Background: MicroRNAs (miRNAs) are important post-transcriptional regulators that have been demonstrated to play an important role in human diseases. Elucidating the associations between miRNAs and diseases at the systematic level will deepen our understanding of the molecular mechanisms of diseases. However, miRNA-disease associations identified by previous computational methods are far from completeness and more effort is needed. Results: We developed a computational framework to identify miRNA-disease associations by performing random walk analysis, and focused on the functional link between miRNA targets and disease genes in protein-protein interaction (PPI) networks. Furthermore, a bipartite miRNA-disease network was constructed, from which several miRNA-disease co-regulated modules were identified by hierarchical clustering analysis. Our approach achieved satisfactory performance in identifying known cancer-related miRNAs for nine human cancers with an area under the ROC curve (AUC) ranging from 71.3% to 91.3%. By systematically analyzing the global properties of the miRNA-disease network, we found that only a small number of miRNAs regulated genes involved in various diseases, genes associated with neurological diseases were preferentially regulated by miRNAs and some immunological diseases were associated with several specific miRNAs. We also observed that most diseases in the same co-regulated module tended to belong to the same disease category, indicating that these diseases might share similar miRNA regulatory mechanisms. Conclusions: In this study, we present a computational framework to identify miRNA-disease associations, and further construct a bipartite miRNA-disease network for systematically analyzing the global properties of miRNA regulation of disease genes. Our findings provide a broad perspective on the relationships between miRNAs and diseases and could potentially aid future research efforts concerning miRNA involvement in disease pathogenesis.

Atherosclerosis (AS), a predominant contributor to global cardiovascular disease burden, exhibits complex interplay with gut microbiota dysbiosis. While the associations between microbial imbalance and AS pathogenesis are well-documented, the pathophysiological mechanisms governing microbe-host crosstalk remain incompletely characterized. Current research limitations stem from methodological heterogeneity across studies and the absence of consensus regarding disease-specific microbial signatures. In this study, we conducted an integrated multi-omics analysis to characterize the functional signatures of gut microbiome in AS. We collected all public AS-related 6 microbiome datasets and 8 peripheral blood host transcriptomic datasets from across the world, comprising 456 metagenomic samples and 111 16S rRNA gene sequencing samples for microbial profiling, alongside 118 RNA-Seq samples and 302 microarray samples. We systematically characterized AS microbial taxa and computationally inferred the metabolic potential for the gut microbiome using metabolomic-related data. Metabolite-host gene interactions were further predicted based on the synergistic effects between microbiome and host transcriptome in AS. Five "microbe-metabolite-host gene" tripartite associations related to AS were identified involving 5 microbial genera (Actinomyces, Bacteroides, Eisenbergiella, Gemella, and Veillonella), 2 metabolites (Ethanol and H₂O₂), and 2 host genes (FANCD2 and GPX2), and the reliability of these associations was validated. Five microbial genera demonstrated robust diagnostic potential as noninvasive biomarkers, with 5-fold cross-validation, study-to-study transfer validation, and leave-one-study-out (LOSO) validation confirming good diagnostic performance. Additionally, the specificity of the biomarkers was validated against hypertension, inflammatory bowel disease (IBD), diabetes, and obesity cohorts. Our study unveiled the functional characteristics of gut microbiota interacting with AS host genes and highlighted the potential of gut microbiota as both diagnostic biomarkers and therapeutic targets for AS. However, the findings should be interpreted considering the inherent heterogeneity of the integrated datasets and the preliminary diagnostic value of the biomarkers.

Hypertrophic cardiomyopathy (HCM) is the most common genetic cardiovascular disease. Although some genes and miRNAs related with HCM have been studied, the molecular regulatory mechanisms between miRNAs and transcription factors (TFs) in HCM have not been systematically elucidated. In this study, we proposed a novel method for identifying dysregulated miRNA‐TF feed‐forward loops (FFLs) by integrating sample matched miRNA and gene expression profiles and experimentally verified interactions of TF‐target gene and miRNA‐target gene. We identified 316 dysregulated miRNA‐TF FFLs in HCM, which were confirmed to be closely related with HCM from various perspectives. Subpathway enrichment analysis demonstrated that the method was outperformed by the existing method. Furthermore, we systematically analysed the global architecture and feature of gene regulation by miRNAs and TFs in HCM, and the FFL composed of hsa‐miR‐17‐5p, FASN and STAT3 was inferred to play critical roles in HCM. Additionally, we identified two panels of biomarkers defined by three TFs (CEBPB, HIF1A, and STAT3) and four miRNAs (hsa‐miR‐155‐5p, hsa‐miR‐17‐5p, hsa‐miR‐20a‐5p, and hsa‐miR‐181a‐5p) in a discovery cohort of 126 samples, which could differentiate HCM patients from healthy controls with better performance. Our work provides HCM‐related dysregulated miRNA‐TF FFLs for further experimental study, and provides candidate biomarkers for HCM diagnosis and treatment.

Analyses of long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) implicated in myocardial infarction (MI) have increased our understanding of gene regulatory mechanisms in MI. However, it is not known how their expression fluctuates over the different stages of MI progression. In this study, we used time-series gene expression data to examine global lncRNA and miRNA expression patterns during the acute phase of MI and at three different time points thereafter. We observed that the largest expression peak for mRNAs, lncRNAs, and miRNAs occurred during the acute phase of MI and involved mainly protein-coding, rather than non-coding RNAs. Functional analysis indicated that the lncRNAs and miRNAs most sensitive to MI and most unstable during MI progression were usually related to fewer biological functions. Additionally, we developed a novel computational method for identifying dysregulated competing endogenous lncRNA-miRNA-mRNA triplets (LmiRM-CTs) during MI onset and progression. As a result, a new panel of candidate diagnostic biomarkers defined by seven lncRNAs was suggested to have high classification performance for patients with or without MI, and a new panel of prognostic biomarkers defined by two lncRNAs evidenced high discriminatory capability for MI patients who developed heart failure from those who did not.

Ischemic cardiomyopathy (ICM) and dilated cardiomyopathy (DCM) are the two main causes of heart failure (HF). Despite similar clinical characteristics and common “HF pathways”, ICM and DCM are expected to have different personalized treatment strategies. The underlying mechanisms of ICM and DCM have yet to be fully elucidated. The present study developed a novel computational method for identifying dysregulated long noncoding RNA (lncRNA)–microRNA (miRNA)–mRNA competing endogenous RNA (ceRNA) triplets. Time-ordered dysregulated ceRNA networks were subsequently constructed to reveal the possible disease progression of ICM and DCM based on the method. Biological functional analysis indicated that ICM and DCM had similar features during myocardial remodeling, whereas their characteristics differed during progression. Specifically, disturbance of myocardial energy metabolism may be the main characteristic during DCM progression, whereas early inflammation and response to oxygen are the characteristics that may be specific to ICM. In addition, several panels of diagnostic biomarkers for differentiating non-heart failure (NF) and ICM (NF-ICM), NF-DCM, and ICM-DCM were identified. Our study reveals biological differences during ICM and DCM progression and provides potential diagnostic biomarkers for ICM and DCM.

The neuroendocrine-immune (NEI) network is fundamental for maintaining body's homeostasis and health. While the roles of microRNAs (miRNAs) and transcription factors (TFs) in disease processes are well-established, their synergistic regulation within the NEI network has yet to be elucidated. In this study, we constructed a background NEI-related miRNA-TF regulatory network (NEI-miRTF-N) by integrating NEI signaling molecules (including miRNAs, genes, and TFs) and identifying miRNA-TF feed-forward loops. Our analysis reveals that the number of immune signaling molecules is the highest and suggests potential directions for signal transduction, primarily from the nervous system to both the endocrine and immune systems, as well as from the endocrine system to the immune system. Furthermore, disease-specific NEI-miRTF-Ns for depression, Alzheimer’s disease (AD) and dilated cardiomyopathy (DCM) were constructed based on the known disease molecules and significantly differentially expressed (SDE) molecules. Additionally, we proposed a novel method using depth-first-search algorithm for identifying significantly dysregulated NEI-related miRNA-TF regulatory pathways (NEI-miRTF-Ps) and verified their reliability from multiple perspectives. Our study provides an effective approach for identifying disease-specific NEI-miRTF-Ps and offers new insights into the synergistic regulation of miRNAs and TFs within the NEI network. Our findings provide information for new therapeutic strategies targeting these regulatory pathways.