RNAInter v4.0: RNA interactome repository with redefined confidence scoring system and improved accessibility

RIscoper 2.0: A deep learning tool to extract RNA biomedical relation sentences from literature

RNA plays an extensive role in a multi-dimensional regulatory system, and its biomedical relationships are scattered across numerous biological studies. However, text mining works dedicated to the extraction of RNA biomedical relations remain limited. In this study, we established a comprehensive and reliable corpus of RNA biomedical relations, recruiting over 30,000 sentences manually curated from more than 15,000 biomedical literature. We also updated RIscoper 2.0, a BERT-based deep learning tool to extract RNA biomedical relation sentences from literature. Benefiting from approximately 100,000 annotated named entities, we integrated the text classification and named entity recognition tasks in this tool. Additionally, RIscoper 2.0 outperformed the original tool in both tasks and can discover new RNA biomedical relations. Additionally, we provided a user-friendly online search tool that enables rapid scanning of RNA biomedical relationships using local and online resources. Both the online tools and data resources of RIscoper 2.0 are available at http://www.rnainter.org/riscoper.

MDDOmics: multi-omics resource of major depressive disorder

Major depressive disorder (MDD) is a pressing global health issue. Its pathogenesis remains elusive, but numerous studies have revealed its intricate associations with various biological factors. Consequently, there is an urgent need for a comprehensive multi-omics resource to help researchers in conducting multi-omics data analysis for MDD. To address this issue, we constructed the MDDOmics database (Major Depressive Disorder Omics, (https://www.csuligroup.com/MDDOmics/), which integrates an extensive collection of published multi-omics data related to MDD. The database contains 41 222 entries of MDD research results and several original datasets, including Single Nucleotide Polymorphisms, genes, non-coding RNAs, DNA methylations, metabolites and proteins, and offers various interfaces for searching and visualization. We also provide extensive downstream analyses of the collected MDD data, including differential analysis, enrichment analysis and disease-gene prediction. Moreover, the database also incorporates multi-omics data for bipolar disorder, schizophrenia and anxiety disorder, due to the challenge in differentiating MDD from similar psychiatric disorders. In conclusion, by leveraging the rich content and online interfaces from MDDOmics, researchers can conduct more comprehensive analyses of MDD and its similar disorders from various perspectives, thereby gaining a deeper understanding of potential MDD biomarkers and intricate disease pathogenesis. Database URL: https://www.csuligroup.com/MDDOmics/.

circADAMTS6 via stabilizing CAMK2A is involved in smoking-induced emphysema through driving M2 macrophage polarization

Cigarette smoke (CS), an indoor environmental pollutant, is a prominent risk factor for emphysema, which is a pathological feature of chronic obstructive pulmonary disease (COPD). Emerging function of circRNAs in immune responses and disease progression shed new light to explore the pathogenesis of emphysema. In this research, we demonstrated, by single-cell RNA sequencing (scRNAseq), that the ratio of M2 macrophages were increased in lung tissues of humans and mice with smoking-related emphysema. Further, our data showed that circADAMTS6 was associated with cigarette smoke extract (CSE)-induced M2 macrophage polarization. Mechanistically, in macrophages, circADAMTS6 stabilized CAMK2A mRNA via forming a circADAMTS6/IGF2BP2/CAMK2A RNA-protein ternary complex to activate CREB, which drives M2 macrophage polarization and leads to emphysema. In addition, in macrophages of mouse lung tissues, downregulation of circADAMTS6 reversed M2 macrophage polarization, the proteinase/anti-proteinase imbalance, and the elastin degradation, which protecting against CS-induced emphysema. Moreover, for macrophages and in a model with co-cultured lung organoids, the target of circADAMTS6 restored the growth of lung organoids compared to CSE-treated macrophages. Our results also demonstrated that, for smokers and COPD smokers, elevation of circADAMTS6 negatively correlated with lung function. Overall, this study reveals a novel mechanism for circADAMTS6-driven M2 macrophage polarization in smoking-related emphysema and postulates that circADAMTS6 could serve as a diagnostic and therapeutic marker for smoking-related emphysema.

Cellular senescence molecules expression in type 2 diabetes mellitus: CDKN2A, CDKN2B, and lncRNA ANRIL

AIMS

Cellular senescence in type 2 diabetes mellitus (T2DM) has received widespread attention. However, the cellular senescence molecules involved in T2DM are unclear. Furthermore, there are no consistent biomarkers for cellular senescence in T2DM. Therefore, this study aimed to identify cellular senescence molecules in T2DM and investigate their expression in peripheral blood mononuclear cells of individuals with T2DM.

METHODS

Patients with T2DM (n = 40) and healthy controls (n = 40) were enrolled. We used different databases to identify cellular senescence molecules in T2DM and confirmed the obtained genes and lncRNA using real-time PCR.

RESULTS

Bioinformatics analysis indicated that CDKN2A and CDKN2B genes, and long noncoding RNA ANRIL are the most effective cellular senescence molecules in T2DM. Furthermore, CDKN2A and ANRIL expression decreased in individuals with T2DM.

CONCLUSIONS

Cellular senescence may have a protective effect against T2DM. In addition, the cellular senescence molecules CDKN2A and ANRIL may be potential biomarkers of cellular senescence in T2DM.

LncRNA HOXC-AS3 accelerates malignant proliferation of cervical cancer cells via stabilizing KDM5B

BACKGROUND

Cervical cancer (CC) is a common malignancy amongst women globally. Ubiquitination plays a dual role in the occurrence and development of cancers. This study analyzed the mechanism of long noncoding RNA HOXC cluster antisense RNA 3 (lncRNA HOXC-AS3) in malignant proliferation of CC cells via mediating ubiquitination of lysine demethylase 5B (KDM5B/JARID1B).

METHODS

The expression patterns of lncRNA HOXC-AS3 and KDM5B were measured by real-time quantitative polymerase chain reaction or Western blot analysis. After transfection with lncRNA HOXC-AS3 siRNA and pcDNA3.1-KDM5B, proliferation of CC cells was assessed by the cell counting kit-8, colony formation, and 5-Ethynyl-2'-deoxyuridine staining assays. The xenograft tumor model was established to confirm the impact of lncRNA HOXC-AS3 on CC cell proliferation in vivo by measuring tumor size and weight and the immunohistochemistry assay. The subcellular location of lncRNA HOXC-AS3 and the binding of lncRNA HOXC-AS3 to KDM5B were analyzed. After treatment of lncRNA HOXC-AS3 siRNA or MG132, the protein and ubiquitination levels of KDM5B were determined. Thereafter, the interaction and the subcellular co-location of tripartite motif-containing 37 (TRIM37) and KDM5B were analyzed by the co-immunoprecipitation and immunofluorescence assays.

RESULTS

LncRNA HOXC-AS3 and KDM5B were upregulated in CC tissues and cells. Depletion of lncRNA HOXC-AS3 repressed CC cell proliferation and in vivo tumor growth. Mechanically, lncRNA HOXC-AS3 located in the nucleus directly bound to KDM5B, inhibited TRIM37-mediated ubiquitination of KDM5B, and upregulated the protein levels of KDM5B. KDM5B overexpression attenuated the inhibitory role of silencing lncRNA HOXC-AS3 in CC cell proliferation in vivo and in vitro.

CONCLUSION

Nucleus-located lncRNA HOXC-AS3 facilitated malignant proliferation of CC cells via stabilization of KDM5B protein levels.

ChimericFragments: computation, analysis and visualization of global RNA networks

RNA-RNA interactions are a key feature of post-transcriptional gene regulation in all domains of life. While ever more experimental protocols are being developed to study RNA duplex formation on a genome-wide scale, computational methods for the analysis and interpretation of the underlying data are lagging behind. Here, we present ChimericFragments, an analysis framework for RNA-seq experiments that produce chimeric RNA molecules. ChimericFragments implements a novel statistical method based on the complementarity of the base-pairing RNAs around their ligation site and provides an interactive graph-based visualization for data exploration and interpretation. ChimericFragments detects true RNA-RNA interactions with high precision and is compatible with several widely used experimental procedures such as RIL-seq, LIGR-seq or CLASH. We further demonstrate that ChimericFragments enables the systematic detection of novel RNA regulators and RNA-target pairs with crucial roles in microbial physiology and virulence. ChimericFragments is written in Julia and available at: https://github.com/maltesie/ChimericFragments.

GEHGAN: CircRNA-disease association prediction via graph embedding and heterogeneous graph attention network

There is growing proof suggested that circRNAs play a crucial function in diverse important biological reactions related to human diseases. Within the area of biochemistry, a massive range of wet experiments have been carried out to find out the connections of circRNA-disease in recent years. Since wet experiments are expensive and laborious, nowadays, calculation-based solutions have increasingly attracted the attention of researchers. However, the performance of these methods is restricted due to the inability to balance the distribution among various types of nodes. To remedy the problem, we present a novel computational method called GEHGAN to forecast the new relationships in this research, leveraging graph embedding and heterogeneous graph attention networks. Firstly, we calculate circRNA sequences similarity, circRNA RBP similarity, disease semantic similarity and corresponding GIP kernel similarity to construct heterogeneous graph. Secondly, a graph embedding method using random walks with jump and stay strategies is applied to obtain the preliminary embeddings of circRNAs and diseases, greatly improving the performance of the model. Thirdly, a multi-head graph attention network is employed to further update the embeddings, followed by the employment of the MLP as a predictor. As a result, the five-fold cross-validation indicates that GEHGAN achieves an outstanding AUC score of 0.9829 and an AUPR value of 0.9815 on the CircR2Diseasev2.0 database, and case studies on osteosarcoma, gastric and colorectal neoplasms further confirm the model's efficacy at identifying circRNA-disease correlations.

Sparassis latifolia and exercise training as complementary medicine mitigated the 5-fluorouracil potent side effects in mice with colorectal cancer: bioinformatics approaches, novel monitoring pathological metrics, screening signatures, and innovative management tactic

BACKGROUND

Prompt identification and assessment of the disease are essential for reducing the death rate associated with colorectal cancer (COL). Identifying specific causal or sensitive components, such as coding RNA (cRNA) and non-coding RNAs (ncRNAs), may greatly aid in the early detection of colorectal cancer.

METHODS

For this purpose, we gave natural chemicals obtained from Sparassis latifolia (SLPs) either alone or in conjunction with chemotherapy (5-Fluorouracil to a mouse colorectal tumor model induced by AOM-DSS. The transcription profile of non-coding RNAs (ncRNAs) and their target hub genes was evaluated using qPCR Real-Time, and ELISA techniques.

RESULTS

MSX2, MMP7, ITIH4, and COL1A2 were identified as factors in inflammation and oxidative stress, leading to the development of COL. The hub genes listed, upstream regulatory factors such as lncRNA PVT1, NEAT1, KCNQ1OT1, SNHG16, and miR-132-3p have been discovered as biomarkers for prognosis and diagnosis of COL. The SLPs and exercise, effectively decreased the size and quantity of tumors.

CONCLUSIONS

This effect may be attributed to the modulation of gene expression levels, including MSX2, MMP7, ITIH4, COL1A2, PVT1, NEAT1, KCNQ1OT1, SNHG16, and miR-132-3p. Ultimately, SLPs and exercise have the capacity to be regarded as complementing and enhancing chemotherapy treatments, owing to their efficacious components.

Meta-analysis of differential gene expression in lower motor neurons isolated by laser capture microdissection from post-mortem ALS spinal cords

Introduction

ALS is a fatal neurodegenerative disease for which underlying mechanisms are incompletely understood. The motor neuron is a central player in ALS pathogenesis but different transcriptome signatures have been derived from bulk analysis of post-mortem tissue and iPSC-derived motor neurons (iPSC-MNs).

Methods

This study performed a meta-analysis of six gene expression studies (microarray and RNA-seq) in which laser capture microdissection (LCM) was used to isolate lower motor neurons from post-mortem spinal cords of ALS and control (CTL) subjects. Differentially expressed genes (DEGs) with consistent ALS versus CTL expression differences across studies were identified.

Results

The analysis identified 222 ALS-increased DEGs (FDR <0.10, SMD >0.80) and 278 ALS-decreased DEGs (FDR <0.10, SMD < -0.80). ALS-increased DEGs were linked to PI3K-AKT signaling, innate immunity, inflammation, motor neuron differentiation and extracellular matrix. ALS-decreased DEGs were associated with the ubiquitin-proteosome system, microtubules, axon growth, RNA-binding proteins and synaptic membrane. ALS-decreased DEG mRNAs frequently interacted with RNA-binding proteins (e.g., FUS, HuR). The complete set of DEGs (increased and decreased) overlapped significantly with genes near ALS-associated SNP loci (p < 0.01). Transcription factor target motifs with increased proximity to ALS-increased DEGs were identified, most notably DNA elements predicted to interact with forkhead transcription factors (e.g., FOXP1) and motor neuron and pancreas homeobox 1 (MNX1). Some of these DNA elements overlie ALS-associated SNPs within known enhancers and are predicted to have genotype-dependent MNX1 interactions. DEGs were compared to those identified from SOD1-G93A mice and bulk spinal cord segments or iPSC-MNs from ALS patients. There was good correspondence with transcriptome changes from SOD1-G93A mice (r ≤ 0.408) but most DEGs were not differentially expressed in bulk spinal cords or iPSC-MNs and transcriptome-wide effect size correlations were weak (bulk tissue: r ≤ 0.207, iPSC-MN: r ≤ 0.037).

Conclusion

This study defines a robust transcriptome signature from LCM-based motor neuron studies of post-mortem tissue from ALS and CTL subjects. This signature differs from those obtained from analysis of bulk spinal cord segments and iPSC-MNs. Results provide insight into mechanisms underlying gene dysregulation in ALS and highlight connections between these mechanisms, ALS genetics, and motor neuron biology.

Identification of a robust biomarker LAPTM4A for glioma based on comprehensive computational biology and experimental verification

BACKGROUND

Glioma, a highly invasive and deadly form of human neoplasm, presents a pressing need for the exploration of potential therapeutic targets. While the lysosomal protein transmembrane 4A (LATPM4A) has been identified as a risk factor in pancreatic cancer patients, its role in glioma remains unexplored.

METHODS

The analysis of differentially expressed genes (DEG) was conducted from The Cancer Genome Atlas (TCGA) glioma dataset and the Genotype Tissue Expression (GTEx) dataset. Through weighted gene co-expression network analysis (WGCNA), the key glioma-related genes were identified. Among these, by using Kaplan-Meier (KM) analysis and univariate/multivariate COX methods, LAPTM4A emerged as the most influential gene. Moreover, the bioinformatics methods and experimental verification were employed to analyze its relationships with diagnosis, clinical parameters, epithelial-mesenchymal transition (EMT), metastasis, immune cell infiltration, immunotherapy, drug sensitivity, and ceRNA network.

RESULTS

Our findings revealed that LAPTM4A was up-regulated in gliomas and was associated with clinicopathological features, leading to poor prognosis. Furthermore, functional enrichment analysis demonstrated that LATPM4A played a role in the immune system and cancer progression. In vitro experiments indicated that LAPTM4A may influence metastasis through the EMT pathway in glioma. Additionally, we found that LAPTM4A was associated with the tumor microenvironment (TME) and immunotherapy. Notably, drug sensitivity analysis revealed that patients with high LAPTM4A expression were sensitive to doxorubicin, which contributed to a reduction in LAPTM4A expression. Finally, we uncovered the FGD5-AS1-hsa-miR-103a-3p-LAPTM4A axis as a facilitator of glioma progression.

CONCLUSIONS

In conclusion, our study identifies LATPM4A as a promising biomarker for prognosis and immune characteristics in glioma.

RBM10 C761Y mutation induced oncogenic ASPM isoforms and regulated β-catenin signaling in cholangiocarcinoma

BACKGROUND

Cholangiocarcinoma (CCA) comprises a heterogeneous group of biliary tract cancer. Our previous CCA mutation pattern study focused on genes in the post-transcription modification process, among which the alternative splicing factor RBM10 captured our attention. However, the roles of RBM10 wild type and mutations in CCA remain unclear.

METHODS

RBM10 mutation spectrum in CCA was clarified using our initial data and other CCA genomic datasets from domestic and international sources. Real-time PCR and tissue microarray were used to detect RBM10 clinical association. Function assays were conducted to investigate the effects of RBM10 wild type and mutations on CCA. RNA sequencing was to investigate the changes in alternative splicing events in the mutation group compared to the wild-type group. Minigene splicing reporter and interaction assays were performed to elucidate the mechanism of mutation influence on alternative splicing events.

RESULTS

RBM10 mutations were more common in Chinese CCA populations and exhibited more protein truncation variants. RBM10 exerted a tumor suppressive effect in CCA and correlated with favorable prognosis of CCA patients. The overexpression of wild-type RBM10 enhanced the ASPM exon18 exon skipping event interacting with SRSF2. The C761Y mutation in the C2H2-type zinc finger domain impaired its interaction with SRSF2, resulting in a loss-of-function mutation. Elevated ASPM203 stabilized DVL2 and enhanced β-catenin signaling, which promoted CCA progression.

CONCLUSIONS

Our results showed that RBM10C761Y-modulated ASPM203 promoted CCA progression in a Wnt/β-catenin signaling-dependent manner. This study may enhance the understanding of the regulatory mechanisms that link mutation-altering splicing variants to CCA.

StellarPath: Hierarchical-vertical multi-omics classifier synergizes stable markers and interpretable similarity networks for patient profiling

The Patient Similarity Network paradigm implies modeling the similarity between patients based on specific data. The similarity can summarize patients' relationships from high-dimensional data, such as biological omics. The end PSN can undergo un/supervised learning tasks while being strongly interpretable, tailored for precision medicine, and ready to be analyzed with graph-theory methods. However, these benefits are not guaranteed and depend on the granularity of the summarized data, the clarity of the similarity measure, the complexity of the network's topology, and the implemented methods for analysis. To date, no patient classifier fully leverages the paradigm's inherent benefits. PSNs remain complex, unexploited, and meaningless. We present StellarPath, a hierarchical-vertical patient classifier that leverages pathway analysis and patient similarity concepts to find meaningful features for both classes and individuals. StellarPath processes omics data, hierarchically integrates them into pathways, and uses a novel similarity to measure how patients' pathway activity is alike. It selects biologically relevant molecules, pathways, and networks, considering molecule stability and topology. A graph convolutional neural network then predicts unknown patients based on known cases. StellarPath excels in classification performances and computational resources across sixteen datasets. It demonstrates proficiency in inferring the class of new patients described in external independent studies, following its initial training and testing phases on a local dataset. It advances the PSN paradigm and provides new markers, insights, and tools for in-depth patient profiling.

LINC01134: a pivotal oncogene with promising predictive maker and therapeutic target in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) represents a leading and fatal malignancy within the gastrointestinal tract. Recent advancements highlight the pivotal role of long non-coding RNAs (lncRNAs) in diverse biological pathways and pathologies, particularly in tumorigenesis. LINC01134, a particular lncRNA, has attracted considerable attention due to its oncogenic potential in hepatoma. Current research underscores LINC01134's potential in augmenting the onset and progression of HCC, with notable implications in drug resistance. This review comprehensively explores the molecular functions and regulatory mechanisms of LINC01134 in HCC, offering a fresh perspective for therapeutic interventions. By delving into LINC01134's multifaceted roles, we aim to foster novel strategies in HCC management.

A Group of Highly Secretory miRNAs Correlates with Lymph Node Metastasis and Poor Prognosis in Oral Squamous Cell Carcinoma

MicroRNAs (miRNAs) in oral squamous cell carcinoma (OSCC)-derived small extracellular vesicles (sEVs) play a pivotal role in modulating intercellular communications between tumor cells and other cells in the microenvironment, thereby influencing tumor progression and the efficacy of therapeutic interventions. However, a comprehensive inventory of these secretory miRNAs in sEVs and their biological and clinical implications remains elusive. This study aims to profile the miRNA content of OSCC cell line sEVs and computationally elucidate their biological and clinical relevance. We conducted miRNA sequencing to compare the miRNA profiles of OSCC cells and their corresponding sEVs. Our motif enrichment analysis identified specific sorting motifs that are implicated in either cellular retention or preferential sEV secretion. Target cell analysis suggested that the sEV miRNAs potentially interact with various immune cell types, including natural killer cells and dendritic cells. Additionally, we explored the clinical relevance of these miRNAs by correlating their expression levels with TNM stages and patient survival outcomes. Intriguingly, our findings revealed that a distinct sEV miRNA signature is associated with lymph node metastasis and poorer survival in patients in TCGA-HNSC dataset. Collectively, this research furthers our understanding of the miRNA sorting mechanisms in OSCC and underscores their clinical implications.

MicroRNA-329-3p inhibits the Wnt/β-catenin pathway and proliferation of osteosarcoma cells by targeting transcription factor 7-like 1

An important factor in the emergence and progression of osteosarcoma (OS) is the dysregulated expression of microRNAs (miRNAs). Transcription factor 7-like 1 (TCF7L1), a member of the T cell factor/lymphoid enhancer factor (TCF/LEF) transcription factor family, interacts with the Wnt signaling pathway regulator β-catenin and acts as a DNA-specific binding protein. This study sought to elucidate the impact of the interaction between miR-329-3p and TCF7L1 on the growth and apoptosis of OS and analyze the regulatory expression relationship between miRNA and mRNA in osteosarcoma cells using a variety of approaches. MiR329-3p was significantly downregulated, while TCF7L1 was considerably up-regulated in all examined OS cell lines. Additionally, a clinical comparison study was performed using the TCGA database. Subsequently, the regulatory relationship between miR-329-3p and TCF7L1 on the proliferation and apoptosis of OS cells was verified through in vitro and in vivo experiments. When miR-329-3p was transfected into the OS cell line, the expression of TCF7L1 decreased, the proliferation of OS cells was inhibited, the cytoskeleton disintegrated, and the nucleus condensed to form apoptotic bodies. The expression of proteins that indicate apoptosis increased simultaneously. The cell cycle was arrested in the G0/G1 phase, and the G1/S transition was blocked. The introduction of miR-329-3p also inhibited downstream Cyclin D1 of the Wnt pathway. Xenograft experiments indicated that the overexpression of miR-329-3p significantly inhibited the growth of OS xenografts in nude mice, and the expression of TCF7L1 and c-Myc in tumor tissues decreased. MiR-329-3p was significantly reduced in OS cells and played a suppressive role in tumorigenesis and proliferation by targeting TCF7L1 both in vitro and in vivo. Osteosarcoma cell cycle arrest and pathway inhibition were observed upon the regulation of TCF7L1 by miR-329-3p. Summarizing these results, it can be inferred that miR-329-3p exerts anticancer effects in osteosarcoma by inhibiting TCF7L1.

Analysis and Validation of Critical Signatures and Immune Cell Infiltration Characteristics in Doxorubicin-Induced Cardiotoxicity by Integrating Bioinformatics and Machine Learning

Purpose

Doxorubicin-induced cardiotoxicity (DIC) is a severe side reaction in cancer chemotherapy that greatly impacts the well-being of cancer patients. Currently, there is still an insufficiency of effective and reliable biomarkers in the field of clinical practice for the early detection of DIC. This study aimed to determine and validate the potential diagnostic and predictive values of critical signatures in DIC.

Methods

We obtained high-throughput sequencing data from the GEO database and performed data analysis and visualization using R software, GO, KEGG and Cytoscape. Machine learning methods and weighted gene coexpression network (WGCNA) were used to identify key genes for diagnostic model construction. Receiver operating characteristic (ROC) analysis and a nomogram were used to assess their diagnostic values. A multiregulatory network was built to reveal the possible regulatory relationships of critical signatures. Cell-type identification by estimating relative subsets of RNA transcript (CIBERSORT) analysis was used to investigate differential immune cell infiltration. Additionally, a cell and animal model were constructed to investigate the relationship between the identified genes and DIC.

Results

Among the 3713 differentially expressed genes, three key genes (CSGALNACT1, ZNF296 and FANCB) were identified. A nomogram and ROC curves based on three key genes showed excellent diagnostic predictive performance. The regulatory network analysis showed that the TFs CREB1, EP300, FLI1, FOXA1, MAX, and MAZ modulated three key genes. An analysis of immune cell infiltration indicated that many immune cells (activated NK cells, M0 macrophages, activated dendritic cells and neutrophils) might be related to the progression of DIC. Furthermore, there may be various degrees of correlation between the three critical signatures and immune cells. RT‒qPCR demonstrated that the mRNA expression of CSGALNACT1 and ZNF296 was significantly upregulated, while FANCB was significantly downregulated in DOX-treated cardiomyocytes in vitro and in vivo.

Conclusion

Our study suggested that the differential expression of CSGALNACT1, ZNF296 and FANCB is associated with cardiotoxicity and is also involved in immune cell infiltration in DIC. They might be potential biomarkers for the early occurrence of DIC.

Identification and validation of an H2AZ1-based index model: a novel prognostic tool for hepatocellular carcinoma

The H2A.Z variant histone 1 (H2AZ1) is aberrantly expressed in various tumors, correlating with an unfavorable prognosis. However, its role in hepatocellular carcinoma (HCC) remains unclear. We aimed to elucidate the pathways affected by H2AZ1 and identify promising therapeutic targets for HCC. Following bioinformatic analysis of gene expression and clinical data from The Cancer Genome Atlas and Gene Expression Omnibus database, we found 6,344 dysregulated genes related to H2AZ1 overexpression in HCC tissues (P < 0.05). We performed weighted gene co-expression network analysis to identify the gene module most related to H2AZ1. The H2AZ1-based index was further developed using Cox regression analysis, which revealed that the poor prognosis in the high H2AZ1-based index group could be attributed to elevated tumor stemness (P < 0.05). Moreover, the clinical model showed good prognostic potential (AUC > 0.7). We found that H2AZ1 knockdown led to reduced superoxide dismutase (SOD) activity, elevated malondialdehyde (MDA) levels, and increased apoptosis rate in tumor cells (P < 0.001). Thus, we developed an H2AZ1-based index model with the potential to predict the prognosis of patients with HCC. Our findings provide initial evidence that H2AZ1 overexpression plays a pivotal role in HCC initiation and progression.

Experimental and computational methods for studying the dynamics of RNA-RNA interactions in SARS-COV2 genomes

Long-range ribonucleic acid (RNA)-RNA interactions (RRI) are prevalent in positive-strand RNA viruses, including Beta-coronaviruses, and these take part in regulatory roles, including the regulation of sub-genomic RNA production rates. Crosslinking of interacting RNAs and short read-based deep sequencing of resulting RNA-RNA hybrids have shown that these long-range structures exist in severe acute respiratory syndrome coronavirus (SARS-CoV)-2 on both genomic and sub-genomic levels and in dynamic topologies. Furthermore, co-evolution of coronaviruses with their hosts is navigated by genetic variations made possible by its large genome, high recombination frequency and a high mutation rate. SARS-CoV-2's mutations are known to occur spontaneously during replication, and thousands of aggregate mutations have been reported since the emergence of the virus. Although many long-range RRIs have been experimentally identified using high-throughput methods for the wild-type SARS-CoV-2 strain, evolutionary trajectory of these RRIs across variants, impact of mutations on RRIs and interaction of SARS-CoV-2 RNAs with the host have been largely open questions in the field. In this review, we summarize recent computational tools and experimental methods that have been enabling the mapping of RRIs in viral genomes, with a specific focus on SARS-CoV-2. We also present available informatics resources to navigate the RRI maps and shed light on the impact of mutations on the RRI space in viral genomes. Investigating the evolution of long-range RNA interactions and that of virus-host interactions can contribute to the understanding of new and emerging variants as well as aid in developing improved RNA therapeutics critical for combating future outbreaks.

Constructing a competitive endogenous RNA network of EndMT-related atherosclerosis through weighted gene co-expression network analysis

Atherosclerosis is a chronic inflammatory disease characterized by endothelial dysfunction and plaque formation. Under pro-inflammatory conditions, endothelial cells can undergo endothelial-to-mesenchymal transition (EndMT), contributing to atherosclerosis development. However, the specific regulatory mechanisms by which EndMT contributes to atherosclerosis remain unclear and require further investigation. Dan-Shen-Yin (DSY), a traditional Chinese herbal formula, is commonly used for cardiovascular diseases, but its molecular mechanisms remain elusive. Emerging evidence indicates that competing endogenous RNA (ceRNA) networks play critical roles in atherosclerosis pathogenesis. In this study, we constructed an EndMT-associated ceRNA network during atherosclerosis progression by integrating gene expression profiles from the Gene Expression Omnibus (GEO) database and weighted gene co-expression network analysis. Functional enrichment analysis revealed this EndMT-related ceRNA network is predominantly involved in inflammatory responses. ROC curve analysis showed the identified hub genes can effectively distinguish between normal vasculature and atherosclerotic lesions. Furthermore, Kaplan-Meier analysis demonstrated that high expression of IL1B significantly predicts ischemic events in atherosclerosis. Molecular docking revealed most DSY bioactive components can bind key EndMT-related lncRNAs, including AC003092.1, MIR181A1HG, MIR155HG, WEE2-AS1, and MIR137HG, suggesting DSY may mitigate EndMT in atherosclerosis by modulating the ceRNA network.

LncSEA 2.0: an updated platform for long non-coding RNA related sets and enrichment analysis

Long non-coding RNAs (lncRNAs) possess a wide range of biological functions, and research has demonstrated their significance in regulating major biological processes such as development, differentiation, and immune response. The accelerating accumulation of lncRNA research has greatly expanded our understanding of lncRNA functions. Here, we introduce LncSEA 2.0 (http://bio.liclab.net/LncSEA/index.php), aiming to provide a more comprehensive set of functional lncRNAs and enhanced enrichment analysis capabilities. Compared with LncSEA 1.0, we have made the following improvements: (i) We updated the lncRNA sets for 11 categories and extremely expanded the lncRNA scopes for each set. (ii) We newly introduced 15 functional lncRNA categories from multiple resources. This update not only included a significant amount of downstream regulatory data for lncRNAs, but also covered numerous epigenetic regulatory data sets, including lncRNA-related transcription co-factor binding, chromatin regulator binding, and chromatin interaction data. (iii) We incorporated two new lncRNA set enrichment analysis functions based on GSEA and GSVA. (iv) We adopted the snakemake analysis pipeline to track data processing and analysis. In summary, LncSEA 2.0 offers a more comprehensive collection of lncRNA sets and a greater variety of enrichment analysis modules, assisting researchers in a more comprehensive study of the functional mechanisms of lncRNAs.

Carcinogenic roles of MAFG-AS1 in human cancers

The MAF bZIP transcription factor G-antisense RNA 1 (MAFG-AS1) is located on chromosome 17. MAFG-AS1 was upregulated in 15 human cancers. MAFG-AS1 not only suppresses 16 miRNAs but also directly impacts 22 protein-coding genes' expression. Notably, abnormal MAFG-AS1 expression is connected to clinicopathological characteristics and a worse prognosis in a variety of cancers. Moreover, MAFG-AS1 takes its part in the tumorigenesis and progression of various human malignancies by suppressing apoptosis and promoting proliferation, migration, invasion, aerobic glycolysis, ferroptosis, angiogenesis, EMT, and metastasis. Besides, it can predict treatment effectiveness in ER + breast cancer, urothelial bladder carcinoma, and liver cancer by functioning as a trigger of resistance to tamoxifen, sorafenib, and cisplatin. This study systematically presents the functions of MAFG-AS1 in various cancers, as well as the findings of bioinformatics analyses of the MAFG-AS1, which should give clear advice for future research.

Decoding LINC00052 role in breast cancer by bioinformatic and experimental analyses

LncRNA is a group of transcripts with a length exceeding 200 nucleotides that contribute to tumour development. Our research group found that LINC00052 expression was repressed during the formation of breast cancer (BC) multicellular spheroids. Intriguingly, LINC00052 precise role in BC remains uncertain. We explored LINC00052 expression in BC patients` RNA samples (TCGA) in silico, as well as in an in-house patient cohort, and inferred its cellular and molecular mechanisms. In vitro studies evaluated LINC00052 relevance in BC cells viability, cell cycle and DNA damage. Results. Bioinformatic RNAseq analysis of BC patients showed that LINC00052 is overexpressed in samples from all BC molecular subtypes. A similar LINC00052 expression pattern was observed in an in-house patient cohort. In addition, higher LINC00052 levels are related to better BC patient´s overall survival. Remarkably, MCF-7 and ZR-75-1 cells treated with estradiol showed increased LINC00052 expression compared to control, while these changes were not observed in MDA-MB-231 cells. In parallel, bioinformatic analyses indicated that LINC00052 influences DNA damage and cell cycle. MCF-7 cells with low LINC00052 levels exhibited increased cellular protection against DNA damage and diminished growth capacity. Furthermore, in cisplatin-resistant MCF-7 cells, LINC00052 expression was downregulated. Conclusion. This work shows that LINC00052 expression is associated with better BC patient survival. Remarkably, LINC00052 expression can be regulated by Estradiol. Additionally, assays suggest that LINC00052 could modulate MCF-7 cells growth and DNA damage repair. Overall, this study highlights the need for further research to unravel LINC00052 molecular mechanisms and potential clinical applications in BC.

System biology analysis to develop diagnostic biomarkers, monitoring pathological indexes, and novel therapeutic approaches for immune targeting based on maggot bioactive compounds and polyphenolic cocktails in mice with gastric cancer

Early diagnosis and prognosis are prerequisites for mitigating mortality in gastric cancer (GaCa). Identifying some causative or sensitive elements (coding RNA (cRNA)-non-cRNAs (ncRNAs)) can be very helpful in the early diagnosis of GaCa. Notably, despite significant development in the GaCa treatment, the outcome of patients does not remain satisfactory due to limitations such as multi-drug resistance and tumor relapse. Therefore, more attention has been drawn to complementary therapies and the use of supplements. In this regard, Polyphenol natural compounds (PNC) and maggot larvae (MaLa) alone or in combination were administered along with chemotherapy (paclitaxel) to N-methyl-N-nitrosourea (MNU)- induced murine tumor model. In addition, in order to identify potential diagnostic or prognostic biomarkers, transcriptomics analysis was performed through a bioinformatics approach. Then transcription profile of ncRNAs with their target hub genes was assessed through qPCR Real-Time, Western blot, and ELISA. According to the bioinformatics results, 17 hub genes (e.g., IL-6, CXCL8, MKI67, IL-2, IL-4, IL-10, IL-1β, SPP1, LOX, COL1A1, and IFN-γ) were explored that contribute towards inflammation and oxidative stress and ultimately GaCa development. Upstream of the mentioned hub genes, regulatory factors (lncRNA XIST and NEAT1) were also identified and introduced as prognosis and diagnosis biomarkers for GaCa. Our results showed that PNC alone and in combination with MaLa was able to reduce the size and number of tumors, which is related to the reduction of genes expression levels (including IL-6, CXCL8, MKI67, IL-2, IL-4, IL-10, IL-1β, SPP1, LOX, COL1A1, IFN-γ, NEAT1, and XIST). In conclusion, PNC and MaLa have the potential to be considered as complementary and improving chemotherapy due to their effective compounds. Also, the introduced hub gene and lncRNA in addition to diagnostic and prognostic biomarkers can be used as druggable proteins for novel therapeutic targeting of GaCa.

A Hitchhiker's guide to RNA-RNA structure and interaction prediction tools

RNA biology has risen to prominence after a remarkable discovery of diverse functions of noncoding RNA (ncRNA). Most untranslated transcripts often exert their regulatory functions into RNA-RNA complexes via base pairing with complementary sequences in other RNAs. An interplay between RNAs is essential, as it possesses various functional roles in human cells, including genetic translation, RNA splicing, editing, ribosomal RNA maturation, RNA degradation and the regulation of metabolic pathways/riboswitches. Moreover, the pervasive transcription of the human genome allows for the discovery of novel genomic functions via RNA interactome investigation. The advancement of experimental procedures has resulted in an explosion of documented data, necessitating the development of efficient and precise computational tools and algorithms. This review provides an extensive update on RNA-RNA interaction (RRI) analysis via thermodynamic- and comparative-based RNA secondary structure prediction (RSP) and RNA-RNA interaction prediction (RIP) tools and their general functions. We also highlighted the current knowledge of RRIs and the limitations of RNA interactome mapping via experimental data. Then, the gap between RSP and RIP, the importance of RNA homologues, the relationship between pseudoknots, and RNA folding thermodynamics are discussed. It is hoped that these emerging prediction tools will deepen the understanding of RNA-associated interactions in human diseases and hasten treatment processes.

Exploring Circulating Long Non-Coding RNAs in Mild Cognitive Impairment Patients' Blood

Mild cognitive impairment (MCI) is a transitional clinical stage prior to dementia. Patients with amnestic MCI have a high risk of progression toward Alzheimer's disease. Both amnestic mild cognitive impairment and sporadic Alzheimer's disease are multifactorial disorders consequential from a multifaceted cross-talk among molecular and biological processes. Non-coding RNAs play an important role in the regulation of gene expression, mainly long non-coding RNAs (lncRNAs), that regulate other RNA transcripts through binding microRNAs. Cross-talk between RNAs, including coding RNAs and non-coding RNAs, produces a significant regulatory network all through the transcriptome. The relationship of genes and non-coding RNAs could improve the knowledge of the genetic factors contributing to the predisposition and pathophysiology of MCI. The objective of this study was to identify the expression patterns and relevant lncRNA-associated miRNA regulatory axes in the blood of MCI patients, which includes lncRNA-SNHG16, lncRNA-H19, and lncRNA-NEAT1. Microarray investigations have demonstrated modifications in the expression of long non-coding RNAs (lncRNA) in the blood of patients with MCI compared with control samples. This is the first study to explore lncRNA profiles in mild cognitive impairment blood. Our study proposes RNAs targets involved in molecular pathways connected to the pathogenesis of MCI.

Transforming drug discovery with a high-throughput AI-powered platform: A 5-year experience with Patrimony

High-throughput computational platforms are being established to accelerate drug discovery. Servier launched the Patrimony platform to harness computational sciences and artificial intelligence (AI) to integrate massive multimodal data from internal and external sources. Patrimony has enabled researchers to prioritize therapeutic targets based on a deep understanding of the pathophysiology of immuno-inflammatory diseases. Herein, we share our experience regarding main challenges and critical success factors faced when industrializing the platform and broadening its applications to neurological diseases. We emphasize the importance of integrating such platforms in an end-to-end drug discovery process and engaging human experts early on to ensure a transforming impact.

Low m6A modification-mediated upregulation of PLAC8 promotes trophoblast cell invasion and migration in preeclampsia

BACKGROUND

The main symptoms of preeclampsia (PE), a specific ailment that develops during pregnancy, are proteinuria and hypertension. The pathological root of the onset and progression of PE is widely regarded as abnormal placental trophoblast cell function. This study aimed to look into the character and mechanism of Placenta-specific 8 (PLAC8) in trophoblast cell invasion and migration.

METHODS

Expressions of PLAC8 and AlkB homologue 5 (ALKBH5) were examined by western blot and quantitative real-time PCR. The m6A level of PLAC8 mRNA was detected by methylated RNA Immunoprecipitation. Using Transwell experiments, cell invasion and migration were examined. The enzyme-linked immunosorbent assay was utilized to analyze the MMP-2 and MMP-9 secretion levels. RNA pull-down and RNA immunoprecipitation were conducted to detect the binding between ALKBH5 and PLAC8.

RESULTS

In PE tissue and hypoxia-treated HTR-8/SVneo cells, levels of ALKBH5 and PLAC8 were increased, and PLAC8 m6A methylation levels were decreased. There was a positive correlation between PLAC8 and ALKBH5 expression in clinical tissues. In addition, overexpressing PLAC8 promoted HTR-8/SVneo cell migration and invasion, and so as the levels of MMP-2 and MMP-9; while interference with PLAC8 reduced the migration and invasion of hypoxia-treated HTR-8/SVneo cells, and so as the levels of MMP-2 and MMP-9. Moreover, the PLAC8 mRNA's m6A modification site was GAACU (Position 1449, Site 2). Increased levels of MMP-2 and MMP-9, as well as migration and invasion of HTR-8/SVneo cells exposed to hypoxia, were all facilitated by the m6A Site2 mutation. Furthermore, ALKBH5 could bind to PLAC8, reduce its m6A modification, and promote its expression.

CONCLUSION

High-expressed ALKBH5 inhibits the m6A level of PLAC8 mRNA and promotes PLAC8 expression, while PLAC8 overexpression can promote hypoxia-induced invasion and migration of HTR-8/Svneo cells, indicating its potential protective function in PE.

Long noncoding RNA MIAT regulates TP53 ubiquitination and expedites prostate adenocarcinoma progression by recruiting TBL1X

Despite recent advances in cancer immunotherapy, their efficacy for treating patients with prostate adenocarcinoma (PRAD) is low due to complex immune evasion mechanisms. However, the function of long non-coding RNA (lncRNAs) in immune evasion has not been fully clarified. This study aimed to expound the role of myocardial infarction-associated transcript (MIAT), a lncRNA significantly upregulated in three PRAD-associated datasets, in immune evasion and try to reveal the potential mechanism. MIAT was highly expressed in PRAD tissues and predicted poor prognosis, and suppression of MIAT inhibited the malignant biological behavior of PRAD cells. Moreover, the depletion of MIAT promoted the immune response of CD8+ T cells and hampered the immune evasion of PRAD cells. In addition, MIAT downregulated TP53 protein expression by recruiting transducin beta-like protein 1X (TBL1X) for ubiquitination modification. Silencing of TP53 or overexpression of TBL1X was enough to abate the tumor suppressive effects of MIAT knockdown in vitro and in vivo. Our results provide evidence for a novel regulation mechanism of CD8+ T cells in PRAD and MIAT may serve as a potential therapeutic target in PRAD.

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.

A New Understanding of Long Non-Coding RNA in Hepatocellular Carcinoma-From m6A Modification to Blood Biomarkers

With recent advancements in biological research, long non-coding RNAs (lncRNAs) with lengths exceeding 200 nucleotides have emerged as pivotal regulators of gene expression and cellular phenotypic modulation. Despite initial skepticism due to their low sequence conservation and expression levels, their significance in various biological processes has become increasingly apparent. We provided an overview of lncRNAs and discussed their defining features and modes of operation. We then explored their crucial function in the hepatocarcinogenesis process, elucidating their complex involvement in hepatocellular carcinoma (HCC). The influential role of lncRNAs within the HCC tumor microenvironment is emphasized, illustrating their potential as key modulators of disease dynamics. We also investigated the significant influence of N6-methyladenosine (m6A) modification on lncRNA function in HCC, enhancing our understanding of both their roles and their upstream regulators. Additionally, the potential of lncRNAs as promising biomarkers was discussed in liver cancer diagnosis, suggesting a novel avenue for future research and clinical application. Finally, our work underscored the dual potential of lncRNAs as both contributors to HCC pathogenesis and innovative tools for its diagnosis. Existing challenges and prospective trajectories in lncRNA research are also discussed, emphasizing their potential in advancing liver cancer research.

SETD1A-mediated H3K4me3 methylation upregulates lncRNA HOXC-AS3 and the binding of HOXC-AS3 to EP300 and increases EP300 stability to suppress the ferroptosis of NSCLC cells

BACKGROUND

Histone methyltransferases are crucial regulators in non-small cell lung cancer (NSCLC) development. This study explored the mechanism of histone methyltransferase SET domain containing 1A (SETD1A)-mediated H3K4me2 methylation in NSCLC cell ferroptosis and provides novel targets for NSCLC treatment.

METHODS

Upon downregulation of SETD1A in NSCLC cell lines, cell proliferation potential, malondialdehyde (MDA), superoxide dismutase (SOD) and glutathione (GSH) activities, iron content, and SETD1A, long noncoding RNA HOXC cluster antisense RNA 3 (lncRNA HOXC-AS3), E1A binding protein p300 (EP300), glutathione peroxidase 4 (GPX4) expressions were determined via cell counting kit-8, ELISA, iron assay kits, RT-qPCR, and western blot. Enrichment levels of SETD1A and H3K4me3 in the HOXC-AS3 promotor were measured via chromatin immunoprecipitation, and the binding of HOXC-AS3 and EP300 was analyzed via RNA immunoprecipitation. Rescue experiments were performed to confirm their roles in NSCLC cell ferroptosis. Xenograft tumor models were established to validate the role of SETD1A in vivo.

RESULTS

SETD1A, H3K4me3, HOXC-AS3, and EP300 were highly-expressed in NSCLC cells. Silencing SETD1A inhibited NSCLC cell proliferation, increased MDA and iron levels, and decreased SOD, GSH, and GPX4 levels. SETD1A downregulation reduced H3K4me3 level, HOXC-AS3 expression, the binding of HOXC-AS3 to EP300, and EP300 stability. Overexpression of HOXC-AS3 or EP300 reversed the promotion of silencing SETD1A on NSCLC cell ferroptosis. Silencing SETD1A reduced tumor volume and weight and positive rate of ki67 and increased ferroptosis through the HOXC-AS3/EP300 axis.

CONCLUSION

SETD1A-mediated H3K4me2 methylation promoted HOXC-AS3 expression, binding of HOXC-AS3 to EP300, and EP300 stability, thereby suppressing NSCLC cell ferroptosis.

The RNA interactome in the Hallmarks of Cancer

Ribonucleic acid (RNA) molecules are indispensable for cellular homeostasis in healthy and malignant cells. However, the functions of RNA extend well beyond that of a protein-coding template. Rather, both coding and non-coding RNA molecules function through critical interactions with a plethora of cellular molecules, including other RNAs, DNA, and proteins. Deconvoluting this RNA interactome, including the interacting partners, the nature of the interaction, and dynamic changes of these interactions in malignancies has yielded fundamental advances in knowledge and are emerging as a novel therapeutic strategy in cancer. Here, we present an RNA-centric review of recent advances in the field of RNA-RNA, RNA-protein, and RNA-DNA interactomic network analysis and their impact across the Hallmarks of Cancer. This article is categorized under: RNA in Disease and Development > RNA in Disease RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes.

Expression of LINC00174 in different cancers: Review of the literature and bioinformatics analyses

LINC00174 is an example of long intergenic non-coding RNAs with important functions in the development of human cancers. The gene encoding this lincRNA is located on 7q11.21. LINC00174 has been demonstrated to play an oncogenic role in a variety of cancers, including colorectal carcinoma, thymic carcinoma, glioma, glioblastoma, hepatocellular carcinoma, kidney renal clear cell carcinoma, breast cancer and non-functioning pituitary adenoma. In lung cancer, there is an obvious discrepancy between different studies regarding the role of this lincRNA. This lincRNA is also involved in the determination of prognosis of different cancers, particularly colorectal cancer. In the current review, we discuss the role of this lincRNA in human carcinogenesis based on the available data in the literature and bioinformatics tools.

Co-expression module analysis reveals high expression homogeneity for both coding and non-coding genes in sepsis

Sepsis is a life-threatening condition characterized by a harmful host response to infection with organ dysfunction. Annually about 20 million people are dead owing to sepsis and its mortality rates is as high as 20%. However, no studies have been carried out to investigate sepsis from the system biology point of view, as previous research predominantly focused on individual genes without considering their interactions and associations. Here, we conducted a comprehensive exploration of genome-wide expression alterations in both mRNAs and long non-coding RNAs (lncRNAs) in sepsis, using six microarray datasets. Co-expression networks were conducted to identify mRNA and lncRNA modules, respectively. Comparing these sepsis modules with normal modules, we observed a homogeneous expression pattern within the mRNA/lncRNA members, with the majority of them displaying consistent expression direction. Moreover, we identified consistent modules across diverse datasets, consisting of 20 common mRNA members and two lncRNAs, namely CHRM3-AS2 and PRKCQ-AS1, which are potential regulators of sepsis. Our results reveal that the up-regulated common mRNAs are mainly involved in the processes of neutrophil mediated immunity, while the down-regulated mRNAs and lncRNAs are significantly overrepresented in T-cell mediated immunity functions. This study sheds light on the co-expression patterns of mRNAs and lncRNAs in sepsis, providing a novel perspective and insight into the sepsis transcriptome, which may facilitate the exploration of candidate therapeutic targets and molecular biomarkers for sepsis.

Identification of hub genes and potential ceRNA networks of diabetic cardiomyopathy

Diabetic cardiomyopathy (DCM), a common complication of diabetes, is defined as ventricular dysfunction in the absence of underlying heart disease. Noncoding RNAs (ncRNAs), including long noncoding RNAs (lncRNAs) and microRNAs (miRNAs), play a crucial role in the development of DCM. Weighted Gene Co-Expression Network Analysis (WGCNA) was used to identify key modules in DCM-related pathways. DCM-related miRNA-mRNA network and DCM-related ceRNA network were constructed by miRNA-seq to identify hub genes in these modules. We identified five hub genes that are associated with the onset of DCM, including Troponin C1 (Tnnc1), Phospholamban (Pln), Fatty acid binding proteins 3 (Fabp3), Popeye domain containing 2 (Popdc2), and Tripartite Motif-containing Protein 63 (Trim63). miRNAs that target the hub genes were mainly involved in TGF-β and Wnt signaling pathways. GO BP enrichment analysis found these miRNAs were involved in the signaling of TGF-β and glucose homeostasis. Q-PCR results found the gene expressions of Pln, Fabp3, Trim63, Tnnc1, and Popdc2 were significantly increased in DCM. Our study identified five hub genes (Tnnc1, Pln, Fabp3, Popdc2, Trim63) whose associated ceRNA networks are responsible for the onset of DCM.

Long Noncoding RNA AC078850.1 Induces NLRP3 Inflammasome-Mediated Pyroptosis in Atherosclerosis by Upregulating ITGB2 Transcription via Transcription Factor HIF-1α

As a chronic progressive inflammatory disease, atherosclerosis constitutes a leading cause of cardiovascular disease, with high mortality and morbidity worldwide. The effect of lncRNA AC078850.1 in atherosclerosis is unknown; this study aims to explore the regulatory mechanism of the lncRNA AC078850.1/HIF-1α complex in atherosclerosis. Initially, we identified the lncRNA AC078850.1 associated with atherosclerosis using multiple bioinformatic methods, finding that the level of lncRNA AC078850.1 in peripheral blood mononuclear cells was positively related to the severity of carotid atherosclerosis. LncRNA AC078850.1 was upregulated, and found to be predominately localized in the nucleus of THP-1 macrophage-derived foam cells. Both the knockdown of lncRNA AC078850.1 and the transcription factor HIF-1α can each markedly suppress ITGB2 gene transcription, ROS production, NLRP3 inflammasome, IL-1β/18 release, lipid accumulation, and pyroptotic cell death in ox-LDL-stimulated THP-1-derived macrophages. Additionally, the downregulation of HIF-1α attenuated the positive effects of lncRNA AC078850.1 on pyroptosis and foam cell formation. In addition, the knockdown of lncRNA AC078850.1 suppressed HIF-1α-aggravated macrophages pyroptosis and foam cell formation. Meanwhile, inhibition of ITGB2 gene expression ameliorated HIF-1α-aggravated ROS generation in THP-1-derived macrophages. Taken together, our study demonstrated that lncRNA AC078850.1 was involved in the regulation of ITGB2 gene transcription by binding to the HIF-1α and lncRNA AC078850.1/HIF-1α complex, promoting both NLRP3 inflammasome-mediated pyroptosis and foam cell formation through the ROS-dependent pathway in cases of atherosclerosis.

Downregulation of N6-methyladenosine-modified LINC00641 promotes EMT, but provides a ferroptotic vulnerability in lung cancer

The prognosis of lung cancer is poor with few effective therapies. Targeting ferroptosis is a new promising strategy for cancer therapy. LINC00641 has been involved in several cancers, however, its specific roles in lung cancer treatment remain largely unknown. Here, we reported that LINC00641 was down-regulated in tumor tissues and its downregulation was associated with poor outcomes in lung adenocarcinoma. LINC00641 was localized primarily in the nucleus and was modified by m⁶A. The nuclear m⁶A reader YTHDC1 regulated LINC00641 expression by affecting its stability. We demonstrated that LINC00641 suppressed lung cancer by inhibiting migration and invasion in vitro and metastasis in vivo. Knockdown of LINC00641 upregulated HuR protein level (especially in the cytoplasm), which subsequently increased N-cadherin levels by stabilizing its mRNA, then ultimately promoted EMT. Interestingly, LINC00641 knockdown in lung cancer cells increased the arachidonic acid metabolism and promoted ferroptosis sensitivity. Our findings identified LINC00641 as a tumor suppressor through inhibiting EMT. In another aspect, low expression of LINC00641 caused a ferroptotic vulnerability in lung cancer cells, which may serve as a potential ferroptosis-related therapeutic target for lung cancer.

N6-Methyladenosine-modified circSAV1 triggers ferroptosis in COPD through recruiting YTHDF1 to facilitate the translation of IREB2

Epithelial cell damage-initiated chronic obstructive pulmonary disease (COPD) is implicated in regulated cell death (RCD) including ferroptosis triggered by complex gene-environment interactions. Our data showed that iron overload and ferroptosis are associated with COPD progression in COPD patients and in experimental COPD. Furthermore, we found that, in lung tissues of COPD patients, circSAV1 was associated with COPD progression by circRNA-seq screening. Knockdown of circSAV1 reversed cigarette smoke extract (CSE)-induced ferroptosis. Mechanistically, m6A-modified circSAV1 formed an RNA-protein ternary complex of circSAV1/YTHDF1/IREB2 to facilitate the translation of IREB2 mRNA. Elevated protein levels of IREB2 disrupted iron homeostasis, resulting in accumulation of a labile iron pool (LIP) and lipid peroxidation, which contribute to ferroptosis. Here we demonstrate, by use of an experimental COPD model induced by cigarette smoke (CS), that silencing of circSAV1 and the treatment with deferoxamine (DFO) blocked CS-induced ferroptosis of lung epithelial cells, which attenuated COPD progression in mice. Our results reveal that N6-methyladenosine-modified circSAV1 triggers ferroptosis in COPD through recruiting YTHDF1 to facilitate the translation of IREB2, indicating that circSAV1 is a mediator of ferroptosis and that circSAV1-dependent ferroptosis is a therapeutic target for COPD. In lung epithelial cell, m6A-modified circSAV1, via recruiting YTHDF1, induces the formation of a circSAV1/YTHDF1/IREB2 mRNA protein ternary complex, which promotes translation of IREB2 mRNA. Further, elevated IREB2 contributes to the accumulation of a labile iron pool (LIP) and lipid peroxidation, then triggers ferroptosis of lung epithelial cells. The ferroptosis of airway epithelial cells and alveolar epithelial cells induces airway remodeling and emphysema, respectively, which causes COPD.

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/.

Global expression and functional analysis of human piRNAs during HSV-1 infection

Piwi-interacting RNAs (piRNAs) are a class of non-coding RNAs that play a key role in spermatogenesis. However, little is known about their expression characterization and role in somatic cells infected with herpes simplex virus type 1 (HSV-1). In this study, we systematically investigated the cellular piRNA expression profiles of HSV-1-infected human lung fibroblasts. Compared with the control group, 69 differentially expressed piRNAs were identified in the infection group, among which 52 were up-regulated and 17 were down-regulated. The changes in the expression of 8 piRNAs were further verified by RT-qPCR with a similar expression trend. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis showed that the target genes of piRNAs were mainly involved in antiviral immunity and various human disease-related signaling pathways. Furthermore, we tested the effects of four up-regulated piRNAs on viral replication by transfecting piRNA mimics. The results showed that the virus titers of the group transfected with piRNA-hsa-28,382 (alias piR-36,233) mimic decreased significantly, and that of the group transfected piRNA-hsa-28,190 (alias piR-36,041) mimic significantly increased. Overall, our results revealed the expression characteristics of piRNAs in HSV-1-infected cells. We also screened two piRNAs that potentially regulate HSV-1 replication. These results may promote a better understanding of the regulatory mechanism of pathophysiological changes induced by HSV-1 infection.

Comprehensive analysis reveals TSEN54 as a robust prognosis biomarker and promising immune-related therapeutic target for hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma represents the most common primary malignancy of all liver cancer types and its prognosis is usually unsatisfactory. TSEN54 encodes a protein constituting a subunit of the tRNA splicing endonuclease heterotetramer. Previous researches concentrated on the contribution of TSEN54 in pontocerebellar hypoplasia, but no studies have yet reported its role in HCC.

METHODS

TIMER, HCCDB, GEPIA, HPA, UALCAN, MEXPRESS, SMART, TargetScan, RNAinter, miRNet, starBase, Kaplan-Meier Plotter, cBioPortal, LinkedOmics, GSEA, TISCH, TISIDB, GeneMANIA, PDB, GSCALite were applied in this research.

RESULTS

We identified the upregulation of TSEN54 expression in HCC and related it to multiple clinicopathological features. Hypomethylation of TSEN54 was closely associated with its high expression. HCC sufferers who held high TSEN54 expression typically had shorter survival expectations. Enrichment analysis showed the involvement of TSEN54 in the cell cycle and metabolic processes. Afterward, we observed that TSEN54 expression level had a positive relationship to the infiltration level of multiple immune cells and the expression of several chemokines. We additionally identified that TSEN54 was related to the expression level of several immune checkpoints and TSEN54 was linked to several m6A-related regulators.

CONCLUSIONS

TSEN54 is a prognostic marker of HCC. TSEN54 could become a prospective candidate for HCC diagnosis and therapy.

A hybrid approach to assess the structural impact of long noncoding RNA mutations uncovers key NEAT1 interactions in colorectal cancer

Long noncoding RNAs (lncRNAs) are emerging players in cancer and they entail potential as prognostic biomarkers or therapeutic targets. Earlier studies have identified somatic mutations in lncRNAs that are associated with tumor relapse after therapy, but the underlying mechanisms behind these associations remain unknown. Given the relevance of secondary structure for the function of some lncRNAs, some of these mutations may have a functional impact through structural disturbance. Here, we examined the potential structural and functional impact of a novel A > G point mutation in NEAT1 that has been recurrently observed in tumors of colorectal cancer patients experiencing relapse after treatment. Here, we used the nextPARS structural probing approach to provide first empirical evidence that this mutation alters NEAT1 structure. We further evaluated the potential effects of this structural alteration using computational tools and found that this mutation likely alters the binding propensities of several NEAT1-interacting miRNAs. Differential expression analysis on these miRNA networks shows upregulation of Vimentin, consistent with previous findings. We propose a hybrid pipeline that can be used to explore the potential functional effects of lncRNA somatic mutations.

Interactomics in plant defence: progress and opportunities

Interactomics is a branch of systems biology that deals with the study of protein-protein interactions and how these interactions influence phenotypes. Identifying the interactomes involved during host-pathogen interaction events may bring us a step closer to deciphering the molecular mechanisms underlying plant defence. Here, we conducted a systematic review of plant interactomics studies over the last two decades and found that while a substantial progress has been made in the field, plant-pathogen interactomics remains a less-travelled route. As an effort to facilitate the progress in this field, we provide here a comprehensive research pipeline for an in planta plant-pathogen interactomics study that encompasses the in silico prediction step to the validation step, unconfined to model plants. We also highlight four challenges in plant-pathogen interactomics with plausible solution(s) for each.

Integrative bioinformatics analysis to identify novel biomarkers associated with non-obstructive azoospermia

Aim

This study aimed to identify autophagy-related genes (ARGs) associated with non-obstructive azoospermia and explore the underlying molecular mechanisms.

Methods

Two datasets associated with azoospermia were downloaded from the Gene Expression Omnibus database, and ARGs were obtained from the Human Autophagy-dedicated Database. Autophagy-related differentially expressed genes were identified in the azoospermia and control groups. These genes were subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, protein–protein interaction (PPI) network, and functional similarity analyses. After identifying the hub genes, immune infiltration and hub gene–RNA-binding protein (RBP)–transcription factor (TF)–miRNA–drug interactions were analyzed.

Results

A total 46 differentially expressed ARGs were identified between the azoospermia and control groups. These genes were enriched in autophagy-associated functions and pathways. Eight hub genes were selected from the PPI network. Functional similarity analysis revealed that HSPA5 may play a key role in azoospermia. Immune cell infiltration analysis revealed that activated dendritic cells were significantly decreased in the azoospermia group compared to those in the control groups. Hub genes, especially ATG3, KIAA0652, MAPK1, and EGFR were strongly correlated with immune cell infiltration. Finally, a hub gene–miRNA–TF–RBP–drug network was constructed.

Conclusion

The eight hub genes, including EGFR, HSPA5, ATG3, KIAA0652, and MAPK1, may serve as biomarkers for the diagnosis and treatment of azoospermia. The study findings suggest potential targets and mechanisms for the occurrence and development of this disease.

Finding miRNA-RNA Network Biomarkers for Predicting Metastasis and Prognosis in Cancer

Despite remarkable progress in cancer research and treatment over the past decades, cancer ranks as a leading cause of death worldwide. In particular, metastasis is the major cause of cancer deaths. After an extensive analysis of miRNAs and RNAs in tumor tissue samples, we derived miRNA-RNA pairs with substantially different correlations from those in normal tissue samples. Using the differential miRNA-RNA correlations, we constructed models for predicting metastasis. A comparison of our model to other models with the same data sets of solid cancer showed that our model is much better than the others in both lymph node metastasis and distant metastasis. The miRNA-RNA correlations were also used in finding prognostic network biomarkers in cancer patients. The results of our study showed that miRNA-RNA correlations and networks consisting of miRNA-RNA pairs were more powerful in predicting prognosis as well as metastasis. Our method and the biomarkers obtained using the method will be useful for predicting metastasis and prognosis, which in turn will help select treatment options for cancer patients and targets of anti-cancer drug discovery.

A passage-dependent network for estimating the in vitro senescence of mesenchymal stromal/stem cells using microarray, bulk and single cell RNA sequencing

Long-term in vitro culture of human mesenchymal stem cells (MSCs) leads to cell lifespan shortening and growth stagnation due to cell senescence. Here, using sequencing data generated in the public domain, we have established a specific regulatory network of "transcription factor (TF)-microRNA (miRNA)-Target" to provide key molecules for evaluating the passage-dependent replicative senescence of mesenchymal stem cells for the quality control and status evaluation of mesenchymal stem cells prepared by different procedures. Short time-series expression miner (STEM) analysis was performed on the RNA-seq and miRNA-seq databases of mesenchymal stem cells from various passages to reveal the dynamic passage-related changes of miRNAs and mRNAs. Potential miRNA targets were predicted using seven miRNA target prediction databases, including TargetScan, miRTarBase, miRDB, miRWalk, RNA22, RNAinter, and TargetMiner. Then use the TransmiR v2.0 database to obtain experimental-supported transcription factor for regulating the selected miRNA. More than ten sequencing data related to mesenchymal stem cells or mesenchymal stem cells reprogramming were used to validate key miRNAs and mRNAs. And gene set variation analysis (GSVA) was performed to calculate the passage-dependent signature. The results showed that during the passage of mesenchymal stem cells, a total of 29 miRNAs were gradually downregulated and 210 mRNA were gradually upregulated. Enrichment analysis showed that the 29 miRNAs acted as multipotent regulatory factors of stem cells and participated in a variety of signaling pathways, including TGF-beta, HIPPO and oxygen related pathways. 210 mRNAs were involved in cell senescence. According to the target prediction results, the targets of these key miRNAs and mRNAs intersect to form a regulatory network of "TF-miRNA-Target" related to replicative senescence of cultured mesenchymal stem cells, across 35 transcription factor, 7 miRNAs (has-mir-454-3p, has-mir-196b-5p, has-mir-130b-5p, has-mir-1271-5p, has-let-7i-5p, has-let-7a-5p, and has-let-7b-5p) and 7 predicted targets (PRUNE2, DIO2, CPA4, PRKAA2, DMD, DDAH1, and GATA6). This network was further validated by analyzing datasets from a variety of mesenchymal stem cells subculture and lineage reprogramming studies, as well as qPCR analysis of early passages mesenchymal stem cells versus mesenchymal stem cells with senescence morphologies (SA-β-Gal⁺). The "TF-miRNA-Target" regulatory network constructed in this study reveals the functional mechanism of miRNAs in promoting the senescence of MSCs during in vitro expansion and provides indicators for monitoring the quality of functional mesenchymal stem cells during the preparation and clinical application.

Hsa_circ_0001944 regulates apoptosis by regulating the binding of PARP1 and HuR in leukemia and malignant transformed cells induced by hydroquinone

Hydroquinone (HQ) is one of the major metabolites of benzene and can cause abnormal gene expression. It is a known carcinogen that alters cell cycle disruption and cell proliferation. However, its chemical mechanism remain a mystery. Circular RNAs (circRNAs) are a subtype of noncoding RNAs (ncRNAs) that play a variety of roles in biological processes. Hsa_circ_001944 expression was upregulated in 30 leukemia patients and HQ-induced malignant transformed TK6 cells. Hsa_circ_001944 silencing inhibited the growth of HQ-TK6 cells and halted the cell cycle. The silencing of hsa_circ_0001944 led to increased cell accumulation in G1 versus S phase, increased apoptosis in the sh1944 versus the shNC group, and increased levels of DNA damage (γ-H2AX), leading to cell cycle arrest. In summary, inhibition of hsa_circ_001944 restricted cell growth by inhibiting cell cycle arrest and induced growth of HQ-TK6 cells by modulating PARP1 expression. Hsa_circ_0001944 targeted HuR, which is a kind of RNA-binding protein, to control PARP1 expression via RNAinter, RBPmap, and RBPdb. Fluorescence in situ hybridization combined with immunofluorescent labeling and western blotting experiments showed that hsa_circ_001944 was able to dissociate HuR and PARP1 binding in HQ-TK6 cells, control PARP1 production, and ultimately alter the PARP1/H-Ras pathway.

Novel diagnostic biomarkers related to immune infiltration in Parkinson's disease by bioinformatics analysis

Background

Parkinson's disease (PD) is Pengfei Zhang Liwen Zhao Pengfei Zhang Liwen Zhao a common neurological disorder involving a complex relationship with immune infiltration. Therefore, we aimed to explore PD immune infiltration patterns and identify novel immune-related diagnostic biomarkers.

Materials and methods

Three substantia nigra expression microarray datasets were integrated with elimination of batch effects. Differentially expressed genes (DEGs) were screened using the "limma" package, and functional enrichment was analyzed. Weighted gene co-expression network analysis (WGCNA) was performed to explore the key module most significantly associated with PD; the intersection of DEGs and the key module in WGCNA were considered common genes (CGs). The CG protein-protein interaction (PPI) network was constructed to identify candidate hub genes by cytoscape. Candidate hub genes were verified by another two datasets. Receiver operating characteristic curve analysis was used to evaluate the hub gene diagnostic ability, with further gene set enrichment analysis (GSEA). The immune infiltration level was evaluated by ssGSEA and CIBERSORT methods. Spearman correlation analysis was used to evaluate the hub genes association with immune cells. Finally, a nomogram model and microRNA-TF-mRNA network were constructed based on immune-related biomarkers.

Results

A total of 263 CGs were identified by the intersection of 319 DEGs and 1539 genes in the key turquoise module. Eleven candidate hub genes were screened by the R package "UpSet." We verified the candidate hub genes based on two validation sets and identified six (SYT1, NEFM, NEFL, SNAP25, GAP43, and GRIA1) that distinguish the PD group from healthy controls. Both CIBERSORT and ssGSEA revealed a significantly increased proportion of neutrophils in the PD group. Correlation between immune cells and hub genes showed SYT1, NEFM, GAP43, and GRIA1 to be significantly related to immune cells. Moreover, the microRNA-TFs-mRNA network revealed that the microRNA-92a family targets all four immune-related genes in PD pathogenesis. Finally, a nomogram exhibited a reliable capability of predicting PD based on the four immune-related genes (AUC = 0.905).

Conclusion

By affecting immune infiltration, SYT1, NEFM, GAP43, and GRIA1, which are regulated by the microRNA-92a family, were identified as diagnostic biomarkers of PD. The correlation of these four genes with neutrophils and the microRNA-92a family in PD needs further investigation.

Multi-view contrastive heterogeneous graph attention network for lncRNA-disease association prediction

MOTIVATION

Exploring the potential long noncoding RNA (lncRNA)-disease associations (LDAs) plays a critical role for understanding disease etiology and pathogenesis. Given the high cost of biological experiments, developing a computational method is a practical necessity to effectively accelerate experimental screening process of candidate LDAs. However, under the high sparsity of LDA dataset, many computational models hardly exploit enough knowledge to learn comprehensive patterns of node representations. Moreover, although the metapath-based GNN has been recently introduced into LDA prediction, it discards intermediate nodes along the meta-path and results in information loss.

RESULTS

This paper presents a new multi-view contrastive heterogeneous graph attention network (GAT) for lncRNA-disease association prediction, MCHNLDA for brevity. Specifically, MCHNLDA firstly leverages rich biological data sources of lncRNA, gene and disease to construct two-view graphs, feature structural graph of feature schema view and lncRNA-gene-disease heterogeneous graph of network topology view. Then, we design a cross-contrastive learning task to collaboratively guide graph embeddings of the two views without relying on any labels. In this way, we can pull closer the nodes of similar features and network topology, and push other nodes away. Furthermore, we propose a heterogeneous contextual GAT, where long short-term memory network is incorporated into attention mechanism to effectively capture sequential structure information along the meta-path. Extensive experimental comparisons against several state-of-the-art methods show the effectiveness of proposed framework.The code and data of proposed framework is freely available at https://github.com/zhaoxs686/MCHNLDA.

Non-coding RNAs in human health and disease: potential function as biomarkers and therapeutic targets

Human diseases have been a critical threat from the beginning of human history. Knowing the origin, course of action and treatment of any disease state is essential. A microscopic approach to the molecular field is a more coherent and accurate way to explore the mechanism, progression, and therapy with the introduction and evolution of technology than a macroscopic approach. Non-coding RNAs (ncRNAs) play increasingly important roles in detecting, developing, and treating all abnormalities related to physiology, pathology, genetics, epigenetics, cancer, and developmental diseases. Noncoding RNAs are becoming increasingly crucial as powerful, multipurpose regulators of all biological processes. Parallel to this, a rising amount of scientific information has revealed links between abnormal noncoding RNA expression and human disorders. Numerous non-coding transcripts with unknown functions have been found in addition to advancements in RNA-sequencing methods. Non-coding linear RNAs come in a variety of forms, including circular RNAs with a continuous closed loop (circRNA), long non-coding RNAs (lncRNA), and microRNAs (miRNA). This comprises specific information on their biogenesis, mode of action, physiological function, and significance concerning disease (such as cancer or cardiovascular diseases and others). This study review focuses on non-coding RNA as specific biomarkers and novel therapeutic targets.