Therapies promoting microvascular flow and endothelial repair require effective identification of new targets in ischemic heart disease (IHD). The aim of this study was to unravel and functionally investigate circular RNA (circRNA)-microRNA (miRNA)-messenger RNA (mRNA) networks in endothelial cells in the context of IHD and type 2 diabetes mellitus (T2DM). We performed RNA sequencing on left ventricle biopsies from patients with IHD, IHD and T2DM, and non-IHD. Next, we created circRNA-miRNA-mRNA networks and mechanistically investigated the top circRNA-miRNA sponging interactions in endothelial cells. We found that circNPHP1 promotes angiogenesis via sponging of miR-221-3p and regulates its downstream target genes, VEGFA and BCL2, in IHD and T2DM.

Objectives Rapid advances in transcriptomics have driven efforts to identify deregulated pathways in multiple sclerosis (MS) tissues, though many detected differentially expressed genes are likely false positives, with only a small fraction reflecting actual pathological events. Robust, integrative methods are essential for accurately understanding the molecular mechanisms underlying MS pathology. Methods We conducted a gene prioritization analysis of MS white matter pathology transcriptomic studies. Articles were sought in Scopus and PubMed up to July 31, 2024. Potentially eligible publications were those that provided either transcriptomics datasets (deposited in GEO) or lists of differentially expressed genes comparing MS white matter to control white matter. Results Applying a vote‐count strategy to search for the intersection of genes reported in multiple independent studies with a consistent fold‐change direction, followed by a Monte Carlo simulation, we identified 528 highly significant differentially expressed multi‐study genes ( p < 0.0001; 10,000 simulations). Functional enrichment analysis revealed deregulation of the folate pathway in MS normal‐appearing white matter, and tumor necrosis factor (TNF) ‐related and complement‐related pathways in active and chronic active lesions, respectively. Network analysis identified 6 key signaling hubs: PTPRC, HLA‐B, MYC, MMP2, COL11A2, MAG. The major nodes identified revealed mechanistic concordance with published in vivo MS models, supporting their value as potential therapeutic targets. Interpretation Our strategy provides a robust framework for integrating gene expression data, effectively identifying the intricate pathways altered in human diseased tissues. This method holds potential for translating findings into drug development strategies. ANN NEUROL 2025

Gene expression quantitative trait loci are widely used to infer relationships between genes and central nervous system (CNS) phenotypes; however, the effect of brain disease on these inferences is unclear. Using 2,348,438 single-nuclei profiles from 391 disease-case and control brains, we report 13,939 genes whose expression correlated with genetic variation, of which 16.7-40.8% (depending on cell type) showed disease-dependent allelic effects. Across 501 colocalizations for 30 CNS traits, 23.6% had a disease dependency, even after adjusting for disease status. To estimate the unconfounded effect of genes on outcomes, we repeated the analysis using nondiseased brains (n = 183) and reported an additional 91 colocalizations not present in the larger mixed disease and control dataset, demonstrating enhanced interpretation of disease-associated variants. Principled implementation of single-cell Mendelian randomization in control-only brains identified 140 putatively causal gene-trait associations, of which 11 were replicated in the UK Biobank, prioritizing candidate peripheral biomarkers predictive of CNS outcomes.

Inhibin, β, which is also known as INHBA, encodes a protein that belongs to the Transforming Growth factor-β (TGF-β) superfamily, which plays a pivotal role in cancer. Gastrointestinal tract (GI tract) cancer refers to the cancers that develop in the colon, liver, esophagus, stomach, rectum, pancreas, and bile ducts of the digestive system. The role of INHBA in all GI tract cancers remains understudied. By utilizing GEPIA2, which uses transcriptomic data from TCGA, we examined the expression of INHBA across different GI tract cancers. The results revealed consistent upregulation of INHBA in all TCGA GI tract cancers, except for liver hepatocellular carcinoma, where it showed downregulation compared to normal tissues, along with GTEx normal samples. Significant differences in INHBA expression were noted in adenocarcinomas of the colon, pancreas, rectum, and stomach, while no such differences were observed in cholangiocarcinoma and liver cancer. Moreover, a comprehensive bioinformatics analysis has been done to demonstrate that the differences in expression levels are significantly related to pathological tumor stages and prognosis in different GI tract cancers. Mucinous adenocarcinoma, esophageal squamous cell carcinoma, and stomach adenocarcinoma show a higher frequency of INHBA alteration and are primarily linked to mutations and amplifications. DNA methylation, immune infiltration, functional enrichment analysis, the genes associated with INHBA, and survival analysis in all TCGA GI tract cancers have been extensively analyzed. In colon and stomach cancers, increased INHBA expression significantly correlates with poorer overall survival (OS). However, in colon and pancreatic adenocarcinoma, higher expression is significantly associated with worse disease-free survival (DFS). Additionally, INHBA expression exhibited a positive correlation with cancer-associated fibroblasts across all gastrointestinal (GI) tract cancers. The KEGG pathway analysis revealed that INHBA and its interacting proteins are involved in several pathways, including TGF-beta signaling, Signalling pathways regulating pluripotency of stem cells, colorectal cancer, pancreatic cancer, AGE-RAGE signaling, and so on as major pathways. These findings demonstrate that INHBA could serve as a potential biomarker therapeutic target for GI tract cancer.

Introduction: Chronic immunopathology contributes to the development of heart failure after a myocardial infarction. Both T and B cells of the adaptive immune system are present in the myocardium and have been suggested to be involved in post-MI immunopathology. Methods: We analyzed the B and T cell populations isolated from previously published single cell RNA-sequencing data sets (PMID: 32130914, PMID: 35948637, PMID: 32971526 and PMID: 35926050), of the mouse and human heart, using differential expression analysis, functional enrichment analysis, gene regulatory inferences, and integration with autoimmune and cardiovascular GWAS. Results: Already at baseline, mature effector B and T cells are present in the human and mouse heart, having increased activity in transcription factors maintaining tolerance (e.g. DEAF1, JDP2, SPI-B). Following MI, T cells upregulate pro-inflammatory transcript levels (e.g. Cd11, Gzmk, Prf1), while B cells upregulate activation markers (e.g. Il6, Il1rn, Ccl6) and collagen (e.g. Col5a2, Col4a1, Col1a2). Importantly, pro-inflammatory and fibrotic transcription factors (e.g. NFKB1, CREM, REL) remain active in T cells, while B cells maintain elevated activity in transcription factors related to immunoglobulin production (e.g. ERG, REL) in both mouse and human post-MI hearts. Notably, genes differentially expressed in post-MI T and B cells are associated with cardiovascular and autoimmune disease. Conclusion: These findings highlight the varied and time-dependent dynamic roles of post-MI T and B cells. They appear ready-to-go and are activated immediately after MI, thus participate in the acute wound healing response. However, they subsequently remain in a state of pro-inflammatory activation contributing to persistent immunopathology.

Liver hepatocellular carcinoma (LIHC) is considered one of the primary contributors to cancer-related mortality on a global scale. The identification of new biomarkers is of utmost importance due to the fact that patients with liver LIHC are frequently detected at advanced stages, leading to an increased mortality rate. The study utilized TCGA-LIHC gene expression datasets to identify biomarkers and to address the complexity of datasets. A combination of feature selection (FS) techniques was used, and the performance of this strategy was assessed using ten machine learning classifiers. The findings were integrated, revealing biomarkers identified through at least five FS techniques. Through our proposed approach, we identified 55 potential biomarkers for LIHC. The Gaussian Naive Bayes Classifier (AUC=0.99) was found to be the most effective classifier, achieving 98.67% accuracy when utilizing the 55 identified biomarkers in the test dataset. Additionally, we conducted differential gene expression, survival analysis, and enrichment analysis for all the identified biomarkers. Subsequently, Lasso-penalized Cox regression further refined the identified biomarkers to thirteen Out of thirteen genes, we singled out B4GALNT1 because of its statistical significance in differential expression analysis and increasing importance across various cancer types, including LIHC. We carried out comprehensive bioinformatics and molecular dynamics simulation studies along with other structural analysis of B4GALNT1 in LIHC. In LIHC, six mutations (P64Q, S131F, A311S, R340Q, D478H, and P507Q) have been predicted to be probably damaging by evaluating in-silico prediction algorithms. In comparison to the wild type, the B4GALNT1 variations, specifically P64Q and S131F, demonstrate increased stability. However, these mutations lead to decreased atomic fluctuations, indicating a rigid protein structure. Again, mutations like A311S and P507Q induce increased flexibility, highlighting their structural impact on B4GALNT1. The study demonstrated the combination of various feature selection methods effectively reveals new biomarkers, thereby directly impacting their biological significance. Furthermore, our findings indicate a link between increased B4GALNT1 expression in individuals with liver cancer and a poorer prognosis, highlighting its potential as a promising therapeutic target.

AbstractTools for predicting COVID-19 outcomes enable personalized healthcare, potentially easing the disease burden. This collaborative study by 15 institutions across Europe aimed to develop a machine learning model for predicting the risk of in-hospital mortality post-SARS-CoV-2 infection. Blood samples and clinical data from 1286 COVID-19 patients collected from 2020 to 2023 across four cohorts in Europe and Canada were analyzed, with 2906 long non-coding RNAs profiled using targeted sequencing. From a discovery cohort combining three European cohorts and 804 patients, age and the long non-coding RNA LEF1-AS1 were identified as predictive features, yielding an AUC of 0.83 (95% CI 0.82–0.84) and a balanced accuracy of 0.78 (95% CI 0.77–0.79) with a feedforward neural network classifier. Validation in an independent Canadian cohort of 482 patients showed consistent performance. Cox regression analysis indicated that higher levels of LEF1-AS1 correlated with reduced mortality risk (age-adjusted hazard ratio 0.54, 95% CI 0.40–0.74). Quantitative PCR validated LEF1-AS1’s adaptability to be measured in hospital settings. Here, we demonstrate a promising predictive model for enhancing COVID-19 patient management.

OBJECTIVE:Structural epilepsies can manifest months or years after the occurrence of an initial epileptogenic insult, making them amenable for secondary prevention. However, development of preventive treatments has been challenged by a lack of biomarkers for identifying the subset of individuals with the highest risk of epilepsy after the epileptogenic insult. METHODS:Four different rat models of epileptogenesis were investigated to identify differentially expressed circulating microRNA (miRNA) and isomiR profiles as biomarkers for epileptogenesis. Plasma samples were collected on day 2 and day 9 during the latency period from animals that did or did not develop epilepsy during long-term video-electroencephalographic monitoring. miRNAs and isomiRs were identified and measured in an unsupervised manner, using a genome-wide small RNA sequencing platform. Receiver operating characteristic analysis was performed to determine the performance of putative biomarkers. RESULTS:Two days after an epileptogenic insult, alterations in the levels of several plasma miRNAs and isomiRs predicted epileptogenesis in a model-specific manner. One miRNA, miR-3085, showed good sensitivity (but low specificity) as a prognostic biomarker for epileptogenesis in all four models (area under the curve = .729, sensitivity = 83%, specificity = 64%, p < .05). SIGNIFICANCE:Identified plasma miRNAs and isomiRs are mostly etiology-specific rather than common prognostic biomarkers of epileptogenesis. These data imply that in preclinical and clinical studies, it may be necessary to identify specific biomarkers for different epilepsy etiologies. Importantly, circulating miRNAs like miR-3085 with high negative predictive value for epileptogenesis in different etiologies could be useful candidates for initial screening purposes of epileptogenesis risk.

The adult heart is a complex, multicellular organ that is subjected to a series of regulatory stimuli and circuits and has poor reparative potential. Despite progress in our understanding of disease mechanisms and in the quality of health care, ischaemic heart disease remains the leading cause of death globally, owing to adverse cardiac remodelling, leading to ischaemic cardiomyopathy and heart failure. Therapeutic targets are urgently required for the protection and repair of the ischaemic heart. Moreover, personalized clinical biomarkers are necessary for clinical diagnosis, medical management and to inform the individual response to treatment. Non-coding RNAs (ncRNAs) deeply influence cardiovascular functions and contribute to communication between cells in the cardiac microenvironment and between the heart and other organs. As such, ncRNAs are candidates for translation into clinical practice. However, ncRNA biology has not yet been completely deciphered, given that classes and modes of action have emerged only in the past 5 years. In this Review, we discuss the latest discoveries from basic research on ncRNAs and highlight both the clinical value and the challenges underscoring the translation of these molecules as biomarkers and therapeutic regulators of the processes contributing to the initiation, progression and potentially the prevention or resolution of ischaemic heart disease and heart failure.

Background Ischemic heart disease (IHD) is characterized by insufficient myocardial blood flow and aggravated by diabetes. IHD is often treated by coronary revascularization. Complementary therapies that increase the microvascular flow by promoting endothelial homeostasis and repair are still lacking and require the identification of new therapeutic targets. The expression of protein coding genes is regulated by multimodal interactions between different classes of non-coding RNAs. This study aimed to 1) unravel circular RNA (circRNA) -microRNA (miRNA) - messenger RNA (mRNA) networks across the human ischemic and control hearts; 2) identify and functionally test the sub-network with highest potential to control endothelial cell (EC) function. Methods Two intraoperative left ventricle biopsies were prospectively collected from type-2 diabetic and non-diabetic people with IHD and from a control group of non-IHD, non-diabetic patients (N=12 patients/group; ARCADIA study) and used for long transcriptome bulk-sequencing (circRNAs and mRNAs datasets) and small RNA-sequencing (miRNAs datasets). Differentially expressed (DE) RNAs were identified with a customized bioinformatics pipeline. We identified potential “sponging associations” between circRNAs and miRNAs and between miRNAs and mRNAs. Next, we created circRNA-miRNA-mRNA networks and retained the subnetworks where individual RNAs were expressed in ECs (using datasets from GEO database) and of potential relevance to endothelial function based on pathway analyses. Next, we screened top differentially expressed circRNAs in cultured ECs exposed to disease-mimic conditions vs control. The circRNA-miRNA sponging interactions of the top emerging circRNA candidate were confirmed after circRNA pulldown in ECs and the functional relevance was mechanistically tested in EC cell biology assays. Results We created circRNA-miRNA-mRNA networks in IHD (with and without T2DM) and controls indicating distinct interactions in each. We unravelled a novel putative proangiogenic subnetwork, circNPHP1/miR-221-3p/VEGFA/BCL2, in the human ischemic heart specially enriched in IHD and T2DM. CircNPHP1 pull-down assay confirmed it’s binding to miR-221-3p. CircNPHP1 silencing in ECs reduced angiogenesis and decreased the expression of miR-221-3p target genes (VEGFA and BCL2). The proangiogenic action of circNPHP1/miR-221-3p was confirmed in ECs by combining circNPHP1 silencing with either miR-221-3p inhibition or overexpression in ECs. Conclusions CircNPHP1 supports endothelial function through sponging of miR-221-3p in IHD and holds potential as novel therapeutic target in IHD. ### Competing Interest Statement The authors have declared no competing interest.

Liver hepatocellular carcinoma (LIHC) is one of the most frequent types of malignant cancer in the globe. The identification of new biomarkers for the LIHC is critical. We used TCGA-LIHC gene expression datasets for this study. Several feature selection methods were used to find the top gene signatures that distinguish LIHC cancer from normal samples. Eleven machine learning algorithms were used on these selected characteristics, and model performance evaluation revealed that Naive Bayes Classifiers (AUC = 0.965) performs the best for a selection of 55 protein coding genes. Among 55 protein coding genes we found B4GALNT1 (Beta-1,4-N-acetyl-galactosaminyltransferase 1) which is differentially regulated in LIHC. With several evidence B4GALNT1 plays crucial role in tumorigenesis in many cancers, therefore we conducted systematic bioinformatics approach with mutational and structural analysis of B4GALNT1 in LIHC. Moreover, survival analysis, immune cell infiltration, most significant associated methylated CpG probe and access the accuracy of B4GALNT1 conducted to find the potential role of B4GALNT1. The results suggested that B4GALNT1 was significantly expressed in most cancers including LIHC. Finally, 16 missense mutations identified through cBioportal, Cosmic Database, and Human Variant Database, among which 6 mutations (P64Q, S131F, A311S, R340Q, D478H, and P507Q) found to be deleterious when analysed by in-silico prediction algorithms such as SIFT, PolyPhen2, I Mutent2 and CADD in LIHC. Molecular Dynamics simulation analysis was performed to understand the atomic details of the structure and functional changes. Results from this study suggest the impact of these missense variants on the structure of the B4GALNT1 protein and its pathogenic relevance. Our study demonstrated that B4GALNT1 may be evaluated as a novel target for liver cancer therapy because it has been found to be overexpressed in Liver and correlates with a poor prognosis.### Competing Interest StatementThe authors have declared no competing interest.* aa : Amino acid ACC : Adrenocortical carcinoma BLCA : Bladder Urothelial Carcinoma BRCA : Breast invasive carcinoma CESC : Cervical squamous cell carcinoma and endocervical adenocarcinoma DLBC : Lymphoid Neoplasm Diffuse Large B-cell Lymphoma GBM : Glioblastoma multiforme HNSC : Head and Neck squamous cell carcinoma KICH : Kidney Chromophobe KIRC : Kidney renal clear cell carcinoma KIRP : Kidney renal papillary cell carcinoma LAML : Acute Myeloid Leukemia LGG : Brain Lower Grade Glioma LUAD : Lung adenocarcinoma LUSC : Lung squamous cell carcinoma OV : Ovarian serous cystadenocarcinoma PCPG : Pheochromocytoma and Paraganglioma PRAD : Prostate adenocarcinoma SARC : Sarcoma SKCM : Skin Cutaneous Melanoma TGCT : Testicular Germ Cell Tumors THCA : Thyroid carcinoma THYM : Thymoma UCEC : Uterine Corpus Endometrial Carcinoma UCS : Uterine Carcinosarcoma CHOL : Cholangiocarcinoma COAD : Colon adenocarcinoma ESCA : Esophageal carcinoma LIHC : Liver hepatocellular carcinoma PAAD : Pancreatic adenocarcinoma READ : Rectum adenocarcinoma. STAD : Stomach adenocarcinoma INHBA : Inhibin subunit beta A GI : Gastrointestinal CPTAC : Clinical proteomic tumor analysis consortium BP : Biological process CC : Cellular component DFS : Disease-free survival GEPIA : Gene expression profiling interactive analysis GO : Gene ontology KEGG : Kyoto encyclopedia of genes and genomes GTEx : Genotype-tissue expression TIMER : Tumor immune estimation resource

Introduction/Background: Cardiovascular symptoms appear in a high proportion of patients in the few months following a severe SARS-CoV-2 infection. Non-invasive methods to predict disease severity could help personalizing healthcare and reducing the occurrence of these symptoms. Research Questions/Hypothesis: We hypothesized that blood long noncoding RNAs (lncRNAs) and machine learning (ML) could help predict COVID-19 severity. Goals/Aims: To develop a model based on lncRNAs and ML for predicting COVID-19 severity. Methods/Approach: Expression data of 2906 lncRNAs were obtained by targeted sequencing in plasma samples collected at baseline from four independent cohorts, totaling 564 COVID-19 patients. Patients were aged 18+ and were recruited from 2020 to 2023 in the PrediCOVID cohort (n=162; Luxembourg), the COVID19_OMICS-COVIRNA cohort (n=100, Italy), the TOCOVID cohort (n=233, Spain), and the MiRCOVID cohort (n=69, Germany). The study complied with the Declaration of Helsinki. Cohorts were approved by ethics committees and patients signed an informed consent. Results/Data: After data curation and pre-processing, 463 complete datasets were included in further analysis, representing 101 severe patients (in-hospital death or ICU admission) and 362 stable patients (no hospital admission or hospital admission but not ICU). Feature selection with Boruta, a random forest-based method, identified age and five lncRNAs (LINC01088-201, FGDP-AS1, LINC01088-209, AKAP13, and a novel lncRNA) associated with disease severity, which were used to build predictive models using six ML algorithms. A naïve Bayes model based on age and five lncRNAs predicted disease severity with an AUC of 0.875 [0.868-0.881] and an accuracy of 0.783 [0.775-0.791]. Conclusion: We developed a ML model including age and five lncRNAs predicting COVID-19 severity. This model could help improve patients’ management and cardiovascular outcomes.

IntroductionCongenital heart disease (CHD) represents the most common birth defect, affecting from 0.4% to 1.2% of children born in developed countries. The survival of these patients has increased significantly, but CHD remains one of the major causes of neonatal and childhood death. The aetiology of CHD is complex, with some evidence of both genetic and environmental causes. However, there is still lack of knowledge regarding modifiable risk factors and molecular and genetic mechanisms underlying the development of CHD. This study aims to develop a prospective cohort of patients undergoing cardiac procedures that will bring together routinely collected clinical data and biological samples from patients and their biological mothers, in order to investigate risk factors and predictors of postoperative-outcomes, as well as better understanding the effect of the surgical intervention on the early and long-term outcomes.Methods and analysisChildren OMACp (OMACp, outcome monitoring after cardiac procedure in congenital heart disease) is a multicentre, prospective cohort study recruiting children with CHD undergoing a cardiac procedure. The study aims to recruit 3000 participants over 5 years (2019–2024) across multiple UK sites. Routine clinical data will be collected, as well as participant questionnaires collecting sociodemographic, NHS resource use and quality of life data. Biological samples (blood, urine and surgical waste tissue from patients, and blood and urine samples from biological mothers) will be collected where consent has been obtained. Follow-up outcome and questionnaire data will be collected for 5 years.Ethics and disseminationThe study was approved by the London–Brent Research Ethics Committee on 30 July 2019 (19/SW/0113). Participants (or their parent/guardian if under 16 years of age) must provide informed consent prior to being recruited into the study. Mothers who wish to take part must also provide informed consent prior to being recruited. The study is sponsored by University Hospitals Bristol and Weston Foundation Trust and is managed by the University of Bristol. Children OMACp is adopted onto the National Institute for Health Research Clinical Research Network portfolio. Findings will be disseminated through peer-reviewed publications, presentation at conference, meetings and through patient organisations and newsletters.Trial registration numberISRCTN17650644.

The number of "omics" approaches is continuously growing. Among others, epigenetics has appeared as an attractive area of investigation by the cardiovascular research community, notably considering its association with disease development. Complex diseases such as cardiovascular diseases have to be tackled using methods integrating different omics levels, so called "multi-omics" approaches. These approaches combine and co-analyze different levels of disease regulation. In this review, we present and discuss the role of epigenetic mechanisms in regulating gene expression and provide an integrated view of how these mechanisms are interlinked and regulate the development of cardiac disease, with a particular attention to heart failure. We focus on DNA, histone, and RNA modifications, and discuss the current methods and tools used for data integration and analysis. Enhancing the knowledge of these regulatory mechanisms may lead to novel therapeutic approaches and biomarkers for precision healthcare and improved clinical outcomes.

RNA editing, a common and potentially highly functional form of RNA modification, encompasses two different RNA modifications, namely adenosine to inosine (A-to-I) and cytidine to uridine (C-to-U) editing. As inosines are interpreted as guanosines by the cellular machinery, both A-to-I and C-to-U editing change the nucleotide sequence of the RNA. Editing events in coding sequences have the potential to change the amino acid sequence of proteins, whereas editing events in noncoding RNAs can, for example, affect microRNA target binding. With advancing RNA sequencing technology, more RNA editing events are being discovered, studied, and reported. However, RNA editing events are still often overlooked or discarded as sequence read quality defects. With this position paper, we aim to provide guidelines and recommendations for the detection, validation, and follow-up experiments to study RNA editing, taking examples from the fields of cardiovascular and brain disease. We discuss all steps, from sample collection, storage, and preparation, to different strategies for RNA sequencing and editing-sensitive data analysis strategies, to validation and follow-up experiments, as well as potential pitfalls and gaps in the available technologies. This paper may be used as an experimental guideline for RNA editing studies in any disease context.

Background Even after 3 years from SARS-CoV-2 identification, COVID-19 is still a persistent and dangerous global infectious disease. Significant improvements in our understanding of the disease pathophysiology have now been achieved. Nonetheless, reliable and accurate biomarkers for the early stratification of COVID-19 severity are still lacking. Long noncoding RNAs (LncRNAs) are ncRNAs longer than 200 nucleotides, regulating the transcription and translation of protein‐coding genes and they can be found in the peripheral blood, thus holding a promising biomarker potential. Specifically, peripheral blood mononuclear cells (PBMCs) have emerged as a source of indirect biomarkers mirroring the conditions of tissues: they include monocytes, B and T lymphocytes, and natural killer T cells (NKT), being highly informative for immune-related events. Methods We profiled by RNA-Sequencing a panel of 2906 lncRNAs to investigate their modulation in PBMCs of a pilot group of COVID-19 patients, followed by qPCR validation in 111 hospitalized COVID-19 patients. Results The levels of four lncRNAs were found to be decreased in association with COVID-19 mortality and disease severity: HLA Complex Group 18-242 and -244 (HCG18-242 and HCG18-244), Lymphoid Enhancer Binding Factor 1-antisense 1 (LEF1-AS1) and lncCEACAM21 (i.e. ENST00000601116.5, a lncRNA in the CEACAM21 locus). Interestingly, these deregulations were confirmed in an independent patient group of hospitalized patients and by the re-analysis of publicly available single-cell transcriptome datasets. The identified lncRNAs were expressed in all of the PBMC cell types and inversely correlated with the neutrophil/lymphocyte ratio (NLR), an inflammatory marker. In vitro, the expression of LEF1-AS1 and lncCEACAM21 was decreased upon THP-1 monocytes exposure to a relevant stimulus, hypoxia. Conclusion The identified COVID-19-lncRNAs are proposed as potential innovative biomarkers of COVID-19 severity and mortality.