lncLocator 2.0: a cell-line-specific subcellular localization predictor for long non-coding RNAs with interpretable deep learning

An ensemble learning method combined with multiple feature representation strategies to predict lncRNA subcellular localizations

Long non-coding RNAs (lncRNAs) are strongly associated with cellular physiological mechanisms and implicated in the numerous diseases. By exploring the subcellular localizations of lncRNAs, we can not only gain crucial insights into the molecular mechanisms of lncRNA-related biological processes but also make valuable contributions towards the diagnosis, prevention, and treatment of various human diseases. However, conventional experimental techniques tend to be laborious and time-intensive. In this context, computational methods are in increased demand. The focus of this paper is the development of an innovative ensemble method that incorporates hybrid features to accurately predict the subcellular localizations of lncRNAs. To address the issue of incomplete reflection of inherent correlation with the intended target using singular source features, the utilization of heterogeneous multi-source features is implemented by introducing information on sequence composition, physicochemical properties, and structure. To address the issue of the imbalance classes in the benchmark dataset, the Synthetic Minority Over-sampling Technique (SMOTE) is employed. Finally, the resulting predictor termed lncSLPre is developed by integrating the outputs of the individual classifiers. Experimental findings suggest that the complementarity of multi-source heterogeneous features improves prediction performance. Additionally, it is demonstrated that the application of SMOTE is effective in mitigating the issue of the imbalanced dataset, while the feature selection approach is critical in eliminating extraneous and redundant features. Compared with existing advanced methods, lncSLPre achieves better performance with an overall accuracy improvement of 13.13%, 2.15%, and 3.23%, respectively, indicating that lncSLPre can effectively predict lncRNA subcellular localizations.

Overexpression of lncRNA22524 from Dongxiang Wild Rice Reduces Drought and Salt Stress Tolerance in Cultivated Rice

Drought and salt stresses are major challenges to rice production, and a deep understanding of the mechanisms for tolerance could help deal with the challenges. Long non-coding RNAs (lncRNAs) play crucial roles in gene regulation. Previously, lncRNA22524 has been identified as a drought stress-responsive lncRNA from Dongxiang wild rice (DXWR). Nevertheless, its reactions to abiotic stresses in genetics and physiology remained unclear. In this study, we employed a rapid amplification of cDNA ends (RACE) to obtain the full-length cDNA of lncRNA22524 from DXWR, analyzed its cellular localization, built an overexpression vector to generate transgenic lines of cultivated rice and evaluated its impact in genetics and physiology. After treated with drought and salt stress, the overexpressed lines exhibited much more injuries and lower rates of survival, more reactive oxygen species (ROS) and malondialdehyde (MDA), lower antioxidant enzymes and lower proline (Pro) and soluble sugar (SS) than their wild-type (WT). Furthermore, transcriptome analysis of overexpressed lines with weaker tolerance than WT revealed 1,233 differentially expressed genes (DEGs), where most DEGs were involved in phenylpropanoid biosynthesis, photosynthesis and glutathione metabolism. These findings demonstrated that lncRNA22524 negatively regulated rice responses to drought and salt stress, which clear way of working from transcription to metabolic products should be worth of further study.

Analysis of TNS3-203 and LRRFIP1-211 Transcripts as Oral Cancer Biomarkers

INTRODUCTION

A recent pan-cancer transcriptome analysis indicated differential activity of alternative promoters of genes TNS3 and LRRFIP1 in head and neck squamous cell carcinoma compared to non-cancerous tissue. The promoters upregulated in head and neck squamous cell carcinoma regulate expression of transcripts TNS3-203 and LRRFIP1-211.

OBJECTIVE

Our aim was to investigate the biomarker potential of TNS3-203 and LRRFIP1-211 transcripts in oral cancer, the most common type of head and neck cancer.

MATERIALS AND METHODS

An in silico approach was used to characterize the promoters and transcripts of interest. Relative expression of TNS3-203 and LRRFIP1-211 transcripts was evaluated by qRT-PCR in a group of 46 oral cancer patients in samples of cancer and adjacent non-cancerous tissue.

RESULTS

TNS3-203 was significantly overexpressed in oral cancer compared with matched non-cancerous tissue, so this transcript can potentially be used as a diagnostic biomarker. There were no differences in LRRFIP1-211 level between analyzed tissues. None of the investigated transcripts has prognostic potential in oral cancer.

CONCLUSION

The results obtained indicate the diagnostic potential of TNS3-203, but not LRRFIP1-211 transcript and its role in oral carcinogenesis.

CFPLncLoc: A multi-label lncRNA subcellular localization prediction based on Chaos game representation and centralized feature pyramid

There is increasing evidence that the subcellular localization of long noncoding RNAs (lncRNAs) can provide valuable insights into their biological functions. In terms of transcriptomes, lncRNAs were usually found in multiple subcellular localizations. Although several computational methods have been developed to predict the subcellular localization of lncRNAs, few of them were designed for lncRNAs that have multiple subcellular localizations. In this study, we propose a novel deep learning model, called CFPLncLoc, which uses chaos game representation (CGR) images of lncRNA sequences to predict multi-label lncRNA subcellular localization. CFPLncLoc utilizes image update strategy (IUS) to enhance the relative feature representation of the CGR images. To extract higher-level features from CGR images, CFPLncLoc introduces the multi-scale feature fusion (MFF) model, centralized feature pyramid (CFP), from the field of computer vision (CV). Ablation studies confirmed the contribution of the IUS and CFP in improving the prediction performance. Statistical test results verify that CFPLncLoc outperforms existing state-of-the-art predictors under the evaluation metric MaAUC on the hold-out/independent test set. The source code can be obtained from https://github.com/ShengWang-XTU/CFPLncLoc.

Pan-cancer analysis of Methyltransferase-like 16 (METTL16) and validated in colorectal cancer

Human Methyltransferase-like 16(METTL16) is an independent N6-methyladenosine (m6A) methyltransferase. Previous studies have proven METTL16 been linked with some types of cancers. However, comparative studies of the relevance of METTL16 across diverse tumors remain sparse. We comprehensively investigated the effect of METTL16 expression on tumor prognosis across human malignancies by analyzing multiple cancer-related databases like Tumor Immune Estimation Resource (TIMER) and human protein atlas (HPA). Bioinformatics data indicated that METTL16 was overexpressed in most of these human malignancies and was significantly associated with the prognosis of patients with cancer, especially in colorectal cancer (CRC). Subsequently, In vitro experiments, the utility of METTL16 that downregulation of its expression could result in reduced proliferation and migration of CRC cells. Our findings reveal novel insights into METTL16 expression and its biological functions in diverse cancer types, indicating that METTL16 could serve as a prognostic biomarker and plays an important role in colorectal cancer.

Circular RNA encoded by PPARG in the peripheral blood and a lipopolysaccharide-induced cardiomyocyte inflammation model is identified as a marker of fulminant myocarditis

BACKGROUND

Fulminant myocarditis (FM), a critical cardiac disease, is characterised by atypical initial symptoms and rapid progression and tends to lead to cardiomyocyte degeneration or necrosis. Reliable biological markers for FM screening remain lacking. Circular RNAs (circRNAs) are highly stable in peripheral blood due to their special closed-loop structure, and reports have described their involvement in regulating inflammatory responses and cell injury in cardiomyocytes. This study attempted to identify the abnormal expression of circRNAs in the peripheral blood of patients with FM and to evaluate the potential diagnostic value.

METHODS

Peripheral blood was collected from 5 children with FM and 5 sex- and age-matched healthy controls; total RNA was extracted from each sample, and the extracted RNA from each group was pooled. After RNase R treatment, high-throughput sequencing was performed to screen for differentially expressed circRNAs in the peripheral blood of patients. Biological function classification and enrichment analysis were used to explore the main action pathways involving differentially expressed circRNAs. A lipopolysaccharide (LPS)-induced cardiomyocyte inflammation model was used to explore the effect of inflammation on the expression of these dysregulated circRNAs. Receiver operating characteristic (ROC) curves were used to evaluate the potential clinical value of FM-related circRNAs as biomarkers in a large sample of patients.

RESULTS

CircRNA expression profiling of peripheral blood samples from patients revealed 6,435 and 3,678 circRNAs with upregulated and downregulated expression, respectively, compared with healthy controls. The expression of 1,749 circRNAs did not significantly differ between the groups. GO and KEGG analysis revealed that the genes encoding the dysregulated circRNAs were associated with various biological functions related to the risk of FM development, including infectious diseases, the immune system, and signal transduction. The high expression of hsa_circ_0064338 (circ_PPARG) was confirmed in both the myocardial cell inflammation model and peripheral blood from a large sample of FM patients. ROC curve analysis showed that the level of circ_PPARG in peripheral blood had a good ability to distinguish patients with FM from healthy controls.

CONCLUSIONS

Large numbers of abnormally regulated circRNAs were identified in peripheral blood from patients with FM; among these, the highly expressed circ_PPARG could distinguish patients from healthy controls to a certain extent. It is expected to become a potential clinical biomarker of FM in the future.

MVSLLnc: LncRNA subcellular localization prediction based on multi-source features and two-stage voting strategy

The subcellular localization of long non-coding RNAs (lncRNAs) is crucial for understanding the function of lncRNAs. Since the traditional biological experimental methods are time-consuming and some existing computational methods rely on high computing power, we are committed to finding a simple and easy-to-implement method to achieve more efficient prediction of the subcellular localization of lncRNAs. In this work, we proposed a model based on multi-source features and two-stage voting strategy for predicting the subcellular localization of lncRNAs (MVSLLnc). The multi-source features include k-mer frequency, features based on the coordinate values of Chaos Game Representation (CGR) and features based on physicochemical property (PhyChe). We feed the multi-source features into the traditional machine learning classifiers RF, SVM and XGBoost, respectively, and perform the final prediction task with two-stage voting strategy. Experimental results on three benchmark datasets show that the accuracy can reach 0.829, 0.793 and 0.968, respectively. The accuracy on three independent test sets is 0.642, 0.737 and 0.518, respectively, which are competitive with the existing methods. Our ablation analyses show that the two-stage voting strategy can make full use of the advantages of multi-source features and multiple classifiers, and obtain more robust results.

Computational Prediction of Gene Regulation by lncRNAs

High-throughput sequencing technologies and innovative bioinformatics tools discovered that most of the genome is transcribed into RNA. However, only a fraction of the RNAs in cell translates into proteins, while the majority of them are categorized as noncoding RNAs (ncRNAs). The ncRNAs with more than 200 nt without protein-coding ability are termed long noncoding RNAs (lncRNAs). Hundreds of studies established that lncRNAs are a crucial RNA family regulating gene expression. Regulatory RNAs, including lncRNAs, modulate gene expression by interacting with RNA, DNA, and proteins. Several databases and computational tools have been developed to explore the functions of lncRNAs in cellular physiology. This chapter discusses the tools available for lncRNA functional analysis and provides a detailed workflow for the computational analysis of lncRNAs.

LncRNA81246 regulates resistance against tea leaf spot by interrupting the miR164d-mediated degradation of NAC1

Non-coding RNAs play crucial roles in plant responses to viral stresses. However, their molecular mechanisms in tea leaf spot responses remain unclear. In this study, using Camellia sinensis, we identified lncRNA81246 as a long non-coding RNA that localizes to both the nucleus and cytoplasm. It functions as a competitive endogenous RNA, thereby disrupting CsNAC1 (encoding NAC domain-containing protein 1) degradation mediated by miR164d. Silencing lncRNA81246 increased the resistance of tea plants to presistanceathogens, whereas transient lncRNA81246-overexpression plants showed decreased resistance to pathogens. Co-expression assays in Nicotiana benthamiana revealed that lncRNA81246 affects the miR164d-CsNAC1 regulatory module. Transient miR164d-overexpression and silencing assays demonstrated its positive regulation of tea plant resistance. Specifically, silencing its target, CsNAC1, enhanced disease resistance, whereas transient overexpression reduced plant resistance. Yeast one-hybrid, dual-luciferase, and RT-qPCR assay results suggested that CsNAC1 alters the expression of CsEXLB1, whereas AsODN and tobacco transient overexpression assays showed that CsEXLB1 negatively regulated tea plant resistance. Thus, our research demonstrated that lncRNA81246 acts as a mediator to interfere with the miR164d-CsNAC1 regulatory module involved in the disease resistance of tea plants.

BiGM-lncLoc: Bi-level Multi-Graph Meta-Learning for Predicting Cell-Specific Long Noncoding RNAs Subcellular Localization

The precise spatiotemporal expression of long noncoding RNAs (lncRNAs) plays a pivotal role in biological regulation, and aberrant expression of lncRNAs in different subcellular localizations has been intricately linked to the onset and progression of a variety of cancers. Computational methods provide effective means for predicting lncRNA subcellular localization, but current studies either ignore cell line and tissue specificity or the correlation and shared information among cell lines. In this study, we propose a novel approach, BiGM-lncLoc, treating the prediction of lncRNA subcellular localization across cell lines as a multi-graph meta-learning task. Our investigation involves two categories of data: the localization data of nucleotide sequences in different cell lines and cell line expression data. BiGM-lncLoc comprises a cell line-specific optimization network learning specific knowledge from cell line expression data and a graph neural network optimized across cell lines. Subsequently, the specific and shared knowledge acquired through bi-level optimization is applied to a new cell-line prediction task without the need for re-training or fine-tuning. Additionally, through key feature analysis of the impact of different nucleotide combinations on the model, we confirm the necessity of cell line-specific studies based on correlation analysis. Finally, experiments conducted on various cell lines with different data sizes indicate that BiGM-lncLoc outperforms other methods in terms of prediction accuracy, with an average accuracy of 97.7%. After removing overlapping samples to ensure data independence for each cell line, the accuracy ranged from 82.4% to 94.7%, still surpassing existing models. Our code can be found at https://github.com/BioCL1/BiGM-lncLoc .

LncSL: A Novel Stacked Ensemble Computing Tool for Subcellular Localization of lncRNA by Amino Acid-Enhanced Features and Two-Stage Automated Selection Strategy

Long non-coding RNA (lncRNA) is a non-coding RNA longer than 200 nucleotides, crucial for functions like cell cycle regulation and gene transcription. Accurate localization prediction from sequence information is vital for understanding lncRNA's biological roles. Computational methods offer an effective alternative to traditional experimental methods for annotating lncRNA subcellular positions. Existing machine learning-based methods are limited and often overlook regions with coding potential that affect the function of lncRNA. Therefore, we propose a new model called LncSL. For feature encoding, both lncRNA sequences and amino acid sequences from open reading frames (ORFs) are employed. And we selected the most suitable features by CatBoost and integrated them into a new feature set. Additionally, a voting process with seven feature selection algorithms identified the higher contributive features for training our final stacked model. Additionally, an automatic model selection strategy is constructed to find a better performance meta-model for assembling LncSL. This study specifically focuses on predicting the subcellular localization of lncRNA in the nucleus and cytoplasm. On two benchmark datasets called S1 and S2 datasets, LncSL outperformed existing methods by 6.3% to 12.3% in the Matthew's correlation coefficient on a balanced test dataset. On an unbalanced independent test dataset sourced from S1, LncSL improved by 4.7% to 18.6% in the Matthew's correlation coefficient, which further demonstrates that LncSL is superior to other compared methods. In all, this study presents an effective method for predicting lncRNA subcellular localization through enhancing sequence information, which is always overlooked by traditional methods, and addressing contributive meta-model selection problems, which can offer new insights for other bioinformatics problems.

Localization is the key to action: regulatory peculiarities of lncRNAs

To understand the transcriptomic profile of an individual cell in a multicellular organism, we must comprehend its surrounding environment and the cellular space where distinct molecular stimuli responses are located. Contradicting the initial perception that RNAs were nonfunctional and that only a few could act in chromatin remodeling, over the last few decades, research has revealed that they are multifaceted, versatile regulators of most cellular processes. Among the various RNAs, long non-coding RNAs (LncRNAs) regulate multiple biological processes and can even impact cell fate. In this sense, the subcellular localization of lncRNAs is the primary determinant of their functions. It affects their behavior by limiting their potential molecular partner and which process it can affect. The fine-tuned activity of lncRNAs is also tissue-specific and modulated by their cis and trans regulation. Hence, the spatial context of lncRNAs is crucial for understanding the regulatory networks by which they influence and are influenced. Therefore, predicting a lncRNA's correct location is not just a technical challenge but a critical step in understanding the biological meaning of its activity. Hence, examining these peculiarities is crucial to researching and discussing lncRNAs. In this review, we debate the spatial regulation of lncRNAs and their tissue-specific roles and regulatory mechanisms. We also briefly highlight how bioinformatic tools can aid research in the area.

GP-HTNLoc: A graph prototype head-tail network-based model for multi-label subcellular localization prediction of ncRNAs

Numerous research results demonstrated that understanding the subcellular localization of non-coding RNAs (ncRNAs) is pivotal in elucidating their roles and regulatory mechanisms in cells. Despite the existence of over ten computational models dedicated to predicting the subcellular localization of ncRNAs, a majority of these models are designed solely for single-label prediction. In reality, ncRNAs often exhibit localization across multiple subcellular compartments. Furthermore, the existing multi-label localization prediction models are insufficient in addressing the challenges posed by the scarcity of training samples and class imbalance in ncRNA dataset. To address these limitations, this study proposes a novel multi-label localization prediction model for ncRNAs, named GP-HTNLoc. To mitigate class imbalance, GP-HTNLoc adopts separate training approaches for head and tail location labels. Additionally, GP-HTNLoc introduces a pioneering graph prototype module to enhance its performance in small-sample, multi-label scenarios. The experimental results based on 10-fold cross-validation on benchmark datasets demonstrate that GP-HTNLoc achieves competitive predictive performance. The average results from 10 rounds of testing on an independent dataset show that GP-HTNLoc outperforms the best existing models on the human lncRNA, human snoRNA, and human miRNA subsets, with average precision improvements of 31.5%, 14.2%, and 5.6%, respectively, reaching 0.685, 0.632, and 0.704. A user-friendly online GP-HTNLoc server is accessible at https://56s8y85390.goho.co.

Rapid prediction of key residues for foldability by machine learning model enables the design of highly functional libraries with hyperstable constrained peptide scaffolds

Peptides are an emerging modality for developing therapeutics that can either agonize or antagonize cellular pathways associated with disease, yet peptides often suffer from poor chemical and physical stability, which limits their potential. However, naturally occurring disulfide-constrained peptides (DCPs) and de novo designed Hyperstable Constrained Peptides (HCPs) exhibiting highly stable and drug-like scaffolds, making them attractive therapeutic modalities. Previously, we established a robust platform for discovering peptide therapeutics by utilizing multiple DCPs as scaffolds. However, we realized that those libraries could be further improved by considering the foldability of peptide scaffolds for library design. We hypothesized that specific sequence patterns within the peptide scaffolds played a crucial role in spontaneous folding into a stable topology, and thus, these sequences should not be subject to randomization in the original library design. Therefore, we developed a method for designing highly diverse DCP libraries while preserving the inherent foldability of each scaffold. To achieve this, we first generated a large-scale dataset from yeast surface display (YSD) combined with shotgun alanine scan experiments to train a machine-learning (ML) model based on techniques used for natural language understanding. Then we validated the ML model with experiments, showing that it is able to not only predict the foldability of peptides with high accuracy across a broad range of sequences but also pinpoint residues critical for foldability. Using the insights gained from the alanine scanning experiment as well as prediction model, we designed a new peptide library based on a de novo-designed HCP, which was optimized for enhanced folding efficiency. Subsequent panning trials using this library yielded promising hits having good folding properties. In summary, this work advances peptide or small protein domain library design practices. These findings could pave the way for the efficient development of peptide-based therapeutics in the future.

Integrated Biochemical and Computational Methods for Deciphering RNA-Processing Codes

RNA processing involves steps such as capping, splicing, polyadenylation, modification, and nuclear export. These steps are essential for transforming genetic information in DNA into proteins and contribute to RNA diversity and complexity. Many biochemical methods have been developed to profile and quantify RNAs, as well as to identify the interactions between RNAs and RNA-binding proteins (RBPs), especially when coupled with high-throughput sequencing technologies. With the rapid accumulation of diverse data, it is crucial to develop computational methods to convert the big data into biological knowledge. In particular, machine learning and deep learning models are commonly utilized to learn the rules or codes governing the transformation from DNA sequences to intriguing RNAs based on manually designed or automatically extracted features. When precise enough, the RNA codes can be incredibly useful for predicting RNA products, decoding the molecular mechanisms, forecasting the impact of disease variants on RNA processing events, and identifying driver mutations. In this review, we systematically summarize the biochemical and computational methods for deciphering five important RNA codes related to alternative splicing, alternative polyadenylation, RNA localization, RNA modifications, and RBP binding. For each code, we review the main types of experimental methods used to generate training data, as well as the key features, strategic model structures, and advantages of representative tools. We also discuss the challenges encountered in developing predictive models using large language models and extensive domain knowledge. Additionally, we highlight useful resources and propose ways to improve computational tools for studying RNA codes.

M1 macrophages deliver CASC19 via exosomes to inhibit the proliferation and migration of colon cancer cells

Colorectal cancer (CRC) continues to be one of the leading causes of cancer-related death worldwide. Exosomes have been established to play an important role in intercellular communication and that long non-coding RNA (lncRNA) CASC19 is enriched within M1 macrophage-derived exosomes (M1-exo). However, the biological functions and underlying molecular mechanisms of exosomal CASC19 from macrophages on CRC remain unknown. Cell proliferation and migration were evaluated by MTS and transwell assays. The exosomes were characterized by western blot, nanoparticle tracking analysis (NTA) and electron microscope imaging. The expression levels of CASC19 and its putative target miR-410-3p were quantified by reverse-transcription polymerase chain reaction (RT-qPCR). The interaction between CASC19 and miR-410-3p was detected by the pull-down assay. We found that the non-contact inhibition of M1 macrophages on the proliferation of colon cancer cells is largely dependent on the CASC19 released from M1 exosomes. M1 exosomes successfully delivered CASC19 to colon cancer cells, exerting an inhibitory effect on cell proliferation and migration. The exosomes secreted by M1 cells with CASC19 knockdown showed less inhibition effect on cell proliferation and migration. Mechanically, CASC19 exerted an inhibitory effect on colon cancer cells by sponging miR-410-3p via tube morphogenesis and TGF-β signaling pathway. We first proved that CASC19 in M1 macrophages is delivered into colon cancer cells via exosomes, exerting an inhibitory effect on their proliferation and migration by sponging miR-410-3p. The study may provide mechanistic insights into the roles of lncRNAs in CRC progression and a potential therapeutic target for the treatment of CRC.

Evaluation of machine learning models that predict lncRNA subcellular localization

The lncATLAS database quantifies the relative cytoplasmic versus nuclear abundance of long non-coding RNAs (lncRNAs) observed in 15 human cell lines. The literature describes several machine learning models trained and evaluated on these and similar datasets. These reports showed moderate performance, e.g. 72-74% accuracy, on test subsets of the data withheld from training. In all these reports, the datasets were filtered to include genes with extreme values while excluding genes with values in the middle range and the filters were applied prior to partitioning the data into training and testing subsets. Using several models and lncATLAS data, we show that this 'middle exclusion' protocol boosts performance metrics without boosting model performance on unfiltered test data. We show that various models achieve only about 60% accuracy when evaluated on unfiltered lncRNA data. We suggest that the problem of predicting lncRNA subcellular localization from nucleotide sequences is more challenging than currently perceived. We provide a basic model and evaluation procedure as a benchmark for future studies of this problem.

STAU1-mediated CNBP mRNA degradation by LINC00665 alters stem cell characteristics in ovarian cancer

BACKGROUND

To investigate the role of lncRNA LINC00665 in modulating ovarian cancer stemness and its influence on treatment resistance and cancer development.

METHODS

We isolated ovarian cancer stem cells (OCSCs) from the COC1 cell line using a combination of chemotherapeutic agents and growth factors, and verified their stemness through western blotting and immunofluorescence for stem cell markers. Employing bioinformatics, we identified lncRNAs associated with ovarian cancer, with a focus on LINC00665 and its interaction with the CNBP mRNA. In situ hybridization, immunohistochemistry, and qPCR were utilized to examine their expression and localization, alongside functional assays to determine the effects of LINC00665 on CNBP.

RESULTS

LINC00665 employs its Alu elements to interact with the 3'-UTR of CNBP mRNA, targeting it for degradation. This molecular crosstalk enhances stemness by promoting the STAU1-mediated decay of CNBP mRNA, thereby modulating the Wnt and Notch signaling cascades that are pivotal for maintaining CSC characteristics and driving tumor progression. These mechanistic insights were corroborated by a series of in vitro assays and validated in vivo using tumor xenograft models. Furthermore, we established a positive correlation between elevated CNBP levels and increased disease-free survival in patients with ovarian cancer, underscoring the prognostic value of CNBP in this context.

CONCLUSIONS

lncRNA LINC00665 enhances stemness in ovarian cancer by mediating the degradation of CNBP mRNA, thereby identifying LINC00665 as a potential therapeutic target to counteract drug resistance and tumor recurrence associated with CSCs.

Decoding the Non-coding: Tools and Databases Unveiling the Hidden World of "Junk" RNAs for Innovative Therapeutic Exploration

Non-coding RNAs are pivotal regulators of gene and protein expression, exerting crucial influences on diverse biological processes. Their dysregulation is frequently implicated in the onset and progression of diseases, notably cancer. A profound comprehension of the intricate mechanisms governing ncRNAs is imperative for devising innovative therapeutic interventions against these debilitating conditions. Significantly, nearly 80% of our genome comprises ncRNAs, underscoring their centrality in cellular processes. The elucidation of ncRNA functions is pivotal for grasping the complexities of gene regulation and its implications for human health. Modern genome sequencing techniques yield vast datasets, stored in specialized databases. To harness this wealth of information and to understand the crosstalk of non-coding RNAs, knowledge of available databases is required, and many new sophisticated computational tools have emerged. These tools play a pivotal role in the identification, prediction, and annotation of ncRNAs, thereby facilitating their experimental validation. This Review succinctly outlines the current understanding of ncRNAs, emphasizing their involvement in disease development. It also highlights the databases and tools instrumental in classifying, annotating, and evaluating ncRNAs. By extracting meaningful biological insights from seemingly "junk" data, these tools empower scientists to unravel the intricate roles of ncRNAs in shaping human health.

LncRNA HMOX1 alleviates renal ischemia-reperfusion-induced ferroptotic injury via the miR-3587/HMOX1 axis

Emerging evidence suggests that long non-coding RNAs (lncRNAs) play significant roles in renal ischemia reperfusion (RIR) injury. However, the specific mechanisms by which lncRNAs regulate ferroptosis in renal tubular epithelial cells remain largely unknown. The objective of this study was to investigate the biological function of lncRNA heme oxygenase 1 (lnc-HMOX1) in RIR and its potential molecular mechanism. Our findings demonstrated that the expression of HMOX1-related lnc-HMOX1 was reduced in renal tubular epithelial cells treated with hypoxia-reoxygenation (HR). Furthermore, the over-expression of lnc-HMOX1 mitigated ferroptotic injury in renal tubular epithelial cells in vivo and in vitro. Mechanistically, lnc-HMOX1, as a competitive endogenous RNA (ceRNA), promoted the expression of HMOX1 by sponging miR-3587. Furthermore, the inhibition of HMOX1 effectively impeded the aforementioned effects exerted by lnc-HMOX1. Ultimately, the inhibitory or mimic action of miR-3587 reversed the promoting or refraining influence of silenced or over-expressed lnc-HMOX1 on ferroptotic injury during HR. In summary, our findings contribute to a comprehensive comprehension of the mechanism underlying ferroptotic injury mediated by lnc-HMOX1 during RIR. Significantly, we identified a novel lnc-HMOX1-miR-3587-HMOX1 axis, which holds promise as a potential therapeutic target for RIR injury.

Copy number gain of FAM131B-AS2 promotes the progression of glioblastoma by mitigating replication stress

BACKGROUND

Glioblastoma (GBM) is characterized by chromosome 7 copy number gains, notably 7q34, potentially contributing to therapeutic resistance, yet the underlying oncogenes have not been fully characterized. Pertinently, the significance of long noncoding RNAs (lncRNAs) in this context has gained attention, necessitating further exploration.

METHODS

FAM131B-AS2 was quantified in GBM samples and cells using qPCR. Overexpression and knockdown of FAM131B-AS2 in GBM cells were used to study its functions in vivo and in vitro. The mechanisms of FAM131B-AS2 were studied using RNA-seq, qPCR, Western blotting, RNA pull-down, coimmunoprecipitation assays, and mass spectrometry analysis. The phenotypic changes that resulted from FAM131B-AS2 variation were evaluated through CCK8 assay, EdU assay, comet assay, and immunofluorescence.

RESULTS

Our analysis of 149 primary GBM patients identified FAM131B-AS2, a lncRNA located in the 7q34 region, whose upregulation predicts poor survival. Mechanistically, FAM131B-AS2 is a crucial regulator of the replication stress response, stabilizing replication protein A1 through recruitment of ubiquitin-specific peptidase 7 and activating the ataxia telangiectasia and rad3-related protein kinase pathway to protect single-stranded DNA from breakage. Furthermore, FAM131B-AS2 overexpression inhibited CD8+ T-cell infiltration, while FAM131B-AS2 inhibition activated the cGAS-STING pathway, increasing lymphocyte infiltration and improving the response to immune checkpoint inhibitors.

CONCLUSIONS

FAM131B-AS2 emerges as a promising indicator for adjuvant therapy response and could also be a viable candidate for combined immunotherapies against GBMs.

Long non‑coding RNA lung cancer‑associated transcript 1 regulates ferroptosis via microRNA‑34a‑5p‑mediated GTP cyclohydrolase 1 downregulation in lung cancer cells

Ferroptosis, a recently discovered type of programmed cell death triggered by excessive accumulation of iron‑dependent lipid peroxidation, is linked to several malignancies, including non‑small cell lung cancer. Long non‑coding RNAs (lncRNAs) are involved in ferroptosis; however, data on their role and mechanism in cancer therapy remains limited. Therefore, the aim of the present study was to identify ferroptosis‑associated mRNAs and lncRNAs in A549 lung cancer cells treated with RAS‑selective lethal 3 (RSL3) and ferrostatin‑1 (Fer‑1) using RNA sequencing. The results demonstrated that lncRNA lung cancer‑associated transcript 1 (LUCAT1) was significantly upregulated in lung adenocarcinoma and lung squamous cell carcinoma tissues. Co‑expression analysis of differentially expressed mRNAs and lncRNAs suggested that LUCAT1 has a crucial role in ferroptosis. LUCAT1 expression was markedly elevated in A549 cells treated with RSL3, which was prevented by co‑incubation with Fer‑1. Functionally, overexpression of LUCAT1 facilitated cell proliferation and reduced the occurrence of ferroptosis induced by RSL3 and Erastin, while inhibition of LUCAT1 expression reduced cell proliferation and increased ferroptosis. Mechanistically, downregulation of LUCAT1 resulted in the downregulation of both GTP cyclohydrolase 1 (GCH1) and ferroptosis suppressor protein 1 (FSP1). Furthermore, inhibition of LUCAT1 expression upregulated microRNA (miR)‑34a‑5p and then downregulated GCH1. These results indicated that inhibition of LUCAT1 expression promoted ferroptosis by modulating the downregulation of GCH1, mediated by miR‑34a‑5p. Therefore, the combination of knocking down LUCAT1 expression with ferroptosis inducers may be a promising strategy for lung cancer treatment.

Long noncoding RNA LINC00675 drives malignancy in acute myeloid leukemia via the miR-6809 -CDK6 axis

Hematological malignancies such as acute myeloid leukemia (AML) have a low cure rate and a high recurrence rate. Long noncoding RNAs (LNCs) are essential regulators of tumorigenesis and progression. The role of lncRNA LINC00675 in AML has rarely been reported. This study revealed elevated LINC00675 expression in AML that promotes proliferation and inhibits apoptosis. Mechanistically, LINC00675 combines with miR-6809 to promote the expression of CDK6 in vitro and in vivo. Immune-checkpoint genes were expressed more highly in LINC00675-high patients. A high level of LINC00675 expression may make patients more susceptible to palbociclib treatments. In conclusion, our study demonstrated that LINC00675 is an oncogenic lncRNA that enhances the malignancy of AML by upregulating CDK6 expression through miR-6809 sponging, providing a new perspective and feasible target for the diagnosis and treatment of AML.

LncLocFormer: a Transformer-based deep learning model for multi-label lncRNA subcellular localization prediction by using localization-specific attention mechanism

MOTIVATION

There is mounting evidence that the subcellular localization of lncRNAs can provide valuable insights into their biological functions. In the real world of transcriptomes, lncRNAs are usually localized in multiple subcellular localizations. Furthermore, lncRNAs have specific localization patterns for different subcellular localizations. Although several computational methods have been developed to predict the subcellular localization of lncRNAs, few of them are designed for lncRNAs that have multiple subcellular localizations, and none of them take motif specificity into consideration.

RESULTS

In this study, we proposed a novel deep learning model, called LncLocFormer, which uses only lncRNA sequences to predict multi-label lncRNA subcellular localization. LncLocFormer utilizes eight Transformer blocks to model long-range dependencies within the lncRNA sequence and shares information across the lncRNA sequence. To exploit the relationship between different subcellular localizations and find distinct localization patterns for different subcellular localizations, LncLocFormer employs a localization-specific attention mechanism. The results demonstrate that LncLocFormer outperforms existing state-of-the-art predictors on the hold-out test set. Furthermore, we conducted a motif analysis and found LncLocFormer can capture known motifs. Ablation studies confirmed the contribution of the localization-specific attention mechanism in improving the prediction performance.

AVAILABILITY AND IMPLEMENTATION

The LncLocFormer web server is available at http://csuligroup.com:9000/LncLocFormer. The source code can be obtained from https://github.com/CSUBioGroup/LncLocFormer.

Identification of IRF1 as a Novel Pyroptosis-Related Prognostic Biomarker of Atopic Dermatitis

Purpose: The aim of this study was to characterize key biomarkers associated with pyroptosis in atopic dermatitis (AD). Materials and methods: To identify the differentially expressed pyroptosis-related genes (DEPRGs), the gene expression profiles GSE16161 and GSE32924 from the Gene Expression Omnibus (GEO) database were utilized. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were conducted to determine the potential biological functions and involved pathways. Furthermore, protein-protein interaction network analyses were performed to identify hub genes. The types and proportions of infiltrating immune cells were detected by immune filtration analysis using CIBERSORT. A 12-axis competing endogenous RNA (ceRNA) network was constructed utilizing the miRNet database. Immunohistochemistry (IHC) further validated the differential expression of a key gene IRF1 in the skin tissues collected from AD patients. The collection of skin tissue from human subjects in this study were reviewed and approved by the IRB of Yueyang Integrated Chinese and Western Medicine Hospital (KYSKSB2020-125). Results: The study identified a total of 76 DEPRGs, which were enriched in genes associated with the inflammatory response and immune regulation. There was a higher percentage of activated dendritic cells and a lower percentage of resting mast cells in AD samples. PVT1 expression was associated with upregulation of hub genes including CXCL8, IRF1, MKI67, and TP53 in the ceRNA network and was correlated with activated dendritic cells in AD. As a transcription factor, IRF1 could regulate the production of downstream inflammatory factors. The IHC study revealed that IRF1 was overexpressed in the skin tissues of AD patients, which were consistent with the results of the bioinformatic study. Conclusions: IRF1 and its related genes were identified as key pyroptosis-related biomarkers in AD, which is a crucial pathway in the pathogenesis of AD.

Prognostic hub gene CBX2 drives a cancer stem cell-like phenotype in HCC revealed by multi-omics and multi-cohorts

Hepatocellular carcinoma (HCC) is a malignant tumor with a high prevalence and fatality rate. CBX2 has been demonstrated to impact the development and advancement of various cancers, albeit it has received limited attention in relation to HCC. In this study, CBX2 and CEP55 were screened out with the refined triple regulatory networks constructed by total RNA-seq datasets (TCGA-LIHC, GSE140845) and a robust prognostic model. Aberrantly higher expression levels of CBX2 and CEP55 in HCC may be caused by CNV alterations, promoter hypo-methylation, open chromatin accessibility, and greater active marks such as H3K4me3, H3K4me1, and H3K27ac. Functionally, CBX2, which was highly correlated with CD44, shaped a cancer stem cell-like phenotype by positively regulating cell-cycle progression, proliferation, invasion, metastasis, wound healing, and radiation resistance, revealed by combining bulk RNA-seq and scRNA-seq datasets. CBX2 knockdown validated its role in affecting the cell cycle. Importantly, we revealed CBX2 could activate gene by cooperating with co-regulators or not rather than a recognizer of the repressive mark H3K27me3. For instance, we uncovered CBX2 bound to promoter of CTNNB1 and CEP55 to augment their expressions. CBX2 showed a highly positive correlation with CEP55 at pan-cancer level. In addition, CBX2 and CEP55 may enhance extracellular matrix reprograming via cancer-associated fibroblast. Surprisingly, patients with high expression of CBX2 or CEP55 exhibited a higher response to immunotherapy, indicating that CBX2 and CEP55 may be promising therapeutic targets for HCC patients.

ncRNALocate-EL: a multi-label ncRNA subcellular locality prediction model based on ensemble learning

Subcellular localizations of ncRNAs are associated with specific functions. Currently, an increasing number of biological researchers are focusing on computational approaches to identify subcellular localizations of ncRNAs. However, the performance of the existing computational methods is low and needs to be further studied. First, most prediction models are trained with outdated databases. Second, only a few predictors can identify multiple subcellular localizations simultaneously. In this work, we establish three human ncRNA subcellular datasets based on the latest RNALocate, including lncRNA, miRNA and snoRNA, and then we propose a novel multi-label classification model based on ensemble learning called ncRNALocate-EL to identify multi-label subcellular localizations of three ncRNAs. The results show that the ncRNALocate-EL outperforms previous methods. Our method achieved an average precision of 0.709,0.977 and 0.730 on three human ncRNA datasets. The web server of ncRNALocate-EL has been established, which can be accessed at https://bliulab.net/ncRNALocate-EL.

RNA trafficking and subcellular localization-a review of mechanisms, experimental and predictive methodologies

RNA localization is essential for regulating spatial translation, where RNAs are trafficked to their target locations via various biological mechanisms. In this review, we discuss RNA localization in the context of molecular mechanisms, experimental techniques and machine learning-based prediction tools. Three main types of molecular mechanisms that control the localization of RNA to distinct cellular compartments are reviewed, including directed transport, protection from mRNA degradation, as well as diffusion and local entrapment. Advances in experimental methods, both image and sequence based, provide substantial data resources, which allow for the design of powerful machine learning models to predict RNA localizations. We review the publicly available predictive tools to serve as a guide for users and inspire developers to build more effective prediction models. Finally, we provide an overview of multimodal learning, which may provide a new avenue for the prediction of RNA localization.

Analysis of lncRNAs in Lupinus mutabilis (Tarwi) and Their Potential Role in Drought Response

Lupinus mutabilis is a legume with high agronomic potential and available transcriptomic data for which lncRNAs have not been studied. Therefore, our objective was to identify, characterize, and validate the drought-responsive lncRNAs in L. mutabilis. To achieve this, we used a multilevel approach based on lncRNA prediction, annotation, subcellular location, thermodynamic characterization, structural conservation, and validation. Thus, 590 lncRNAs were identified by at least two algorithms of lncRNA identification. Annotation with the PLncDB database showed 571 lncRNAs unique to tarwi and 19 lncRNAs with homology in 28 botanical families including Solanaceae (19), Fabaceae (17), Brassicaceae (17), Rutaceae (17), Rosaceae (16), and Malvaceae (16), among others. In total, 12 lncRNAs had homology in more than 40 species. A total of 67% of lncRNAs were located in the cytoplasm and 33% in exosomes. Thermodynamic characterization of S03 showed a stable secondary structure with -105.67 kcal/mol. This structure included three regions, with a multibranch loop containing a hairpin with a SECIS-like element. Evaluation of the structural conservation by CROSSalign revealed partial similarities between L. mutabilis (S03) and S. lycopersicum (Solyc04r022210.1). RT-PCR validation demonstrated that S03 was upregulated in a drought-tolerant accession of L. mutabilis. Finally, these results highlighted the importance of lncRNAs in tarwi improvement under drought conditions.

A review from biological mapping to computation-based subcellular localization

Subcellular localization is crucial to the study of virus and diseases. Specifically, research on protein subcellular localization can help identify clues between virus and host cells that can aid in the design of targeted drugs. Research on RNA subcellular localization is significant for human diseases (such as Alzheimer's disease, colon cancer, etc.). To date, only reviews addressing subcellular localization of proteins have been published, which are outdated for reference, and reviews of RNA subcellular localization are not comprehensive. Therefore, we collated (the most up-to-date) literature on protein and RNA subcellular localization to help researchers understand changes in the field of protein and RNA subcellular localization. Extensive and complete methods for constructing subcellular localization models have also been summarized, which can help readers understand the changes in application of biotechnology and computer science in subcellular localization research and explore how to use biological data to construct improved subcellular localization models. This paper is the first review to cover both protein subcellular localization and RNA subcellular localization. We urge researchers from biology and computational biology to jointly pay attention to transformation patterns, interrelationships, differences, and causality of protein subcellular localization and RNA subcellular localization.

LINC01116 modulates EMT process via binding with AGO1 mRNA in oesophageal squamous cell carcinoma

Recent researches have uncovered that long non-coding RNAs (lncRNAs) are closely correlated with the development of different diseases, while biological functions and hidden molecular mechanisms of antisense lncRNAs in oesophageal squamous cell carcinoma (OSCC) remain unclear. Here, we identified upregulation of LINC01116 in RNA sequencing data, online database, and in OSCC and intraepithelial neoplasia (IEN) specimens. Functionally, LINC01116 facilitates OSCC advancement and metastasis in vitro and vivo. Mechanistically, elevated expression of LINC01116 in OSCC cells other than tumor stroma and cytoplasmic enables it to activate AGO1 expression via complementary binding with AGO1 mRNA to facilitate EMT process of OSCC.

Different Recognition of Protein Features Depending on Deep Learning Models: A Case Study of Aromatic Decarboxylase UbiD

The number of unannotated protein sequences is explosively increasing due to genome sequence technology. A more comprehensive understanding of protein functions for protein annotation requires the discovery of new features that cannot be captured from conventional methods. Deep learning can extract important features from input data and predict protein functions based on the features. Here, protein feature vectors generated by 3 deep learning models are analyzed using Integrated Gradients to explore important features of amino acid sites. As a case study, prediction and feature extraction models for UbiD enzymes were built using these models. The important amino acid residues extracted from the models were different from secondary structures, conserved regions and active sites of known UbiD information. Interestingly, the different amino acid residues within UbiD sequences were regarded as important factors depending on the type of models and sequences. The Transformer models focused on more specific regions than the other models. These results suggest that each deep learning model understands protein features with different aspects from existing knowledge and has the potential to discover new laws of protein functions. This study will help to extract new protein features for the other protein annotations.

BCLncRDB: a comprehensive database of LncRNAs associated with breast cancer

Breast cancer, the most common cancer in women, is characterized by high morbidity and mortality worldwide. Recent evidence has shown that long non-coding RNAs (lncRNAs) play a crucial role in the development and progression of breast cancer. However, despite increasing data and evidence indicating the implication of lncRNAs in breast cancer, no web resource or database exists primarily for lncRNAs associated with only breast cancer. Therefore, we developed a manually curated, comprehensive database, "BCLncRDB," for lncRNAs associated with breast cancer. For this, we collected, processed, and analyzed available data on breast cancer-associated lncRNAs from different sources, including previously published research articles, the Gene Expression Omnibus (GEO) Database of the National Centre for Biotechnology Information (NCBI), The Cancer Genome Atlas (TCGA), and the Ensembl database; subsequently, these data were hosted at BCLncRDB for public access. Currently, the database contains 5324 unique breast cancer-lncRNA associations and has the following features: (i) a user-friendly, easy-to-use web interface for searching and browsing about lncRNAs of the user's interest, (ii) differentially expressed and methylated lncRNAs, (iii) stage- and subtype-specific lncRNAs, and (iv) drugs, subcellular localization, sequence, and chromosome information of these lncRNAs. Thus, the BCLncRDB provides a one-stop dedicated platform for exploring breast cancer-related lncRNAs to advance and support the ongoing research on this disease. The BCLncRDB is publicly available for use at http://sls.uohyd.ac.in/new/bclncrdb_v1 .

Integrative bioinformatics and validation studies reveal KDM6B and its associated molecules as crucial modulators in Idiopathic Pulmonary Fibrosis

Background

Idiopathic Pulmonary Fibrosis (IPF) can be described as a debilitating lung disease that is characterized by the complex interactions between various immune cell types and signaling pathways. Chromatin-modifying enzymes are significantly involved in regulating gene expression during immune cell development, yet their role in IPF is not well understood.

Methods

In this study, differential gene expression analysis and chromatin-modifying enzyme-related gene data were conducted to identify hub genes, common pathways, immune cell infiltration, and potential drug targets for IPF. Additionally, a murine model was employed for investigating the expression levels of candidate hub genes and determining the infiltration of different immune cells in IPF.

Results

We identified 33 differentially expressed genes associated with chromatin-modifying enzymes. Enrichment analyses of these genes demonstrated a strong association with histone lysine demethylation, Sin3-type complexes, and protein demethylase activity. Protein-protein interaction network analysis further highlighted six hub genes, specifically KDM6B, KDM5A, SETD7, SUZ12, HDAC2, and CHD4. Notably, KDM6B expression was significantly increased in the lungs of bleomycin-induced pulmonary fibrosis mice, showing a positive correlation with fibronectin and α-SMA, two essential indicators of pulmonary fibrosis. Moreover, we established a diagnostic model for IPF focusing on KDM6B and we also identified 10 potential therapeutic drugs targeting KDM6B for IPF treatment.

Conclusion

Our findings suggest that molecules related to chromatin-modifying enzymes, primarily KDM6B, play a critical role in the pathogenesis and progression of IPF.

Crosstalk between Noncoding RNAs and the Epigenetics Machinery in Pediatric Tumors and Their Microenvironment

According to the World Health Organization, every year, an estimated 400,000+ new cancer cases affect children under the age of 20 worldwide. Unlike adult cancers, pediatric cancers develop very early in life due to alterations in signaling pathways that regulate embryonic development, and environmental factors do not contribute much to cancer development. The highly organized complex microenvironment controlled by synchronized gene expression patterns plays an essential role in the embryonic stages of development. Dysregulated development can lead to tumor initiation and growth. The low mutational burden in pediatric tumors suggests the predominant role of epigenetic changes in driving the cancer phenotype. However, one more upstream layer of regulation driven by ncRNAs regulates gene expression and signaling pathways involved in the development. Deregulation of ncRNAs can alter the epigenetic machinery of a cell, affecting the transcription and translation profiles of gene regulatory networks required for cellular proliferation and differentiation during embryonic development. Therefore, it is essential to understand the role of ncRNAs in pediatric tumor development to accelerate translational research to discover new treatments for childhood cancers. This review focuses on the role of ncRNA in regulating the epigenetics of pediatric tumors and their tumor microenvironment, the impact of their deregulation on driving pediatric tumor progress, and their potential as effective therapeutic targets.

ELMo4m6A: A Contextual Language Embedding-Based Predictor for Detecting RNA N6-Methyladenosine Sites

N6-methyladenosine (m6A) is a universal post-transcriptional modification of RNAs, and it is widely involved in various biological processes. Identifying m6A modification sites accurately is indispensable to further investigate m6A-mediated biological functions. How to better represent RNA sequences is crucial for building effective computational methods for detecting m6A modification sites. However, traditional encoding methods require complex biological prior knowledge and are time-consuming. Furthermore, most of the existing m6A sites prediction methods are limited to single species, and few methods are able to predict m6A sites across different species and tissues. Thus, it is necessary to design a more efficient computational method to predict m6A sites across multiple species and tissues. In this paper, we proposed ELMo4m6A, a contextual language embedding-based method for predicting m6A sites from RNA sequences without any prior knowledge. ELMo4m6A first learns embeddings of RNA sequences using a language model ELMo, then uses a hybrid convolutional neural network (CNN) and long short-term memory (LSTM) to identify m6A sites. The results of 5-fold cross-validation and independent testing demonstrate that ELMo4m6A is superior to state-of-the-art methods. Moreover, we applied integrated gradients to find potential sequence patterns contributing to m6A sites.

DeepmRNALoc: A Novel Predictor of Eukaryotic mRNA Subcellular Localization Based on Deep Learning

The subcellular localization of messenger RNA (mRNA) precisely controls where protein products are synthesized and where they function. However, obtaining an mRNA's subcellular localization through wet-lab experiments is time-consuming and expensive, and many existing mRNA subcellular localization prediction algorithms need to be improved. In this study, a deep neural network-based eukaryotic mRNA subcellular location prediction method, DeepmRNALoc, was proposed, utilizing a two-stage feature extraction strategy that featured bimodal information splitting and fusing for the first stage and a VGGNet-like CNN module for the second stage. The five-fold cross-validation accuracies of DeepmRNALoc in the cytoplasm, endoplasmic reticulum, extracellular region, mitochondria, and nucleus were 0.895, 0.594, 0.308, 0.944, and 0.865, respectively, demonstrating that it outperforms existing models and techniques.

GM-lncLoc: LncRNAs subcellular localization prediction based on graph neural network with meta-learning

In recent years, a large number of studies have shown that the subcellular localization of long non-coding RNAs (lncRNAs) can bring crucial information to the recognition of lncRNAs function. Therefore, it is of great significance to establish a computational method to accurately predict the subcellular localization of lncRNA. Previous prediction models are based on low-level sequences information and are troubled by the few samples problem. In this study, we propose a new prediction model, GM-lncLoc, which is based on the initial information extracted from the lncRNA sequence, and also combines the graph structure information to extract high level features of lncRNA. In addition, the training mode of meta-learning is introduced to obtain meta-parameters by training a series of tasks. With the meta-parameters, the final parameters of other similar tasks can be learned quickly, so as to solve the problem of few samples in lncRNA subcellular localization. Compared with the previous methods, GM-lncLoc achieved the best results with an accuracy of 93.4 and 94.2% in the benchmark datasets of 5 and 4 subcellular compartments, respectively. Furthermore, the prediction performance of GM-lncLoc was also better on the independent dataset. It shows the effectiveness and great potential of our proposed method for lncRNA subcellular localization prediction. The datasets and source code are freely available at https://github.com/JunzheCai/GM-lncLoc .

GraphLncLoc: long non-coding RNA subcellular localization prediction using graph convolutional networks based on sequence to graph transformation

The subcellular localization of long non-coding RNAs (lncRNAs) is crucial for understanding lncRNA functions. Most of existing lncRNA subcellular localization prediction methods use k-mer frequency features to encode lncRNA sequences. However, k-mer frequency features lose sequence order information and fail to capture sequence patterns and motifs of different lengths. In this paper, we proposed GraphLncLoc, a graph convolutional network-based deep learning model, for predicting lncRNA subcellular localization. Unlike previous studies encoding lncRNA sequences by using k-mer frequency features, GraphLncLoc transforms lncRNA sequences into de Bruijn graphs, which transforms the sequence classification problem into a graph classification problem. To extract the high-level features from the de Bruijn graph, GraphLncLoc employs graph convolutional networks to learn latent representations. Then, the high-level feature vectors derived from de Bruijn graph are fed into a fully connected layer to perform the prediction task. Extensive experiments show that GraphLncLoc achieves better performance than traditional machine learning models and existing predictors. In addition, our analyses show that transforming sequences into graphs has more distinguishable features and is more robust than k-mer frequency features. The case study shows that GraphLncLoc can uncover important motifs for nucleus subcellular localization. GraphLncLoc web server is available at http://csuligroup.com:8000/GraphLncLoc/.

Identification of lncRNA-miRNA-mRNA networks in circulating exosomes as potential biomarkers for systemic sclerosis

Objective

This study aimed to analyze potential biomarkers for systemic sclerosis (SSc) by constructing lncRNA-miRNA-mRNA networks in circulating exosomes (cirexos).

Materials and methods

Differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) in SSc cirexos were screened using high-throughput sequencing and detected with real-time quantitative PCR (RT-qPCR). Differentially expressed genes (DEGs) were analyzed using the DisGeNET, GeneCards, GSEA4.2.3, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Receiver operating characteristic (ROC) curves, correlation analyses, and a double-luciferase reporter gene detection assay were used to analyze competing endogenous RNA (ceRNA) networks and clinical data.

Results

In this study, 286 DEmRNAs and 192 DElncRNAs were screened, of which 18 DEGs were the same as the SSc-related genes. The main SSc-related pathways included extracellular matrix (ECM) receptor interaction, local adhesion, platelet activation, and IgA production by the intestinal immune network. A hub gene, COL1A1, was obtained by a protein-protein interaction (PPI) network. Four ceRNA networks were predicted through Cytoscape. The relative expression levels of COL1A1, ENST0000313807, and NON-HSAT194388.1 were significantly higher in SSc, while the relative expression levels of hsa-miR-29a-3p, hsa-miR-29b-3p, and hsa-miR-29c-3p were significantly lower in SSc (P < 0.05). The ROC curve showed that the ENST00000313807-hsa-miR-29a-3p-COL1A1 network as a combined biomarker of SSc is more valuable than independent diagnosis, and that it is correlated with high-resolution CT (HRCT), Scl-70, C-reactive protein (CRP), Ro-52, IL-10, IgM, lymphocyte percentage, neutrophil percentage, albumin divided by globulin, urea, and RDW-SD (P < 0.05). Double-luciferase reporter gene detection showed that ENST00000313807 interacts with hsa-miR-29a-3p, which interacts with COL1A1.

Conclusion

The ENST00000313807-hsa-miR-29a-3p-COL1A1 network in plasma cirexos represents a potential combined biomarker for the clinical diagnosis and treatment of SSc.

Hsa_circ_0015278 Regulates FLT3-ITD AML Progression via Ferroptosis-Related Genes

AML with the FLT3-ITD mutation seriously threatens human health. The mechanism by which circRNAs regulate the pathogenesis of FLT3-ITD mutant-type AML through ferroptosis-related genes (FerRGs) remains unclear. Differentially expressed circRNAs and mRNAs were identified from multiple integrated data sources. The target miRNAs and mRNAs of the circRNAs were predicted using various databases. The PPI network, ceRNA regulatory network, GO, and KEGG enrichment analyses were performed. The "survival" and the "pROC" R packages were used for K-M and ROC analysis, respectively. GSEA, immune infiltration analysis, and clinical subgroup analysis were performed. Finally, circRNAs were validated by Sanger sequencing and qRT-PCR. In our study, 77 DECircs-1 and 690 DECircs-2 were identified. Subsequently, 11 co-up-regulated DECircs were obtained by intersecting DECircs-1 and DECircs-2. The target miRNAs of the circRNAs were screened by CircInteractome, circbank, and circAtlas. Utilizing TargetScan, ENCORI, and miRWalk, the target mRNAs of the miRNAs were uncovered. Ultimately, 73 FerRGs were obtained, and the ceRNA regulatory network was constructed. Furthermore, MAPK3 and CD44 were significantly associated with prognosis. qRT-PCR results confirmed that has_circ_0015278 was significantly overexpressed in FLT3-ITD mutant-type AML. In summary, we constructed the hsa_circ_0015278/miRNAs/FerRGs signaling axis, which provides new insight into the pathogenesis and therapeutic targets of AML with FLT3-ITD mutation.

LncBook 2.0: integrating human long non-coding RNAs with multi-omics annotations

LncBook, a comprehensive resource of human long non-coding RNAs (lncRNAs), has been used in a wide range of lncRNA studies across various biological contexts. Here, we present LncBook 2.0 (https://ngdc.cncb.ac.cn/lncbook), with significant updates and enhancements as follows: (i) incorporation of 119 722 new transcripts, 9632 new genes, and gene structure update of 21 305 lncRNAs; (ii) characterization of conservation features of human lncRNA genes across 40 vertebrates; (iii) integration of lncRNA-encoded small proteins; (iv) enrichment of expression and DNA methylation profiles with more biological contexts and (v) identification of lncRNA-protein interactions and improved prediction of lncRNA-miRNA interactions. Collectively, LncBook 2.0 accommodates a high-quality collection of 95 243 lncRNA genes and 323 950 transcripts and incorporates their abundant annotations at different omics levels, thereby enabling users to decipher functional significance of lncRNAs in different biological contexts.

LINC00589-dominated ceRNA networks regulate multiple chemoresistance and cancer stem cell-like properties in HER2+ breast cancer

Resistance to human epidermal growth factor receptor 2 (HER2)-targeted therapy (trastuzumab), cancer stem cell (CSC)-like properties and multiple chemoresistance often concur and intersect in breast cancer, but molecular links that may serve as effective therapeutic targets remain largely unknown. Here, we identified the long noncoding RNA, LINC00589 as a key regulatory node for concurrent intervention of these processes in breast cancer cells in vitro and in vivo. We demonstrated that the expression of LINC00589 is clinically valuable as an independent prognostic factor for discriminating trastuzumab responders. Mechanistically, LINC00589 serves as a ceRNA platform that simultaneously sponges miR-100 and miR-452 and relieves their repression of tumor suppressors, including discs large homolog 5 (DLG5) and PR/SET domain 16 (PRDM16, a transcription suppressor of mucin4), thereby exerting multiple cancer inhibitory functions and counteracting drug resistance. Collectively, our results disclose two LINC00589-initiated ceRNA networks, the LINC00589-miR-100-DLG5 and LINC00589-miR-452-PRDM16- mucin4 axes, which regulate trastuzumab resistance, CSC-like properties and multiple chemoresistance of breast cancer, thus providing potential diagnostic and prognostic markers and therapeutic targets for HER2-positive breast cancer.

KIF26B-AS1 Regulates TLR4 and Activates the TLR4 Signaling Pathway to Promote Malignant Progression of Laryngeal Cancer

Laryngeal cancer is one of the highest incidence, most prevalently diagnosed head and neck cancers, making it critically necessary to probe effective targets for laryngeal cancer treatment. Here, real-time quantitative reverse transcription PCR (qRT-PCR) and western blot analysis were used to detect gene expression levels in laryngeal cancer cell lines. Fluorescence in situ hybridization (FISH) and subcellular fractionation assays were used to detect the subcellular location. Functional assays encompassing Cell Counting Kit-8 (CCK-8), 5-ethynyl-2'-deoxyuridine (EdU), transwell and wound healing assays were performed to examine the effects of target genes on cell proliferation and migration in laryngeal cancer. The in vivo effects were proved by animal experiments. RNA-binding protein immunoprecipitation (RIP), RNA pulldown and luciferase reporter assays were used to investigate the underlying regulatory mechanisms. The results showed that KIF26B antisense RNA 1 (KIF26B-AS1) propels cell proliferation and migration in laryngeal cancer and regulates the toll-like receptor 4 (TLR4) signaling pathway. KIF26B-AS1 also recruits FUS to stabilize TLR4 mRNA, consequently activating the TLR4 signaling pathway. Furthermore, KIF26B-AS1 plays an oncogenic role in laryngeal cancer via upregulating TLR4 expression as well as the FUS/TLR4 pathway axis, findings which offer novel insight for targeted therapies in the treatment of laryngeal cancer patients.

Integrative analyses of prognosis, tumor immunity, and ceRNA network of the ferroptosis-associated gene FANCD2 in hepatocellular carcinoma

Extensive evidence has revealed that ferroptosis plays a vital role in HCC development and progression. Fanconi anemia complementation group D2 (FANCD2) has been reported to serve as a ferroptosis-associated gene and has a close relationship with tumorigenesis and drug resistance. However, the impact of the FANCD2-related immune response and its mechanisms in HCC remains incompletely understood. In the current research, we evaluated the prognostic significance and immune-associated mechanism of FANCD2 based on multiple bioinformatics methods and databases. The results demonstrated that FANCD2 was commonly upregulated in 15/33 tumors, and only the high expression of FANCD2 in HCC was closely correlated with worse clinical outcomes by OS and DFS analyses. Moreover, ncRNAs, including two major types, miRNAs and lncRNAs, were closely involved in mediating FANCD2 upregulation in HCC and were established in a ceRNA network by performing various in silico analyses. The DUXAP8-miR-29c-FANCD2 and LINC00511-miR-29c-FANCD2 axes were identified as the most likely ncRNA-associated upstream regulatory axis of FANCD2 in HCC. Finally, FANCD2 expression was confirmed to be positively related to HCC immune cell infiltration, immune checkpoints, and IPS analysis, and GSEA results also revealed that this ferroptosis-associated gene was primarily involved in cancer-associated pathways in HCC. In conclusion, our investigations indicate that ncRNA-related modulatory overexpression of FANCD2 might act as a promising prognostic and immunotherapeutic target against HCC.

iPro-WAEL: a comprehensive and robust framework for identifying promoters in multiple species

Promoters are consensus DNA sequences located near the transcription start sites and they play an important role in transcription initiation. Due to their importance in biological processes, the identification of promoters is significantly important for characterizing the expression of the genes. Numerous computational methods have been proposed to predict promoters. However, it is difficult for these methods to achieve satisfactory performance in multiple species. In this study, we propose a novel weighted average ensemble learning model, termed iPro-WAEL, for identifying promoters in multiple species, including Human, Mouse, E.coli, Arabidopsis, B.amyloliquefaciens, B.subtilis and R.capsulatus. Extensive benchmarking experiments illustrate that iPro-WAEL has optimal performance and is superior to the current methods in promoter prediction. The experimental results also demonstrate a satisfactory prediction ability of iPro-WAEL on cross-cell lines, promoters annotated by other methods and distinguishing between promoters and enhancers. Moreover, we identify the most important transcription factor binding site (TFBS) motif in promoter regions to facilitate the study of identifying important motifs in the promoter regions. The source code of iPro-WAEL is freely available at https://github.com/HaoWuLab-Bioinformatics/iPro-WAEL.

Analysis of herbivore-responsive long noncoding ribonucleic acids reveals a subset of small peptide-coding transcripts in Nicotiana tabacum

Long non-coding RNAs (lncRNAs) regulate many biological processes in plants, including defense against pathogens and herbivores. Recently, many small ORFs embedded in lncRNAs have been identified to encode biologically functional peptides (small ORF-encoded peptides [SEPs]) in many species. However, it is unknown whether lncRNAs mediate defense against herbivore attack and whether there are novel functional SEPs for these lncRNAs. By sequencing Spodoptera litura-treated leaves at six time-points in Nicotiana tabacum, 22,436 lncRNAs were identified, of which 787 were differentially expressed. Using a comprehensive mass spectrometry (MS) pipeline, 302 novel SEPs derived from 115 tobacco lncRNAs were identified. Moreover, 61 SEPs showed differential expression after S. litura attack. Importantly, several of these peptides were characterized through 3D structure prediction, subcellular localization validation by laser confocal microscopy, and western blotting. Subsequent bioinformatic analysis revealed some specific chemical and physical properties of these novel SEPs, which probably represent the largest number of SEPs identified in plants to date. Our study not only identifies potential lncRNA regulators of plant response to herbivore attack but also serves as a valuable resource for the functional characterization of SEP-encoding lncRNAs.

Circular RNA circFIRRE drives osteosarcoma progression and metastasis through tumorigenic-angiogenic coupling

Background

Disappointing clinical efficacy of standard treatment has been proven in refractory metastatic osteosarcoma, and the emerging anti-angiogenic regimens are still in the infantile stage. Thus, there is an urgent need to develop novel therapeutic approach for osteosarcoma lung metastasis.

Methods

circFIRRE was selected from RNA-sequencing of 4 matched osteosarcoma and adjacent samples. The expression of circFIRRE was verified in clinical osteosarcoma samples and cell lines via quantitative real-time polymerase chain reaction (RT-qPCR). The effect of circFIRRE was investigated in cell lines in vitro models, ex vivo models and in vivo xenograft tumor models, including proliferation, invasion, migration, metastasis and angiogenesis. Signaling regulatory mechanism was evaluated by RT-qPCR, Western blot, RNA pull-down and dual-luciferase reporter assays.

Results

In this article, a novel circular RNA, circFIRRE (hsa_circ_0001944) was screened out and identified from RNA-sequencing, and was upregulated in both osteosarcoma cell lines and tissues. Clinically, aberrantly upregulated circFIRRE portended higher metastatic risk and worse prognosis in osteosarcoma patients. Functionally, in vitro, ex vivo and in vivo experiments demonstrated that circFIRRE could drive primary osteosarcoma progression and lung metastasis by inducing both tumor cells and blood vessels, we call as “tumorigenic-angiogenic coupling”. Mechanistically, upregulated circFIRRE was induced by transcription factor YY1, and partially boosted the mRNA and protein level of LUZP1 by sponging miR-486-3p and miR-1225-5p.

Conclusions

We identified circFIRRE as a master regulator in the tumorigenesis and angiogenesis of osteosarcoma, which could be purposed as a novel prognostic biomarker and therapeutic target for refractory osteosarcoma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12943-022-01624-7.

A novel glycosyltransferase-related lncRNA signature correlates with lung adenocarcinoma prognosis

Background

Lung adenocarcinoma (LUAD) is one of the most fatal cancers in the world. Previous studies have shown the increase in glycosylation level, and abnormal expressions of related enzymes are closely related to various cancers. Long non-coding RNAs (lncRNAs) play an important role in the proliferation, metabolism, and migration of cancer cells, but the underlying role of glycosyltransferase (GT)-related lncRNAs in LUAD remains to be elucidated.

Methods

We abstracted 14,056 lncRNAs from The Cancer Genome Atlas (TCGA) dataset and 257 GT-related genes from the Gene Set Enrichment Analysis (GSEA) database. Univariate, LASSO-penalized, and multivariate Cox regression analyses were conducted to construct a GT-related lncRNA prognosis model.

Results

A total of 2,726 GT-related lncRNAs were identified through Pearson's correlation analysis, and eight of them were utilized to construct a GT-related lncRNA model. The overall survival (OS) of the low-risk group continued to be superior to that of the high-risk group according to the subgroups classified by clinical features. The risk model was proved to have independent prognostic characteristics for LUAD by univariate and multivariate Cox regression analyses. The status of the tumor immune microenvironment and the relevant immunotherapy response was significantly different between the two risk groups. The candidate drugs aimed at LUAD subtype differentiation were identified.

Conclusion

We constructed a risk model comprising eight GT-related lncRNAs which was identified as an independent predictor of prognoses to predict patient survival and guide-related treatments for patients with LUAD.

A comprehensive analysis of ncRNA-mediated interactions reveals potential prognostic biomarkers in prostate adenocarcinoma

As one of common malignancies, prostate adenocarcinoma (PRAD) has been a growing health problem and a leading cause of cancer-related death. To obtain expression and functional relevant RNAs, we firstly screened candidate hub mRNAs and characterized their associations with cancer. Eight deregulated genes were identified and used to build a risk model (AUC was 0.972 at 10 years) that may be a specific biomarker for cancer prognosis. Then, relevant miRNAs and lncRNAs were screened, and the constructed primarily interaction networks showed the potential cross-talks among diverse RNAs. IsomiR landscapes were surveyed to understand the detailed isomiRs in relevant homologous miRNA loci, which largely enriched RNA interaction network due to diversities of sequence and expression. We finally characterized TK1, miR-222-3p and SNHG3 as crucial RNAs, and the abnormal expression patterns of them were correlated with poor survival outcomes. TK1 was found synthetic lethal interactions with other genes, implicating potential therapeutic target in precision medicine. LncRNA SNHG3 can sponge miR-222-3p to perturb RNA regulatory network and TK1 expression. These results demonstrate that TK1:miR-222-3p:SNHG3 axis may be a potential prognostic biomarker, which will contribute to further understanding cancer pathophysiology and providing potential therapeutic targets in precision medicine.