Cancer LncRNA Census reveals evidence for deep functional conservation of long noncoding RNAs in tumorigenesis

Acquired sensorineural hearing loss, oxidative stress, and microRNAs

Hearing loss is the third leading cause of human disability. Age-related hearing loss, one type of acquired sensorineural hearing loss, is largely responsible for this escalating global health burden. Noise-induced, ototoxic, and idiopathic sudden sensorineural are other less common types of acquired hearing loss. The etiology of these conditions is complex and multi-factorial involving an interplay of genetic and environmental factors. Oxidative stress has recently been proposed as a likely linking cause in most types of acquired sensorineural hearing loss. Short non-coding RNA sequences known as microRNAs (miRNAs) have increasingly been shown to play a role in cellular hypoxia and oxidative stress responses including promoting an apoptotic response. Sensory hair cell death is a central histopathological finding in sensorineural hearing loss. As these cells do not regenerate in humans, it underlies the irreversibility of human age-related hearing loss. Ovid EMBASE, Ovid MEDLINE, Web of Science Core Collection, and ClinicalTrials.gov databases over the period August 1, 2018 to July 31, 2023 were searched with "hearing loss," "hypoxamiRs," "hypoxia," "microRNAs," "ischemia," and "oxidative stress" text words for English language primary study publications or registered clinical trials. Registered clinical trials known to the senior author were also assessed. A total of 222 studies were thus identified. After excluding duplicates, editorials, retractions, secondary research studies, and non-English language articles, 39 primary studies and clinical trials underwent full-text screening. This resulted in 11 animal, in vitro , and/or human subject journal articles and 8 registered clinical trial database entries which form the basis of this narrative review. MiRNAs miR-34a and miR-29b levels increase with age in mice. These miRNAs were demonstrated in human neuroblastoma and murine cochlear cell lines to target Sirtuin 1/peroxisome proliferator-activated receptor gamma coactivator-1-alpha (SIRT1/PGC-1α), SIRT1/p53, and SIRT1/hypoxia-inducible factor 1-alpha signaling pathways resulting in increased apoptosis. Furthermore, hypoxia and oxidative stress had a similar adverse apoptotic effect, which was inhibited by resveratrol and a myocardial inhibitor-associated transcript, a miR-29b competing endogenous mRNA. Gentamicin reduced miR-182-5p levels and increased cochlear oxidative stress and cell death in mice - an effect that was corrected by inner ear stem cell-derived exosomes. There is ongoing work seeking to determine if these findings can be effectively translated to humans.

Recent advances in the delivery of microRNAs via exosomes derived from MSCs, and their role in regulation of ferroptosis

Mesenchymal stem cell (MSC) therapy, with its unique properties, has garnered interest in cancer treatment. Exosomes (EXOs)-derived from MSC retain the paracrine components of MSCs and demonstrate increased stability, minimal immunogenicity, and low risk of unintended tumorigenesis. Enhanced endocytosis methods make them versatile delivery vehicles for therapeutic cargo. MSC-EXOs can either promote or inhibit carcinogenesis, mediated by paracrine factors and various RNA molecules, particularly microRNAs (miRNAs). The prospect of using MSC-EXOs as a delivery tool for antitumor miRNAs in solid tumor therapy is promising. Exosomes' intrinsic tumor-targeting abilities and low immunogenicity make them ideal for delivering miRNAs, which have shown potential as cancer therapeutics. miRNAs within MSC-EXOs molecules can stimulate tumor growth or induce non-apoptotic cell death pathways, such as ferroptosis, depending on context. Ferroptosis is a kind of controlled cell death that is associated with the pathophysiology of several illnesses and includes iron metabolism. There is growing evidence that miRNAs carried by exosomes derived from MSCs may control ferroptosis in tumor cells by altering key genes related to antioxidant defense, lipid peroxidation, and iron metabolism. Understanding their complex mechanisms in the tumor microenvironment and optimizing their cargo are critical steps toward harnessing their full therapeutic potential. This review provides a comprehensive overview of MSC-EXOs and their role in cancer treatment. We also discuss the potential of MSC-EXOs as delivery vehicles for miRNAs to enhance therapeutic efficacy, as well as the role of exosomal miRNAs in the induction of ferroptosis.

POCALI: Prediction and Insight on CAncer LncRNAs by Integrating Multi-Omics Data with Machine Learning

Long non-coding RNAs (lncRNAs) are receiving increasing attention as biomarkers for cancer diagnosis and therapy. Although there are many computational methods to identify cancer lncRNAs, they do not comprehensively integrate multi-omics features for predictions or systematically evaluate the contribution of each omics to the multifaceted landscape of cancer lncRNAs. In this study, an algorithm, POCALI, is developed to identify cancer lncRNAs by integrating 44 omics features across six categories. The contributions of different omics are explored to identifying cancer lncRNAs and, more specifically, how each feature contributes to a single prediction. The model is evaluated and benchmarked POCALI with existing methods. Finally, the cancer phenotype and genomics characteristics of the predicted novel cancer lncRNAs are validated. POCALI identifies secondary structure and gene expression-related features as strong predictors of cancer lncRNAs, and epigenomic features as moderate predictors. POCALI performed better than other methods, especially in terms of sensitivity, and predicted more candidates. Novel POCALI-predicted cancer lncRNAs have strong relationships with cancer phenotypes, similar to known cancer lncRNAs. Overall, this study facilitates the identification of previously undetected cancer lncRNAs and the comprehensive exploration of the multifaceted feature contributions to cancer lncRNA prediction.

siRNA-based knockdown of lncRNAs: A new modality to target tumor progression

This study examines the potential of small interfering RNA (siRNA) as a therapeutic agent for cancer targeting long non-coding RNAs (lncRNAs). The article begins with an analysis of the structure and biogenesis of lncRNA. It explains the diverse functions of lncRNAs in cancer, establishing a foundation for assessing approaches to inhibit these molecules. The analysis focuses on the consequences of lncRNA suppression through siRNA on signaling pathways associated with cancer, connecting theoretical understanding to practical applications. An evaluation of ongoing clinical trials and applications contributes to the discourse by revealing the potential for siRNA-mediated interventions to be practiced. Furthermore, an evaluation of the advantages and disadvantages of this therapeutic approach offers a nuanced viewpoint. In conclusion, the paper synthesizes significant discoveries and outlines potential avenues for future research, contributing to the dialogue surrounding personalized cancer therapeutics and precision medicine. Future challenges in using siRNA to target lncRNAs in oncology include optimizing delivery systems for efficient tumor cell uptake, minimizing off-target effects, enhancing RNA stability for a longer therapeutic window, and overcoming barriers in the tumor microenvironment. Addressing these factors is essential for the practical application of siRNA-based cancer therapies.

Selectively expressed RNA molecules as a versatile tool for functionalized cell targeting

Targeting of diseased cells is one of the most urgently needed prerequisites for a next generation of potent pharmaceuticals. Different approaches pursued fail mainly due to a lack of specific surface markers. Developing an RNA-based methodology, we can now ensure precise cell targeting combined with selective expression of effector proteins for therapy, diagnostics or cell steering. The specific combination of the molecular properties of antisense technology and mRNA therapy with functional RNA secondary structures allowed us to develop selectively expressed RNA molecules for medical applications. These seRNAs remain inactive in non-target cells and induce translation by partial degradation only in preselected cell types of interest. Cell specificity and type of functionalization are easily adaptable based on a modular system. In proof-of-concept studies we use seRNAs as platform technology for highly selective cell targeting. We effectively treat breast tumor cell clusters in mixed cell systems and shrink early U87 glioblastoma cell clusters in the brain of male mice without detectable side effects. Our data open up potential avenues for various therapeutic applications.

Computational Resources for lncRNA Functions and Targetome

Long non-coding RNAs (lncRNAs) are a type of non-coding RNA molecules exceeding 200 nucleotides in length and that do not encode proteins. The dysregulated expression of lncRNAs has been identified in various diseases, holding therapeutic significance. Over the past decade, numerous computational resources have been published in the field of lncRNA. In this chapter, we have provided a comprehensive review of the databases as well as predictive tools, that is, lncRNA databases, machine learning based algorithms, and tools predicting lncRNAs utilizing different techniques. The chapter will focus on the importance of lncRNA resources developed for different organisms specifically for humans, mouse, plants, and other model organisms. We have enlisted important databases, primarily focusing on comprehensive information related to lncRNA registries, associations with diseases, differential expression, lncRNA transcriptome, target regulations, and all-in-one resources. Further, we have also included the updated version of lncRNA resources. Additionally, computational identification of lncRNAs using algorithms like Deep learning, Support Vector Machine (SVM), and Random Forest (RF) was also discussed. In conclusion, this comprehensive overview concludes by summarizing vital in silico resources, empowering biologists to choose the most suitable tools for their lncRNA research endeavors. This chapter serves as a valuable guide, emphasizing the significance of computational approaches in understanding lncRNAs and their implications in various biological contexts.

Small and long non-coding RNAs: Past, present, and future

Since the introduction of the central dogma of molecular biology in 1958, various RNA species have been discovered. Messenger RNAs transmit genetic instructions from DNA to make proteins, a process facilitated by housekeeping non-coding RNAs (ncRNAs) such as small nuclear RNAs (snRNAs), ribosomal RNAs (rRNAs), and transfer RNAs (tRNAs). Over the past four decades, a wide array of regulatory ncRNAs have emerged as crucial players in gene regulation. In celebration of Cell's 50th anniversary, this Review explores our current understanding of the most extensively studied regulatory ncRNAs-small RNAs and long non-coding RNAs (lncRNAs)-which have profoundly shaped the field of RNA biology and beyond. While small RNA pathways have been well documented with clearly defined mechanisms, lncRNAs exhibit a greater diversity of mechanisms, many of which remain unknown. This Review covers pivotal events in their discovery, biogenesis pathways, evolutionary traits, action mechanisms, functions, and crosstalks among ncRNAs. We also highlight their roles in pathophysiological contexts and propose future research directions to decipher the unknowns of lncRNAs by leveraging lessons from small RNAs.

Long noncoding RNA AK144717 exacerbates pathological cardiac hypertrophy through modulating the cellular distribution of HMGB1 and subsequent DNA damage response

DNA damage induced by oxidative stress during cardiac hypertrophy activates the ataxia telangiectasia mutated (ATM)-mediated DNA damage response (DDR) signaling, in turn aggravating the pathological cardiomyocyte growth. This study aims to identify the functional associations of long noncoding RNA (lncRNAs) with cardiac hypertrophy and DDR. The altered ventricular lncRNAs in the mice between sham and transverse aortic constriction (TAC) group were identified by microarray analysis, and a novel lncRNA AK144717 was found to gradually upregulate during the development of pathological cardiac hypertrophy induced by TAC surgery or angiotensin II (Ang II) stimulation. Silencing AK144717 had a similar anti-hypertrophic effect to that of ATM inhibitor KU55933 and also suppressed the activated ATM-DDR signaling induced by hypertrophic stimuli. The involvement of AK144717 in DDR and cardiac hypertrophy was closely related to its interaction with HMGB1, as silencing HMGB1 abolished the effects of AK144717 knockdown. The binding of AK144717 to HMGB1 prevented the interaction between HMGB1 and SIRT1, contributing to the increased acetylation and then cytosolic translocation of HMGB1. Overall, our study highlights the role of AK144717 in the hypertrophic response by interacting with HMGB1 and regulating DDR, hinting that AK144717 is a promising therapeutic target for pathological cardiac growth.

Long noncoding RNA EPCART regulates translation through PI3K/AKT/mTOR pathway and PDCD4 in prostate cancer

While hundreds of cancer-associated long noncoding RNAs (lncRNAs) have been discovered, their functional role in cancer cells is still largely a mystery. An increasing number of lncRNAs are recognized to function in the cytoplasm, e.g., as modulators of translation. Here, we investigated the detailed molecular identity and functional role of EPCART, a lncRNA we previously discovered to be a potential oncogene in prostate cancer (PCa). First, we interrogated the transcript structure of EPCART and then confirmed EPCART to be a non-peptide-coding lncRNA using in silico methods. Pathway analysis of differentially expressed protein-coding genes in EPCART knockout cells implied that EPCART modulates the translational machinery of PCa cells. EPCART was also largely located in the cytoplasm and at the sites of translation. With quantitative proteome analysis on EPCART knockout cells we discovered PDCD4, an inhibitor of protein translation, to be increased by EPCART reduction. Further studies indicated that the inhibitory effect of EPCART silencing on translation was mediated by reduced activation of AKT and inhibition of the mTORC1 pathway. Together, our findings identify EPCART as a translation-associated lncRNA that functions via modulation of the PI3K/AKT/mTORC1 pathway in PCa cells. Furthermore, we provide evidence for the prognostic potential of PDCD4 in PCa tumors in connection with EPCART.

The oxidative stress-associated long non-coding RNAs in pancreatic cancer

PURPOSE

A lot of people are dying from pancreatic cancer (PC) annually. The early detection of this cancer is particularly challenging due to the fact that symptoms tend to appear in advanced stages. It has been suggested that oxidative stress may play a role in the development of PC. Several genes regulate this process, including long noncoding RNAs (lncRNAs). There is no comprehensive study on the expression pattern of lncRNAs related to oxidative stress in PC patients. In the present case-control study, we quantified levels of oxidative stress-associated lncRNAs in PC patients versus healthy controls.

PATIENTS AND METHODS

In the present study, we investigated the expression levels of lincRNA-p21, LUCAT, RMST, FOXD3-AS1, and MT1DP lncRNAs in the peripheral blood mononuclear cells (PBMCs) of 53 ​PC patients and 50 healthy controls. The association between lncRNA expression and clinical and pathological characteristics was also evaluated.

RESULTS

The expression of lincRNA-P21 and rhabdomyosarcoma 2-associated transcript (RMST) lncRNAs in PC patients has significantly decreased. Expression of lncRNA RMST was significantly higher in TNM stage III-IV patients in comparison to TNM stage I-II patients. In addition, a significant positive association was recognized between candidate lncRNA expression, and finally, the AUC values of the expression levels of lincRNA-p21 and RMST were 0.60 and 0.61, respectively.

CONCLUSIONS

Altogether, our study suggests a possible role of lincRNA-p21 and RMST lncRNAs in the etiology of PC pathobiology, and their biomarker role may be understood in future studies.

Long non-coding RNAs involved in Drosophila development and regeneration

The discovery of functional long non-coding RNAs (lncRNAs) changed their initial concept as transcriptional noise. LncRNAs have been identified as regulators of multiple biological processes, including chromatin structure, gene expression, splicing, mRNA degradation, and translation. However, functional studies of lncRNAs are hindered by the usual lack of phenotypes upon deletion or inhibition. Here, we used Drosophila imaginal discs as a model system to identify lncRNAs involved in development and regeneration. We examined a subset of lncRNAs expressed in the wing, leg, and eye disc development. Additionally, we analyzed transcriptomic data from regenerating wing discs to profile the expression pattern of lncRNAs during tissue repair. We focused on the lncRNA CR40469, which is upregulated during regeneration. We generated CR40469 mutant flies that developed normally but showed impaired wing regeneration upon cell death induction. The ability of these mutants to regenerate was restored by the ectopic expression of CR40469. Furthermore, we found that the lncRNA CR34335 has a high degree of sequence similarity with CR40469 and can partially compensate for its function during regeneration in the absence of CR40469. Our findings point to a potential role of the lncRNA CR40469 in trans during the response to damage in the wing imaginal disc.

Long Non-Coding RNAs, Nuclear Receptors and Their Cross-Talks in Cancer-Implications and Perspectives

Long non-coding RNAs (lncRNAs) play key roles in various epigenetic and post-transcriptional events in the cell, thereby significantly influencing cellular processes including gene expression, development and diseases such as cancer. Nuclear receptors (NRs) are a family of ligand-regulated transcription factors that typically regulate transcription of genes involved in a broad spectrum of cellular processes, immune responses and in many diseases including cancer. Owing to their many overlapping roles as modulators of gene expression, the paths traversed by lncRNA and NR-mediated signaling often cross each other; these lncRNA-NR cross-talks are being increasingly recognized as important players in many cellular processes and diseases such as cancer. Here, we review the individual roles of lncRNAs and NRs, especially growth factor modulated receptors such as androgen receptors (ARs), in various types of cancers and how the cross-talks between lncRNAs and NRs are involved in cancer progression and metastasis. We discuss the challenges involved in characterizing lncRNA-NR associations and how to overcome them. Furthering our understanding of the mechanisms of lncRNA-NR associations is crucial to realizing their potential as prognostic features, diagnostic biomarkers and therapeutic targets in cancer biology.

A novel partnership between lncTCF7 and SND1 regulates the expression of the TCF7 gene via recruitment of the SWI/SNF complex

Long non-coding RNAs (lncRNAs) play key roles in cellular pathways and disease progression, yet their molecular mechanisms remain largely understudied. The lncRNA lncTCF7 has been shown to promote tumor progression by recruiting the SWI/SNF complex to the TCF7 promoter, activating its expression and the WNT signaling pathway. However, how lncTCF7 recruits SWI/SNF remains to be determined, and lncTCF7-specific binding partners are unknown. Using RNA-pulldown and quantitative mass spectrometry, we identified a novel interacting protein partner for lncTCF7, SND1, a multifunctional RNA binding protein that can also function as a transcription co-activator. Knockdown analysis of lncTCF7 and SND1 reveals that they are both required for the expression of TCF7. Chromatin immunoprecipitation assays suggest that both SND1 and lncTCF7 are required for recruiting the SWI/SNF chromatin remodeling complex, and these functions, in tandem, activate the expression of TCF7. Finally, using structural probing and RNA-pulldown of lncTCF7 and its subdomains, we highlight the potential binding region for SND1 in the 3'-end of lncTCF7. Overall, this study highlights the critical roles lncRNAs play in regulating gene expression and provides new insights into the complex network of interactions that underlie this process.

SNHG1: Redefining the Landscape of Hepatocellular Carcinoma through Long Noncoding RNAs

Hepatocellular carcinoma (HCC) represents a global health concern, ranking as the sixth most common malignancy worldwide and the third leading cause of cancer-related mortality. Despite advances in research, the diagnosis and prognosis of such malignancy remain challenging. Alpha-fetoprotein, the current serum biomarker used in the management of HCC, has limited sensitivity and specificity, making early detection and effective management more difficult. Thus, new management approaches in diagnosis and prognosis are needed to improve the outcome and survival of HCC patients. SNHG1 is a long noncoding RNA mainly expressed in the cell and cytoplasm of cells and is consistently upregulated in tissues and cell lines of HCC, where it acts as an important regulator of various processes: modulation of p53 activity, sponging of microRNAs with consequent upregulation of their target mRNAs, regulation of fatty acid, iron and glucose metabolism, and interaction with immune cells. The deregulation of these processes results in abnormal cell division, angiogenesis, and apoptosis, thus promoting various aspects of tumorigenesis, including proliferation, invasion, and migration of cells. Clinically, a higher expression of SNHG1 predicts poorer clinical outcomes by significantly correlating with bigger, less differentiated, and more aggressive tumors, more advanced disease stages, and lower overall survival in HCC patients. This article comprehensively summarizes the current understanding of the multifaceted roles of SNHG1 in the pathogenesis of HCC, while also highlighting its clinicopathological correlations, therefore concluding that it has potential as a biomarker in HCC diagnosis and prognosis.

TGF-β1-Induced LINC01094 promotes epithelial-mesenchymal transition in hepatocellular carcinoma through the miR-122-5p/TGFBR2-SAMD2-SMAD3 Axis

Hepatocellular carcinoma (HCC) is a common malignancy with a poor prognosis. It has been proven that long non-coding RNAs (lncRNAs) play an essential role in regulating HCC progression. However, the involvement of LINC01094 in regulating epithelial-mesenchymal transition (EMT) in HCC remains unclear. LINC01094 expression in HCC patients was retrieved from the Cancer Genome Atlas database. Overexpressing and downregulating LINC01094 were conducted to investigate its biological functions using Hep3B, SNU-387, and HuH-7 cells. Western blotting and morphological observation were performed to study the EMT in HCC cells. Transwell assay was adopted to determine the migration and invasion of HCC cells. The underlying mechanism of competitive endogenous RNAs (ceRNAs) was investigated using bioinformatics analysis, quantitative reverse-transcription polymerase chain reaction, and rescue experiments. Elevated LINC01094 expression was observed in HCC and associated with a poor prognosis. Knockdown of LINC01094 expression in SNU-387 and HuH-7 cells could inhibit migration, invasion, and EMT markers. Overexpression of LINC01094 indicated that LINC01094 promoted EMT via the TGF-β/SMAD signaling pathway. The bioinformatics analysis revealed that miR-122-5p was a target of LINC01094. The miRWalk database analysis showed that TGFBR2, SMAD2, and SMAD3 were downstream targets of miR-122-5p. Mechanically, LINC01094 acted as a ceRNA that facilitated HCC metastasis by sponging miR-122-5p to regulate the expression of TGFBR2, SMAD2, and SMAD3. Further, TGF-β1 could enhance the expression of LINC01094, forming a positive feedback loop. TGF-β1-induced LINC01094 expression promotes HCC cell migration and invasion by targeting the miR-122-5p/TGFBR2-SMAD2-SMAD3 axis. LINC01094 may be a potential prognostic biomarker and therapeutic target for HCC metastasis.

Early detection and diagnosis of cancer with interpretable machine learning to uncover cancer-specific DNA methylation patterns

Cancer, a collection of more than two hundred different diseases, remains a leading cause of morbidity and mortality worldwide. Usually detected at the advanced stages of disease, metastatic cancer accounts for 90% of cancer-associated deaths. Therefore, the early detection of cancer, combined with current therapies, would have a significant impact on survival and treatment of various cancer types. Epigenetic changes such as DNA methylation are some of the early events underlying carcinogenesis. Here, we report on an interpretable machine learning model that can classify 13 cancer types as well as non-cancer tissue samples using only DNA methylome data, with 98.2% accuracy. We utilize the features identified by this model to develop EMethylNET, a robust model consisting of an XGBoost model that provides information to a deep neural network that can generalize to independent data sets. We also demonstrate that the methylation-associated genomic loci detected by the classifier are associated with genes, pathways and networks involved in cancer, providing insights into the epigenomic regulation of carcinogenesis.

A Network of Circular RNA and MicroRNA Sequencing Provides Insights into Pigment Deposition of Changshun Blue Eggshell Chickens

Eggshell color plays important biological roles and attracts the attention of both egg retailers and researchers. However, whether non-coding RNAs are involved in pigment deposition among different eggshell colors remains unknown. In this study, RNA sequencing was used to analyse the uterine gland transcriptome (CircRNA and miRNA) of Changshun chicken blue-shell hens producing four different eggshell color eggs including dark blue PK(DB) and light blue (LB), dark brown and greenish (between blue and pink, DP) and pink (p). We found that miR-192-x, targeting SLC16a7, was expressed in DB, DP, and LB groups compared with the PK group, which indicates that miR-192-x may play a role in the blue eggshell color. KEGG and GO analyses showed that the "metabolic pathways" with targeted genes such BLVRA and HMOX1 were detected in dark and light blue color eggshell chickens, which confirms the different ratios of biliverdin and HO-1 involved in the deposition of blue color. As annotated by connectivity analysis, RASGRF1 and RASGRF2, belonging to the RASGRF family, are involved in the Ras signaling pathway, which plays an important role in cell growth, differentiation, metastasis and apoptosis. Our findings enrich the database of circRNA, miRNAs and genes for chicken uterine tissue, which will be useful in accelerating molecular selection for blue eggshell color layers.

LncRNA taurine upregulated gene 1 in liver disease

Long non-coding RNAs (lncRNAs) are RNA sequences exceeding 200 nucleotides in length that lack protein-coding capacity and participate in diverse biological processes in the human body, particularly exerting a pivotal role in disease surveillance, diagnosis, and progression. Taurine upregulated gene 1 (TUG1) is a versatile lncRNA, and recent studies have revealed that the aberrant expression or function of TUG1 is intricately linked to the pathogenesis of liver diseases. Consequently, we have summarized the current understanding of the mechanism of TUG1 in liver diseases such as liver fibrosis, fatty liver, cirrhosis, liver injury, hepatitis, and liver cancer. Moreover, mounting evidence suggests that interventions targeting TUG1 or its downstream pathways may hold therapeutic promise for liver diseases. This review elucidates the characteristics, mechanisms, and targets of TUG1 in liver diseases, offering a theoretical basis for the prevention, diagnosis, treatment, and prognostic biomarkers of liver diseases.

Developing a prognostic model using machine learning for disulfidptosis related lncRNA in lung adenocarcinoma

Disulfidptosis represents a novel cell death mechanism triggered by disulfide stress, with potential implications for advancements in cancer treatments. Although emerging evidence highlights the critical regulatory roles of long non-coding RNAs (lncRNAs) in the pathobiology of lung adenocarcinoma (LUAD), research into lncRNAs specifically associated with disulfidptosis in LUAD, termed disulfidptosis-related lncRNAs (DRLs), remains insufficiently explored. Using The Cancer Genome Atlas (TCGA)-LUAD dataset, we implemented ten machine learning techniques, resulting in 101 distinct model configurations. To assess the predictive accuracy of our model, we employed both the concordance index (C-index) and receiver operating characteristic (ROC) curve analyses. For a deeper understanding of the underlying biological pathways, we referred to the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) for functional enrichment analysis. Moreover, we explored differences in the tumor microenvironment between high-risk and low-risk patient cohorts. Additionally, we thoroughly assessed the prognostic value of the DRLs signatures in predicting treatment outcomes. The Kaplan-Meier (KM) survival analysis demonstrated a significant difference in overall survival (OS) between the high-risk and low-risk cohorts (p < 0.001). The prognostic model showed robust performance, with an area under the ROC curve exceeding 0.75 at one year and maintaining a value above 0.72 in the two and three-year follow-ups. Further research identified variations in tumor mutational burden (TMB) and differential responses to immunotherapies and chemotherapies. Our validation, using three GEO datasets (GSE31210, GSE30219, and GSE50081), revealed that the C-index exceeded 0.67 for GSE31210 and GSE30219. Significant differences in disease-free survival (DFS) and OS were observed across all validation cohorts among different risk groups. The prognostic model offers potential as a molecular biomarker for LUAD prognosis.

Osteosarcoma in a ceRNET perspective

Osteosarcoma (OS) is the most prevalent and fatal type of bone tumor. It is characterized by great heterogeneity of genomic aberrations, mutated genes, and cell types contribution, making therapy and patients management particularly challenging. A unifying picture of molecular mechanisms underlying the disease could help to transform those challenges into opportunities.This review deeply explores the occurrence in OS of large-scale RNA regulatory networks, denominated "competing endogenous RNA network" (ceRNET), wherein different RNA biotypes, such as long non-coding RNAs, circular RNAs and mRNAs can functionally interact each other by competitively binding to shared microRNAs. Here, we discuss how the unbalancing of any network component can derail the entire circuit, driving OS onset and progression by impacting on cell proliferation, migration, invasion, tumor growth and metastasis, and even chemotherapeutic resistance, as distilled from many studies. Intriguingly, the aberrant expression of the networks components in OS cells can be triggered also by the surroundings, through cytokines and vesicles, with their bioactive cargo of proteins and non-coding RNAs, highlighting the relevance of tumor microenvironment. A comprehensive picture of RNA regulatory networks underlying OS could pave the way for the development of innovative RNA-targeted and RNA-based therapies and new diagnostic tools, also in the perspective of precision oncology.

Quantitative estimates of the regulatory influence of long non-coding RNAs on global gene expression variation using TCGA breast cancer transcriptomic data

Long non-coding RNAs (lncRNAs) have received attention in recent years for their regulatory roles in diverse biological contexts including cancer, yet large gaps remain in our understanding of their mechanisms and global maps of their targets. In this work, we investigated a basic unanswered question of lncRNA systems biology: to what extent can gene expression variation across individuals be attributed to lncRNA-driven regulation? To answer this, we analyzed RNA-seq data from a cohort of breast cancer patients, explaining each gene's expression variation using a small set of automatically selected lncRNA regulators. A key aspect of this analysis is that it accounts for confounding effects of transcription factors (TFs) as common regulators of a lncRNA-mRNA pair, to enrich the explained gene expression for lncRNA-mediated regulation. We found that for 16% of analyzed genes, lncRNAs can explain more than 20% of expression variation. We observed 25-50% of the putative regulator lncRNAs to be in 'cis' to, i.e., overlapping or located proximally to the target gene. This led us to quantify the global regulatory impact of such cis-located lncRNAs, which was found to be substantially greater than that of trans-located lncRNAs. Additionally, by including statistical interaction terms involving lncRNA-protein pairs as predictors in our regression models, we identified cases where a lncRNA's regulatory effect depends on the presence of a TF or RNA-binding protein. Finally, we created a high-confidence lncRNA-gene regulatory network whose edges are supported by co-expression as well as a plausible mechanism such as cis-action, protein scaffolding or competing endogenous RNAs. Our work is a first attempt to quantify the extent of gene expression control exerted globally by lncRNAs, especially those located proximally to their regulatory targets, in a specific biological (breast cancer) context. It also marks a first step towards systematic reconstruction of lncRNA regulatory networks, going beyond the current paradigm of co-expression networks, and motivates future analyses assessing the generalizability of our findings to additional biological contexts.

Predicting prostate cancer progression with a Multi-lncRNA expression-based risk score and nomogram integrating ISUP grading

Prostate cancer is a highly heterogeneous disease; therefore, estimating patient prognosis accurately is challenging due to the lack of biomarkers with sufficient specificity and sensitivity. One of the current challenges lies in integrating genomic and transcriptomic data with clinico-pathological features and in incorporating their application in everyday clinical practice. Therefore, we aimed to model a risk score and nomogram containing long non-coding RNA (lncRNA) expression and clinico-pathological data to better predict the probability of prostate cancer progression. We performed bioinformatics analyses to identify lncRNAs differentially expressed across various prostate cancer stages and associated with progression-free survival. This information was further integrated into a prognostic risk score and nomogram containing transcriptomic and clinico-pathological features to estimate the risk of disease progression. We used RNA-seq data from 5 datasets from public repositories (total n = 178) comprising different stages of prostate cancer: pre-treatment primary prostate adenocarcinomas, post-treatment tumors and metastatic castration resistant prostate cancer. We found 30 lncRNAs with consistent differential expression in all comparisons made using two R-based packages. Multivariate progression-free survival analysis including the ISUP group as covariate, revealed that 7/30 lncRNAs were significantly associated with time-to-progression. Next, we combined the expression of these 7 lncRNAs into a multi-lncRNA score and dichotomized the patients into low- or high-score. Patients with a high-score showed a 4-fold risk of disease progression (HR = 4.30, 95 %CI = 2.66-6.97, p = 3.1e-9). Furthermore, we modelled a combined risk-score containing information on the multi-lncRNA score and ISUP group. We found that patients with a high-risk score had nearly 8-fold risk of progression (HR = 7.65, 95 %CI = 4.05-14.44, p = 3.4e-10). Finally, we created and validated a nomogram to help uro-oncologists to better predict patient's risk of progression at 3- and 5-years post-diagnosis. In conclusion, the integration of lncRNA expression data and clinico-pathological features of prostate tumors into predictive models might aid in tailored disease risk assessment and treatment for patients with prostate cancer.

Cis-regulatory effect of HPV integration is constrained by host chromatin architecture in cervical cancers

Human papillomavirus (HPV) infections are the primary drivers of cervical cancers, and often HPV DNA gets integrated into the host genome. Although the oncogenic impact of HPV encoded genes is relatively well known, the cis-regulatory effect of integrated HPV DNA on host chromatin structure and gene regulation remains less understood. We investigated genome-wide patterns of HPV integrations and associated host gene expression changes in the context of host chromatin states and topologically associating domains (TADs). HPV integrations were significantly enriched in active chromatin regions and depleted in inactive ones. Interestingly, regardless of chromatin state, genomic regions flanking HPV integrations showed transcriptional upregulation. Nevertheless, upregulation (both local and long-range) was mostly confined to TADs with integration, but not affecting adjacent TADs. Few TADs showed recurrent integrations associated with overexpression of oncogenes within them (e.g. MYC, PVT1, TP63 and ERBB2) regardless of proximity. Hi-C and 4C-seq analyses in cervical cancer cell line (HeLa) demonstrated chromatin looping interactions between integrated HPV and MYC/PVT1 regions (~ 500 kb apart), leading to allele-specific overexpression. Based on these, we propose HPV integrations can trigger multimodal oncogenic activation to promote cancer progression.

Breaking paradigms: Long non-coding RNAs forming gene fusions with potential implications in cancer

Long non-coding RNAs (lncRNAs) are non-coding RNAs longer than 200 nucleotides with dynamic regulatory functions. They interact with a wide range of molecules such as DNA, RNA, and proteins to modulate diverse cellular functions through several mechanisms and, if deregulated, they can lead to cancer development and progression. Recently, it has been described that lncRNAs are susceptible to form gene fusions with mRNAs or other lncRNAs, breaking the paradigm of gene fusions consisting mainly of protein-coding genes. However, their biological significance in the tumor phenotype is still uncertain. Therefore, their recent identification opens a new line of research to study their biological role in tumorigenesis, and their potential as biomarkers with clinical relevance or as therapeutic targets. The present study aimed to review the lncRNA fusions identified so far and to know which of them have been associated with a potential function. We address the current challenges to deepen their study as well as the reasons why they represent a future therapeutic window in cancer.

Recent applications of artificial intelligence in RNA-targeted small molecule drug discovery

INTRODUCTION

Targeting RNAs with small molecules offers an alternative to the conventional protein-targeted drug discovery and can potentially address unmet and emerging medical needs. The recent rise of interest in the strategy has already resulted in large amounts of data on disease associated RNAs, as well as on small molecules that bind to such RNAs. Artificial intelligence (AI) approaches, including machine learning and deep learning, present an opportunity to speed up the discovery of RNA-targeted small molecules by improving decision-making efficiency and quality.

AREAS COVERED

The topics described in this review include the recent applications of AI in the identification of RNA targets, RNA structure determination, screening of chemical compound libraries, and hit-to-lead optimization. The impact and limitations of the recent AI applications are discussed, along with an outlook on the possible applications of next-generation AI tools for the discovery of novel RNA-targeted small molecule drugs.

EXPERT OPINION

Key areas for improvement include developing AI tools for understanding RNA dynamics and RNA - small molecule interactions. High-quality and comprehensive data still need to be generated especially on the biological activity of small molecules that target RNAs.

Hypoxia/inflammation-induced upregulation of HIF-1α and C/EBPβ promotes nephroblastoma cell EMT by improving HOXA11-AS transcription

Background

Homeobox (HOX) A11 antisense RNA (HOXA11-AS) has been identified as a cancer promoting lncRNA and is overexpressed in nephroblastoma. However, how HOXA11-AS is regulated in a hypoxic inflammatory environment has not been studied.

Methods

In this study, gene expression and epithelial-mesenchymal transition (EMT) ability were detected in the nephroblastoma cell line WiT49 under conditions of hypoxia and inflammation. Next, HOXA11-AS transcription factors were predicted by datasets and subsequently confirmed by CHIP-QPCR, EMSA, and dual-luciferase reporter assays. Moreover, the regulatory relationships of HOXA11-AS and its transcription factors were further confirmed by rescue experiments.

Results

Our results showed that a hypoxic microenvironment promoted HOXA11-AS expression and nephroblastoma progression, induced EMT, and activated the Wnt signaling pathway. Combined hypoxia and inflammation had a more substantial effect on nephroblastoma than either hypoxia or inflammation alone. HIF-1α and C/EBPβ were confirmed to be the transcription factors for HOXA11-AS. Silencing of HIF-1α or C/EBPβ downregulated HOXA11-AS expression and suppressed EMT and the Wnt signaling pathway in nephroblastoma cells exposed to a hypoxic or inflammatory microenvironment. HOXA11-AS overexpression partly reversed the effect of HIF-1α or C/EBPβ knockdown.

Conclusion

We demonstrated that hypoxia/inflammation-induced upregulation of HIF-1α and C/EBPβ promoted nephroblastoma EMT by improving HOXA11-AS transcription. HOXA11-AS might be a therapy target for nephroblastoma.

Co-Expression Network Analysis Unveiled lncRNA-mRNA Links Correlated to Epidermal Growth Factor Receptor-Tyrosine Kinase Inhibitor Resistance and/or Intermediate Epithelial-to-Mesenchymal Transition Phenotypes in a Human Non-Small Cell Lung Cancer Cellular Model System

We investigated mRNA-lncRNA co-expression patterns in a cellular model system of non-small cell lung cancer (NSCLC) sensitive and resistant to the epithelial growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) erlotinib/gefitinib. The aim of this study was to unveil insights into the complex mechanisms of NSCLC targeted therapy resistance and epithelial-to-mesenchymal transition (EMT). Genome-wide RNA expression was quantified for weighted gene co-expression network analysis (WGCNA) to correlate the expression levels of mRNAs and lncRNAs. Functional enrichment analysis and identification of lncRNAs were conducted on modules associated with the EGFR-TKI response and/or intermediate EMT phenotypes. We constructed lncRNA-mRNA co-expression networks and identified key modules and their enriched biological functions. Processes enriched in the selected modules included RHO (A, B, C) GTPase and regulatory signaling pathways, apoptosis, inflammatory and interleukin signaling pathways, cell adhesion, cell migration, cell and extracellular matrix organization, metabolism, and lipid metabolism. Interestingly, several lncRNAs, already shown to be dysregulated in cancer, are connected to a small number of mRNAs, and several lncRNAs are interlinked with each other in the co-expression network.

Detecting subtle transcriptomic perturbations induced by lncRNAs knock-down in single-cell CRISPRi screening using a new sparse supervised autoencoder neural network

Single-cell CRISPR-based transcriptome screens are potent genetic tools for concomitantly assessing the expression profiles of cells targeted by a set of guides RNA (gRNA), and inferring target gene functions from the observed perturbations. However, due to various limitations, this approach lacks sensitivity in detecting weak perturbations and is essentially reliable when studying master regulators such as transcription factors. To overcome the challenge of detecting subtle gRNA induced transcriptomic perturbations and classifying the most responsive cells, we developed a new supervised autoencoder neural network method. Our Sparse supervised autoencoder (SSAE) neural network provides selection of both relevant features (genes) and actual perturbed cells. We applied this method on an in-house single-cell CRISPR-interference-based (CRISPRi) transcriptome screening (CROP-Seq) focusing on a subset of long non-coding RNAs (lncRNAs) regulated by hypoxia, a condition that promote tumor aggressiveness and drug resistance, in the context of lung adenocarcinoma (LUAD). The CROP-seq library of validated gRNA against a subset of lncRNAs and, as positive controls, HIF1A and HIF2A, the 2 main transcription factors of the hypoxic response, was transduced in A549 LUAD cells cultured in normoxia or exposed to hypoxic conditions during 3, 6 or 24 h. We first validated the SSAE approach on HIF1A and HIF2 by confirming the specific effect of their knock-down during the temporal switch of the hypoxic response. Next, the SSAE method was able to detect stable short hypoxia-dependent transcriptomic signatures induced by the knock-down of some lncRNAs candidates, outperforming previously published machine learning approaches. This proof of concept demonstrates the relevance of the SSAE approach for deciphering weak perturbations in single-cell transcriptomic data readout as part of CRISPR-based screening.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

CMC: Cancer miRNA Census - a list of cancer-related miRNA genes

A growing body of evidence indicates an important role of miRNAs in cancer; however, there is no definitive, convenient-to-use list of cancer-related miRNAs or miRNA genes that may serve as a reference for analyses of miRNAs in cancer. To this end, we created a list of 165 cancer-related miRNA genes called the Cancer miRNA Census (CMC). The list is based on a score, built on various types of functional and genetic evidence for the role of particular miRNAs in cancer, e.g. miRNA-cancer associations reported in databases, associations of miRNAs with cancer hallmarks, or signals of positive selection of genetic alterations in cancer. The presence of well-recognized cancer-related miRNA genes, such as MIR21, MIR155, MIR15A, MIR17 or MIRLET7s, at the top of the CMC ranking directly confirms the accuracy and robustness of the list. Additionally, to verify and indicate the reliability of CMC, we performed a validation of criteria used to build CMC, comparison of CMC with various cancer data (publications and databases), and enrichment analyses of biological pathways and processes such as Gene Ontology or DisGeNET. All validation steps showed a strong association of CMC with cancer/cancer-related processes confirming its usefulness as a reference list of miRNA genes associated with cancer.

Role of oncogenic long noncoding RNA KCNQ1OT1 in colon cancer

The role of lncRNA KCNQ1 opposite strand/antisense transcript 1 (KCNQ1OT1) in colon cancer involves various tumorigenic processes and has been studied widely. However, the mechanism by which it promotes colon cancer remains unclear. Retroviral vector pSEB61 was retrofitted in established HCT116-siKCN and SW480-siKCN cells to silence KCNQ1OT1. Cellular proliferation was measured using CCK8 assay, and flow cytometry (FCM) detected cell cycle changes. RNA sequencing (RNA-Seq) analysis showed differentially expressed genes (DEGs). Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were carried out to analyze enriched functions and signaling pathways. RT-qPCR, immunofluorescence, and western blotting were carried out to validate downstream gene expressions. The effects of tumorigenesis were evaluated in BALB/c nude mice by tumor xenografts. Our data revealed that the silencing of KCNQ1OT1 in HCT116 and SW480 cells slowed cell growth and decreased the number of cells in the G2/M phase. RNA-Seq analysis showed the data of DEGs enriched in various GO and KEGG pathways such as DNA replication and cell cycle. RT-qPCR, immunofluorescence, and western blotting confirmed downstream CCNE2 and PCNA gene expressions. HCT116-siKCN cells significantly suppressed tumorigenesis in BALB/c nude mice. Our study suggests that lncRNA KCNQ1OT1 may provide a promising therapeutic strategy for colon cancer.

Development of a Novel Pyroptosis-Associated lncRNA Biomarker Signature in Lung Adenocarcinoma

Pyroptosis is a novel type of cell death observed in various diseases. Our study aimed to investigate the relationship between pyroptosis-associated-long non-coding RNAs (lncRNAs), immune infiltration, and expression of immune checkpoints in the setting of lung adenocarcinoma and the prognostic value of pyroptosis-related lncRNAs. RNA-seq transcriptome data and clinical information from The Cancer Genome Atlas (TCGA) were downloaded, and consensus clustering analysis was used to separate the samples into two groups. Least absolute shrinkage and selection operator (LASSO) analyses were conducted to construct a risk signature. The association between pyroptosis-associated lncRNAs, immune infiltration, and expression of immune checkpoints were analysed. The cBioPortal tool was used to discover genomic alterations. Gene set enrichment analysis (GSEA) was utilized to investigate downstream pathways of the two clusters. Drug sensitivity was also examined. A total of 43 DEGs and 3643 differentially expressed lncRNAs were identified between 497 lung adenocarcinoma tissues and 54 normal samples. A signature consisting of 11 pyroptosis-related lncRNAs was established as prognostic for overall survival. Patients in the low-risk group have a significant overall survival advantage over those in the high-risk group in the training group. Immune checkpoints were expressed differently between the two risk groups. Risk scores were validated to develop an independent prognostic model based on multivariate Cox regression analysis. The area under time-dependent receiver operating characteristic curve (AUC of the ROC) at 1-, 3-, and 5-years measured0.778, 0.757, and 0.735, respectively. The high-risk group was more sensitive to chemotherapeutic drugs than the low-risk group. This study demonstrates the association between pyroptosis-associated lncRNAs and prognosis in lung adenocarcinoma and enables a robust predictive signature of 11 lncRNAs to inform overall survival.

PVT1 interacts with polycomb repressive complex 2 to suppress genomic regions with pro-apoptotic and tumour suppressor functions in multiple myeloma

Multiple myeloma is a heterogeneous hematological disease that originates from the bone marrow and is characterized by the monoclonal expansion of malignant plasma cells. Despite novel therapies, multiple myeloma remains clinically challenging. A common feature among patients with poor prognosis is the increased activity of the epigenetic silencer EZH2, which is the catalytic subunit of the PRC2. Interestingly, the recruitment of PRC2 lacks sequence specificity and, to date, the molecular mechanisms that define which genomic locations are destined for PRC2-mediated silencing remain unknown. The presence of a long non-coding RNA (lncRNA)-binding pocket on EZH2 suggests that lncRNA could potentially mediate PRC2 recruitment to specific genomic regions. Here, we coupled RNA immunoprecipitation sequencing, RNA-sequencing and chromatin immunoprecipitation-sequencing analysis of human multiple myeloma primary cells and cell lines to identify potential lncRNA partners to EZH2. We found that the lncRNA plasmacytoma variant translocation 1 (PVT1) directly interacts with EZH2 and is overexpressed in patients with a poor prognosis. Moreover, genes predicted to be targets of PVT1 exhibited H3K27me3 enrichment and were associated with pro-apoptotic and tumor suppressor functions. In fact, PVT1 inhibition independently promotes the expression of the PRC2 target genes ZBTB7C, RNF144A and CCDC136. Altogether, our work suggests that PVT1 is an interacting partner in PRC2-mediated silencing of tumor suppressor and pro-apoptotic genes in multiple myeloma, making it a highly interesting potential therapeutic target.

Epigenome editing in cancer: Advances and challenges for potential therapeutic options

Cancers are diseases caused by genetic and non-genetic environmental factors. Epigenetic alterations, some attributed to non-genetic factors, can lead to cancer development. Epigenetic changes can occur in tumor suppressors or oncogenes, or they may contribute to global cell state changes, making cells abnormal. Recent advances in gene editing technology show potential for cancer treatment. Herein, we will discuss our current knowledge of epigenetic alterations occurring in cancer and epigenetic editing technologies that can be applied to developing therapeutic options.

Optimal decision-making in high-throughput virtual screening pipelines

The need for efficient computational screening of molecular candidates that possess desired properties frequently arises in various scientific and engineering problems, including drug discovery and materials design. However, the enormous search space containing the candidates and the substantial computational cost of high-fidelity property prediction models make screening practically challenging. In this work, we propose a general framework for constructing and optimizing a high-throughput virtual screening (HTVS) pipeline that consists of multi-fidelity models. The central idea is to optimally allocate the computational resources to models with varying costs and accuracy to optimize the return on computational investment. Based on both simulated and real-world data, we demonstrate that the proposed optimal HTVS framework can significantly accelerate virtual screening without any degradation in terms of accuracy. Furthermore, it enables an adaptive operational strategy for HTVS, where one can trade accuracy for efficiency.

Exploring the landscape of tools and resources for the analysis of long non-coding RNAs

In recent years, research on long non-coding RNAs (lncRNAs) has gained considerable attention due to the increasing number of newly identified transcripts. Several characteristics make their functional evaluation challenging, which called for the urgent need to combine molecular biology with other disciplines, including bioinformatics. Indeed, the recent development of computational pipelines and resources has greatly facilitated both the discovery and the mechanisms of action of lncRNAs. In this review, we present a curated collection of the most recent computational resources, which have been categorized into distinct groups: databases and annotation, identification and classification, interaction prediction, and structure prediction. As the repertoire of lncRNAs and their analysis tools continues to expand over the years, standardizing the computational pipelines and improving the existing annotation of lncRNAs will be crucial to facilitate functional genomics studies.

Globally invariant behavior of oncogenes and random genes at population but not at single cell level

Cancer is widely considered a genetic disease. Notably, recent works have highlighted that every human gene may possibly be associated with cancer. Thus, the distinction between genes that drive oncogenesis and those that are associated to the disease, but do not play a role, requires attention. Here we investigated single cells and bulk (cell-population) datasets of several cancer transcriptomes and proteomes in relation to their healthy counterparts. When analyzed by machine learning and statistical approaches in bulk datasets, both general and cancer-specific oncogenes, as defined by the Cancer Genes Census, show invariant behavior to randomly selected gene sets of the same size for all cancers. However, when protein-protein interaction analyses were performed, the oncogenes-derived networks show higher connectivity than those relative to random genes. Moreover, at single-cell scale, we observe variant behavior in a subset of oncogenes for each considered cancer type. Moving forward, we concur that the role of oncogenes needs to be further scrutinized by adopting protein causality and higher-resolution single-cell analyses.

Plumbing mysterious RNAs in "dark genome" for the conquest of human diseases

Next-generation sequencing has revealed that less than 2% of transcribed genes are translated into proteins, with a large portion transcribed into noncoding RNAs (ncRNAs). Among these, long noncoding RNAs (lncRNAs) represent the largest group and are pervasively transcribed throughout the genome. Dysfunctions in lncRNAs have been found in various diseases, highlighting their potential as therapeutic, diagnostic, and prognostic targets. However, challenges, such as unknown molecular mechanisms and nonspecific immune responses, and issues of drug specificity and delivery present obstacles in translating lncRNAs into clinical applications. In this review, we summarize recent publications that have explored lncRNA functions in human diseases. We also discuss challenges and future directions for developing lncRNA treatments, aiming to bridge the gap between functional studies and clinical potential and inspire further exploration in the field.

Long non-coding RNAs: definitions, functions, challenges and recommendations

Genes specifying long non-coding RNAs (lncRNAs) occupy a large fraction of the genomes of complex organisms. The term 'lncRNAs' encompasses RNA polymerase I (Pol I), Pol II and Pol III transcribed RNAs, and RNAs from processed introns. The various functions of lncRNAs and their many isoforms and interleaved relationships with other genes make lncRNA classification and annotation difficult. Most lncRNAs evolve more rapidly than protein-coding sequences, are cell type specific and regulate many aspects of cell differentiation and development and other physiological processes. Many lncRNAs associate with chromatin-modifying complexes, are transcribed from enhancers and nucleate phase separation of nuclear condensates and domains, indicating an intimate link between lncRNA expression and the spatial control of gene expression during development. lncRNAs also have important roles in the cytoplasm and beyond, including in the regulation of translation, metabolism and signalling. lncRNAs often have a modular structure and are rich in repeats, which are increasingly being shown to be relevant to their function. In this Consensus Statement, we address the definition and nomenclature of lncRNAs and their conservation, expression, phenotypic visibility, structure and functions. We also discuss research challenges and provide recommendations to advance the understanding of the roles of lncRNAs in development, cell biology and disease.

An updated review of contribution of long noncoding RNA-NEAT1 to the progression of human cancers

Long non-coding RNAs (lncRNAs) present pivotal roles in cancer tumorigenesis and progression. Recently, nuclear paraspeckle assembly transcript 1 (NEAT1) as a lncRNA has been shown to mediate cell proliferation, migration, and EMT in tumor cells. NEAT1 by targeting several miRNAs/mRNA axes could regulate cancer cell behavior. Therefore, NEAT1 may function as a potent biomarker for the prediction and treatment of some human cancers. In this review, we summarized various NEAT1-related signaling pathways that are critical in cancer initiation and progression.

Leveraging base-pair mammalian constraint to understand genetic variation and human disease

Thousands of genomic regions have been associated with heritable human diseases, but attempts to elucidate biological mechanisms are impeded by an inability to discern which genomic positions are functionally important. Evolutionary constraint is a powerful predictor of function, agnostic to cell type or disease mechanism. Single-base phyloP scores from 240 mammals identified 3.3% of the human genome as significantly constrained and likely functional. We compared phyloP scores to genome annotation, association studies, copy-number variation, clinical genetics findings, and cancer data. Constrained positions are enriched for variants that explain common disease heritability more than other functional annotations. Our results improve variant annotation but also highlight that the regulatory landscape of the human genome still needs to be further explored and linked to disease.

RNA-Chrom: a manually curated analytical database of RNA-chromatin interactome

Every year there is more and more evidence that non-coding RNAs play an important role in biological processes affecting various levels of organization of living systems: from the cellular (regulation of gene expression, remodeling and maintenance of chromatin structure, co-transcriptional suppression of transposons, splicing, post-transcriptional RNA modifications, etc.) to cell populations and even organismal ones (development, aging, cancer, cardiovascular and many other diseases). The development and creation of mutually complementary databases that will aggregate, unify and structure different types of data can help to reach the system level of studying non-coding RNAs. Here we present the RNA-Chrom manually curated analytical database, which contains the coordinates of billions of contacts of thousands of human and mouse RNAs with chromatin. Through the user-friendly web interface (https://rnachrom2.bioinf.fbb.msu.ru/), two approaches to the analysis of the RNA-chromatin interactome were implemented. Firstly, to find out whether the RNA of interest to a user contacts with chromatin, and if so, with which genes or DNA loci? Secondly, to find out which RNAs are in contact with the DNA locus of interest to a user (and probably participate in its regulation), and if there are such, what is the nature of their interaction? For a more detailed study of contact maps and their comparison with other data, the web interface allows a user to view them in the UCSC Genome Browser. Database URL https://genome.ucsc.edu/.

Knocking Down Gm16685 Decreases Liver Granuloma in Murine Schistosomiasis Japonica

Long noncoding RNAs (lncRNAs) can regulate key genes and pathways in liver disease development. Moreover, macrophages are speculated to play an important role in regulating granulomatous inflammation during schistosomiasis. However, the role of lncRNAs in the formation of liver granulomas by influencing the polarization of macrophages in Schistosoma japonicum infection is unclear. Our study aimed to determine whether lncRNAs can play a role in S. japonicum-induced hepatic egg granulomas and elucidate their effect on macrophages. We established S. japonicum infection models and screened the target lncRNA Gm16685 highly expressed in schistosomiasis mice using high-throughput sequencing. Hematoxylin and eosin staining revealed that the knockdown of Gm16685 reduced the area of egg granulomas. Moreover, M1 macrophage factor genes were significantly downregulated in Gm16685 knockdown livers. Meanwhile, M2 macrophage factor genes were significantly upregulated, which was consistent with the protein detection results. Hepatocytes, hepatic stellate cells, and macrophages were isolated from mouse models infected with S. japonicum, with Gm16685 being significantly upregulated in macrophages. Moreover, the knockdown of Gm16685 in RAW264.7 cells revealed similar results to in liver tissue. RNA fluorescence in situ hybridization (FISH) and nucleocytoplasmic separation experiments revealed that Gm16685 was predominantly localized in the cytoplasm of cells. We found that miR-205-5p was upregulated after Gm16685 was knocked down. After overexpression of miR-205-5p, the expression of Gm16685 and inflammatory factors was significantly downregulated. These results indicate that Gm16685 can participate in the pathogenesis of hepatic disease in schistosomiasis and promote M1 macrophage polarization by regulating miR-205-5p. Thus, our study may provide a new target for schistosomiasis japonica treatment.

Histone acetylation-related IncRNA: Potential biomarkers for predicting prognosis and immune response in lung adenocarcinoma, and distinguishing hot and cold tumours

Background

Histone acetylation-related lncRNAs (HARlncRNAs) play significant roles in various cancers, but their impact on lung adenocarcinoma (LUAD) remains unclear. This study aimed to develop a new HARlncRNA-based prognostic model for LUAD and to explore its potential biological mechanisms.

Methods

We identified 77 histone acetylation genes based on previous studies. HARlncRNAs related to prognosis were screened by co-expression, univariate and multivariate analyses, and least absolute shrinkage selection operator regression (LASSO). Afterward, a prognostic model was established based on the screened HARlncRNAs. We analysed the relationship between the model and immune cell infiltration characteristics, immune checkpoint molecule expression, drug sensitivity, and tumour mutational burden (TMB). Finally, the entire sample was divided into three clusters to further distinguish between hot and cold tumours.

Results

A seven-HARlncRNA-based prognostic model was established for LUAD. The area under the curve (AUC) of the risk score was the highest among all the analysed prognostic factors, indicating the accuracy and robustness of the model. The patients in the high-risk group were predicted to be more sensitive to chemotherapeutic, targeted, and immunotherapeutic drugs. It was worth noting that clusters could effectively identify hot and cold tumours. In our study, clusters 1 and 3 were considered hot tumours that were more sensitive to immunotherapy drugs.

Conclusion

We developed a risk-scoring model based on seven prognostic HARlncRNAs that promises to be a new tool for evaluating the prognosis and efficacy of immunotherapy in patients with LUAD.

Emerging role of oncogenic long noncoding RNA as cancer biomarkers

The view of long noncoding RNAs as nonfunctional "garbage" has been definitely outdated by the large body of evidence indicating this class of ncRNAs as "golden junk", especially in precision oncology. Indeed, in light of their oncogenic role and the higher expression in multiple cancer types compared with paired adjacent tissues, the clinical interest for lncRNAs as diagnostic and/or prognostic biomarkers has been rapidly increasing. The emergence of large-scale sequencing technologies, their subsequent diffusion even in small research and clinical centers, the technological advances for the detection of low-copy lncRNAs in body fluids, coupled to the huge reduction of operating costs, have nowadays made possible to rapidly and comprehensively profile them in multiple tumors and large cohorts. In this review, we first summarize some relevant data about the oncogenic role of well-studied lncRNAs having a clinical relevance. Then, we focus on the description of their potential use as diagnostic/prognostic biomarkers, including an updated overview about licensed patents or clinical trials on lncRNAs in oncology.

Pan-Cancer Analysis Reveals Functional Similarity of Three lncRNAs across Multiple Tumors

Long non-coding RNAs (lncRNAs) are emerging as key regulators in many biological processes. The dysregulation of lncRNA expression has been associated with many diseases, including cancer. Mounting evidence suggests lncRNAs to be involved in cancer initiation, progression, and metastasis. Thus, understanding the functional implications of lncRNAs in tumorigenesis can aid in developing novel biomarkers and therapeutic targets. Rich cancer datasets, documenting genomic and transcriptomic alterations together with advancement in bioinformatics tools, have presented an opportunity to perform pan-cancer analyses across different cancer types. This study is aimed at conducting a pan-cancer analysis of lncRNAs by performing differential expression and functional analyses between tumor and non-neoplastic adjacent samples across eight cancer types. Among dysregulated lncRNAs, seven were shared across all cancer types. We focused on three lncRNAs, found to be consistently dysregulated among tumors. It has been observed that these three lncRNAs of interest are interacting with a wide range of genes across different tissues, yet enriching substantially similar biological processes, found to be implicated in cancer progression and proliferation.

Long Non-Coding RNA MEG3 in Metal Carcinogenesis

Most transcripts from human genomes are non-coding RNAs (ncRNAs) that are not translated into proteins. ncRNAs are divided into long (lncRNAs) and small non-coding RNAs (sncRNAs). LncRNAs regulate their target genes both transcriptionally and post-transcriptionally through interactions with proteins, RNAs, and DNAs. Maternally expressed gene 3 (MEG3), a lncRNA, functions as a tumor suppressor. MEG3 regulates cell proliferation, cell cycle, apoptosis, hypoxia, autophagy, and many other processes involved in tumor development. MEG3 is downregulated in various cancer cell lines and primary human cancers. Heavy metals, such as hexavalent chromium (Cr(VI)), arsenic, nickel, and cadmium, are confirmed human carcinogens. The exposure of cells to these metals causes a variety of cancers. Among them, lung cancer is the one that can be induced by exposure to all of these metals. In vitro studies have demonstrated that the chronic exposure of normal human bronchial epithelial cells (BEAS-2B) to these metals can cause malignant cell transformation. Metal-transformed cells have the capability to cause an increase in cell proliferation, resistance to apoptosis, elevated migration and invasion, and properties of cancer stem-like cells. Studies have revealed that MEG is downregulated in Cr(VI)-transformed cells, nickel-transformed cells, and cadmium (Cd)-transformed cells. The forced expression of MEG3 reduces the migration and invasion of Cr(VI)-transformed cells through the downregulation of the neuronal precursor of developmentally downregulated protein 9 (NEDD9). MEG3 suppresses the malignant cell transformation of nickel-transformed cells. The overexpression of MEG3 decreases Bcl-xL, causing reduced apoptosis resistance in Cd-transformed cells. This paper reviews the current knowledge of lncRNA MEG3 in metal carcinogenesis.

ORLNC1 Suppresses Cell Growth in HER2-Positive Breast Cancer via miRNA-296 Sponging

BACKGROUND

Accumulating research has demonstrated that aberrant levels of long noncoding RNAs (LncRNAs) are related to cancer progression. The effects of ORLNC1 in HER2+ breast cancer have yet to be explored.

METHODS

Real-time PCR was used to examine the expression of LncRNA ORLNC1 in HER+ breast cancer. CCK-8, wound healing and cell invasion assays were used to examine the effect of LncRNA ORLNC1 on HER+ breast cancer cells. Luciferase reporter assay was utilized to determine the regulatory relationship between LncRNA ORLNC1 and miR-296. Western blotting was used to measure the expression of PTEN. Xenograft mouse model was used to examine the effect of LncRNA ORLNC1 on tumor progression in vivo.

RESULTS

In this study, our findings revealed downregulation of ORLNC1 in HER2+ breast cancer specimens and cell lines. Low levels of ORLNC1 were related to poor prognosis and advanced cancer stage. Using gain- and loss-of-function assays, the ability of these tumor cells to proliferate was found to be inhibited by ORLNC1 in vitro and in vivo. Further analyses revealed that miR-296/PTEN axis is directly targeted by ORLNC1. Consequently, over-expression of miR-296 efficiently abrogated the upregulation of PTEN induced by ORLNC1, suggesting that ORLNC1 positively regulates PTEN expression by competitively binding to miR-296.

CONCLUSION

Our results indicate that lncRNA ORLNC1 acts as a tumor suppressor by regulating the miR-296/PTEN axis in HER2+ breast cancer.

CCAT 1- A Pivotal Oncogenic Long Non-Coding RNA in Colorectal Cancer

Colorectal cancer (CRC) is ranked as the third most common cancer and second deadliest cancer in both men and women in the world. Currently, the cure rate and 5-year survival rate of CRC patients remain relatively low. Therefore, discovering a novel molecular biomarker that can be used to improve CRC screening, diagnosis, prognosis, and treatment would be beneficial. Long non-coding RNA colon cancer-associated transcript 1 (CCAT 1) has been found overexpressed in CRC and is associated with CRC tumorigenesis and treatment outcome. CCAT 1 has a high degree of specificity and sensitivity, it is readily detected in CRC tissues and is significantly overexpressed in both premalignant and malignant CRC tissues. Besides, CCAT 1 is associated with clinical manifestation and advanced features of CRC, such as lymph node metastasis, high tumor node metastasis stage, differentiation, invasion, and distant metastasis. In addition, they can upregulate oncogenic c-MYC and negatively modulate microRNAs via different mechanisms of action. Furthermore, dysregulated CCAT 1 also enhances the chemoresistance in CRC cells while downregulation of them reverses the malignant phenotypes of cancer cells. In brief, CCAT 1 serves as a potential screening, diagnostic and prognostic biomarker in CRC, it also serves as a potential therapeutic marker to treat CRC patients.

Mechanism and function of miR-140 in human cancers: A review and in silico study

MicroRNA-140 (miR-140) acts as a tumor suppressor and plays a vital role in cell biological functions such as cell proliferation, apoptosis, and DNA repair. The expression of this miRNA has been shown to be considerably decreased in cancer tissues and cell lines compared with normal adjacent tissues. Consequently, aberrant expression of some miR-140 target genes can lead to the initiation and progression of various human cancers, such as breast cancer, gastrointestinal cancers, lung cancer, and prostate cancer. The dysregulation of the miR-140 network also affects cell proliferation, invasion, metastasis, and apoptosis of cancer cells by affecting various signaling pathways. Besides, up-regulation of miR-140 could enhance the efficacy of chemotherapeutic agents in different cancer. We aimed to cover most aspects of miR-140 function in cancer development and address its importance in different stages of cancer progression.