miRNA-target Network Research Articles

Integrated Analysis of Serum and Tissue microRNA Transcriptome for Biomarker Discovery in Gastric Cancer.

Gastric cancer (GC) poses a significant global health challenge, demanding a detailed exploration of its molecular landscape. Studies suggest that exposure to environmental pollutants can lead to changes in microRNA (miRNA) expression patterns, which may contribute to the development and progression of GC. MiRNAs have emerged as crucial regulators implicated in GC pathogenesis. The largest GC serum miRNA dataset to date, comprising 1417 non-cancer controls and 1417 GC samples was used. We conducted a comprehensive analysis of miRNA expression profiles. Differential expression analysis, co-expression network construction, and machine learning models were employed to identify key serum miRNAs and their association with clinical parameters. Weighted Gene Co-expression Network Analysis (WGCNA) and immune infiltration analysis were used to validate the importance of the key miRNA. A total of 1766 differentially expressed miRNAs were identified, with miR-1290, miR-1246, and miR-451a among the top up-regulated, and miR-6875-5p, miR-6784-5p, miR-1228-5p, and miR-6765-5p among the top down-regulated. WGCNA revealed that modules M1 and M5 were significantly associated with GC subtypes and disease status. MiRNA-target gene network analysis identified prognostically significant genes TP53, EMCN, CBX8, and ALDH1A3. Machine learning models LASSO, SVM, randomforest, and XGBOOST demonstrated the diagnostic potential of miRNA profiles. Tissue and serum miR-187 emerged as an independent prognostic factor, influencing patient survival across clinical parameters. Gene expression and immune cell infiltration were different in tissues stratified by miR-187 expression. In summary, the integration of differential gene expression, co-expression analysis, and immune cell profiling provided insights into the molecular intricacies of GC progression.

RNA-Seq Analysis Revealed circRNAs Associated with Resveratrol-Induced Apoptosis of Porcine Ovarian Granulosa Cells.

Circular RNAs (circRNAs) are a class of circular non-coding RNAs that play essential roles in the intricate and dynamic networks governing cell growth, development, and apoptosis. Resveratrol (RSV), a non-flavonoid polyphenol, is known to participate in follicular development and ovulation. In our previous research, we established a model using porcine ovarian granulosa cells (POGCs) treated with resveratrol, which confirmed its regulatory effects on long non-coding RNAs (lncRNAs) and microRNAs (miRNAs) within these cells. However, the influence of resveratrol on circRNA expression has not been thoroughly investigated. To explore how resveratrol affects circRNA levels in POGCs, we designed an experiment with three groups: a control group (CON, n = 3, 0 μM RSV), a low-dose RSV group (LOW, n = 3, 50 μM RSV), and a high-dose RSV group (HIGH, n = 3, 100 μM RSV) for circRNA sequencing. We identified a total of 10,045 candidate circRNAs from POGCs treated with different concentrations of resveratrol (0, 50, and 100 μM). Differential expression analysis indicated that 96 circRNAs were significantly altered in the LOW vs. CON group, while 109 circRNAs showed significant changes in the HIGH vs. CON group. These circRNAs were notably enriched in biological processes associated with cell metabolism, apoptosis, and oxidative stress. Functional enrichment analysis of the host genes revealed their involvement in critical signaling pathways, including mTOR, AMPK, and apoptosis pathways. Additionally, we identified potential miRNA sponge candidates among the differentially expressed circRNAs, particularly novel_circ_0012954 and novel_circ_0004762, which exhibited strong connectivity within miRNA-target networks. Our findings provide valuable insights into the regulatory mechanisms of circRNAs in the context of resveratrol-induced apoptosis in POGCs, highlighting their potential as innovative therapeutic targets in reproductive biology.

Identification and validation of miRNA-target genes network in pediatric brain tumors

Alterations in miRNA levels have been observed in various types of cancer, impacting numerous cellular processes and increasing their potential usefulness in combination therapies also in brain tumors. Recent advances in understanding the genetics and epigenetics of brain tumours point to new aberrations and associations, making it essential to continually update knowledge and classification. Here we conducted molecular analysis of 123 samples of childhood brain tumors (pilocytic astrocytoma, medulloblastoma, ependymoma), focusing on identification of genes that could potentially be regulated by crucial representatives of OncomiR-1: miR-17-5p and miR-20a-5p. On the basis of microarray gene expression analysis and qRTPCR profiling, we selected six (WEE1, CCND1, VEGFA, PTPRO, TP53INP1, BCL2L11) the most promising target genes for further experiments. The WEE1, CCND1, PTPRO, TP53INP1 genes showed increased expression levels in all tested entities with the lowest increase in the pilocytic astrocytoma compared to the ependymoma and medulloblastoma. The obtained results indicate a correlation between gene expression and the WHO grade and subtype. Furthermore, our analysis showed that the integration between genomic and epigenetic pathways should now point the way to further molecular research.

Bioinformatic Exploration of Circulating microRNAs Related to Functional Outcomes in Patients With Acute Ischemic Stroke: An Exploratory Prospective Study.

Background Although epigenetic modifications have been expected to play an important role in neuroplasticity for stroke recovery, the role of dynamic microRNA (miRNA) regulation related to functional outcomes after ischemic stroke remains unclear. Therefore, the current study performed a comprehensive miRNA expression analysis in serum to identify specifically altered circulating miRNAs associated with different grades of functional outcomes in patients with acute ischemic stroke (AIS). Methods Twelve patients with AIS in the middle cerebral artery region were included in this study. Peripheral blood samples were collected from patients one or two days after hospitalization. Total RNA, including small RNAs, was extracted from 400 µL of serum, and comprehensive miRNA expression analysis was performed to identify specifically altered circulating miRNAs associated with different grades of functional outcomes. Functional outcomes were evaluated three months after stroke onset using the modified Rankin Scale (mRS), classified as favorable (mRS score of 0 or 1) or unfavorable (mRS score of 2 to 5). Differentially expressed miRNAs were analyzed using the DESeq2 package. Target genes of the miRNAs were explored using miRTargetLink 2.0. Results Acute miRNA expression dynamics were characterized by differences in the patients' functional outcomes following ischemic stroke. The favorable outcome group exhibited significantly downregulated miRNAs, including hsa-miR-218-1, hsa-miR-218-2, hsa-miR-320e, hsa-miR-320d-1, hsa-miR-320d-2, hsa-miR-326, and hsa-miR-4429. In addition, 15 miRNAs, including hsa-miR-223, hsa-miR-18a, hsa-miR-411, and hsa-miR-128-1, were significantly upregulated in the favorable outcome group compared to the unfavorable outcome group. Interesting and strong validated networks between miRNAs and their target genes were identified. Conclusion This study identified specifically altered circulating miRNAs in serum associated with varying grades of functional outcomes in AIS patients and explored miRNA-target gene networks that might contribute to these outcomes. Although further studies are needed, this study highlights their potential role as biomarkers for predicting functional outcomes in patients with AIS.

Towards an enhanced understanding of osteoanabolic effects of PTH-induced microRNAs on osteoblasts using a bioinformatic approach.

In this study, we used a bioinformatic approach to construct a miRNA-target gene interaction network potentially involved in the anabolic effect of parathyroid hormone analogue teriparatide [PTH (1-34)] on osteoblasts. We extracted a dataset of 26 microRNAs (miRNAs) from previously published studies and predicted miRNA target interactions (MTIs) using four software tools: DIANA, miRWalk, miRDB, and TargetScan. By constructing an interactome of PTH-regulated miRNAs and their predicted target genes, we elucidated signaling pathways regulating pluripotency of stem cells, the Hippo signaling pathway, and the TGF-beta signaling pathway as the most significant pathways in the effects of PTH on osteoblasts. Furthermore, we constructed intersection of MTI networks for these three pathways and added validated interactions. There are 8 genes present in all three selected pathways and a set of 18 miRNAs are predicted to target these genes, according to literature data. The most important genes in all three pathways were BMPR1A, BMPR2 and SMAD2 having the most interactions with miRNAs. Among these miRNAs, only miR-146a-5p and miR-346 have validated interactions in these pathways and were shown to be important regulators of these pathways. In addition, we also propose miR-551b-5p and miR-338-5p for further experimental validation, as they have been predicted to target important genes in these pathways but none of their target interactions have yet been verified. Our wet-lab experiment on miRNAs differentially expressed between PTH (1-34) treated and untreated mesenchymal stem cells supports miR-186-5p from the literature obtained data as another prominent miRNA. The meticulous selection of miRNAs outlined will significantly support and guide future research aimed at discovering and understanding the crucial pathways of osteoanabolic PTH-epigenetic effects on osteoblasts. Additionally, they hold potential for the discovery of new PTH target genes, innovative biomarkers for the effectiveness and safety of osteoporosis-affected treatment, as well as novel therapeutic targets.

Identification of potential pathogenic genes related to osteoporosis and osteoarthritis.

Osteoarthritis (OA) and osteoporosis (OS) are the most common orthopedic diseases. To identify important genes as biomarkers for the pathogenesis of OA and OS. Microarray data for OA and OS were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between the OA and healthy control groups and between the OS and healthy control groups were identified using the Limma software package. Overlapping hub DEGs were selected using MCC, MNC, DEGREE, and EPC. Weighted gene co-expression network analysis (WGCNA) was used to mine OA- and OS-related modules. Shared hub DEGs were identified, human microRNA disease database was used to screen microRNAs associated with OA and OS, and an miRNA-target gene network was constructed. Finally, the expression of shared hub DEGs was evaluated. A total of 104 overlapping DEGs were identified in both the OA and OS groups, which were mainly related to inflammatory biological processes, such as the Akt and TNF signaling pathways Forty-six hub DEGs were identified using MCC, MNC, DEGREE, and EPC modules using different algorithms. Seven modules with 392 genes that highly correlated with disease were identified in the WGCNA. Furthermore, 10 shared hub DEGs were identified between the OA and OS groups, including OGN, FAP, COL6A3, THBS4, IGFBP2, LRRC15, DDR2, RND3, EFNB2, and CD48. A network consisting of 8 shared hub DEGs and 55 miRNAs was constructed. Furthermore, CD48 was significantly upregulated in the OA and OS groups, whereas EFNB2, DR2, COL6A3, and RND3 were significantly downregulated in OA and OS. Other hub DEGs were significantly upregulated in OA and downregulated in OS. The ten genes may be promising biomarkers for modulating the development of both OA and OS.

Identification of miRNA-Target Gene-Transcription Factor Regulatory Network as Functional Motifs Involved in Glomerular Diabetic Nephropathy

Identification of miRNA-Target Gene-Transcription Factor Regulatory Network as Functional Motifs Involved in Glomerular Diabetic Nephropathy

Exploring miRNA-target modules in Paeonia lactiflora stamen petalization through integrated miRNAome, transcriptome, and degradome analysis

Variation in floral organ number in plants is triggered by extremely complex transcriptional and post-transcriptional regulatory processes. MicroRNAs (miRNAs) play a pivotal role in the post-transcriptional regulation of floral organs. Paeonia lactiflora Pall., originating from China, is a precious herbaceous plant with ornamental, medicinal, and oil properties. Building on previous transcriptional studies, this work examined post-transcriptional regulation of peony stamen petalization using critical-stage flower buds. Using high-throughput small RNA sequencing, a miRNA expression profile was constructed. This was used to identify 10 known, 334 conserved, and 255 novel miRNAs, 155, 151, and 44 of which were differentially expressed at three developmental stages (stamen primordium, stamen primordium partly petaloid, and stamen primordium completely petaloid). Degradome analysis identified 55 miRNAs and 127 potential target genes. A miRNA-target network suggested that miR172-PlRAP2, miR160-PlARF, miR396-PlGRF, miR319-PlTCP4, and miR384\\394-PlFBOX modules are key regulators of peony stamen petalization. The regulation of miR319 and PlTCP4 during peony flower development was validated through transient expression in tobacco. This study provides insight into miRNA-mediated regulation of petalized stamen formation in herbaceous peony and refines the post-transcriptional network at critical stages of this process.

Expression changes of miRNA-regulated genes associated with the formation of the leafy head in cabbage

The vegetative development of cabbage (Brassicaoleracea var. capitata) passes through seedling, rosette, folding and heading stages. Leaves that form the rosette are large and mostly flat. In the following developmental stages, the plants produce leaves that curve inward to produce the leafy head. Many microRNAs and their target genes have been described participating in leaf development and leaf curvature. The aim of this study is to investigate the role of miRNA-regulated genes in the transition from the rosette to the heading stage. We compared the miRNA and gene abundances between emerging rosette and heading leaves. To remove transcripts (miRNAs and genes) whose regulation was most likely associated with plant age rather than the change from rosette to heading stage, we utilized a non-heading collard green (B. oleracea var. acephala) morphotype as control. This resulted in 33 DEMs and 1 998 DEGs with likely roles in the transition from rosette to heading stage in cabbage. Among these 1 998 DEGs, we found enriched GO terms related to DNA-binding transcription factor activity, transcription regulator activity, iron ion binding, and photosynthesis. We predicted the target genes of these 33 DEMs and focused on the subset that was differentially expressed (1 998 DEGs) between rosette and heading stage leaves to construct miRNA-target gene interaction networks. Our main finding is a role for miR396b-5p targeting two Arabidopsis thaliana orthologues of GROWTH REGULATING FACTORs 3 (GRF3) and 4 (GRF4) in pointed cabbage head formation.

Identification of renal ischemia reperfusion injury-characteristic genes, pathways and immunological micro-environment features through bioinformatics approaches.

Biomarkers and pathways associated with renal ischemia reperfusion injury (IRI) had not been well unveiled. This study was intended to investigate and summarize the regulatory networks for related hub genes. Besides, the immunological micro-environment features were evaluated and the correlations between immune cells and hub genes were also explored. GSE98622 containing mouse samples with multiple IRI stages and controls was collected from the GEO database. Differentially expressed genes (DEGs) were recognized by the R package limma, and the GO and KEGG analyses were conducted by DAVID. Gene set variation analysis (GSVA) and weighted gene coexpression network analysis (WGCNA) had been implemented to uncover changed pathways and gene modules related to IRI. Besides the known pathways such as apoptosis pathway, metabolic pathway, and cell cycle pathways, some novel pathways were also discovered to be critical in IRI. A series of novel genes associated with IRI was also dug out. An IRI mouse model was constructed to validate the results. The well-known IRI marker genes (Kim1 and Lcn2) and novel hub genes (Hbegf, Serpine2, Apbb1ip, Trip13, Atf3, and Ncaph) had been proved by the quantitative real-time polymerase chain reaction (qRT-PCR). Thereafter, miRNAs targeted to the dysregulated genes were predicted and the miRNA-target network was constructed. Furthermore, the immune infiltration for these samples was predicted and the results showed that macrophages infiltrated to the injured kidney to affect the tissue repair or fibrosis. Hub genes were significantly positively or negatively correlated with the macrophage abundance indicating they played a crucial role in macrophage infiltration. Consequently, the pathways, hub genes, miRNAs, and the immune microenvironment may explain the mechanism of IRI and might be the potential targets for IRI treatments.

Exploring small non-coding RNAs as blood-based biomarkers to predict Alzheimer’s disease

BackgroundAlzheimer’s disease (AD) diagnosis relies on clinical symptoms complemented with biological biomarkers, the Amyloid Tau Neurodegeneration (ATN) framework. Small non-coding RNA (sncRNA) in the blood have emerged as potential predictors of AD. We identified sncRNA signatures specific to ATN and AD, and evaluated both their contribution to improving AD conversion prediction beyond ATN alone.MethodsThis nested case–control study was conducted within the ACE cohort and included MCI patients matched by sex. Patients free of type 2 diabetes underwent cerebrospinal fluid (CSF) and plasma collection and were followed-up for a median of 2.45-years. Plasma sncRNAs were profiled using small RNA-sequencing. Conditional logistic and Cox regression analyses with elastic net penalties were performed to identify sncRNA signatures for A+(T|N)+ and AD. Weighted scores were computed using cross-validation, and the association of these scores with AD risk was assessed using multivariable Cox regression models. Gene ontology (GO) and Kyoto encyclopaedia of genes and genomes (KEGG) enrichment analysis of the identified signatures were performed.ResultsThe study sample consisted of 192 patients, including 96 A+(T|N)+ and 96 A-T-N- patients. We constructed a classification model based on a 6-miRNAs signature for ATN. The model could classify MCI patients into A-T-N- and A+(T|N)+ groups with an area under the curve of 0.7335 (95% CI, 0.7327 to 0.7342). However, the addition of the model to conventional risk factors did not improve the prediction of AD beyond the conventional model plus ATN status (C-statistic: 0.805 [95% CI, 0.758 to 0.852] compared to 0.829 [95% CI, 0.786, 0.872]). The AD-related 15-sncRNAs signature exhibited better predictive performance than the conventional model plus ATN status (C-statistic: 0.849 [95% CI, 0.808 to 0.890]). When ATN was included in this model, the prediction further improved to 0.875 (95% CI, 0.840 to 0.910). The miRNA-target interaction network and functional analysis, including GO and KEGG pathway enrichment analysis, suggested that the miRNAs in both signatures are involved in neuronal pathways associated with AD.ConclusionsThe AD-related sncRNA signature holds promise in predicting AD conversion, providing insights into early AD development and potential targets for prevention.

Circulating hsa-miR-320a and its regulatory network in type 1 diabetes mellitus.

Increasing evidence from human and animal model studies indicates the significant role of microRNAs (miRNAs) in pancreatic beta cell function, insulin signaling, immune responses, and pathogenesis of type 1 diabetes (T1D). We aimed, using next-generation sequencing, to screen miRNAs from peripheral blood mononuclear cells of eight independent Kuwaiti-Arab families with T1D affected siblings, consisting of 18 T1D patients and 18 unaffected members, characterized by no parent-to-child inheritance pattern. Our analysis revealed 20 miRNAs that are differentially expressed in T1D patients compared with healthy controls. Module-based weighted gene co-expression network analysis prioritized key consensus miRNAs in T1D pathogenesis. These included hsa-miR-320a-3p, hsa-miR-139-3p, hsa-miR-200-3p, hsa-miR-99b-5p and hsa-miR-6808-3p. Functional enrichment analysis of differentially expressed miRNAs indicated that PI3K-AKT is one of the key pathways perturbed in T1D. Gene ontology analysis of hub miRNAs also implicated PI3K-AKT, along with mTOR, MAPK, and interleukin signaling pathways, in T1D. Using quantitative RT-PCR, we validated one of the key predicted miRNA-target gene-transcription factor networks in an extended cohort of children with new-onset T1D positive for islet autoantibodies. Our analysis revealed that hsa-miR-320a-3p and its key targets, including PTEN, AKT1, BCL2, FOXO1 and MYC, are dysregulated in T1D, along with their interacting partners namely BLIMP3, GSK3B, CAV1, CXCL3, TGFB, and IL10. Receiver Operating Characteristic analysis highlighted the diagnostic potential of hsa-miR-320a-3p, CAV1, GSK3B and MYC for T1D. Our study presents a novel link between hsa-miR-320a-3p and T1D, and highlights its key regulatory role in the network of mRNA markers and transcription factors involved in T1D pathogenesis.

Transcriptional and functional analysis of plasma exosomal microRNAs in acute viral myocarditis

AimTo assess the differential expression profiles of exosome-derived microRNA (miRNA) and reveal their potential functions in patients with acute viral myocarditis (AVMC). Materials & methodsPeripheral blood samples were collected from 9 patients diagnosed with AVMC and 9 healthy controls (HC) in the Affiliated Hospital of Qingdao University from July 2021 to September 2022. The exosomal miRNA expression were tested using RNA high-throughput sequencing. We conducted the GO and KEGG functional analysis to predict the potential molecular, biological functions and related signaling pathways of miRNAs in exosomes. Target genes of exosomal miRNAs were predicted and miRNA-target gene network was mapped using gene databases. Differentially expressed exosomal miRNAs were selected and their expression levels were detected by quantitative real-time polymerase chain reaction (qRT-PCR) to verify the sequencing results. ResultsP < 0.05 and Fold Change>2 were considered as cut-off value to screen miRNAs that were differently expressed. This study identified 14 upregulated and 14 downregulated exosome-derived miRNAs. GO and KEGG analysis showed that differentially expressed miRNAs may be related to β-catenin binding, DNA transcription activities, ubiquitin ligase, PI3K-Akt, FoxO, P53, MAPK, and etc.. The target genes of differentially expressed miRNAs were predicted using gene databases. Real-time PCR confirmed the upregulation of hsa-miR-548a-3p and downregulation of hsa-miR-500b-5p in AVMC. ConclusionsHsa-miR-548a-3p and hsa-miR-500b-5p could serve as a promising biomarker of AVMC. Exosomal miRNAs may have substantial roles in the mechanisms of AVMC.

Cladribine and ocrelizumab induce differential miRNA profiles in peripheral blood mononucleated cells from relapsing-remitting multiple sclerosis patients.

Multiple sclerosis (MS) is a chronic, progressive neurological disease characterized by early-stage neuroinflammation, neurodegeneration, and demyelination that involves a spectrum of heterogeneous clinical manifestations in terms of disease course and response to therapy. Even though several disease-modifying therapies (DMTs) are available to prevent MS-related brain damage-acting on the peripheral immune system with an indirect effect on MS lesions-individualizing therapy according to disease characteristics and prognostic factors is still an unmet need. Given that deregulated miRNAs have been proposed as diagnostic tools in neurodegenerative/neuroinflammatory diseases such as MS, we aimed to explore miRNA profiles as potential classifiers of the relapsing-remitting MS (RRMS) patients' prospects to gain a more effective DMT choice and achieve a preferential drug response. A total of 25 adult patients with RRMS were enrolled in a cohort study, according to the latest McDonald criteria before (pre-cladribine, pre-CLA; pre-ocrelizumab, pre-OCRE, time T0) and after high-efficacy DMTs, time T1, 6 months post-CLA (n = 10, 7F and 3 M, age 39.0 ± 7.5) or post-OCRE (n = 15, 10F and 5 M, age 40.5 ± 10.4) treatment. A total of 15 age- and sex-matched healthy control subjects (9F and 6 M, age 36.3 ± 3.0) were also selected. By using Agilent microarrays, we analyzed miRNA profiles from peripheral blood mononuclear cells (PBMC). miRNA-target networks were obtained by miRTargetLink, and Pearson's correlation served to estimate the association between miRNAs and outcome clinical features. First, the miRNA profiles of pre-CLA or pre-OCRE RRMS patients compared to healthy controls identified modulated miRNA patterns (40 and seven miRNAs, respectively). A direct comparison of the two pre-treatment groups at T0 and T1 revealed more pro-inflammatory patterns in the pre-CLA miRNA profiles. Moreover, both DMTs emerged as being capable of reverting some dysregulated miRNAs toward a protective phenotype. Both drug-dependent miRNA profiles and specific miRNAs, such as miR-199a-3p, miR-29b-3p, and miR-151a-3p, emerged as potentially involved in these drug-induced mechanisms. This enabled the selection of miRNAs correlated to clinical features and the related miRNA-mRNA network. These data support the hypothesis of specific deregulated miRNAs as putative biomarkers in RRMS patients' stratification and DMT drug response.

Drug target genes and molecular mechanism investigation in isoflurane-induced anesthesia based on WGCNA and machine learning methods

PurposeThis study sought to identify drug target genes and their associated molecular mechanisms during isoflurane-induced anesthesia in clinical applications.MethodsMicroarray data (ID: GSE64617; isoflurane-treated vs. normal samples) were downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were screened and hub genes were investigated using weighted correlation network analysis (WGCNA). Protein–protein interactions (PPIs) were constructed among the co-DEGs (common genes between DEGs and hub genes), followed by functional enrichment analyses. Then, three machine learning methods were used to reveal drug targets, followed by validation, nomogram analysis, and gene set enrichment analysis. Finally, an miRNA-target network was constructed.ResultsA total of 686 DEGs were identified between the two groups—of which, 183 DEGs integrated with genes revealed by WCGNA were identified as co-genes. These genes, including contactin-associated protein 1 (CNTNAP1), are mainly involved in functions such as action potentials. PPI network analysis revealed three models, with the machine learning analysis exploring four drug target genes: A2H, FAM155B, SCARF2, and SDR16C5. ROC and nomogram analyses demonstrated the ideal diagnostic value of these target genes. Finally, miRNA–mRNA pairs were constructed based on the four mRNAs and associated 174 miRNAs.ConclusionFA2H, FAM155B, SCARF2, and SDR16C5 may be novel drug target genes for isoflurane-induced anesthesia. CNTNAP1 may participate in the progression of isoflurane-induced anesthesia via its action potential function.

Identifying the driver miRNAs with somatic copy number alterations driving dysregulated ceRNA networks in cancers

BackgroundMicroRNAs (miRNAs) play critical roles in cancer initiation and progression, which were critical components to maintain the dynamic balance of competing endogenous RNA (ceRNA) networks. Somatic copy number alterations (SCNAs) in the cancer genome could disturb the transcriptome level of miRNA to deregulate this balance. However, the driving effects of SCNAs of miRNAs were insufficiently understood.MethodsIn this study, we proposed a method to dissect the functional roles of miRNAs under different copy number states and identify driver miRNAs by integrating miRNA SCNAs profile, miRNA-target relationships and expression profiles of miRNA, mRNA and lncRNA.ResultsApplying our method to 813 TCGA breast cancer (BRCA) samples, we identified 29 driver miRNAs whose SCNAs significantly and concordantly regulated their own expression levels and further inversely dysregulated expression levels of their targets or disturbed the miRNA-target networks they directly involved. Based on miRNA-target networks, we further constructed dynamic ceRNA networks driven by driver SCNAs of miRNAs and identified three different patterns of SCNA interference in the miRNA-mediated dynamic ceRNA networks. Survival analysis of driver miRNAs showed that high-level amplifications of four driver miRNAs (including has-miR-30d-3p, has-mir-30b-5p, has-miR-30d-5p and has-miR-151a-3p) in 8q24 characterized a new BRCA subtype with poor prognosis and contributed to the dysfunction of cancer-associated hallmarks in a complementary way. The SCNAs of driver miRNAs across different cancer types contributed to the cancer development by dysregulating different components of the same cancer hallmarks, suggesting the cancer specificity of driver miRNA.ConclusionsThese results demonstrate the efficacy of our method in identifying driver miRNAs and elucidating their functional roles driven by endogenous SCNAs, which is useful for interpreting cancer genomes and pathogenic mechanisms.

Small RNA sequencing of exosomal microRNAs reveals differential expression of microRNAs in preeclampsia.

Preeclampsia (PE) is one of the most common hypertensive disorders of pregnancy. It is a dangerous condition with a high mortality rate in mothers and fetuses and is associated with a lack of early diagnosis and effective treatment. While the etiology of the disease is complex and obscure, it is now clear that the placenta is central to disease progression. Exosomal microRNAs (miRNAs) are possible mediators that regulate placenta-related physiological and pathological processes. Placental mesenchymal stem cells have considerable potential to help us understand the pathogenesis and treatment of pregnancy-related diseases. Here, we investigate the exosomal miRNA profiles of human placenta-derived mesenchymal stem cells between healthy pregnant women and those with PE. We performed small RNA sequencing to obtain miRNA profiles, and conducted enrichment analysis of the miRNA target genes to identify differentially expressed miRNAs associated with PE. Overall, we detected 1795 miRNAs; among them, 206 were differentially expressed in women with PE, including 35 upregulated and 171 downregulated miRNAs, when compared with healthy pregnant women. Moreover, we identified possible functions and pathways associated with PE, including angiogenesis, cell proliferation, migration and invasion, and the coagulation-fibrinolysis balance. Eventually, we proposed hsa-miR-675-5p, hsa-miR-3614-5p, and hsa-miR-615-5p as potential regulators of the pathogenesis of PE, and constructed a miRNA-target gene network. Our study identifies possible candidate biomarkers for the diagnosis of PE, and introduces a new direction for further understanding the pathogenesis of PE.

Differentially Expressed Genes, miRNAs and Network Models: A Strategy to Shed Light on Molecular Interactions Driving HNSCC Tumorigenesis.

Head and neck squamous cell carcinoma (HNSCC) is among the most common cancer worldwide, accounting for hundreds thousands deaths annually. Unfortunately, most patients are diagnosed in an advanced stage and only a percentage respond favorably to therapies. To help fill this gap, we hereby propose a retrospective in silico study to shed light on gene-miRNA interactions driving the development of HNSCC. Moreover, to identify topological biomarkers as a source for designing new drugs. To achieve this, gene and miRNA profiles from patients and controls are holistically reevaluated using protein-protein interaction (PPI) and bipartite miRNA-target networks. Cytoskeletal remodeling, extracellular matrix (ECM), immune system, proteolysis, and energy metabolism have emerged as major functional modules involved in the pathogenesis of HNSCC. Of note, the landscape of our findings depicts a concerted molecular action in activating genes promoting cell cycle and proliferation, and inactivating those suppressive. In this scenario, genes, including VEGFA, EMP1, PPL, KRAS, MET, TP53, MMPs and HOXs, and miRNAs, including mir-6728 and mir-99a, emerge as key players in the molecular interactions driving HNSCC tumorigenesis. Despite the heterogeneity characterizing these HNSCC subtypes, and the limitations of a study pointing to relationships that could be context dependent, the overlap with previously published studies is encouraging. Hence, it supports further investigation for key molecules, both those already and not correlated to HNSCC.

Systems biology based miRNA–mRNA expression pattern analysis of Emodin in breast cancer cell lines

Breast cancer has been among the most prominent cancers with high mortality. Currently most of the offered therapeutics are toxic; hence, less toxic therapeutic intervention is required. Here, we studied the molecular mechanisms of the effect of a phytoestrogen Emodin on estrogen receptor positive MCF-7 and negative MDA-MB-231 cells by carrying out a comprehensive network assessment. Differentially expressed microRNAs along with their previously identified differentially expressed mRNAs were analyzed through microarrays by using integrative systems biology approach. For each cell line miRNA-target gene networks were built, gene ontology and pathway enrichment analyses were performed, enrichment maps were constructed and the potential key genes, miRNAs and miRNA-gene interactions were studied.

Ovarian cancer classification and prognosis assessment model based on prognostic target genes in key microRNA-target gene networks.

The present study was designed to screen key microRNA (miRNA)-target gene networks for ovarian cancer (OC) and to classify and construct a risk assessment system for OC based on the target genes. OC sample data of The Cancer Genome Atlas dataset and GSE26193, GSE30161, GSE63885 and GSE9891 datasets were retrospectively collected. Pearson correlation analysis and targeted analysis of miRNA and target gene were performed to screen key miRNA-target gene networks. Target genes associated with the prognosis of OC were screened from key miRNA-target gene networks for consensus clustering and least absolute shrinkage and selection operator-based regression machine learning analysis of OC samples. Twenty target genes of 2651 key miRNA-target gene pairs had significant prognostic correlation in each OC cohort, and OC was divided into three clusters. There were differences in prognostic outcome, biological pathways, immune cell abundance and susceptibility to immune checkpoint blockade (ICB) therapy and anti-tumor drugs among the three molecular clusters. S2 exhibited the least advantage in prognosis and immunotherapy response rate in the three molecular clusters, and the pathways regulating immunity, hypoxia, metabolism and promoting malignant progression of cancer, as well as infiltrating immune and stromal cell population abundance, were the highest in this cluster. An eight-target gene prognostic model was created, and the risk index obtained by using this model not only significantly distinguished the immune characteristics of the sample, but also predicted the response of the sample to ICB treatment, and helped to screen 36 potential anti-OC drugs. The present study provides a classification strategy for OC based on prognostic target genes in key miRNA-target gene networks, and creates a risk assessment system for predicting prognosis and response to ICB therapy in OC patients, providing molecular basis for prognosis and precise treatment of OC.