Hepatocellular carcinoma (HCC) is one of leading causes of cancer-related death and new approaches are urgently needed, given current dearth of therapies. Long non-coding RNAs (lncRNAs) have been linked to cancer formation and impact cell regulatory pathways. We have investigated molecular mechanisms of action of "lung cancer associated transcript 1" (LUCAT1, a lncRNA) in HCC and studied the potential role of targeting these. We analyzed expression levels of LUCAT1 in TCGA dataset and another 148 HCC patients in Peking Union Medical College Hospital (PUMCH). Expression analysis using TCGA database and patient cohort revealed LUCAT1 to be upregulated in HCC. LUCAT1 levels were closely associated with clinical prognosis. Subcellular localization patterns of LUCAT1 in HCC tissues and cells were identified by RNAscope. Engineered antisense oligonucleotides (ASOs) targeting LUCAT1 were used to test anti-tumor effectiveness using in vitro and in vivo models. CHIRP-MS and RNA pull-down assays were conducted to demonstrate the mechanism of action of LUCAT1.LUCAT1 expression facilitated nuclear accumulation of HSP90. This interaction evoked persistent phosphorylation and constitutive activation of signal transducer and activator of transcription 3 (STAT3), potentially driving HCC growth and metastases. These tumor-promoting effects were substantively diminished using ASOs against LUCAT1, both in vitro and in vivo. LUCAT1 selectively boosts oncogenic property of HSP90 in driving STAT3 activation, which can be effectively and precisely inhibited by designer ASOs. We propose novel potential therapeutic avenues that are selective, cost-effective and seemingly nontoxic.

C-terminal binding protein 1 divergent transcript (CTBP1-DT) is a novel long non-coding RNA (lncRNA) located on human chromosome 4p16.3. Numerous studies have shown that CTBP1-DT plays a critical regulatory role in various human malignancies and non-malignant diseases. In several cancers, the expression of CTBP1-DT is upregulated, closely associated with the risk of 12 types of cancer, and strongly correlated with the clinical pathological features and poor prognosis of 10 of these cancers. Mechanistically, CTBP1-DT is stimulated by the transcription factors ETV5 and Sp1, or methylated by YTHDC1. By competitively inhibiting 12 microRNAs, it activates 3 signaling pathways that influence malignant behaviors of tumor cells, including proliferation, apoptosis, cell cycle arrest, migration, invasion, immune evasion, and chemoresistance. Importantly, it also encodes the microprotein DNA damage up-regulated protein (DDUP), which mediates cisplatin resistance through sustained response to DNA damage signals. Furthermore, CTBP1-DT has been implicated in the progression of non-malignant diseases such as diabetes and related conditions, cardiovascular diseases, and osteoarthritis. This review summarizes the latest research on the RNA and protein functions of CTBP1-DT in human diseases, outlines various molecular regulatory networks centered around CTBP1-DT, and discusses the opportunities and challenges of its clinical applications.

Long non-coding RNA (lncRNA) may be involved in dysfunction of pulmonary artery endothelial cells (PAEC) and, thus, in pulmonary arterial hypertension (PAH) pathobiology. We screened the RNA expression profile of commercial human PAEC (hPAEC) exposed to increased hydrostatic pressure, and found that the lncRNA Down syndrome critical region 9 (DSCR9) was the most regulated transcript (log2FC 1.89 vs control). We confirmed by RT-qPCR that DSCR9 levels were higher in PAEC isolated from patients with idiopathic PAH (iPAH-PAEC), as well as in induced pluripotent stem cell-derived endothelial cells (iPSC-EC) from a patient with BMPR2-mutated PAH, than in relevant controls. Moreover, a re-analysis of the publicly available GSE117261 microarray dataset revealed that DSCR9 was upregulated in the lung tissue of PAH patients. In silico simulation indicated that DSCR9 would be mainly located in the nucleus and could interact with calcium/calmodulin-dependent protein kinase II beta (CAMK2B) and nitric oxide synthase 3 (NOS3, encoding eNOS). CAMK2B levels resulted 3.4-fold higher (p < 0.05) in iPAH-PAEC transfected with a DSCR9-GFP carrying plasmid than with a GFP-only-carrying one. A trend for higher NOS3 expression was also noted. GFP immunostaining was predominantly nuclear and cytoplasmic upon DSCR9-GFP or GFP-only transfection, respectively. CAMK2B and NOS3 mRNA were also higher in iPAH-PAEC than control-PAEC in basal conditions. Instead, variations in total and phosphorylated CAMK2B, eNOS, and NO synthesis were inconsistent. We conclude that DSCR9 is upregulated in PAH-related endothelial cell models and influences CAMK2B and NOS3 expression. Future studies are necessary to determine whether DSCR9 affects NO availability, including in PAH.

The oncogenic and tumor suppressor roles of lnc-MAPKAPK5-AS1 in multiple cancers suggest its complexity in modulating cancer progression. The expression and promoter methylation level of lnc-MAPKAPK5-AS1 in hepatocellular carcinoma (HCC) was investigated through data mining from The Cancer Genome Atlas and Gene Expression Omnibus and its significance in prognosis and immunity was explored. lnc-MAPKAPK5-AS1 was co-expressed with its protein-coding gene MAPKAPK5 in HCC and exhibited upregulation in HCC tissues as a result of hypomethylation of its promoter region. High expression of lnc-MAPKAPK5-AS1 was associated with poor prognosis. Enrichment analysis revealed that lnc-MAPKAPK5-AS1 is involved in immune and metabolic-related pathways. Changes in the expression of lnc-MAPKAPK5-AS1 affected plasma cells, T cells CD4+ memory resting, NK cells, macrophages M0/M1, and mast cells resting in the tumor microenvironment. lnc-MAPKAPK5-AS1 was found to correlate with multiple immune checkpoints. Analysis of the Sangerbox database revealed positive relationships between expression of lnc-MAPKAPK5-AS1, tumor mutational burden and microsatellite instability, which suggested that immunotherapy may be effective in tumors with high expression of lnc-MAPKAPK5-AS1. The expression of lnc-MAPKAPK5-AS1 was verified to indicate sensitivity to 16 common targeted drugs. Immunohistochemistry confirmed the expression of MAPKAPK5 protein in HCC and its prognostic significance. Weighted gene co-expression network analysis was applied to identify hub genes related to both immunoreactive score and gene expression. These results revealed that lnc-MAPKAPK5-AS1 may be involved in the occurrence and development of HCC as an oncogene and may represent a potential therapeutic target through modulating the substance metabolism and immune response.

This study investigates the mechanism of lncRNA ZFAS1 in pyroptosis of TNF-α-induced nucleus pulposus cells (NPCs) in intervertebral disc degeneration (IDD).

Mouse NPCs were isolated and induced by TNF-α to establish a cell model of IDD. The cell viability was evaluated by MTT assay. NLRP3, GSDMD-N, and cleaved-Caspase1 expressions were detected by Western blot. IL-1β and IL-18 contents were detected by ELISA. RT-qPCR was performed to determine ZFAS1, miR-155-3p, and METTL14 expressions. After intervening in ZFAS1 expression, the effect of ZFAS1 on pyroptosis was verified by Western blot and ELISA assays. RNA pull down or dual luciferase assay verified the binding between ZFAS1, miR-155-3p, and METTL14.

TNF-α induced pyroptosis of NPCs and promoted ZFAS1 expression. Silence of ZFAS1 repressed pyroptosis of TNF-α-induced NPCs. Mechanistically, ZFAS1 upregulated the transcription of METTL14 by competitively binding to miR-155-3p, thus enhancing NLRP3/Caspase-1-mediated NPC pyroptosis. Inhibition of miR-155-3p or overexpression of METTL14 alleviated the inhibitory effect of ZFAS1 silencing on TNF-α-treated NPC pyroptosis.

ZFAS1 facilitates NLRP3/Caspase-1-mediated pyroptosis of NPCs in IDD via miR-155-3p/METTL14 axis.

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.

Hepatocellular carcinoma (HCC) is an aggressive malignancy with poor prognosis. LncRNA MAPKAPK5-AS1 is a potential oncogene and contributes to HCC cell malignant proliferation. This study explores the role of MAPKAPK5-AS1 carried by carcinoma-associated fibroblasts-derived extracellular vesicles (CAF-EVs) in HCC cell proliferation. Our findings reveal that CAF-EVs promotes HCC cell proliferation by delivering MAPKAPK5-AS1, which binds to and inhibits SMURF2 and stabilizes TCF12. SMURF2 leads to TCF12 ubiquitination and degradation. TCF12 upregulates FOXH1 expression. In animal model, CAF-EVs enhances tumor growth by stabilizing TCF12 via MAPKAPK5-AS1 and activating FOXH1 transcription. In conclusion, CAF-EVs carrying MAPKAPK5-AS1 stabilizes TCF12 expression by competitively inhibiting SMURF2, thus promoting TCF12-mediated FOXH1 transcription and driving HCC cell proliferation. Our findings may offer insights for HCC treatment and suggest potential targets for future treatments, opening avenues for HCC therapies.

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 .

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.

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.

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.

Recent research highlights the significant impact of methionine metabolism on glioma progression. An increasing amount of compelling evidence bridges long non-coding RNAs to abnormal metabolism in gliomas. However, the specific role of long non-coding RNAs in methionine metabolism regulating glioma progression remains unclear. This study reveals that methionine deprivation inhibits the proliferation, migration, and invasion capabilities of gliomas. Interestingly, the expression of TP53TG1, a long non-coding RNA, is also suppressed. TP53TG1 is highly expressed in gliomas and associated with poor patient outcomes. Subsequently, our data proves that inhibition of TP53TG1 suppresses glioma cell proliferation and the epithelial-mesenchymal transition process both in vitro and in vivo. Ultimately, we found that the underlying mechanism involves a competing endogenous RNA regulating network, in which TP53TG1 modulates the target protein STK17B by competitively binding to miR-96-5p, thus regulating glioma progression. These findings suggest that targeting methionine deprivation could be a promising approach for the clinical treatment of glioma.

Bipolar disorder is a complex polygenic disorder that is characterized by recurrent episodes of depression and mania, the heterogeneity of which is likely complicated by epigenetic modifications that remain to be elucidated.

We performed transcriptomic analysis of peripheral blood RNA from monozygotic (MZ) twins discordant for bipolar disorder to identify disease-associated differentially expressed long noncoding RNAs (DE-lncRNAs), which were further validated in the PsychENCODE brain RNA-seq dataset. We then performed behavioral tests, electrophysiological assays, chromatin immunoprecipitation, and PCR to investigate the function of DE-lncRNAs in the mouse and cell models. Statistical analyses were performed using GraphPad Prism 9.0 or SPSS.

We identified a bipolar disorder-associated upregulated long non-coding RNA (lncRNA), AP1AR-DT. We observed that overexpression of AP1AR-DT in the mouse medial prefrontal cortex (mPFC) resulted in a reduction of both the total spine density and the spontaneous excitatory postsynaptic current (sEPSC) frequency of mPFC neurons as well as depressive and anxiety-like behaviors. A combination of the results of brain transcriptome analysis of AP1AR-DT overexpressing mice brains with the known genes associated with bipolar disorder revealed that NEGR1, which encodes neuronal growth regulator 1, is one of the AP1AR-DT targets and is reduced in vivo upon gain of AP1AR-DT in mice. We further demonstrated that overexpression of recombinant Negr1 in the mPFC neurons of AP1AR-DTOE mice ameliorates depressive and anxiety-like behaviors and normalizes the reduced excitatory synaptic transmission induced by the gain of AP1AR-DT. We finally identified that AP1AR-DT reduces NEGR1 expression by competing for the transcriptional activator NRF1 in the overlapping binding site of the NEGR1 promoter region.

The epigenetic and pathophysiological mechanism linking AP1AR-DT to the modulation of depressive and anxiety-like behaviors and excitatory synaptic function provides etiological implications for bipolar disorder.

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.

Chronic myeloid leukemia (CML) is a clonal myeloproliferative neoplasm in which all the patients has the translocation (9;22) that generates de BCR::ABL1 tyrosine kinase. Despite this disease possessing a good biomarker (BCR::ABL1 transcripts level) for diagnosis and prognosis, many studies has been performed to investigate other molecules, such as the long noncoding RNAs (lncRNAs) and mRNAs, as potential biomarkers with the aim of predicting a change in BCR::ABL1 levels and as an associated biomarker. A RNAseq was performed comparing 6 CML patients with high BCR::ABL1 expression with 6 healthy control individuals, comprising the investigation cohort to investigate these molecules. To validate the results obtained by RNAseq, samples of 87 CML patients and 42 healthy controls were used in the validation cohort by RT-qPCR assays. The results showed lower expression of HOTAIR and PTGS2 in CML patients. The HOTAIR expression is inversely associated with BCR::ABL1 expression in imatinib-treated CML patients, and to PTGS2 showing that CML patients with high BCR::ABL1 expression showed reduced PTGS2 expression.

Cervical cancer (CC) poses a threat to human health. Enhancing pyroptosis can prevent the proliferation and epithelial-mesenchymal transition (EMT) of tumor cells. This study aims to reveal the candidates that modulate pyroptosis in CC. Accordingly, the common microRNAs (miRNAs/miRs) that were sponged by RBPMS antisense RNA 1 (RBPMS-AS1) and could target Phospholipase C-Like 1 (PLCL1) were intersected. The expression of PBPMS-AS1/miR-19a-3p (candidate miRNA)/PLCL1 was predicted in cervical squamous cell carcinoma (CESC), by which the expression location of RBPMS-AS1 and the binding between RBPMS-AS1/PLCL1 and miR-19a-3p were analyzed. The targeting relationship between RBPMS-AS1/PLCL1 and miR-19a-3p was confirmed by dual-luciferase reporter assay. After the transfection, cell counting kit-8 assay, colony formation assay, quantitative reverse transcription PCR, and Western blot were implemented for cell viability and proliferation analysis as well as gene and protein expression quantification analysis. Based on the results, RBPMS-AS1 and PLCL1 were lowly expressed, yet miR-19a-3p was highly expressed in CESC. RBPMS-AS1 overexpression diminished the proliferation and expressions of N-cadherin, vimentin, and miR-19a-3p, yet enhanced those of E-cadherin, PLCL1, and pyroptosis-relevant proteins (inteleukin-1β, caspase-1, and gasdermin D N-terminal). However, the above RBPMS-AS1 overexpression-induced effects were counteracted in the presence of miR-19a-3p. There also existed a targeting relationship and negative interplay between PLCL1 and miR-19a-3p. In short, RBPMS-AS1 sponges miR-19a-3p and represses the growth and EMT of CC cells via enhancing PLCL1-mediated pyroptosis.

MicroRNA isoforms (isomiRs), tRNA-derived fragments (tRFs), and rRNA-derived fragments (rRFs) represent most of the small non-coding RNAs (sncRNAs) found in cells. Members of these three classes modulate messenger RNA (mRNA) and protein abundance and are dysregulated in diseases. Experimental studies to date have assumed that the subcellular distribution of these molecules is well-understood, independent of cell type, and the same for all isoforms of a sncRNA.

We tested these assumptions by investigating the subcellular distribution of isomiRs, tRFs, and rRFs in biological replicates from three cell lines from the same tissue and same-sex donors that model the same cancer subtype. In each cell line, we profiled the isomiRs, tRFs, and rRFs in the nucleus, cytoplasm, whole mitochondrion (MT), mitoplast (MP), and whole cell. Using a rigorous mathematical model we developed, we accounted for cross-fraction contamination and technical errors and adjusted the measured abundances accordingly. Analyses of the adjusted abundances show that isomiRs, tRFs, and rRFs exhibit complex patterns of subcellular distributions. These patterns depend on each sncRNA's exact sequence and the cell type. Even in the same cell line, isoforms of the same sncRNA whose sequences differ by a few nucleotides (nts) can have different subcellular distributions.

SncRNAs with similar sequences have different subcellular distributions within and across cell lines, suggesting that each isoform could have a different function. Future computational and experimental studies of isomiRs, tRFs, and rRFs will need to distinguish among each molecule's various isoforms and account for differences in each isoform's subcellular distribution in the cell line at hand. While the findings add to a growing body of evidence that isomiRs, tRFs, rRFs, tRNAs, and rRNAs follow complex intracellular trafficking rules, further investigation is needed to exclude alternative explanations for the observed subcellular distribution of sncRNAs.

Non-coding RNAs (ncRNAs) and the innate immune system are closely related, acting as defense mechanisms and regulating gene expression and innate immunity. Both are modulators in the initiation, development and progression of cancer. We aimed to review the major types of ncRNAs, including small interfering RNAs (siRNAs), microRNAs (miRNAs), piwi-interacting RNAs (piRNAs), and long non-coding RNAs (lncRNAs), with a focus on cancer, innate immunity, and inflammation. We found that ncRNAs are closely related to innate immunity, epigenetics, chronic inflammation, and cancer and share properties such as inducibility, specificity, memory, and transfer. These similarities and interrelationships suggest that ncRNAs and modulators of trained immunity, together with the control of chronic inflammation, can be combined to develop novel therapeutic approaches for personalized cancer treatment. In conclusion, the close relationship between ncRNAs, the innate immune system, and inflammation highlights their importance in cancer pathways and their potential as targets for novel therapeutic strategies.

Triple-negative breast cancer (TNBC) represents the most formidable subtype of breast cancer, characterized by a notable dearth in targeted therapeutic options. Deciphering the underlying molecular mechanisms of TNBC is pivotal for improving patient outcomes. Recent scientific advancements have spotlighted long non-coding RNAs (lncRNAs) as key players in the genesis, progression, and metastasis of cancers. This review delineates the significant influence of lncRNAs on the advancement, detection, and neoadjuvant chemotherapy efficacy in TNBC, detailing the diverse expression patterns of aberrant lncRNAs. The paper explores the specific mechanisms by which lncRNAs regulate gene expression in both the nucleus and cytoplasm, with a special focus on their involvement in TNBC's post-transcriptional landscape. Thorough investigations into TNBC-associated lncRNAs not only forge new avenues for early diagnosis and potent treatment strategies but also highlight these molecules as promising therapeutic targets, heralding an era of personalized and precision medicine in TNBC management.

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.

The "RNA world" represents a novel frontier for the study of fundamental biological processes and human diseases and is paving the way for the development of new drugs tailored to each patient's biomolecular characteristics. Although scientific data about coding and non-coding RNA molecules are constantly produced and available from public repositories, they are scattered across different databases and a centralized, uniform, and semantically consistent representation of the "RNA world" is still lacking. We propose RNA-KG, a knowledge graph (KG) encompassing biological knowledge about RNAs gathered from more than 60 public databases, integrating functional relationships with genes, proteins, and chemicals and ontologically grounded biomedical concepts. To develop RNA-KG, we first identified, pre-processed, and characterized each data source; next, we built a meta-graph that provides an ontological description of the KG by representing all the bio-molecular entities and medical concepts of interest in this domain, as well as the types of interactions connecting them. Finally, we leveraged an instance-based semantically abstracted knowledge model to specify the ontological alignment according to which RNA-KG was generated. RNA-KG can be downloaded in different formats and also queried by a SPARQL endpoint. A thorough topological analysis of the resulting heterogeneous graph provides further insights into the characteristics of the "RNA world". RNA-KG can be both directly explored and visualized, and/or analyzed by applying computational methods to infer bio-medical knowledge from its heterogeneous nodes and edges. The resource can be easily updated with new experimental data, and specific views of the overall KG can be extracted according to the bio-medical problem to be studied.

Cancer stemness plays an important role in cancer initiation and progression, and is the major cause of tumor invasion, metastasis, recurrence, and poor prognosis. Non-coding RNAs (ncRNAs) are a class of RNA transcripts that generally cannot encode proteins and have been demonstrated to play a critical role in regulating cancer stemness. Here, we developed the ncStem database to record manually curated and predicted ncRNAs associated with cancer stemness. In total, ncStem contains 645 experimentally verified entries, including 159 long non-coding RNAs (lncRNAs), 254 microRNAs (miRNAs), 39 circular RNAs (circRNAs), and 5 other ncRNAs. The detailed information of each entry includes the ncRNA name, ncRNA identifier, disease, reference, expression direction, tissue, species, and so on. In addition, ncStem also provides computationally predicted cancer stemness-associated ncRNAs for 33 TCGA cancers, which were prioritized using the random walk with restart (RWR) algorithm based on regulatory and co-expression networks. The total predicted cancer stemness-associated ncRNAs included 11 132 lncRNAs and 972 miRNAs. Moreover, ncStem provides tools for functional enrichment analysis, survival analysis, and cell location interrogation for cancer stemness-associated ncRNAs. In summary, ncStem provides a platform to retrieve cancer stemness-associated ncRNAs, which may facilitate research on cancer stemness and offer potential targets for cancer treatment. Database URL: http://www.nidmarker-db.cn/ncStem/index.html.

This is a mini-review capturing the views and opinions of selected participants at the 2021 IEEE BIBM 3rd Annual LncRNA Workshop, held in Dubai, UAE. The views and opinions are expressed on five broad themes related to problems in lncRNA, namely, challenges in the computational analysis of lncRNAs, lncRNAs and cancer, lncRNAs in sports, lncRNAs and COVID-19, and lncRNAs in human brain activity.

Non-coding RNA (ncRNA) therapeutics can target either ncRNAs or conventional messenger RNA, offering both superior pharmacokinetics and selectivity to conventional therapies and addressing new, previously unexplored pathways. Although no ncRNA has yet been approved for the treatment of heart failure, in this review we present five most promising pathways and agents that either are in human clinical trials or offer great promise in the near future.

The incidence and mortality of liver hepatocellular carcinoma (LIHC) were increasing year by year. The aim of this study was to investigate the comprehensive roles of lncRNA FAM99A and FAM99B in LIHC.

According to the data of TCGA and GTEx, the expression levels of FAM99A and FAM99B in LIHC were evaluated, and the overall survival (OS), disease-free survival (DFS), immune cell infiltration and tumor stage were analyzed. The subcellular localization of FAM99A and FAM99B in various cancer cell lines was predicted by lncATLAS database. In addition, we also used ENCORI, KEGG, LinkedOmics, Metascape and other databases. It was verified by in vivo and in vitro experiments.

Compared with adjacent normal tissues, FAM99A and FAM99B were down-regulated in LIHC tissues, and significantly correlated with immune cell infiltration. With the progression of tumor stage and grade, the expression of FAM99A and FAM99B showed a decreasing trend, and the prognosis of patients were also poor. In addition, the biological functions, signaling pathways and protein interactions of FAM99A and FAM99B in LIHC were enriched to study the potential molecular mechanisms. The overlapping RNA binding proteins (RBP) of FAM99A and FAM99B mainly included CSTF2T, BCCIP, RBFOX2 and SF3B4. Finally, experiments showed that overexpression of FAM99A attenuated the proliferation, invasion, colony formation and tumor growth of LIHC cells.

Taken together, the above studies demonstrated that FAM99A and FAM99B had an inhibitory effect on the progression of LIHC, which might be promising diagnostic biomarkers and therapeutic targets for LIHC patients.

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.

With increasing significance of developmental programming effects associated with placental dysfunction, more investigations are devoted to improving the characterization and understanding of placental signatures in health and disease. The placenta is a transitory but dynamic organ adapting to the shifting demands of fetal development and available resources of the maternal supply throughout pregnancy. Trophoblasts (cytotrophoblasts, syncytiotrophoblasts, and extravillous trophoblasts) are placental-specific cell types responsible for the main placental exchanges and adaptations. Transcriptomic studies with single-cell resolution have led to advances in understanding the placenta's role in health and disease. These studies, however, often show discrepancies in characterization of the different placental cell types.

We aim to review the knowledge regarding placental structure and function gained from the use of single-cell RNA sequencing (scRNAseq), followed by comparing cell-type-specific genes, highlighting their similarities and differences. Moreover, we intend to identify consensus marker genes for the various trophoblast cell types across studies. Finally, we will discuss the contributions and potential applications of scRNAseq in studying pregnancy-related diseases.

We conducted a comprehensive systematic literature review to identify different cell types and their functions at the human maternal-fetal interface, focusing on all original scRNAseq studies on placentas published before March 2023 and published reviews (total of 28 studies identified) using PubMed search. Our approach involved curating cell types and subtypes that had previously been defined using scRNAseq and comparing the genes used as markers or identified as potential new markers. Next, we reanalyzed expression matrices from the six available scRNAseq raw datasets with cell annotations (four from first trimester and two at term), using Wilcoxon rank-sum tests to compare gene expression among studies and annotate trophoblast cell markers in both first trimester and term placentas. Furthermore, we integrated scRNAseq raw data available from 18 healthy first trimester and nine term placentas, and performed clustering and differential gene expression analysis. We further compared markers obtained with the analysis of annotated and raw datasets with the literature to obtain a common signature gene list for major placental cell types.

Variations in the sampling site, gestational age, fetal sex, and subsequent sequencing and analysis methods were observed between the studies. Although their proportions varied, the three trophoblast types were consistently identified across all scRNAseq studies, unlike other non-trophoblast cell types. Notably, no marker genes were shared by all studies for any of the investigated cell types. Moreover, most of the newly defined markers in one study were not observed in other studies. These discrepancies were confirmed by our analysis on trophoblast cell types, where hundreds of potential marker genes were identified in each study but with little overlap across studies. From 35 461 and 23 378 cells of high quality in the first trimester and term placentas, respectively, we obtained major placental cell types, including perivascular cells that previously had not been identified in the first trimester. Importantly, our meta-analysis provides marker genes for major placental cell types based on our extensive curation.

This review and meta-analysis emphasizes the need for establishing a consensus for annotating placental cell types from scRNAseq data. The marker genes identified here can be deployed for defining human placental cell types, thereby facilitating and improving the reproducibility of trophoblast cell annotation.

It is apparent that various functional units within the cellular machinery are derived from RNAs. The evolution of sequencing techniques has resulted in significant insights into approaches for transcriptome studies. Organisms utilize RNA to govern cellular systems, and a heterogeneous class of RNAs is involved in regulatory functions. In particular, regulatory RNAs are increasingly recognized to participate in intricately functioning machinery across almost all levels of biological systems. These systems include those mediating chromatin arrangement, transcription, suborganelle stabilization, and posttranscriptional modifications. Any class of RNA exhibiting regulatory activity can be termed a class of regulatory RNA and is typically represented by noncoding RNAs, which constitute a substantial portion of the genome. These RNAs function based on the principle of structural changes through cis and/or trans regulation to facilitate mutual RNA‒RNA, RNA‒DNA, and RNA‒protein interactions. It has not been clearly elucidated whether regulatory RNAs identified through deep sequencing actually function in the anticipated mechanisms. This review addresses the dominant properties of regulatory RNAs at various layers of the cellular machinery and covers regulatory activities, structural dynamics, modifications, associated molecules, and further challenges related to therapeutics and deep learning.

The surge in reports describing non-coding RNAs (ncRNAs) has focused attention on their possible biological roles and effects on development and disease. ncRNAs have been touted as previously uncharacterized regulators of gene expression and cellular processes, possibly working to fine-tune these functions. The sheer number of ncRNAs identified has outpaced the capacity to characterize each molecule thoroughly and to reliably establish its clinical relevance; it has, nonetheless, created excitement about their potential as molecular targets for novel therapeutic approaches to treat human disease. In this Review, we focus on one category of ncRNAs - long non-coding RNAs - and their expression, functions and molecular mechanisms in cardiac hypertrophy and heart failure. We further discuss the prospects for this specific class of ncRNAs as novel targets for the diagnosis and treatment of these conditions.

Competitive endogenous RNA (ceRNA) is an innovative way of gene expression modulation, which plays a crucial part in neoplasia. However, the intricacy and behavioral characteristics of the ceRNA network in hepatocellular carcinoma (HCC) remain dismal.

To establish a cyclin dependent kinase inhibitor 2A (CDKN2A)-related ceRNA network and recognize potential prognostic indicators for HCC.

The mutation landscape of CDKN2A in HCC was first explored using the cBioPortal database. Differential expression analysis was implemented between CDKN2Ahigh and CDKN2Alow expression HCC samples. The targeted microRNAs were predicted by lncBasev3.0, and the targeted mRNAs were predicted by miRDB, and Targetscan database. The univariate and multivariate analysis were utilized to identify independent prognostic indicators.

CDKN2A was frequently mutated and deleted in HCC. The single-cell RNA-sequencing analysis revealed that CDKN2A participated in cell cycle pathways. The CDKN2A-related ceRNA network-growth arrest specific 5 (GAS5)/miR-25-3p/SRY-box transcription factor 11 (SOX11) was successfully established. GAS5 was recognized as an independent prognostic biomarker, whose overexpression was correlated with a poor prognosis in HCC patients. The association between GAS5 expression and methylation, immune infiltration was explored. Besides, traditional Chinese medicine effective components targeting GAS5 were obtained.

This CDKN2A-related ceRNA network provides innovative insights into the molecular mechanism of HCC formation and progression. Moreover, GAS5 might be a significant prognostic biomarker and therapeutic target in HCC.

A key attribute of some long noncoding RNAs (lncRNAs) is their ability to regulate expression of neighbouring genes in cis. However, such 'cis-lncRNAs' are presently defined using ad hoc criteria that, we show, are prone to false-positive predictions. The resulting lack of cis-lncRNA catalogues hinders our understanding of their extent, characteristics and mechanisms. Here, we introduce TransCistor, a framework for defining and identifying cis-lncRNAs based on enrichment of targets amongst proximal genes. TransCistor's simple and conservative statistical models are compatible with functionally defined target gene maps generated by existing and future technologies. Using transcriptome-wide perturbation experiments for 268 human and 134 mouse lncRNAs, we provide the first large-scale survey of cis-lncRNAs. Known cis-lncRNAs are correctly identified, including XIST, LINC00240 and UMLILO, and predictions are consistent across analysis methods, perturbation types and independent experiments. We detect cis-activity in a minority of lncRNAs, primarily involving activators over repressors. Cis-lncRNAs are detected by both RNA interference and antisense oligonucleotide perturbations. Mechanistically, cis-lncRNA transcripts are observed to physically associate with their target genes and are weakly enriched with enhancer elements. In summary, TransCistor establishes a quantitative foundation for cis-lncRNAs, opening a path to elucidating their molecular mechanisms and biological significance.

Metastasis is the leading cause of death in colorectal cancer (CRC) patients. By mediating intercellular communication, exosomes exhibit considerable value in regulating tumor metastasis. Long non-coding RNAs (lncRNAs) are abundant in exosomes and participate in regulating tumor progression. However, it is poorly understood how the cancer-secreted exosomal lncRNAs affect CRC proliferation and metastasis. Here, by analyzing the public databases we identified a lncRNA SNHG3 and demonstrated that SNHG3 was delivered through CRC cells-derived exosomes to promote metastasis in CRC. Mechanistically, exosomal SNHG3 was internalized by CRC cells and afterward upregulated the expression of β-catenin by facilitating the intranuclear transport of hnRNPC. Consequently, the RNA stability of β-catenin was enhanced which led to the activation of EMT and metastasis of CRC cells. Our findings expand the oncogenic mechanisms of exosomal SNHG3 and identify it as a diagnostic marker for CRC.

N6-methyladenosine (m6A) and 5-methylcytosine (m5C) RNA modifications have garnered significant attention in the field of epigenetic research due to their close association with human cancers. This study we focus on elucidating the expression patterns of m6A/m5C-related long non-coding RNAs (lncRNAs) in esophageal squamous cell carcinoma (ESCC) and assessing their prognostic significance and therapeutic potential. Transcriptomic profiles of ESCC were derived from public resources. m6A/m5C-related lncRNAs were obtained from TCGA using Spearman's correlations analysis. The m6A/m5C-lncRNAs prognostic signature was selected to construct a RiskScore model for survival prediction, and their correlation with the immune microenvironment and immunotherapy response was analyzed. A total of 606 m6A/m5C-lncRNAs were screened, and ESCC cases in the TCGA cohort were stratified into three clusters, which showed significantly distinct in various clinical features and immune landscapes. A RiskScore model comprising ten m6A/m5C-lncRNAs prognostic signature were constructed and displayed good independent prediction ability in validation datasets. Patients in the low-RiskScore group had a better prognosis, a higher abundance of immune cells (CD4 + T cell, CD4 + naive T cell, class-switched memory B cell, and Treg), and enhanced expression of most immune checkpoint genes. Importantly, patients with low-RiskScore were more cline benefit from immune checkpoint inhibitor treatment (P < 0.05). Our findings underscore the potential of RiskScore system comprising ten m6A/m5C-related lncRNAs as effective biomarkers for predicting survival outcomes, characterizing the immune landscape, and assessing response to immunotherapy in ESCC.

RNASEH1-AS1, a long non-coding RNA (lncRNA) divergently transcribed from the antisense strand of its neighboring protein-coding gene ribonuclease H1 (RNASEH1), has recently been demonstrated to be involved in tumor progression. However, the association between RNASEH1-AS1 and hepatocellular carcinoma (HCC) remains unclear. In the present study, first, the expression of RNASEH1-AS1 in HCC and its correlation with clinicopathological features, prognosis, diagnosis, immune cell infiltration of HCC patients was inspected using relevant R packages based on The Cancer Genome Atlas (TCGA) data. RNASEH1-AS1 was found to be up-regulated in most cancer types, including HCC, and its overexpression was significantly associated with histologic grade and AFP level as well as poor prognosis, and was an independent risk factor affecting overall survival with good diagnostic and prognostic values for HCC. RNASEH1-AS1 was inversely associated with the infiltration of most immune cell types, including plasmacytoid dendritic cells (pDC), B cells and neutrophils. Second, a total of 1109 positively co-expressed genes (PCEGs) of RNASEH1-AS1 were screened out in HCC by correlation analysis in batches (|Spearman's r| >0.4 and adjusted P value <0.01). GO and KEGG enrichment analysis indicated that PCEGs of RNASEH1-AS1 were mainly related to RNA processing, ribosome biogenesis, transcription and histone acetylation. The top 10 hub genes (EIF4A3, WDR43, WDR12, DKC1, NAT10, UTP18, DDX18, BYSL, DDX10, PDCD11) were identified by constructing the protein-protein interaction (PPI) network, and they were all highly expressed in HCC and positively correlated with histological grade. Third, a risk model was constructed based on four RNASEH1-AS1-related hub genes (EIF4A3, WDR12, DKC1, and NAT10) with good prognostic predictive potential via univariate Cox and the least absolute selection operator (LASSO) regression analysis. Fourth, experimental validation revealed that RNASEH1-AS1 was significantly elevated in HCC tissues and several cell lines, and its knockdown could suppress the proliferation, migration, and invasion of HCC cells. Finally, mechanistic studies demonstrated that the stability of RNASEH1-AS1 could be regulated by DKC1 via their direct interaction. Taken together, RNASEH1-AS1 may serve as a potential prognostic and diagnostic biomarker and oncogenic lncRNA for HCC.

Exosomes play a crucial role in intercellular communication and can be used as biomarkers for diagnostic and therapeutic clinical applications. However, systematic studies in cancer-associated exosomal nucleic acids remain a big challenge. Here, we developed ExMdb, a comprehensive database of exosomal nucleic acid biomarkers and disease-gene associations curated from published literature and high-throughput datasets. We performed a comprehensive curation of exosome properties including 4,586 experimentally supported gene-disease associations, 13,768 diagnostic and therapeutic biomarkers, and 312,049 nucleic acid subcellular locations. To characterize expression variation of exosomal molecules and identify causal factors of complex diseases, we have also collected 164 high-throughput datasets, including bulk and single-cell RNA sequencing (scRNA-seq) data. Based on these datasets, we performed various bioinformatics and statistical analyses to support our conclusions and advance our knowledge of exosome biology. Collectively, our dataset will serve as an essential resource for investigating the regulatory mechanisms of complex diseases and improving the development of diagnostic and therapeutic biomarkers.

Molecular entities showing dysregulation in multiple cancers may hold great biomarker or therapeutic potential. There is accumulating evidence that highlights the dysregulation of a long non-coding RNA, MIR210HG, in various cancers and its oncogenic role. However, a comprehensive analysis of MIR210HG expression pattern, molecular mechanisms, diagnostic or prognostic significance or evaluation of its interaction with tumor microenvironment across various cancers remains unstudied.

A systematic pan-cancer analysis was done using multiple public databases and bioinformatic tools to study the molecular role and clinical significance of MIR210HG. We have analyzed expression patterns, genome alteration, transcriptional and epigenetic regulation, correlation with patient survival, immune infiltrates, co-expressed genes, interacting proteins, and pathways associated with MIR210HG.

The Pan cancer expression analysis of MIR210HG through various tumor datasets demonstrated that MIR210HG is significantly upregulated in most cancers and increased with the tumor stage in a subset of them. Furthermore, prognostic analysis revealed high MIR210HG expression is associated with poor overall and disease-free survival in specific cancer types. Genetic alteration analysis showed minimal alterations in the MIR210HG locus, indicating that overexpression in cancers is not due to gene amplification. The exploration of SNPs on MIR210HG suggested possible structural changes that may affect its interactions with the miRNAs. The correlation of MIR210HG with promoter methylation was found to be significantly negative in nature in majority of cancers depicting the possible epigenetic regulation of expression of MIR210HG. Additionally, MIR210HG showed negative correlations with immune cells and thus may have strong impact on the tumor microenvironment. Functional analysis indicates its association with hypoxia, angiogenesis, metastasis, and DNA damage repair processes. MIR210HG was found to interact with several proteins and potentially regulate chromatin modifications and transcriptional regulation.

A first pan-can cancer analysis of MIR210HG highlights its transcriptional and epigenetic deregulation and oncogenic role in the majority of cancers, its correlation with tumor microenvironment factors such as hypoxia and immune infiltration, and its potential as a prognostic biomarker and therapeutic target in several cancers.

In the past, several methods have been developed for predicting the single-label subcellular localization of messenger RNA (mRNA). However, only limited methods are designed to predict the multi-label subcellular localization of mRNA. Furthermore, the existing methods are slow and cannot be implemented at a transcriptome scale. In this study, a fast and reliable method has been developed for predicting the multi-label subcellular localization of mRNA that can be implemented at a genome scale. Machine learning-based methods have been developed using mRNA sequence composition, where the XGBoost-based classifier achieved an average area under the receiver operator characteristic (AUROC) of 0.709 (0.668-0.732). In addition to alignment-free methods, we developed alignment-based methods using motif search techniques. Finally, a hybrid technique that combines the XGBoost model and the motif-based approach has been developed, achieving an average AUROC of 0.742 (0.708-0.816). Our method-MRSLpred-outperforms the existing state-of-the-art classifier in terms of performance and computation efficiency. A publicly accessible webserver and a standalone tool have been developed to facilitate researchers (webserver: https://webs.iiitd.edu.in/raghava/mrslpred/).

UPF1, a novel posttranscriptional regulator, regulates the abundance of transcripts, including long noncoding RNAs (lncRNAs), and thus plays an important role in cell homeostasis. In this study, we revealed that UPF1 regulates the abundance of hepatocellular carcinoma upregulated EZH2-associated lncRNA (lncRNA-HEIH) by binding the CG-rich motif, thereby regulating hepatocellular carcinoma (HCC) tumorigenesis. UPF1-bound lncRNA-HEIH was susceptible to degradation mediated by UPF1 phosphorylation via SMG1 and SMG5. According to analysis of RNA-seq and public data on patients with liver cancer, the expression of lncRNA-HEIH increased the levels of miR-194-5p targets and was inversely correlated with miR-194-5p expression in HCC patients. Furthermore, UPF1 depletion upregulated lncRNA-HEIH, which acts as a decoy of miR-194-5p that targets GNA13, thereby promoting GNA13 expression and HCC proliferation. The UPF1/lncRNA-HEIH/miR-194-5p/GNA13 regulatory axis is suggested to play a crucial role in cell progression and may be a suitable target for HCC therapy.

Cytochrome P450 3A4 (CYP3A4) is the most important and abundant drug-metabolizing enzyme in the human liver. Inter-individual differences in the expression and activity of CYP3A4 affect clinical and precision medicine. Increasing evidence indicates that long noncoding RNAs (lncRNAs) play crucial roles in the regulation of CYP3A4 expression. Here, we showed that lncRNA hepatocyte nuclear factor 1 alpha-antisense 1 (HNF1A-AS1) exerted dual functions in regulating CYP3A4 expression in Huh7 and HepG2 cells. Mechanistically, HNF1A-AS1 served as an RNA scaffold to interact with both protein arginine methyltransferase 1 and pregnane X receptor (PXR), thereby facilitating their protein interactions and resulting in the transactivation of PXR and transcriptional alteration of CYP3A4 via histone modifications. Furthermore, HNF1A-AS1 bound to the HNF1A protein, a liver-specific transcription factor, thereby blocking its interaction with the E3 ubiquitin ligase tripartite motif containing 25, ultimately preventing HNF1A ubiquitination and protein degradation, further regulating the expression of CYP3A4. In summary, these results reveal the novel functions of HNF1A-AS1 as the transcriptional and post-translational regulator of CYP3A4; thus, HNF1A-AS1 may serve as a new indicator for establishing or predicting individual differences in CYP3A4 expression.