellular localization information of target mRNAs greatly improves prediction performance.

Serum Exosomal lncRNA AC007099.1 Regulates the Expression of Neuropeptide-Related FAP, as a Potential Biomarker for Hepatocarcinogenesis

Neuropeptide-associated fibroblast activation protein (FAP) may be an important risk factor for neurovascular metastasis in hepatocellular carcinoma. Analysis of The Cancer Genome Atlas (TCGA) database showed that FAP mRNA was highly expressed in most human tumor tissues. The HPA database then verified that FAP was highly expressed in tumor tissues following protein translation. Survival analysis then showed that the level of FAP expression significantly affected the overall survival (OS), progress free interval (PFI), and disease specific survival (DSS) of patients with hepatocellular carcinoma. A high expression of FAP in tumor tissue is associated with poor patient prognosis. According to the results of spearman correlation, AC009099 and FAP were negatively correlated with miR-7152 expression, while AC009099 and FAP expression were positively correlated. The lncRNA AC007099.1, which may serve as a potential target for the treatment of hepatocellular carcinoma, was associated with liver cancer. AC007099.1/miR-7152/FAP was found to be associated with immune infiltration in patients with hepatocellular carcinoma. Enrichment analysis suggests that the AC009099/miR-7152/FAP ceRNA regulatory network is associated with neuropeptide functional pathways. In conclusion, a neuropeptide-related AC009099/miR-7152/FAP ceRNA regulatory network was constructed in this study.

FOXM1/lncRNA TYMSOS/miR-214-3p–Mediated High Expression of NCAPG Correlates With Poor Prognosis and Cell Proliferation in Non–Small Cell Lung Carcinoma

Lung cancer is the most common cancer with high mortality. Increasing evidence has demonstrated that nonstructural maintenance of chromosomes condensin I complex subunit G (NCAPG) plays a crucial role in the progression of human cancers. However, the biological function and underlying mechanism of NCAPG in non–small cell lung cancer (NSCLC) are still unclear. Here, we utilized diverse public databases to analyze the expression of NCAPG in pan-cancer. We found that NCAPG was highly expressed in various human cancers, especially in NSCLC. NCAPG expression was significantly positively correlated with poor clinical-pathological features, poor prognosis, tumor mutational burden, DNA microsatellite instability, and immune cell infiltration in NSCLC. In addition, our results showed that depletion of NCAPG significantly inhibited NSCLC cell proliferation, migration, and self-renewal abilities, yet these could be reversed by adding microRNA (miRNA)-214-3p. Knockdown of long noncoding RNA (lncRNA) thymidylate synthetase opposite strand (TYMSOS) also inhibits the NSCLC cell proliferation, migration, and self-renewal abilities. In summary, our findings demonstrated that the crucial roles of the FOXM1/lncRNA-TYMSOS/miRNA-214-3p/NCAPG axis in NSCLC may shed light on how NCAPG may act as a therapeutic target for NSCLC.

Long non‑coding RNA LINC00238 suppresses the malignant phenotype of liver cancer by sponging miR‑522

Long non‑coding RNAs can regulate the malignant tumor phenotype either as tumor suppressors or oncogenes. The present study investigated the underlying mechanism of LINC00238 in liver cancer. LINC00238 was identified as a downregulated molecule in The Cancer Genome Atlas liver hepatocellular carcinoma dataset through Gene Expression Profiling Interactive Analysis software. Through gain‑ and loss‑of‑function experiments, LINC00238 was confirmed as a tumor suppressor that could not only decrease cell viability, migration and invasion in vitro, but also tumorigenesis and tumor metastasis in vivo. By cytoplasmic and nuclear RNA isolation, LINC00238 was confirmed to be predominantly cytoplasmic. Mechanistically, RNA pull‑down assays showed that LINC00238 sponged microRNA (miR)‑522 and then reversed the inhibitory effects on two downstream targets, secreted frizzled related protein 2 and dickkopf1. Collectively, LINC00238 was identified as a tumor suppressor that acts via sponging miR‑522 followed by silencing of downstream targets, suggesting that LINC00238 may have a key role in suppressing the malignant phenotype of liver cancer cells.

The Emerging Role of Long Non-Coding RNAs in Development and Function of Gilthead Sea Bream (Sparus aurata) Fast Skeletal Muscle

Long non-coding RNAs (lncRNAs) are an emerging group of ncRNAs that can modulate gene expression at the transcriptional or translational levels. In the present work, previously published transcriptomic data were used to identify lncRNAs expressed in gilthead sea bream skeletal muscle, and their transcription levels were studied under different physiological conditions. Two hundred and ninety lncRNAs were identified and, based on transcriptomic differences between juveniles and adults, a total of seven lncRNAs showed potential to be important for muscle development. Our data suggest that the downregulation of most of the studied lncRNAs might be linked to increased myoblast proliferation, while their upregulation might be necessary for differentiation. However, with these data, as it is not possible to propose a formal mechanism to explain their effect, bioinformatic analysis suggests two possible mechanisms. First, the lncRNAs may act as sponges of myoblast proliferation inducers microRNAs (miRNAs) such as miR-206, miR-208, and miR-133 (binding energy MEF < -25.0 kcal). Secondly, lncRNA20194 had a strong predicted interaction towards the myod1 mRNA (ndG = -0.17) that, based on the positive correlation between the two genes, might promote its function. Our study represents the first characterization of lncRNAs in gilthead sea bream fast skeletal muscle and provides evidence regarding their involvement in muscle development.

Differentially Expressed lncRNAs Related to the Development of Abdominal Fat in Gushi Chickens and Their Interaction Regulatory Network

Chickens are one of the most important sources of meat worldwide, and the growth status of abdominal fat is closely related to production efficiency. Long noncoding RNAs (lncRNAs) play an important role in lipid metabolism and deposition regulation. However, research on the expression profile of lncRNAs related to the development of abdominal fat in chickens after hatching and their interaction regulatory networks is still lacking. To characterize the lncRNA expression profile during the development of chicken abdominal fat, abdominal adipose tissues from 6-, 14-, 22-, and 30-week-old Chinese Gushi chickens were herein used to construct 12 cDNA libraries, and a total of 3,827 new lncRNAs and 5,466 previously annotated lncRNAs were revealed. At the same time, based on the comparative analysis of five combinations, 276 differentially expressed lncRNAs (DE-lncRNAs) were screened. Functional enrichment analysis showed that the predicted target genes of these DE-lncRNAs were significantly enriched in pathways related to the posttranscriptional regulation of gene expression, negative regulation of cell proliferation, cell adhesion and other biological processes, glycosphingolipid biosynthesis, PPAR signaling, fatty acid degradation, fatty acid synthesis and others. In addition, association analysis of the lncRNA transcriptome profile was performed, and DE-lncRNA-related lncRNA-mRNA, lncRNA-miRNA and lncRNA-miRNA-mRNA interaction regulatory networks were constructed. The results showed that DE-lncRNA formed a complex network with PPAR pathway components, including PPARD, ACOX1, ADIPOQ, CPT1A, FABP5, ASBG2, LPL, PLIN2 and related miRNAs, including mir-200b-3p, mir-130b-3p, mir-215-5p, mir-122-5p, mir-223 and mir-125b-5p, and played an important regulatory role in biological processes such as lipid metabolism, adipocyte proliferation and differentiation. This study described the dynamic expression profile of lncRNAs in the abdominal fat of Gushi chickens for the first time and constructed the DE-lncRNA interaction regulatory network. The results expand the number of known lncRNAs in chicken abdominal fat and provide valuable resources for further elucidating the posttranscriptional regulatory mechanism of chicken abdominal fat development or deposition.

Illuminating lncRNA Function Through Target Prediction

Most of the transcribed human genome codes for noncoding RNAs (ncRNAs), and long noncoding RNAs (lncRNAs) make for the lion's share of the human ncRNA space. Despite growing interest in lncRNAs, because there are so many of them, and because of their tissue specialization and, often, lower abundance, their catalog remains incomplete and there are multiple ongoing efforts to improve it. Consequently, the number of human lncRNA genes may be lower than 10,000 or higher than 200,000. A key open challenge for lncRNA research, now that so many lncRNA species have been identified, is the characterization of lncRNA function and the interpretation of the roles of genetic and epigenetic alterations at their loci. After all, the most important human genes to catalog and study are those that contribute to important cellular functions-that affect development or cell differentiation and whose dysregulation may play a role in the genesis and progression of human diseases. Multiple efforts have used screens based on RNA-mediated interference (RNAi), antisense oligonucleotide (ASO), and CRISPR screens to identify the consequences of lncRNA dysregulation and predict lncRNA function in select contexts, but these approaches have unresolved scalability and accuracy challenges. Instead-as was the case for better-studied ncRNAs in the past-researchers often focus on characterizing lncRNA interactions and investigating their effects on genes and pathways with known functions. Here, we focus most of our review on computational methods to identify lncRNA interactions and to predict the effects of their alterations and dysregulation on human disease pathways.

The LncRNA MIR155HG is Upregulated by SP1 in Melanoma Cells and Drives Melanoma Progression via Modulating the MiR-485-3p/PSIP1 Axis

BACKGROUND

MIR155HG is a long non-coding RNA (lncRNA) that has been shown to be dysregulated in a range of tumor types, but the functions of this lncRNA in melanoma remain to be explored.

OBJECTIVES

We explored the functions of lncRNA MIR155HG in melanoma progression.

METHODS

The expression of miR155HG was analyzed in clinical melanoma. Bioinformatics analysis was performed to assess the potential tumor-related functions of miR155HG. The interaction of miR155HG and SP1 and the inhibition of PSIP1 by miR-485-3p were analyzed by ChIP, luciferase reporter experiments, and the biological effects in melanoma were explored by colony formation assays, EdU cell proliferation assays, Transwell analysis, and intracranial melanoma mouse model.

RESULTS

Herein, we found that MIR155HG was markedly upregulated in melanoma cell lines and tissues. We further determined that the SP1 transcription factor was responsible for driving MIR155HG upregulation in melanoma. Elevated MIR155HG levels were linked to decreased overall survival (OS) in melanoma patients, and we further determined that MIR155HG expression was an independent predictor of melanoma patient prognosis. When MIR155HG was knocked down in melanoma cells, this impaired their proliferative, migratory, and invasive activity. By using predictive bioinformatics analyses, we identified miR-485-3p as a microRNA (miRNA) capable of binding to both MIR155HG and the 3' UTR of PSIP1.

CONCLUSION

Together, these results suggest that MIR155HG is capable of promoting melanoma cell proliferation via the miR-485-3p/PSIP1 axis. These novel findings provide new insights into the development of melanoma, potentially highlighting future avenues for therapeutic intervention.

OUP accepted manuscript

Motivation

There is a plethora of measures to evaluate functional similarity (FS) of genes based on their co-expression, protein–protein interactions and sequence similarity. These measures are typically derived from hand-engineered and application-specific metrics to quantify the degree of shared information between two genes using their Gene Ontology (GO) annotations.

Results

We introduce deepSimDEF, a deep learning method to automatically learn FS estimation of gene pairs given a set of genes and their GO annotations. deepSimDEF’s key novelty is its ability to learn low-dimensional embedding vector representations of GO terms and gene products and then calculate FS using these learned vectors. We show that deepSimDEF can predict the FS of new genes using their annotations: it outperformed all other FS measures by >5–10% on yeast and human reference datasets on protein–protein interactions, gene co-expression and sequence homology tasks. Thus, deepSimDEF offers a powerful and adaptable deep neural architecture that can benefit a wide range of problems in genomics and proteomics, and its architecture is flexible enough to support its extension to any organism.

Availability and implementation

Source code and data are available at https://github.com/ahmadpgh/deepSimDEF

Supplementary information

Supplementary data are available at Bioinformatics online.

LncRNA LINC00667 aggravates the progression of hepatocellular carcinoma by regulating androgen receptor expression as a miRNA-130a-3p sponge

Emerging studies have found long noncoding RNAs, widely expressed in eukaryotes, crucial regulators in the progression of human cancers, including hepatocellular carcinoma (HCC). Although the long intergenic noncoding RNA 667 (LINC00667) can promote the progression of a variety of cancer types, the expression pattern, the role in cancer progression, and the molecular mechanism involved in HCC remain unclear. This study aims to investigate the function and mechanism of LINC00667 in HCC progression. The effects of LINC00667 silencing in cell proliferation, cell migration, and cell invasion, and androgen receptor (AR) expression were determined with loss-of-function phenotypic analysis in Huh-7 and HCCLM3 cells, and subsequently testified in vivo in tumor growth. We found that the expression of LINC00667 was upregulated in HCC tissues and cell lines. Upregulation of LINC00667 was significantly associated with the unfavorable prognosis of HCC in our study patients. On the other hand, low expression of LINC00667 significantly inhibited the cell proliferation, cell migration and cell invasion of HCC in vitro and tumor growth in vivo. This inhibitory effect could be counteracted by miR-130a-3p inhibitor. LINC00667 reduced the inhibition of AR expression by miR-130a-3p, which correlated with the progression of HCC. Our finding suggests LINC00667 is a molecular sponge in the miR-130s-3p/AR signal pathway in the progression of HCC, in which it relieves the repressive function of miR-130a-3p on the AR expression. This indicates LINC00667 functions as a tumor promotor in promoting HCC progression through targeting miR-130a-3p/AR axis, making a novel biomarker and potential therapeutic target for HCC.

Integrated Analysis of the m6A-Related lncRNA Identified lncRNA ABALON/miR-139-3p/NOB1 Axis Was Involved in the Occurrence of Lung Cancer

Background

Lung cancer has the characteristics of early metastasis, high recurrence, and high mortality rate despite emerging advances in diagnostic. Early diagnosis can significantly improve the patient’s chances of cure and survival.

Purpose

This study aimed to identify and assess a prognostic lncRNA/miRNA/gene signature in patients with lung cancer.

Methods

Pearson correlation analysis, univariate Cox analysis and LASSO Cox analysis were used to construct a lung cancer prognostic risk model based on m6A-related lncRNA. The interaction between lncRNA-miRNA-gene was verified by luciferase reporter gene experiment.

Results

The Pearson correlation analysis determined that 1655 lncRNAs significantly correlated with the expression of m6A genes. A lung cancer prognostic risk model, including 14 m6A-related lncRNAs, was constructed through univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) Cox analysis. ABALON was identified as the key lncRNA through cluster analysis and gene expression difference analysis.

Conclusion

It was experimentally verified that ABALON acted as a competing endogenous RNA by sponging miR-139-3p and indirectly regulated the expression of NOB1. This study provided a new biological target for the early diagnosis of lung cancer and a new direction for studying the mechanism of lung cancer.

LINC00265 promotes the viability, proliferation, and migration of bladder cancer cells via the miR-4677-3p/FGF6 axis

BACKGROUND

Bladder cancer (BCa) is a common genitourinary malignancy with higher incidence in males. Long intergenic non-protein coding RNA 265 (LINC00265) is identified as an oncogene in many malignancies, while its role in BCa development remains unknown.

PURPOSE

To explore the functions and mechanism of LINC00265 in BCa.

RESEARCH DESIGN

Reverse transcription quantitative polymerase chain reaction was performed to examine LINC00265 expression in BCa cells. Cell counting kit-8 assays, colony formation assays, TdT-mediated dUTP Nick-End Labeling assays, and Transwell assays were conducted to examine BCa cell viability, proliferation, apoptosis, and migration. Luciferase reporter assays and RNA immunoprecipitation assays were carried out to explore the binding capacity between miR-4677-3p and messenger RNA fibroblast growth factor 6 (FGF6) (or LINC00265). Xenograft tumor model was established to explore the role of LINC00265 in vivo.

RESULTS

LINC00265 was highly expressed in BCa cells. LINC00265 knockdown inhibited xenograft tumor growth and BCa cell viability, proliferation and migration while enhancing cell apoptosis. Moreover, LINC00265 interacted with miR-4677-3p to upregulate the expression of FGF6. FGF6 overexpression reversed the suppressive effect of LINC00265 knockdown on malignant phenotypes of BCa cells.

CONCLUSIONS

LINC00265 promotes the viability, proliferation, and migration of BCa cells by binding with miR-4677-3p to upregulate FGF6 expression.

Silencing long noncoding RNA LINC01138 inhibits aerobic glycolysis to reduce glioma cell proliferation by regulating the microRNA‑375/SP1 axis

Glioma is a primary cerebral neoplasm that originates from glial tissue and spreads to the central nervous system. Long noncoding RNAs are known to play a role in glioma cells by regulating cell proliferation, migration and invasion. The aim of the present study was to investigate the mechanism by which long intergenic non‑protein coding RNA (LINC) 01138 affects glycolysis and proliferation in glioma cells via the microRNA (miR)‑375/specificity protein 1 (SP1) axis. LINC01138 expression was assessed in glioma tissues and cells using reverse transcription‑quantitative PCR and the association between LINC01138 and patient clinicopathological features was analyzed. Glucose uptake, lactic acid secretion, cell proliferation, and glycolysis‑related enzyme levels were detected following LINC01138 silencing using CCK‑8, EDU assay and western blot analysis. miR‑375 and SP1 expression levels were also assessed, and the distribution of LINC01138 in the nucleus and cytoplasm was investigated using subcellular fractionation localization. Furthermore, the binding relationships between LINC01138 and miR‑375, and between miR‑375 and SP1 were assessed via dual‑luciferase experiment, RIP and RNA pull‑down assays. Finally, xenograft transplantation models were used to verify the in vitro results. LINC01138 was highly expressed in glioma, which was independent of patient sex or age but was significantly related to tumor diameter, the World Health Organization tumor grade and lymph node metastasis. Silencing LINC01138 significantly reduced glioma glycolysis and cell proliferation. Moreover, LINC01138 acted as a competing endogenous RNA to sponge miR‑375 and promote SP1 expression. miR‑375 inhibition significantly reversed the effect of LINC01138 silencing. In addition, silencing LINC01138 significantly reduced tumor growth in vivo. The present study demonstrated that silencing LINC01138 inhibited aerobic glycolysis and thus reduced glioma cell proliferation, potentially by modulating the miR‑375/SP1 axis.

Mechanism of M2 macrophage-derived extracellular vesicles carrying lncRNA MEG3 in inflammatory responses in ulcerative colitis

Ulcerative colitis (UC) is a chronic inflammatory disease of the colon. M2 macrophages possess certain anti-inflammation activity. Accordingly, the current study set out to investigate the potential mechanism of M2 macrophage-derived extracellular vesicles (M2-EVs) in UC inflammation. Firstly, mouse peritoneal macrophages were induced to M2 phenotype, and M2-EVs were isolated. , the murine model of UC was established, and the length and weight of the colon, disease activity index (DAI), apoptosis, and inflammatory response of UC mice were measured. Young adult mouse colon (YAMC) cells were induced with the help of lipopolysaccharide. LncRNA maternally expressed 3 (LncRNA MEG3), miR-20b-5p, and cAMP responsive element binding protein 1 (CREB1) expression patterns were detected in UC models. In addition, we analyzed the binding relationship among MEG3, miR-20b-5p, and CREB1. UC mice presented with shortened colon length, lightened weight, increased DAI score, enhanced apoptosis, and significant inflammatory cell infiltration, while M2-EVs reversed these trends. In vitro, M2-EVs increased UC cell viability and reduced inflammation. Mechanistic experimentation revealed that M2-EVs transferred MEG3 into YAMC cells to up-regulate MEG3 expression and promote CREB1 transcription by competitively binding to miR-20b-5p. Moreover, up-regulation of MEG3 in M2-EVs enhanced the protective effect of M2-EVs on UC cells, while over-expression of miR-20b-5p attenuated the aforementioned protective effect of M2-EVs on UC mice and cells. Collectively, our findings revealed that M2-EVs carrying MEG3 enhanced UC cell viability and reduced inflammatory responses via the miR-20b-5p/CREB1 axis, thus alleviating UC inflammation.

Prediction of RNA subcellular localization: Learning from heterogeneous data sources

RNA subcellular localization has recently emerged as a widespread phenomenon, which may apply to the majority of RNAs. The two main sources of data for characterization of RNA localization are sequence features and microscopy images, such as obtained from single-molecule fluorescent in situ hybridization-based techniques. Although such imaging data are ideal for characterization of RNA distribution, these techniques remain costly, time-consuming, and technically challenging. Given these limitations, imaging data exist only for a limited number of RNAs. We argue that the field of RNA localization would greatly benefit from complementary techniques able to characterize location of RNA. Here we discuss the importance of RNA localization and the current methodology in the field, followed by an introduction on prediction of location of molecules. We then suggest a machine learning approach based on the integration between imaging localization data and sequence-based data to assist in characterization of RNA localization on a transcriptome level.

Comprehensive analysis of competitive endogenous RNAs networks reveals potential prognostic biomarkers associated with epithelial ovarian cancer

Ovarian cancer (OC) is a major health threat to females, as it has high morbidity and mortality. Evidence has increasingly demonstrated that long non-coding RNAs (lncRNAs) regulate OC progression and they may have value as early diagnostic biomarkers, prognostic biomarkers and/or therapeutic targets. In the present study, the regulatory mechanisms and prognosis associated with cancer-specific lncRNAs and their related competing endogenous (ce)RNA network in OC were investigated. The differential expression profiles and prognostic significance of lncRNAs and mRNAs were systematically explored based on data from 359 OC cases from The Cancer Genome Atlas and 180 healthy individuals from the Genotype-Tissue Expression database. Functional enrichment analyses, RNA-RNA interactome prediction, ceRNA network analysis, correlation analysis and survival analysis were utilized to identify hub lncRNAs and biomarkers associated with OC diagnosis or prognosis. A total of 1,049 differentially expressed lncRNAs and 6,516 differentially expressed mRNAs between OC and healthy tissues were detected. An lncRNA-micro (mi)RNA-mRNA regulatory network in OC was further established, containing 91 lncRNAs, 23 miRNAs and 179 mRNAs. After survival analysis based on the expression of the RNAs in the ceRNA network, 8 lncRNAs, 4 miRNAs and 11 mRNAs that were significantly associated with OC patient survival (P<0.05) were obtained. Using least absolute shrinkage and selection operator-penalized Cox regression, an eight-lncRNA risk score model was generated, which was able to readily discriminate between OC and healthy individuals and predict the survival of patients with OC. In addition, the differential expression of several key lncRNAs and mRNAs was verified by reverse transcription-quantitative PCR and western blot analysis. The current study presents a novel lncRNA-miRNA-mRNA network, which provides insight into the potential pathogenesis of OC and allows the identification of prognostic biomarkers and treatment strategies for OC.

Comprehensive Pan-Cancer Analysis of the Prognostic and Immunological Roles of the METTL3/lncRNA-SNHG1/miRNA-140-3p/UBE2C Axis

Growing evidence has demonstrated that UBE2C plays a critical role in cancer progression, but there is no study focusing on the prognosis, upstream regulation mechanism, and immunological roles of UBE2C across diverse tumor types. In this study, we found that UBE2C was elevated in this human pan-cancer analysis, and high expression of UBE2C was correlated with poor prognosis. In addition, UBE2C expression was markedly associated with tumor mutation burden (TMB), microsatellite instability (MSI), immune cell infiltration, and diverse drug sensitivities. Finally, we showed that the METTL3/SNHG1/miRNA-140-3p axis could potentially regulate UBE2C expression. N(6)-Methyladenosine (m6A) modifications improved the stability of methylated SNHG1 transcripts by decreasing the rate of RNA degradation, which lead to upregulation of SNHG1 in non-small cell lung cancer (NSCLC). In vitro functional experiments showed that SNHG1, as a competing endogenous RNA, sponges miR-140-3p to increase UBE2C expression in NSCLC cell lines. Our study elucidates the clinical importance and regulatory mechanism of the METTL3/SNHG1/miRNA-140-3p/UBE2C axis in NSCLC and provides a prognostic indicator, as well as a promising therapeutic target for patients with NSCLC.

Linc-ROR facilitates progression and angiogenesis of hepatocellular carcinoma by modulating DEPDC1 expression

Linc-ROR have been well-demonstrated to play important roles in cancer progression and angiogenesis. However, the underlying oncogenic mechanism of Linc-ROR in hepatocellular carcinoma is poorly understood. In this study, we demonstrate that Linc-ROR plays an oncogenic role in part through its positive regulation of DEPDC1 expression. Mechanistically, Linc-ROR acts as competing endogenous RNA to stabilize DEPDC1 mRNA and regulates DEPDC1 mRNA stability by binding HNRNPK. Thus, these findings suggest that function of Linc-ROR-mediated DEPDC1 could predispose hepatocellular carcinoma patients to progression and angiogenesis, and may serve as a potential target for anticancer therapies.

DLpTCR: an ensemble deep learning framework for predicting immunogenic peptide recognized by T cell receptor

Accurate prediction of immunogenic peptide recognized by T cell receptor (TCR) can greatly benefit vaccine development and cancer immunotherapy. However, identifying immunogenic peptides accurately is still a huge challenge. Most of the antigen peptides predicted in silico fail to elicit immune responses in vivo without considering TCR as a key factor. This inevitably causes costly and time-consuming experimental validation test for predicted antigens. Therefore, it is necessary to develop novel computational methods for precisely and effectively predicting immunogenic peptide recognized by TCR. Here, we described DLpTCR, a multimodal ensemble deep learning framework for predicting the likelihood of interaction between single/paired chain(s) of TCR and peptide presented by major histocompatibility complex molecules. To investigate the generality and robustness of the proposed model, COVID-19 data and IEDB data were constructed for independent evaluation. The DLpTCR model exhibited high predictive power with area under the curve up to 0.91 on COVID-19 data while predicting the interaction between peptide and single TCR chain. Additionally, the DLpTCR model achieved the overall accuracy of 81.03% on IEDB data while predicting the interaction between peptide and paired TCR chains. The results demonstrate that DLpTCR has the ability to learn general interaction rules and generalize to antigen peptide recognition by TCR. A user-friendly webserver is available at http://jianglab.org.cn/DLpTCR/. Additionally, a stand-alone software package that can be downloaded from https://github.com/jiangBiolab/DLpTCR.

Predicting subcellular location of protein with evolution information and sequence-based deep learning

BACKGROUND

Protein subcellular localization prediction plays an important role in biology research. Since traditional methods are laborious and time-consuming, many machine learning-based prediction methods have been proposed. However, most of the proposed methods ignore the evolution information of proteins. In order to improve the prediction accuracy, we present a deep learning-based method to predict protein subcellular locations.

RESULTS

Our method utilizes not only amino acid compositions sequence but also evolution matrices of proteins. Our method uses a bidirectional long short-term memory network that processes the entire protein sequence and a convolutional neural network that extracts features from protein sequences. The position specific scoring matrix is used as a supplement to protein sequences. Our method was trained and tested on two benchmark datasets. The experiment results show that our method yields accurate results on the two datasets with an average precision of 0.7901, ranking loss of 0.0758 and coverage of 1.2848.

CONCLUSION

The experiment results show that our method outperforms five methods currently available. According to those experiments, we can see that our method is an acceptable alternative to predict protein subcellular location.

A novel prognostic cancer-related lncRNA signature in papillary renal cell carcinoma

Background

Papillary renal cell carcinoma (pRCC) ranks second in renal cell carcinoma and the prognosis of pRCC remains poor. Here, we aimed to screen and identify a novel prognostic cancer-related lncRNA signature in pRCC.

Methods

The RNA-seq profile and clinical feature of pRCC cases were downloaded from TCGA database. Significant cancer-related lncRNAs were obtained from the Immlnc database. Differentially expressed cancer-related lncRNAs (DECRLs) in pRCC were screened for further analysis. Cox regression report was implemented to identify prognostic cancer-related lncRNAs and establish a prognostic risk model, and ROC curve analysis was used to evaluate its precision. The correlation between RP11-63A11.1 and clinical characteristics was further analyzed. Finally, the expression level and role of RP11-63A11.1 were studied in vitro.

Results

A total of 367 DECRLs were finally screened and 26 prognostic cancer-related lncRNAs were identified. Among them, ten lncRNAs (RP11-573D15.8, LINC01317, RNF144A-AS1, TFAP2A-AS1, LINC00702, GAS6-AS1, RP11-400K9.4, LUCAT1, RP11-63A11.1, and RP11-156L14.1) were independently associated with prognosis of pRCC. These ten lncRNAs were incorporated into a prognostic risk model. In accordance with the median value of the riskscore, pRCC cases were separated into high and low risk groups. Survival analysis indicated that there was a significant difference on overall survival (OS) rate between the two groups. The area under curve (AUC) in different years indicated that the model was of high efficiency in prognosis prediction. RP11-63A11.1 was mainly expressed in renal tissues and it correlated with the tumor stage, T, M, N classifications, OS, PFS, and DSS of pRCC patients. Consistent with the expression in pRCC tissue samples, RP11-63A11.1 was also down-regulated in pRCC cells. More importantly, up-regulation of RP11-63A11.1 attenuated cell survival and induced apoptosis.

Conclusions

Ten cancer-related lncRNAs were incorporated into a powerful model for prognosis evaluation. RP11-63A11.1 functioned as a cancer suppressor in pRCC and it might be a potential therapeutic target for treating pRCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02247-6.