Predicting lncRNA-miRNA interactions based on interactome network and graphlet interaction

A heterogeneous information network learning model with neighborhood-level structural representation for predicting lncRNA-miRNA interactions

Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are closely related to the treatment of human diseases. Traditional biological experiments often require time-consuming and labor-intensive in their search for mechanisms of disease. Computational methods are regarded as an effective way to predict unknown lncRNA-miRNA interactions (LMIs). However, most of them complete their tasks by mainly focusing on a single lncRNA-miRNA network without considering the complex mechanism between biomolecular in life activities, which are believed to be useful for improving the accuracy of LMI prediction. To address this, a heterogeneous information network (HIN) learning model with neighborhood-level structural representation, called HINLMI, to precisely identify LMIs. In particular, HINLMI first constructs a HIN by integrating nine interactions of five biomolecules. After that, different representation learning strategies are applied to learn the biological and network representations of lncRNAs and miRNAs in the HIN from different perspectives. Finally, HINLMI incorporates the XGBoost classifier to predict unknown LMIs using final embeddings of lncRNAs and miRNAs. Experimental results show that HINLMI yields a best performance on the real dataset when compared with state-of-the-art computational models. Moreover, several analysis experiments indicate that the simultaneous consideration of biological knowledge and network topology of lncRNAs and miRNAs allows HINLMI to accurately predict LMIs from a more comprehensive perspective. The promising performance of HINLMI also reveals that the utilization of rich heterogeneous information can provide an alternative insight for HINLMI to identify novel interactions between lncRNAs and miRNAs.

Bioinformatics analysis of the role of cuproptosis gene in acute myocardial infarction

BACKGROUND

Immune infiltration plays a vital role in the course of acute myocardial infarction (AMI). Cuproptosis is a new type of programmed cell death discovered recently. Currently, there is no study on the mechanism of cuproptosis gene regulating immune infiltration in AMI. Therefore, by integrating cuproptosis-related genes and GEO database-related microarray data, this study analyzed the association between cuproptosis genes and immune infiltration and built a risk model.

METHODS

The GSE59867 was used to extract cuproptosis gene expression profile. The R limma package was used to analyze the differentially expressed genes associated with AMI-Cuproptosis. The risk model was constructed according to AMI-cuproptosis differentially expressed genes. Prediction of AMI-cuproptosis-related gene drugs through Coremine Medical database. The upstream miRNAs were predicted using miRWalk, TargetScan, and miRDB libraries, and a miRNA-mRNA network was constructed.

RESULTS

Cuproptosis-related genes (DLST, LIAS, DBT, ATP7A, LIPT1, PDHB, GCSH, DLD, DLAT) were down-regulated in AMI patients. One (ATP7B) gene was up-regulated in AMI patients (P<0.05). These 10 Cuproptosis-related genes were significantly associated with immune cell infiltration. Based on these 10 differential genes, the AMI risk prediction model was constructed, and the AUC value was 0.825, among which the abnormal expression of DLST was a risk factor for AMI. Additionally, we also predicted DLAT upstream miRNAs and associated drug targets, finding that 9 miRNAs were upstream of DLST.

CONCLUSIONS

DLST is a potential cuproptosis gene associated with AMI, but its specific mechanism remains unclear and requires further investigation in future studies.

HERC5: a comprehensive in silico analysis of its diagnostic, prognostic, and therapeutic potential in cancer

HERC5, a vital protein in the HERC family, plays crucial roles in immune response, cancer progression, and antiviral defense. This bioinformatic study comprehensively assessed HERC5's significance across various malignancies by analyzing its gene expression, immune and molecular subtype expressions, target proteins, biological functions, and prognostic and diagnostic values in pan-cancer. We further examined its correlation with clinical features, co-expressed and differentially expressed genes, and prognosis in clinical subgroups, focusing on endometrial cancer (UCEC). Our findings showed that HERC5 RNA is expressed at low levels in most cancers and significantly differs across immune and molecular subtypes. HERC5 accurately predicts cancer and correlates with most cancer prognoses. In UCEC, HERC5 was significantly associated with age, hormonal status, clinical stage, treatment status, and metastasis. Elevated HERC5 expression was linked to worse progression-free interval, disease-specific survival, and overall survival in UCEC, particularly in diverse clinical subgroups. Significant differences in HERC5 expression were also observed in various human cancer cell line validations. In summary, HERC5 may be a critical biomarker for pan-cancer prognosis, progression, and diagnosis, as well as a promising new target for cancer therapy.

Improving plant miRNA-target prediction with self-supervised k-mer embedding and spectral graph convolutional neural network

Deciphering the targets of microRNAs (miRNAs) in plants is crucial for comprehending their function and the variation in phenotype that they cause. As the highly cell-specific nature of miRNA regulation, recent computational approaches usually utilize expression data to identify the most physiologically relevant targets. Although these methods are effective, they typically require a large sample size and high-depth sequencing to detect potential miRNA-target pairs, thereby limiting their applicability in improving plant breeding. In this study, we propose a novel miRNA-target prediction framework named kmerPMTF (k-mer-based prediction framework for plant miRNA-target). Our framework effectively extracts the latent semantic embeddings of sequences by utilizing k-mer splitting and a deep self-supervised neural network. We construct multiple similarity networks based on k-mer embeddings and employ graph convolutional networks to derive deep representations of miRNAs and targets and calculate the probabilities of potential associations. We evaluated the performance of kmerPMTF on four typical plant datasets: Arabidopsis thaliana, Oryza sativa, Solanum lycopersicum, and Prunus persica. The results demonstrate its ability to achieve AUPRC values of 84.9%, 91.0%, 80.1%, and 82.1% in 5-fold cross-validation, respectively. Compared with several state-of-the-art existing methods, our framework achieves better performance on threshold-independent evaluation metrics. Overall, our study provides an efficient and simplified methodology for identifying plant miRNA-target associations, which will contribute to a deeper comprehension of miRNA regulatory mechanisms in plants.

Machine Learning Strategies in MicroRNA Research: Bridging Genome to Phenome

MicroRNAs (miRNAs) have emerged as a prominent layer of regulation of gene expression. This article offers the salient and current aspects of machine learning (ML) tools and approaches from genome to phenome in miRNA research. First, we underline that the complexity in the analysis of miRNA function ranges from their modes of biogenesis to the target diversity in diverse biological conditions. Therefore, it is imperative to first ascertain the miRNA coding potential of genomes and understand the regulatory mechanisms of their expression. This knowledge enables the efficient classification of miRNA precursors and the identification of their mature forms and respective target genes. Second, and because one miRNA can target multiple mRNAs and vice versa, another challenge is the assessment of the miRNA-mRNA target interaction network. Furthermore, long-noncoding RNA (lncRNA)and circular RNAs (circRNAs) also contribute to this complexity. ML has been used to tackle these challenges at the high-dimensional data level. The present expert review covers more than 100 tools adopting various ML approaches pertaining to, for example, (1) miRNA promoter prediction, (2) precursor classification, (3) mature miRNA prediction, (4) miRNA target prediction, (5) miRNA- lncRNA and miRNA-circRNA interactions, (6) miRNA-mRNA expression profiling, (7) miRNA regulatory module detection, (8) miRNA-disease association, and (9) miRNA essentiality prediction. Taken together, we unpack, critically examine, and highlight the cutting-edge synergy of ML approaches and miRNA research so as to develop a dynamic and microlevel understanding of human health and diseases.

Multicomponent comprehensive confirms that erythroferrone is a molecular biomarker of pan-cancer

All vertebrates organisms produce erythroferrone, a secretory hormone with structure-related functions during iron homeostasis. However, limited knowledge exists regarding the effect of this hormone on the occurrence and progression of cancer. To systematically and comprehensively identify the diverse implications of Erythroferrone (ERFE) in various malignant tumors, we conducted an in-depth analysis of multiple datasets, including the expression levels of oncogenes and target proteins, biological functions, and molecular characteristics. This analysis aimed to assess the diagnostic and prognostic value of ERFE in pan-cancer. Our findings revealed a significant elevation in ERFE expression across 20 distinct cancer types, with notable increases in gastrointestinal cancers. Utilizing the Cytoscape and STRING databases, we identified 35 ERFE-targeted binding proteins. Survival prognosis studies, particularly gastrointestinal cancers indicated by Colon adenocarcinoma (COAD), demonstrated a poor prognosis in patients with high ERFE expression (p < 0.001), consistently observed across various clinical subgroups. Furthermore, the ROC curve underscored the high predictive ability of EFRE for gastrointestinal cancer (AUC >0.9). Understanding the roles and interactions of ERFE in biological processes can also be aided by examining the genes co-expressed with ERFE in the coat and ranking the top 50 positive and negative genes. In the correlation analysis between the ERFE gene and different immune cells in COAD, we discovered that the expression of ERFE was positively correlated with Th1 cells, cytotoxic cells, and activated DC (aDC) abundance, and negatively correlated with Tcm (T central memory) abundance (P < 0.001). in summary, ERFE emerges as strongly associated with various malignant cancers, positioning it as a prospective biological target for cancer treatment. It stands out as a key molecular biomarker for diagnosing and prognosticating pancreatic cancer, also serves as an independent prognostic risk factor for COAD.

Prognostic value of oxidative stress-related genes in colorectal cancer and its correlation with tumor immunity

Oxidative stress (OS) plays an essential role in chronic diseases such as colorectal cancer (CRC). In this study, we aimed to explore the relation between oxidative stress-related genes and CRC prognosis and their involvement in the immune microenvironment. Totally 101 OS-related genes were selected from the MsigDB database. Then, univariate Cox regression was used to explore the prognostic value of the selected genes correlated with the CRC patient survival in the TCGA database. A total of 9 prognostic OS-related genes in CRC were identified. Based on consensus clustering, CRC patients were then categorized into two molecular subtypes. A prognostic risk model containing 8 genes was established using Lasso regression, and CRC patients were divided into high or low-risk groups based on the median risk scores. The predictive value of the 8 genes in CRC prognosis was validated using ROC curves, which indicate that CTNNB1, STK25, RNF112, SFPQ, MMP3, and NOL3 were promising prognostic biomarkers in CRC. Furthermore, the immune cell infiltration levels in different risk groups or CRC subtypes were analyzed. We found that the high-risk or C1 subtype had immunosuppressive microenvironment, which might explain the unfavorable prognosis in the two groups of CRC patients. Additionally, functional experiments were conducted to investigate the effects of OS-related genes on CRC cell proliferation, stemness, and apoptosis. We found that CTNNB1, HSPB1, MMP3, and NOL3 were upregulated in CRC tissues and cells. Knockdown of CTNNB1, HSPB1, MMP3, and NOL3 significantly suppressed CRC cell proliferation, stemness and facilitated CRC cell apoptosis. In conclusion, we established prognostic CRC subtypes and an eight-gene risk model, which may provide novel prognostic indicators and benefit the design of individualized therapeutic strategies for CRC patients.

Disulfidptosis-related Protein RPN1 may be a Novel Anti-osteoporosis Target of Kaempferol

BACKGROUND

Osteoporosis (OP) is an age-related skeletal disease. Kaempferol can regulate bone mesenchymal stem cells (BMSCs) osteogenesis to improve OP, but its mechanism related to disulfidptosis, a newly discovered cell death mechanism, remains unclear.

OBJECTIVE

The study aimed to investigate the biological function and immune mechanism of disulfidptosis- related ribophorin I (RPN1) in OP and to experimentally confirm that RPN1 is the target for the treatment of OP with kaempferol.

METHODS

Differential expression analysis was conducted on disulfide-related genes extracted from the GSE56815 and GSE7158 datasets. Four machine learning algorithms identified disease signature genes, with RPN1 identified as a significant risk factor for OP through the nomogram. Validation of RPN1 differential expression in OP patients was performed using the GSE56116 dataset. The impact of RPN1 on immune alterations and biological processes was explored. Predictive ceRNA regulatory networks associated with RPN1 were generated via miRanda, miRDB, and TargetScan databases. Molecular docking estimated the binding model between kaempferol and RPN1. The targeting mechanism of kaempferol on RPN1 was confirmed through pathological HE staining and immunohistochemistry in ovariectomized (OVX) rats.

RESULTS

RPN1 was abnormally overexpressed in the OP cohort, associated with TNF signaling, hematopoietic cell lineage, and NF-kappa B pathway. Immune infiltration analysis showed a positive correlation between RPN1 expression and CD8+ T cells and resting NK cells, while a negative correlation with CD4+ naive T cells, macrophage M1, T cell gamma delta, T cell follicular helper cells, activated mast cells, NK cells, and dendritic cells, was found. Four miRNAs and 17 lncRNAs associated with RPN1 were identified. Kaempferol exhibited high binding affinity (-7.2 kcal/mol) and good stability towards the RPN1. The experimental results verified that kaempferol could improve bone microstructure destruction and reverse the abnormally high expression of RPN1 in the femur of ovariectomized rats.

CONCLUSION

RPN1 may be a new diagnostic biomarker in patients with OP, and may serve as a new target for kaempferol to improve OP.

Predicting drug-induced liver injury using graph attention mechanism and molecular fingerprints

Drug-induced liver injury (DILI) is a significant issue in drug development and clinical treatment due to its potential to cause liver dysfunction or damage, which, in severe cases, can lead to liver failure or even fatality. DILI has numerous pathogenic factors, many of which remain incompletely understood. Consequently, it is imperative to devise methodologies and tools for anticipatory assessment of DILI risk in the initial phases of drug development. In this study, we present DMFPGA, a novel deep learning predictive model designed to predict DILI. To provide a comprehensive description of molecular properties, we employ a multi-head graph attention mechanism to extract features from the molecular graphs, representing characteristics at the level of compound nodes. Additionally, we combine multiple fingerprints of molecules to capture features at the molecular level of compounds. The fusion of molecular fingerprints and graph features can more fully express the properties of compounds. Subsequently, we employ a fully connected neural network to classify compounds as either DILI-positive or DILI-negative. To rigorously evaluate DMFPGA's performance, we conduct a 5-fold cross-validation experiment. The obtained results demonstrate the superiority of our method over four existing state-of-the-art computational approaches, exhibiting an average AUC of 0.935 and an average ACC of 0.934. We believe that DMFPGA is helpful for early-stage DILI prediction and assessment in drug development.

Graph Reasoning Method Based on Affinity Identification and Representation Decoupling for Predicting lncRNA-Disease Associations

An increasing number of studies have shown that dysregulation of lncRNAs is related to the occurrence of various diseases. Most of the previous methods, however, are designed based on homogeneity assumption that the representation of a target lncRNA (or disease) node should be updated by aggregating the attributes of its neighbor nodes. However, the assumption ignores the affinity nodes that are far from the target node. We present a novel prediction method, GAIRD, to fully leverage the heterogeneous information in the network and the decoupled node features. The first major innovation is a random walk strategy based on width-first searching and depth-first searching. Different from previous methods that only focus on homogeneous information, our new strategy learns both the homogeneous information within local neighborhoods and the heterogeneous information within higher-order neighborhoods. The second innovation is a representation decoupling module to extract the purer attributes and the purer topologies. Third, a module based on group convolution and deep separable convolution is developed to promote the pairwise intrachannel and interchannel feature learning. The experimental results show that GAIRD outperforms comparing state-of-the-art methods, and the ablation studies prove the contributions of major innovations. We also performed case studies on 3 diseases to further demonstrate the effectiveness of the GAIRD model in applications.

LncRNA RARA-AS1 could serve as a novel prognostic biomarker in pan-cancer and promote proliferation and migration in glioblastoma

Long non-coding RNAs (lncRNAs) have emerged as crucial regulators of cancer progression and are potential biomarkers for diagnosis and treatment. This study investigates the role of RARA Antisense RNA 1 (RARA-AS1) in cancer and its implications for diagnosis and treatment. Various bioinformatics tools were conducted to analyze the expression patterns, immune-related functions, methylation, and gene expression correlations of RARA-AS1, mainly including the comparisons of different subgroups and correlation analyses between RARA-AS1 expression and other factors. Furthermore, we used short hairpin RNA to perform knockdown experiments, investigating the effects of RARA-AS1 on cell proliferation, invasion, and migration in glioblastoma. Our results revealed that RARA-AS1 has distinct expression patterns in different cancers and exhibits notable correlation with prognosis. Additionally, RARA-AS1 is highly correlated with certain immune checkpoints and mismatch repair genes, indicating its potential role in immune infiltration and related immunotherapy. Further analysis identified potential effective drugs for RARA-AS1 and demonstrated its potential RNA binding protein (RBP) mechanism in glioblastoma. Besides, a series of functional experiments indicated inhibiting RARA-AS1 could decrease cell proliferation, invasion, and migration of glioblastoma cell lines. Finally, RARA-AS1 could act as an independent prognostic factor for glioblastoma patients and may serve as a promising therapeutic target. All in all, Our study provides a comprehensive understanding of the functions and implications of RARA-AS1 in pan-cancer, highlighting it as a promising biomarker for survival. It is also an independent risk factor affecting prognosis in glioblastoma and an important factor affecting proliferation and migration in glioblastoma, setting the stage for further mechanistic investigations.

A Survey of Computational Methods and Databases for lncRNA-MiRNA Interaction Prediction

Long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) are two prevalent non-coding RNAs in current research. They play critical regulatory roles in the life processes of animals and plants. Studies have shown that lncRNAs can interact with miRNAs to participate in post-transcriptional regulatory processes, mainly involved in regulating cancer development, metastatic progression, and drug resistance. Additionally, these interactions have significant effects on plant growth, development, and responses to biotic and abiotic stresses. Deciphering the potential relationships between lncRNAs and miRNAs may provide new insights into our understanding of the biological functions of lncRNAs and miRNAs, and the pathogenesis of complex diseases. In contrast, gathering information on lncRNA-miRNA interactions (LMIs) through biological experiments is expensive and time-consuming. With the accumulation of multi-omics data, computational models are extremely attractive in systematically exploring potential LMIs. To the best of our knowledge, this is the first comprehensive review of computational methods for identifying LMIs. Specifically, we first summarized the available public databases for predicting animal and plant LMIs. Second, we comprehensively reviewed the computational methods for predicting LMIs and classified them into two categories, including network-based methods and sequence-based methods. Third, we analyzed the standard evaluation methods and metrics used in LMI prediction. Finally, we pointed out some problems in the current study and discuss future research directions. Relevant databases and the latest advances in LMI prediction are summarized in a GitHub repository https://github.com/sheng-n/lncRNA-miRNA-interaction-methods, and we'll keep it updated.

Identification of associations between lncRNA and drug resistance based on deep learning and attention mechanism

Introduction

Abnormal lncRNA expression can lead to the resistance of tumor cells to anticancer drugs, which is a crucial factor leading to high cancer mortality. Studying the relationship between lncRNA and drug resistance becomes necessary. Recently, deep learning has achieved promising results in predicting biomolecular associations. However, to our knowledge, deep learning-based lncRNA-drug resistance associations prediction has yet to be studied.

Methods

Here, we proposed a new computational model, DeepLDA, which used deep neural networks and graph attention mechanisms to learn lncRNA and drug embeddings for predicting potential relationships between lncRNAs and drug resistance. DeepLDA first constructed similarity networks for lncRNAs and drugs using known association information. Subsequently, deep graph neural networks were utilized to automatically extract features from multiple attributes of lncRNAs and drugs. These features were fed into graph attention networks to learn lncRNA and drug embeddings. Finally, the embeddings were used to predict potential associations between lncRNAs and drug resistance.

Results

Experimental results on the given datasets show that DeepLDA outperforms other machine learning-related prediction methods, and the deep neural network and attention mechanism can improve model performance.

Dicsussion

In summary, this study proposes a powerful deep-learning model that can effectively predict lncRNA-drug resistance associations and facilitate the development of lncRNA-targeted drugs. DeepLDA is available at https://github.com/meihonggao/DeepLDA.

Investigation of Hippo pathway-related prognostic lncRNAs and molecular subtypes in liver hepatocellular carcinoma

This study aimed to investigate Hippo pathway-related prognostic long noncoding RNAs (lncRNAs) and their prognostic value in liver hepatocellular carcinoma (LIHC). Expression and clinical data regarding LIHC were acquired from The Cancer Genome Atlas and European Bioinformatics Institute array databases. Hippo pathway-related lncRNAs and their prognostic value were revealed, followed by molecular subtype investigations. Differences in survival, clinical characteristics, immune cell infiltration, and checkpoint expression between the subtypes were explored. LASSO regression was used to determine the most valuable prognostic lncRNAs, followed by the establishment of a prognostic model. Survival and differential expression analyses were conducted between two groups (high- and low-risk). A total of 313 Hippo pathway-related lncRNAs were identified from LIHC, of which 88 were associated with prognosis, and two molecular subtypes were identified based on their expression patterns. These two subtypes showed significant differences in overall survival, pathological stage and grade, vascular invasion, infiltration abundance of seven immune cells, and expression of several checkpoints, such as CTLA-4 and PD-1/L1 (P < 0.05). LASSO regression identified the six most valuable independent prognostic lncRNAs for establishing a prognosis risk model. Risk scores calculated by the risk model assigned patients into two risk groups with an AUC of 0.913 and 0.731, respectively, indicating that the high-risk group had poor survival. The risk score had an independent prognostic value with an HR of 2.198. In total, 3007 genes were dysregulated between the two risk groups, and the expression of most genes was elevated in the high-risk group, involving the cell cycle and pathways in cancers. Hippo pathway-related lncRNAs could stratify patients for personalized treatment and predict the prognosis of patients with LIHC.

High-throughput sequencing approach for the identification of lncRNA biomarkers in hepatocellular carcinoma and revealing the effect of ZFAS1/miR-150-5p on hepatocellular carcinoma progression

Aims

To screen abnormal lncRNAs and diagnostic biomarkers in the progression of hepatocellular carcinoma through high-throughput sequencing and explore the underlying mechanisms of abnormal lncRNAs in the progression of hepatocellular carcinoma.

Methods

The transcriptome sequencing was used to analyze the RNA expression profile and identify differentially expressed RNAs. Hub lncRNAs were screened by combining (WGCNA, ceRNA regulatory network, PPI, GO and KEGG analyses, Kaplan-Meier curve analysis, Cox analysis, risk model construction and qPCR). Thereafter, the correlation between the expression of hub lncRNAs and tumor clinicopathological parameters was analyzed, and the hub lncRNAs were analyzed by GSEA. Finally, the effects of hub RNAs on the proliferation, migration and invasion of HepG2 cells were investigated in vitro.

Results

Compared with the control group, a total of 610 lncRNAs, 2,593 mRNAs and 26 miRNAs were screened in patients with hepatocellular carcinoma. Through miRNA target prediction and WGCNA, a ceRNA was constructed, comprising 324 nodes and 621 edges. Enrichment analysis showed that mRNAs in ceRNA were involved mainly in cancer development progression. Then, the ZFAS1/miR-150-5p interaction pair was screened out by Kaplan Meier curve analysis, Cox analysis and qPCR analysis. Its expression was related to tumor stage, TNM stage and patient age. ROC curve analysis showed that it has a good predictive value for the risk of hepatocellular carcinoma. GSEA showed that ZFAS1 was also enriched in the regulation of immune response, cell differentiation and proliferation. Loss-of-function experiments revealed that ZFAS1 inhibition could remarkably suppress HepG2 cell proliferation, migration and invasion in vitro. Bioinformatic analysis and luciferase reporter assays revealed that ZFAS1 directly interacted with miR-150-5p. Rescue experiments showed that a miR-150-5p inhibitor reversed the cell proliferation, migration and invasion functions of ZFAS1 knockdown in vitro.

Conclusion

ZFAS1 is associated with the malignant status and prognosis of patients with hepatocellular carcinoma, and the ZFAS1/miR-150-5p axis is involved in hepatocellular carcinoma progression.

Myocardial Infarction-Induced INSL6 Decrease Contributes to Breast Cancer Progression

Myocardial infarction (MI) induces early-stage breast cancer progression and increases breast cancer patients' mortality and morbidity. Insulin-like peptide 6 (INSL6) overexpression can impede cardiotoxin-induced injury through myofiber regeneration, playing a significant role in MI progression. To investigate the diverse significance of INSL6 in a variety of malignant tumors, we explored INSL6 through MI GEO dataset and multiple omics data integrative analysis, such as gene expression level, enriched pathway analysis, protein-protein interaction (PPI) analysis, and immune subtypes as well as diagnostic value and prognostic value in pancancer. INSL6 expression was downregulated in the MI group, and overall survival analysis demonstrated that INSL6 could be the prognostic biomarkers in the overall survival of breast cancer (BRCA). INSL6 expression differs significantly not only in most cancers but also in different molecular and immune subtypes of cancers. INSL6 might be a potential diagnostic and prognostic biomarker of cancers due to the high accuracy in diagnostic and prognostic value. Furthermore, we focused on BRCA and further investigated INSL6 from the perspective of the correlations with clinical characteristics, prognosis in different clinical subgroups, coexpression genes, and differentially expressed genes (DEGs) and PPI analysis. Overall survival and disease-specific survival analysis of subgroups in BRCA demonstrated that lower INSL6 expression had a worse prognosis. Therefore, INSL6 aberrant expression is associated with the progression and immune cell infiltration of the tumor, especially in KIRP and BRCA. Therefore, INSL6 may serve as a potential prognostic biomarker and the crosstalk between MI and tumor progression.

Investigating cardiotoxicity related with hERG channel blockers using molecular fingerprints and graph attention mechanism

Human ether-a-go-go-related gene (hERG) channel blockade by small molecules is a big concern during drug development in the pharmaceutical industry. Failure or inhibition of hERG channel activity caused by drug molecules can lead to prolonging QT interval, which will result in serious cardiotoxicity. Thus, evaluating the hERG blocking activity of all these small molecular compounds is technically challenging, and the relevant procedures are expensive and time-consuming. In this study, we develop a novel deep learning predictive model named DMFGAM for predicting hERG blockers. In order to characterize the molecule more comprehensively, we first consider the fusion of multiple molecular fingerprint features to characterize its final molecular fingerprint features. Then, we use the multi-head attention mechanism to extract the molecular graph features. Both molecular fingerprint features and molecular graph features are fused as the final features of the compounds to make the feature expression of compounds more comprehensive. Finally, the molecules are classified into hERG blockers or hERG non-blockers through the fully connected neural network. We conduct 5-fold cross-validation experiment to evaluate the performance of DMFGAM, and verify the robustness of DMFGAM on external validation datasets. We believe DMFGAM can serve as a powerful tool to predict hERG channel blockers in the early stages of drug discovery and development.

Predicting potential interactions between lncRNAs and proteins via combined graph auto-encoder methods

Long noncoding RNA (lncRNA) is a kind of noncoding RNA with a length of more than 200 nucleotide units. Numerous research studies have proven that although lncRNAs cannot be directly translated into proteins, lncRNAs still play an important role in human growth processes by interacting with proteins. Since traditional biological experiments often require a lot of time and material costs to explore potential lncRNA-protein interactions (LPI), several computational models have been proposed for this task. In this study, we introduce a novel deep learning method known as combined graph auto-encoders (LPICGAE) to predict potential human LPIs. First, we apply a variational graph auto-encoder to learn the low dimensional representations from the high-dimensional features of lncRNAs and proteins. Then the graph auto-encoder is used to reconstruct the adjacency matrix for inferring potential interactions between lncRNAs and proteins. Finally, we minimize the loss of the two processes alternately to gain the final predicted interaction matrix. The result in 5-fold cross-validation experiments illustrates that our method achieves an average area under receiver operating characteristic curve of 0.974 and an average accuracy of 0.985, which is better than those of existing six state-of-the-art computational methods. We believe that LPICGAE can help researchers to gain more potential relationships between lncRNAs and proteins effectively.

SCCPMD: Probability matrix decomposition method subject to corrected similarity constraints for inferring long non-coding RNA-disease associations

Accumulating evidence has demonstrated various associations of long non-coding RNAs (lncRNAs) with human diseases, such as abnormal expression due to microbial influences that cause disease. Gaining a deeper understanding of lncRNA-disease associations is essential for disease diagnosis, treatment, and prevention. In recent years, many matrix decomposition methods have also been used to predict potential lncRNA-disease associations. However, these methods do not consider the use of microbe-disease association information to enrich disease similarity, and also do not make more use of similarity information in the decomposition process. To address these issues, we here propose a correction-based similarity-constrained probability matrix decomposition method (SCCPMD) to predict lncRNA-disease associations. The microbe-disease associations are first used to enrich the disease semantic similarity matrix, and then the logistic function is used to correct the lncRNA and disease similarity matrix, and then these two corrected similarity matrices are added to the probability matrix decomposition as constraints to finally predict the potential lncRNA-disease associations. The experimental results show that SCCPMD outperforms the five advanced comparison algorithms. In addition, SCCPMD demonstrated excellent prediction performance in a case study for breast cancer, lung cancer, and renal cell carcinoma, with prediction accuracy reaching 80, 100, and 100%, respectively. Therefore, SCCPMD shows excellent predictive performance in identifying unknown lncRNA-disease associations.

lncRNA-disease association prediction based on the weight matrix and projection score

With the development of medical science, long noncoding RNA (lncRNA), originally considered as a noise gene, has been found to participate in a variety of biological activities. Several recent studies have shown the involvement of lncRNA in various human diseases, such as gastric cancer, prostate cancer, lung cancer, and so forth. However, obtaining lncRNA-disease relationship only through biological experiments not only costs manpower and material resources but also gains little. Therefore, developing effective computational models for predicting lncRNA-disease association relationship is extremely important. This study aimed to propose an lncRNA-disease association prediction model based on the weight matrix and projection score (LDAP-WMPS). The model used the relatively perfect lncRNA-miRNA relationship data and miRNA-disease relationship data to predict the lncRNA-disease relationship. The integrated lncRNA similarity matrix and the integrated disease similarity matrix were established by fusing various methods to calculate the similarity between lncRNA and disease. This study improved the existing weight algorithm, applied it to the lncRNA-miRNA-disease triple network, and thus proposed a new lncRNA-disease weight matrix calculation method. Combined with the improved projection algorithm, the lncRNA-miRNA relationship and miRNA-disease relationship were used to predict the lncRNA-disease relationship. The simulation results showed that under the Leave-One-Out-Cross-Validation framework, the area under the receiver operating characteristic curve of LDAP-WMPS could reach 0.8822, which was better than the latest result. Taking adenocarcinoma and colorectal cancer as examples, the LDAP-WMPS model was found to effectively infer the lncRNA-disease relationship. The simulation results showed good prediction performance of the LDAP-WMPS model, which was an important supplement to the research of lncRNA-disease association prediction without lncRNA-disease relationship data.

Identification and validation of a new pyroptosis-associated lncRNA signature to predict survival outcomes, immunological responses and drug sensitivity in patients with gastric cancer

BACKGROUND

Gastric cancer (GC) ranks fifth in prevalence among carcinomas worldwide. Both pyroptosis and long noncoding RNAs (lncRNAs) play crucial roles in the occurrence and development of gastric cancer. Therefore, we aimed to construct a pyroptosis-associated lncRNA model to predict the outcomes of patients with gastric cancer.

METHODS

Pyroptosis-associated lncRNAs were identified through co-expression analysis. Univariate and multivariate Cox regression analyses were performed using the least absolute shrinkage and selection operator (LASSO). Prognostic values were tested through principal component analysis, a predictive nomogram, functional analysis and Kaplan‒Meier analysis. Finally, immunotherapy and drug susceptibility predictions and hub lncRNA validation were performed.

RESULTS

Using the risk model, GC individuals were classified into two groups: low-risk and high-risk groups. The prognostic signature could distinguish the different risk groups based on principal component analysis. The area under the curve and the conformance index suggested that this risk model was capable of correctly predicting GC patient outcomes. The predicted incidences of the one-, three-, and five-year overall survivals exhibited perfect conformance. Distinct changes in immunological markers were noted between the two risk groups. Finally, greater levels of appropriate chemotherapies were required in the high-risk group. AC005332.1, AC009812.4 and AP000695.1 levels were significantly increased in gastric tumor tissue compared with normal tissue.

CONCLUSIONS

We created a predictive model based on 10 pyroptosis-associated lncRNAs that could accurately predict the outcomes of GC patients and provide a promising treatment option in the future.

Bioinformatics-based analysis of potential candidates chromatin regulators for immune infiltration in osteoarthritis

BACKGROUND

Through the bioinformatics analysis to screen out the potential chromatin regulators (CRs) under the immune infiltration of osteoarthritis (OA), thus providing some theoretical support for future studies of epigenetic mechanisms under OA immune infiltration.

METHODS

By integrating CRs and the OA gene expression matrix, we performed weighted gene co-expression network analysis (WGCNA), differential analysis, and further screened Hub genes by protein-protein interaction (PPI) analysis. Using the OA gene expression matrix, immune infiltration extraction and quantification were performed to analyze the correlations and differences between immune infiltrating cells and their functions. By virtue of these Hub genes, Hub gene association analysis was completed and their upstream miRNAs were predicted by the FunRich software. Moreover, a risk model was established to analyze the risk probability of associated CRs in OA, and the confidence of the results was validated by the validation dataset.

RESULTS

This research acquired a total of 32 overlapping genes, and 10 Hub genes were further identified. The strongest positive correlation between dendritic cells and mast cells and the strongest negative correlation between parainflammation and Type I IFN reponse. In the OA group DCs, iDCs, macrophages, MCs, APC co-inhibition, and CCR were significantly increased, whereas B cells, NK cells, Th2 cells, TIL, and T cell co-stimulation were significantly decreased. The risk model results revealed that BRD1 might be an independent risk factor for OA, and the validation dataset results are consistent with it. 60 upstream miRNAs of OA-related CRs were predicted by the FunRich software.

CONCLUSION

This study identified certain potential CRs and miRNAs that could regulate OA immunity, thus providing certain theoretical supports for future epigenetic mechanism studies on the immune infiltration of OA.

lncRNA-disease association prediction method based on the nearest neighbor matrix completion model

State-of-the-art medical studies proved that long noncoding ribonucleic acids (lncRNAs) are closely related to various diseases. However, their large-scale detection in biological experiments is problematic and expensive. To aid screening and improve the efficiency of biological experiments, this study introduced a prediction model based on the nearest neighbor concept for lncRNA-disease association prediction. We used a new similarity algorithm in the model that fused potential associations. The experimental validation of the proposed algorithm proved its superiority over the available Cosine, Pearson, and Jaccard similarity algorithms. Satisfactory results in the comparative leave-one-out cross-validation test (with AUC = 0.96) confirmed its excellent predictive performance. Finally, the proposed model's reliability was confirmed by performing predictions using a new dataset, yielding AUC = 0.92.

Hypoxia-associated prognostic markers and competing endogenous RNA coexpression networks in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common form of non-small cell lung cancer (NSCLC). Hypoxia has been found in 50-60% of locally advanced solid tumors and is associated with poor prognosis in various tumors, including NSCLC. This study focused on hypoxia-associated molecular hallmarks in LUAD. Fifteen hypoxia-related genes were selected to define the hypoxia status of LUAD by ConsensusClusterPlus based on data from The Cancer Genome Atlas (TCGA). Then, we investigated the immune status under different hypoxia statuses. Subsequently, we constructed prognostic models based on hypoxia-related differentially expressed genes (DEGs), identified hypoxia-related microRNAs, lncRNAs and mRNAs, and built a network based on the competing endogenous RNA (ceRNA) theory. Two clusters (Cluster 1 and Cluster 2) were identified with different hypoxia statuses. Cluster 1 was defined as the hypoxia subgroup, in which all 15 hypoxia-associated genes were upregulated. The infiltration of CD4+ T cells and Tfh cells was lower, while the infiltration of regulatory T (Treg) cells, the expression of PD-1/PD-L1 and TMB scores were higher in Cluster 1, indicating an immunosuppressive status. Based on the DEGs, a risk signature containing 7 genes was established. Furthermore, three differentially expressed microRNAs (hsa-miR-9, hsa-miR-31, hsa-miR-196b) associated with prognosis under different hypoxia clusters and their related mRNAs and lncRNAs were identified, and a ceRNA network was built. This study showed that hypoxia was associated with poor prognosis in LUAD and explored the potential mechanism from the perspective of the gene signature and ceRNA theory.

ncRNAInter: a novel strategy based on graph neural network to discover interactions between lncRNA and miRNA

In recent years, many studies have illustrated the significant role that non-coding RNA (ncRNA) plays in biological activities, in which lncRNA, miRNA and especially their interactions have been proved to affect many biological processes. Some in silico methods have been proposed and applied to identify novel lncRNA-miRNA interactions (LMIs), but there are still imperfections in their RNA representation and information extraction approaches, which imply there is still room for further improving their performances. Meanwhile, only a few of them are accessible at present, which limits their practical applications. The construction of a new tool for LMI prediction is thus imperative for the better understanding of their relevant biological mechanisms. This study proposed a novel method, ncRNAInter, for LMI prediction. A comprehensive strategy for RNA representation and an optimized deep learning algorithm of graph neural network were utilized in this study. ncRNAInter was robust and showed better performance of 26.7% higher Matthews correlation coefficient than existing reputable methods for human LMI prediction. In addition, ncRNAInter proved its universal applicability in dealing with LMIs from various species and successfully identified novel LMIs associated with various diseases, which further verified its effectiveness and usability. All source code and datasets are freely available at https://github.com/idrblab/ncRNAInter.

WLLP: A weighted reconstruction-based linear label propagation algorithm for predicting potential therapeutic agents for COVID-19

The global coronavirus disease 2019 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV) has led to a huge health and economic crises. However, the research required to develop new drugs and vaccines is very expensive in terms of labor, money, and time. Owing to recent advances in data science, drug-repositioning technologies have become one of the most promising strategies available for developing effective treatment options. Using the previously reported human drug virus database (HDVD), we proposed a model to predict possible drug regimens based on a weighted reconstruction-based linear label propagation algorithm (WLLP). For the drug–virus association matrix, we used the weighted K-nearest known neighbors method for preprocessing and label propagation of the network based on the linear neighborhood similarity of drugs and viruses to obtain the final prediction results. In the framework of 10 times 10-fold cross-validated area under the receiver operating characteristic (ROC) curve (AUC), WLLP exhibited excellent performance with an AUC of 0.8828 ± 0.0037 and an area under the precision-recall curve of 0.5277 ± 0.0053, outperforming the other four models used for comparison. We also predicted effective drug regimens against SARS-CoV-2, and this case study showed that WLLP can be used to suggest potential drugs for the treatment of COVID-19.

Inferring Latent MicroRNA-Disease Associations on a Gene-Mediated Tripartite Heterogeneous Multiplexing Network

MicroRNA (miRNA) is a class of non-coding single-stranded RNA molecules encoded by endogenous genes with a length of about 22 nucleotides. MiRNAs have been successfully identified as differentially expressed in various cancers. There is evidence that disorders of miRNAs are associated with a variety of complex diseases. Therefore, inferring potential miRNA-disease associations (MDAs) is very important for understanding the aetiology and pathogenesis of many diseases and is useful to disease diagnosis, prognosis and treatment. First, We creatively fused multiple similarity subnetworks from multi-sources for miRNAs, genes and diseases by multiplexing technology, respectively. Then, three multiplexed biological subnetworks are connected through the extended binary association to form a tripartite complete heterogeneous multiplexed network (Tri-HM). Finally, because the constructed Tri-HM network can retain subnetworks' original topology and biological functions and expands the binary association and dependence between the three biological entities, rich neighbourhood information is obtained iteratively from neighbours by a non-equilibrium random walk. Through cross-validation, our tri-HM-RWR model obtained an AUC value of 0.8657, and an AUPR value of 0.2139 in the global 5-fold cross-validation, which shows that our model can more fully speculate disease-related miRNAs.

JSCSNCP-LMA: a method for predicting the association of lncRNA-miRNA

Non-coding RNAs (ncRNAs) have long been considered the "white elephant" on the genome because they lack the ability to encode proteins. However, in recent years, more and more biological experiments and clinical reports have proved that ncRNAs account for a large proportion in organisms. At the same time, they play a decisive role in the biological processes such as gene expression and cell growth and development. Recently, it has been found that short sequence non-coding RNA(miRNA) and long sequence non-coding RNA(lncRNA) can regulate each other, which plays an important role in various complex human diseases. In this paper, we used a new method (JSCSNCP-LMA) to predict lncRNA-miRNA with unknown associations. This method combined Jaccard similarity algorithm, self-tuning spectral clustering similarity algorithm, cosine similarity algorithm and known lncRNA-miRNA association networks, and used the consistency projection to complete the final prediction. The results showed that the AUC values of JSCSNCP-LMA in fivefold cross validation (fivefold CV) and leave-one-out cross validation (LOOCV) were 0.9145 and 0.9268, respectively. Compared with other models, we have successfully proved its superiority and good extensibility. Meanwhile, the model also used three different lncRNA-miRNA datasets in the fivefold CV experiment and obtained good results with AUC values of 0.9145, 0.9662 and 0.9505, respectively. Therefore, JSCSNCP-LMA will help to predict the associations between lncRNA and miRNA.

SVMMDR: Prediction of miRNAs-drug resistance using support vector machines based on heterogeneous network

In recent years, the miRNA is considered as a potential high-value therapeutic target because of its complex and delicate mechanism of gene regulation. The abnormal expression of miRNA can cause drug resistance, affecting the therapeutic effect of the disease. Revealing the associations between miRNAs-drug resistance can help in the design of effective drugs or possible drug combinations. However, current conventional experiments for identification of miRNAs-drug resistance are time-consuming and high-cost. Therefore, it’s of pretty realistic value to develop an accurate and efficient computational method to predicting miRNAs-drug resistance. In this paper, a method based on the Support Vector Machines (SVM) to predict the association between MiRNA and Drug Resistance (SVMMDR) is proposed. The SVMMDR integrates miRNAs-drug resistance association, miRNAs sequence similarity, drug chemical structure similarity and other similarities, extracts path-based Hetesim features, and obtains inclined diffusion feature through restart random walk. By combining the multiple feature, the prediction score between miRNAs and drug resistance is obtained based on the SVM. The innovation of the SVMMDR is that the inclined diffusion feature is obtained by inclined restart random walk, the node information and path information in heterogeneous network are integrated, and the SVM is used to predict potential miRNAs-drug resistance associations. The average AUC of SVMMDR obtained is 0.978 in 10-fold cross-validation.

Predicting miRNA-lncRNA interactions on plant datasets based on bipartite network embedding method

The research of miRNA-lncRNA interactions (MLIs) has received great attention recently due to their vital roles in microbiology and profound significance in diseases. Currently, many related studies mainly focus on animals and the link prediction problem on plants is rarely discussed comprehensively. Motivated by this, we achieve link prediction task based on the concept of bipartite graph and verify encouraging performance of our conclusions by conducting experiments on plant datasets. In this work, we firstly extract attribute information and structure information as base features and further process these information for network embedding. Intra-partition and inter-partition proximity modelling are conducted to construct the loss function, which facilitates the training of parameters. Finally, the superiority of our presented approach is shown by carrying out experiments on four plant datasets, which reflects the significance of this work to the research of microbiology and disease.

Construction of a three commitment points for S phase entry cell cycle model and immune-related ceRNA network to explore novel therapeutic options for psoriasis

While competing endogenous RNAs (ceRNAs) play pivotal roles in various diseases, the proliferation and differentiation of keratinocytes are becoming a research focus in psoriasis. Therefore, the three commitment points for S phase entry (CP1-3) cell cycle model has pointed to a new research direction in these areas. However, it is unclear what role ceRNA regulatory mechanisms play in the interaction between keratinocytes and the immune system in psoriasis. In addition, the ceRNA network-based screening of potential therapeutic agents for psoriasis has not been explored. Therefore, we used multiple bioinformatics approaches to construct a ceRNA network for psoriasis, identified CTGF as the hub gene, and constructed a ceRNA subnetwork, after which validation datasets authenticated the results' accuracy. Subsequently, we used multiple online databases and the single-sample gene-set enrichment analysis algorithm, including the CP1-3 cell cycle model, to explore the mechanisms accounting for the increased proliferation and differentiation of keratinocytes and the possible roles of the ceRNA subnetwork in psoriasis. Next, we performed cell cycle and cell trajectory analyses based on a single-cell RNA-seq dataset of psoriatic skin biopsies. We also used weighted gene co-expression network analysis and single-gene batch correlation analysis-based gene set enrichment analysis to explore the functions of CTGF. Finally, we used the Connectivity Map to identify MS-275 (entinostat) as a novel treatment for psoriasis, SwissTargetPrediction to predict drug targets, and molecular docking to investigate the minimum binding energy and binding sites of the drug to target proteins.

BoT-Net: a lightweight bag of tricks-based neural network for efficient LncRNA–miRNA interaction prediction

Background and objective:

Interactions of long non-coding ribonucleic acids (lncRNAs) with micro-ribonucleic acids (miRNAs) play an essential role in gene regulation, cellular metabolic, and pathological processes. Existing purely sequence based computational approaches lack robustness and efficiency mainly due to the high length variability of lncRNA sequences. Hence, the prime focus of the current study is to find optimal length trade-offs between highly flexible length lncRNA sequences.

Method

The paper at hand performs in-depth exploration of diverse copy padding, sequence truncation approaches, and presents a novel idea of utilizing only subregions of lncRNA sequences to generate fixed-length lncRNA sequences. Furthermore, it presents a novel bag of tricks-based deep learning approach “Bot-Net” which leverages a single layer long-short-term memory network regularized through DropConnect to capture higher order residue dependencies, pooling to retain most salient features, normalization to prevent exploding and vanishing gradient issues, learning rate decay, and dropout to regularize precise neural network for lncRNA–miRNA interaction prediction.

Results

BoT-Net outperforms the state-of-the-art lncRNA–miRNA interaction prediction approach by 2%, 8%, and 4% in terms of accuracy, specificity, and matthews correlation coefficient. Furthermore, a case study analysis indicates that BoT-Net also outperforms state-of-the-art lncRNA–protein interaction predictor on a benchmark dataset by accuracy of 10%, sensitivity of 19%, specificity of 6%, precision of 14%, and matthews correlation coefficient of 26%.

Conclusion

In the benchmark lncRNA–miRNA interaction prediction dataset, the length of the lncRNA sequence varies from 213 residues to 22,743 residues and in the benchmark lncRNA–protein interaction prediction dataset, lncRNA sequences vary from 15 residues to 1504 residues. For such highly flexible length sequences, fixed length generation using copy padding introduces a significant level of bias which makes a large number of lncRNA sequences very much identical to each other and eventually derail classifier generalizeability. Empirical evaluation reveals that within 50 residues of only the starting region of long lncRNA sequences, a highly informative distribution for lncRNA–miRNA interaction prediction is contained, a crucial finding exploited by the proposed BoT-Net approach to optimize the lncRNA fixed length generation process.

Availability:

BoT-Net web server can be accessed at https://sds_genetic_analysis.opendfki.de/lncmiRNA/.

Graphic Abstract

Research on a Weighted Gene Co-expression Network Analysis method for mining pathogenic genes in thyroid cancer

Thyroid cancer (TC) is one of the most common thyroid malignancies occurring worldwide, and accounts for about 1% of all the malignant tumors. It is one of the fastest growing tumor and can occur at any age, but it is more common in women. It is important to find the pathogenesis and treatment targets of TC. In this pursuit, the present study was envisaged to investigate the effective carcinogenic biological macromolecules, so as to provide a better understanding of the occurrence and development of TC. The clinical and gene expression data were collected from The Cancer Genome Atlas (TCGA). We clustered mRNA and long non-coding RNA (lncRNA) into different modules by Weighted Gene Co-expression Network Analysis (WGCNA), and calculated the correlation coefficient between the genes and clinical phenotypes. Using WGCNA, we identified the module with the highest correlation coefficient. Subsequently, by using the differential genes expression analysis to screen the differential micro-RNA (miRNA), the univariate Cox proportional hazard regression was employed to screen the hub genes related to overall survival (OS), with P < 0.05 as the statistical significance threshold. Finally, we designed a hub competitive endogenous RNA(ceRNA) network of disease-associated lncRNAs, miRNAs, and mRNAs. From the results of enrichment analysis, the association of these genes could be related to the occurrence and development of TC, and these hub RNAs can be valuable prognostic markers and therapeutic targets in TC.

Comprehensive Analysis of Potential ceRNA Network and Different Degrees of Immune Cell Infiltration in Acute Respiratory Distress Syndrome

Acute respiratory distress syndrome (ARDS) is a leading cause of death in critically ill patients due to hypoxemic respiratory failure. The specific pathogenesis underlying ARDS has not been fully elucidated. In this study, we constructed a triple regulatory network involving competing endogenous RNA (ceRNA) to investigate the potential mechanism of ARDS and evaluated the immune cell infiltration patterns in ARDS patients. Overall, we downloaded three microarray datasets that included 60 patients with sepsis-induced ARDS and 79 patients with sepsis alone from the public Gene Expression Omnibus (GEO) database and identified differentially expressed genes (DEGs, including 9 DElncRNAs, 9 DEmiRNAs, and 269 DEmRNAs) by R software. The DEGs were subjected to the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) for functional enrichment analysis, and a protein–protein interaction (PPI) network was generated for uncovering interactive relationships among DEmRNAs. Then, a ceRNA network that contained 5 DElncRNAs, 7 DEmiRNAs, and 71 DEmRNAs was established according to the overlapping genes in both DEGs and predicted genes by public databases. Finally, we identified the TUG1/miR-140-5p/NFE2L2 pathway as the hub pathway in the whole network through Cytoscape. In addition, we evaluated the distribution of 22 subtypes of immune cells and recognized three differentially expressed immune cells in patients with sepsis-induced ARDS by “Cell Type Identification by Estimating Relative Subsets of Known RNA Transcripts (CIBERSORT)” algorithm, namely, naive B cells, regulatory T cells, and eosinophils. Correlations between differentially expressed immune cells and hub genes in the ceRNA network were also performed. In conclusion, we demonstrated a new potential regulatory mechanism underlying ARDS (the TUG1/miR-140-5p/NFE2L2 ceRNA regulatory pathway), which may help in further exploring the pathogenesis of ARDS.

Construction of long non-coding RNA- and microRNA-mediated competing endogenous RNA networks in alcohol-related esophageal cancer

The current study aimed to explore the lncRNA–miRNA–mRNA networks associated with alcohol-related esophageal cancer (EC). RNA-sequencing and clinical data were downloaded from The Cancer Genome Atlas and the differentially expressed genes (DEGs), long non-coding RNAs (lncRNAs, DELs), and miRNAs (DEMs) in patients with alcohol-related and non-alcohol-related EC were identified. Prognostic RNAs were identified by performing Kaplan–Meier survival analyses. Weighted gene co-expression network analysis was employed to build the gene modules. The lncRNA–miRNA–mRNA competing endogenous RNA (ceRNA) networks were constructed based on our in silico analyses using data from miRcode, starBase, and miRTarBase databases. Functional enrichment analysis was performed for the genes in the identified ceRNA networks. A total of 906 DEGs, 40 DELs, and 52 DEMs were identified. There were eight lncRNAs and miRNAs each, including ST7-AS2 and miR-1269, which were significantly associated with the survival rate of patients with EC. Of the seven gene modules, the blue and turquoise modules were closely related to disease progression; the genes in this module were selected to construct the ceRNA networks. SNHG12–miR-1–ST6GAL1, SNHG3–miR-1–ST6GAL1, SPAG5-AS1–miR-133a–ST6GAL1, and SNHG12–hsa-miR-33a–ST6GA interactions, associated with the N-glycan biosynthesis pathway, may have key roles in alcohol-related EC. Thus, the identified biomarkers provide a novel insight into the molecular mechanism of alcohol-related EC.

De Novo design of potential inhibitors against SARS-CoV-2 Mpro

The impact of the ravages of COVID-19 on people's lives is obvious, and the development of novel potential inhibitors against SARS-CoV-2 main protease (Mpro), which has been validated as a potential target for drug design, is urgently needed. This study developed a model named MproI-GEN, which can be used for the de novo design of potential Mpro inhibitors (MproIs) based on deep learning. The model was mainly composed of long-short term memory modules, and the last layer was re-trained with transfer learning. The validity (0.9248), novelty (0.9668), and uniqueness (0.0652) of the designed potential MproI library (PMproIL) were evaluated, and the results showed that MproI-GEN could be used to design structurally novel and reasonable molecules. Additionally, PMproIL was filtered based on machine learning models and molecular docking. After filtering, the potential MproIs were verified with molecular dynamics simulations to evaluate the binding stability levels of these MproIs and SARS-CoV-2 Mpro, thereby illustrating the inhibitory effects of the potential MproIs against Mpro. Two potential MproIs were proposed in this study. This study provides not only new possibilities for the development of COVID-19 drugs but also a complete pipeline for the discovery of novel lead compounds.

Identification of miRNA biomarkers for stomach adenocarcinoma

BACKGROUND

Stomach adenocarcinoma (STAD) is a common malignant tumor in the world and its prognosis is poor, miRNA plays a role mainly by influencing the expression of mRNAs, and participates in the occurrence and development of tumors. However, reliable miRNA prognostic models for stomach adenocarcinoma remain to be identified.

RESULTS

Using the data from the Cancer Genome Atlas (TCGA), a prognostic model of stomach adenocarcinoma was established including tumor stage and expression levels of 4 miRNAs (hsa-miR-379-3p, hsa-miR-2681-3p, hsa-miR-6499-5p and hsa-miR-6807-3p). A total of 50 ultimate target genes of these miRNAs were obtained through prediction. Enrichment analysis revealed that target genes were mainly concentrated in neural function and TGF-β and FoxO signaling pathways. Survival analysis showed that three model miRNAs (hsa-miR-379-3p, hsa-miR-2681-3p and hsa-miR-6807-3p) and five final target genes (DLC1, LRFN5, NOVA1, POU3F2 and PRICKLE2) were associated with the patient's overall survival outcome.

CONCLUSIONS

We used bioinformatics methods to screen new prognostic miRNA markers from TCGA and established a prognostic model of STAD, so as to provide a basis for the diagnosis, prognosis, and treatment of STAD in the future.

High expression of lncRNA PELATON serves as a risk factor for the incidence and prognosis of acute coronary syndrome

Atherosclerosis is the primary origin of acute coronary syndrome (ACS) diseases. Previous studies have shown that lncRNA plaque-enriched long noncoding RNA in atherosclerotic macrophage regulation (lncRNA PELATON) is a specific lncRNA in macrophage nuclei. This study aims to identify serum lncRNA PELATON as a biomarker for assessing the incidence and prognosis of ACS. Levels of serum lncRNA PELATON were detected by real-time polymerase chain reaction (RT–PCR) in patients with ACS and healthy individuals. The clinical significance of lncRNA PELATON in patients with ACS was assessed by analyzing receiver operating characteristic and survival curves. The serum levels of lncRNA PELATON in patients with ACS were significantly higher than those in healthy individuals. LncRNA PELATON expression was positively correlated with the expression levels of high sensitivity C-reactive protein (hs-CRP), cardiac troponin T (cTnT) and creatine kinase MB (CK-MB) (p < 0.05). LncRNA PELATON can be used as a potential diagnostic index with an AUC of 0.706 for unstable angina pectoris (UA), 0.782 for acute non-ST-segment elevation myocardial infarction (NSTEMI) and 0.900 for acute ST-segment elevation myocardial infarction (STEMI). The incidence of major cardiovascular events in patients with ACS with high lncRNA PELATON expression was higher than that in those with low lncRNA PELATON expression. However, the mortality between patients in the high and low lncRNA PELATON groups was not significantly different. This study showed that higher levels of lncRNA PELATON were negatively correlated with the prognosis of ACS, revealing the potential of this measurement to serve as an index to assess the incidence and prognosis of ACS.

MicroRNAs (miRNAs) in Cardiovascular Complications of Rheumatoid Arthritis (RA): What Is New?

Rheumatoid Arthritis (RA) is among the most prevalent and impactful rheumatologic chronic autoimmune diseases (AIDs) worldwide. Within a framework that recognizes both immunological activation and inflammatory pathways, the exact cause of RA remains unclear. It seems however, that RA is initiated by a combination between genetic susceptibility, and environmental triggers, which result in an auto-perpetuating process. The subsequently, systemic inflammation associated with RA is linked with a variety of extra-articular comorbidities, including cardiovascular disease (CVD), resulting in increased mortality and morbidity. Hitherto, vast evidence demonstrated the key role of non-coding RNAs such as microRNAs (miRNAs) in RA, and in RA-CVD related complications. In this descriptive review, we aim to highlight the specific role of miRNAs in autoimmune processes, explicitly on their regulatory roles in the pathogenesis of RA, and its CV consequences, their main role as novel biomarkers, and their possible role as therapeutic targets.

LINC00922 promotes deterioration of gastric cancer

Several studies have demonstrated the association of lncRNAs with a variety of cancers. Here, we explored the role of LINC00922 in gastric cancer (GC) using bioinformatics approaches and in vitro experiments. We examined the expression of LINC00922 and the prognosis of GC patients based on data from The Cancer Genome Atlas (TCGA) and Gene Expression Profiling Interactive Analysis (GEPIA). LINC00922-related genes were identified by the Multi Experiment Matrix (MEM) database and The Atlas of Noncoding RNAs in Cancer (TANRIC), followed by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and protein-protein interaction analysis. The significance of LINC00922 in cell proliferation, apoptosis, invasion and migration was assessed by MTT assay, flow cytometry, Transwell assay and wound-healing assay. The expression of LINC00922 was increased in GC tissues compared with adjacent non-tumor tissues, and increased LINC00922 expression was correlated with poor overall survival and disease-free survival. In addition, 336 overlapping genes were identified by the MEM database and TANRIC and found to be involved in GC-related biological processes, such as cell adhesion and migration, as well as TGF-β signaling. In the protein-protein interaction network, hub genes, such as FSTL3 and LAMC1, were identified. LINC00922 overexpression significantly promoted cell proliferation and invasion in vitro, whereas LINC00922 knockdown exerted opposite effects. In summary, our findings indicate that LINC00922 is overexpressed in GC tissues, suggesting that it might play a role in the development and progression of GC, and thus, it might serve as a prognostic indicator of GC.

HNRNPA2B1 Demonstrates Diagnostic and Prognostic Values Based on Pan-Cancer Analyses

Some studies have suggested heterogeneous nuclear ribonucleoprotein A2/B1 (HNRNPA2B1) to be a promoter in cancer development. Nonetheless, no detailed pan-cancer investigation has been reported. Thus, this study explored the possible oncogenic role of HNRNPA2B1, such as its expression levels, gene alteration, protein–protein interaction network, immune infiltration, and prognostic value in different cancer types using The Cancer Genome Atlas web platform. Many types of cancer exhibit HNRNPA2B1 overexpression, which is notably associated with poor prognosis. We also found that HNRNPA2B1 with different methylation levels causes a varied prognosis in lung adenocarcinoma (LUAD). It is noteworthy that HNRNPA2B1 levels are connected with cancer-associated fibroblasts in cancers, such as adrenocortical carcinoma, LUAD, and stomach adenocarcinoma. In addition, HNRNPA2B1 participates in the spliceosome- and cell cycle-associated pathways. Finally, HNRNPA2B1 is highly valued in the diagnosis of LUAD, lung squamous cell carcinoma, breast invasive carcinoma, esophageal carcinoma, and liver hepatocellular carcinoma. This systematic study highlighted the role of HNRNPA2B1 in pan-cancer progression.

Astragaloside IV exhibits anti-tumor function in gastric cancer via targeting circRNA dihydrolipoamide S-succinyltransferase (circDLST)/miR-489-3p/ eukaryotic translation initiation factor 4A1(EIF4A1) pathway

Astragaloside IV (AS-IV) is an inartificial saponin separated from astragalus membranaceus, which has exhibited key anti-tumor regulation in some cancers. Circular RNAs (circRNAs) are important regulators in malignant development of gastric cancer (GC). Herein, we focused on the molecular mechanism of AS-IV with circRNA dihydrolipoamide S-succinyltransferase (circDLST) in GC. CircDLST, microRNA-489-3p (miR-489-3p), and eukaryotic translation initiation factor 4A1 (EIF4A1) levels were detected by quantitative real-time polymerase-chain reaction and western blot. Cell functions were assessed by cell counting kit-8 assay, ethynyl-2’-deoxyuridine assay, colony formation assay, and transwell assay. The interaction between miR-489-3p and circDLST or EIF4A1 was analyzed by dual-luciferase reporter assay. Xenograft tumor assay was adopted to check the role of circDLST and AS-IV in vivo. CircDLST and EIF4A1 were upregulated but miR-489-3p was downregulated in GC cells. AS-IV restrained cell proliferation and metastasis in GC cells by downregulating circDLST. CircDLST served as a miR-489-3p sponge, and miR-489-3p inhibition reversed anti-tumor function of AS-IV. EIF4A1 was a target for miR-489-3p and circDLST sponged miR-489-3p to regulate EIF4A1. AS-IV suppressed GC cell progression via circDLST-mediated downregulation of EIF4A1. Also, AS-IV recued tumor growth in vivo via targeting circDLST to regulate miR-489-3p/EIF4A1 axis. AS-IV inhibited the development of GC through circDLST/miR-489-3p/EIF4A1 axis.

RNAI-FRID: novel feature representation method with information enhancement and dimension reduction for RNA-RNA interaction

Different ribonucleic acids (RNAs) can interact to form regulatory networks that play important role in many life activities. Molecular biology experiments can confirm RNA-RNA interactions to facilitate the exploration of their biological functions, but they are expensive and time-consuming. Machine learning models can predict potential RNA-RNA interactions, which provide candidates for molecular biology experiments to save a lot of time and cost. Using a set of suitable features to represent the sample is crucial for training powerful models, but there is a lack of effective feature representation for RNA-RNA interaction. This study proposes a novel feature representation method with information enhancement and dimension reduction for RNA-RNA interaction (named RNAI-FRID). Diverse base features are first extracted from RNA data to contain more sample information. Then, the extracted base features are used to construct the complex features through an arithmetic-level method. It greatly reduces the feature dimension while keeping the relationship between molecule features. Since the dimension reduction may cause information loss, in the process of complex feature construction, the arithmetic mean strategy is adopted to enhance the sample information further. Finally, three feature ranking methods are integrated for feature selection on constructed complex features. It can adaptively retain important features and remove redundant ones. Extensive experiment results show that RNAI-FRID can provide reliable feature representation for RNA-RNA interaction with higher efficiency and the model trained with generated features obtain better performance than other deep neural network predictors.

Revealing key lncRNAs in cytogenetically normal acute myeloid leukemia by reconstruction of the lncRNA-miRNA-mRNA network

Cytogenetically normal acute myeloid leukemia (CN-AML) is a heterogeneous disease with different prognoses. Researches on prognostic biomarkers and therapy targets of CN-AML are still ongoing. Instead of protein-coding genes, more and more researches were focused on the non-coding RNAs especially long non-coding RNAs (lncRNAs) which may play an important role in the development of AML. Although a large number of lncRNAs have been found, our knowledge of their functions and pathological process is still in its infancy. The purpose of this research is to identify the key lncRNAs and explore their functions in CN-AML by reconstructing the lncRNA-miRNA-mRNA network based on the competitive endogenous RNA (ceRNA) theory. We reconstructed a global triple network based on the ceRNA theory using the data from National Center for Biotechnology Information Gene Expression Omnibus and published literature. According to the topological algorithm, we identified the key lncRNAs which had both the higher node degrees and the higher numbers of lncRNA-miRNA pairs and total pairs in the ceRNA network. Meanwhile, Gene Ontology (GO) and pathway analysis were performed using databases such as DAVID, KOBAS and Cytoscape plug-in ClueGO respectively. The lncRNA-miRNA-mRNA network was composed of 90 lncRNAs,33mRNAs,26 miRNAs and 259 edges in the lncRNA upregulated group, and 18 lncRNAs,11 mRNAs,6 miRNAs and 45 edges in the lncRNA downregulated group. The functional assay showed that 53 pathways and 108 GO terms were enriched. Three lncRNAs (XIST, TUG1, GABPB1-AS1) could possibly be selected as key lncRNAs which may play an important role in the development of CN-AML. Particularly, GABPB1-AS1 was highly expressed in CN-AML by both bioinformatic analysis and experimental verification in AML cell line (THP-1) with quantitative real-time polymerase chain reaction. In addition, GABPB1-AS1 was also negatively correlated with overall survival of AML patients. The lncRNA-miRNA-mRNA network revealed key lncRNAs and their functions in CN-AML. Particularly, lncRNA GABPB1-AS1 was firstly proposed in AML. We believe that GABPB1-AS1 is expected to become a candidate prognostic biomarker or a potential therapeutic target.

Identification of miRNA signature for predicting the prognostic biomarker of squamous cell lung carcinoma

As explorations deepen, the role of microRNAs (miRNAs) in lung squamous cell carcinoma (LUSC), from its emergence to metastasis and prognosis, has elicited extensive concern. LUSC-related miRNA and mRNA samples were acquired from The Cancer Genome Atlas (TCGA) database. The data were initially screened and pretreated, and the R platform and series analytical tools were used to identify the specific and sensitive biomarkers. Seven miRNAs and 15 hub genes were found to be closely related to the overall survival of patients with LUSC. Determination of the expression of these miRNAs can help improve the overall survival of LUSC patients. The 15 hub genes correlated with overall survival (OS). The new miRNA markers were identified to predict the prognosis of LUSC. The findings of this study offer novel views on the evolution of precise cancer treatment approaches with high reliability.

Long non-coding RNAs and microRNAs as crucial regulators in cardio-oncology

Cancer is one of the leading causes of morbidity and mortality worldwide. Significant improvements in the modern era of anticancer therapeutic strategies have increased the survival rate of cancer patients. Unfortunately, cancer survivors have an increased risk of cardiovascular diseases, which is believed to result from anticancer therapies. The emergence of cardiovascular diseases among cancer survivors has served as the basis for establishing a novel field termed cardio-oncology. Cardio-oncology primarily focuses on investigating the underlying molecular mechanisms by which anticancer treatments lead to cardiovascular dysfunction and the development of novel cardioprotective strategies to counteract cardiotoxic effects of cancer therapies. Advances in genome biology have revealed that most of the genome is transcribed into non-coding RNAs (ncRNAs), which are recognized as being instrumental in cancer, cardiovascular health, and disease. Emerging studies have demonstrated that alterations of these ncRNAs have pathophysiological roles in multiple diseases in humans. As it relates to cardio-oncology, though, there is limited knowledge of the role of ncRNAs. In the present review, we summarize the up-to-date knowledge regarding the roles of long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) in cancer therapy-induced cardiotoxicities. Moreover, we also discuss prospective therapeutic strategies and the translational relevance of these ncRNAs.

Integrated RNA gene expression analysis identified potential immune-related biomarkers and RNA regulatory pathways of acute myocardial infarction

Background

Acute lesions are among the most important causes of death due to vascular lesions worldwide. However, there are no accurate genetic markers for Acute myocardial infarction (AMI). This project will use microarray integration analysis in bioinformatics analysis to find and validate relevant AMI gene markers.

Methods

Five microarray gene expression datasets were downloaded through the GEO database. We identified 50 significant DEGs by comparing and analyzing gene expression between 92 AMI and 57 standard samples. The BioGPS database screened differentially expressed genes specific to the immune system. DEGs were mainly involved in immune-related biological processes based on Enrichment analysis. Eight hub genes and three-gene cluster modules were subsequently screened using Cytoscape and validated using Box plot’s grouping comparison and ROC curves. Combined group comparison results and ROC curves analysis concluded that AQP9, IL1B, and IL1RN might be potential gene markers for the AMI process. We used the StarBase database to predict target miRNAs for eight essential genes. The expected results were used to screen and obtain target lncRNAs. Then Cytoscape was used to create CeRNA networks. By searching the literature in PubMed, we concluded that AQP9, IL1B, and IL1RN could be used as gene markers for AMI, while FSTL3-miR3303p-IL1B/IL1RN and ACSL4-miR5905p-IL1B could be used as RNA regulatory pathways affecting AMI disease progression.

Conclusions

Our study identified three genes that may be potential genetic markers for AMI’s early diagnosis and treatment. In addition, we suggest that FSTL3-miR-330-3p-IL1B/IL1RN and ACSL4-miR-590-5p-IL1B may be possible RNA regulatory pathways to control AMI disease progression.

The Role of miR-4256/HOXC8 Signaling Axis in the Gastric Cancer Progression: Evidence From lncRNA-miRNA-mRNA Network Analysis

Gastric cancer is a deadly human malignancy and the molecular mechanisms underlying gastric cancer pathophysiology are very complicated. Thus, further investigations are warranted to decipher the underlying molecular mechanisms. With the development of high-throughput screening and bioinformatics, gene expression profiles with large scale have been performed in gastric cancer. In the present study, we mined The Cancer Genome Atlas (TCGA) database and analyzed the gene expression profiles between gastric cancer tissues and normal gastric tissues. A series of differentially expressed lncRNAs, miRNAs and mRNAs between gastric cancer tissues and normal gastric tissues were identified. Based on the differentially expressed genes, we constructed miRNA-mRNA network, lncRNA-mRNA network and transcriptional factors-mRNA-miRNA-lncRNA network. Furthermore, the Kaplan survival analysis showed that high expression levels of EVX1, GBX2, GCM1, HOXC8, HOXC9, HOXC10, HOXC11, HOXC12 and HOXC13 were all significantly correlated with shorter overall survival of the patients with gastric cancer. On the other hand, low expression level of HOXA13 was associated with shorter overall survival of patients with gastric cancer. Among these hub genes, we performed the in vitro functional studies of HOXC8 in the gastric cancer cells. Knockdown of HOXC8 and overexpression of miR-4256 both significantly repressed the gastric cancer cell proliferation and migration, and miR-4256 repressed the expression of HOXC8 via targeting its 3' untranslated region in gastric cancer cells. Collectively, our results revealed that a complex interaction networks of differentially expressed genes in gastric cancer, and further functional studies indicated that miR-4256/HOXC8 may be an important axis in regulating gastric cancer progression.

Identification of hub genes and construction of an mRNA-miRNA-lncRNA network of gastric carcinoma using integrated bioinformatics analysis

Background

Gastric carcinoma (GC) is one of the most common cancer globally. Despite its worldwide decline in incidence and mortality over the past decades, gastric cancer still has a poor prognosis. However, the key regulators driving this process and their exact mechanisms have not been thoroughly studied. This study aimed to identify hub genes to improve the prognostic prediction of GC and construct a messenger RNA-microRNA-long non-coding RNA(mRNA-miRNA-lncRNA) regulatory network.

Methods

The GSE66229 dataset, from the Gene Expression Omnibus (GEO) database, and The Cancer Genome Atlas (TCGA) database were used for the bioinformatic analysis. Differential gene expression analysis methods and Weighted Gene Co-expression Network Analysis (WGCNA) were used to identify a common set of differentially co-expressed genes in GC. The genes were validated using samples from TCGA database and further validation using the online tools GEPIA database and Kaplan-Meier(KM) plotter database. Gene set enrichment analysis(GSEA) was used to identify hub genes related to signaling pathways in GC. The RNAInter database and Cytoscape software were used to construct an mRNA-miRNA-lncRNA network.

Results

A total of 12 genes were identified as the common set of differentially co-expressed genes in GC. After verification of these genes, 3 hub genes, namely CTHRC1, FNDC1, and INHBA, were found to be upregulated in tumor and associated with poor GC patient survival. In addition, an mRNA-miRNA-lncRNA regulatory network was established, which included 12 lncRNAs, 5 miRNAs, and the 3 hub genes.

Conclusions

In summary, the identification of these hub genes and the establishment of the mRNA-miRNA-lncRNA regulatory network provide new insights into the underlying mechanisms of gastric carcinogenesis. In addition, the identified hub genes, CTHRC1, FNDC1, and INHBA, may serve as novel prognostic biomarkers and therapeutic targets.