miRNA Network Research Articles

Bioinformatics and system biology approach to identify potential common pathogenesis for COVID-19 infection and sarcopenia.

Sarcopenia is a condition characterized by age-related loss of muscle mass and strength. Increasing evidence suggests that patients with sarcopenia have higher rates of coronavirus 2019 (COVID-19) infection and poorer post-infection outcomes. However, the exact mechanism and connections between the two is unknown. In this study, we used high-throughput data from the GEO database for sarcopenia (GSE111016) and COVID-19 (GSE171110) to identify common differentially expressed genes (DEGs). We conducted GO and KEGG pathway analyses, as well as PPI network analysis on these DEGs. Using seven algorithms from the Cytoscape plug-in cytoHubba, we identified 15 common hub genes. Further analyses included enrichment, PPI interaction, TF-gene and miRNA-gene regulatory networks, gene-disease associations, and drug prediction. Additionally, we evaluated immune cell infiltration with CIBERSORT and assessed the diagnostic accuracy of hub genes for sarcopenia and COVID-19 using ROC curves. In total, we identified 66 DEGs (34 up-regulated and 32 down-regulated) and 15 hub genes associated with sarcopenia and COVID-19. GO and KEGG analyses revealed functions and pathways between the two diseases. TF-genes and TF-miRNA regulatory network suggest that FOXOC1 and hsa-mir-155-5p may be identified as key regulators, while gene-disease analysis showed strong correlations with hub genes in schizophrenia and bipolar disorder. Immune infiltration showed a correlation between the degree of immune infiltration and the level of infiltration of different immune cell subpopulations of hub genes in different datasets. The ROC curves for ALDH1L2 and KLF5 genes demonstrated their potential as diagnostic markers for both sarcopenia and COVID-19. This study suggests that sarcopenia and COVID-19 may share pathogenic pathways, and these pathways and hub genes offer new targets and strategies for early diagnosis, effective treatment, and tailored therapies for sarcopenia patients with COVID-19.

BIOM-13. THE MIR-17/92 CO-EXPRESSED NETWORK IS ENRICHED IN AGGRESSIVE PEDIATRIC BRAIN TUMORS

Abstract BACKGROUND Over the past two decades, the understanding of miRNA biology has drastically increased as miRNAs have been found to pervade most aspects of molecular biology–from cellular growth to apoptosis. Nevertheless, there has been little understanding of the role of miRNAs in pediatric brain tumors. In this study, we sought to correlate miRNA expression with pediatric brain tumor histologies. METHODS Weighted gene co-expression network analysis (WGCNA) was performed to determine co-expressed miRNA networks that correlated with distinct brain tumor histologies. Our study cohort consisted of miRNA-sequencing of 262 pediatric brain tumor histologies consisting of Atypical Teratoid/Rhabdoid tumors, Craniopharyngioma, Ependymomas, Ganglioglioma, Low-grade astrocytic tumors, High-grade astrocytic tumors, and Medulloblastoma. We utilized associated multi-omics data from the Open Pediatric Brain Tumor Atlas to validate the results. RESULTS WGCNA analysis revealed a distinct miRNA network consisting of 32 miRNAs with miR-17-5p as the hub miRNA that was markedly upregulated in the aggressive brain tumor group (p<0.001). Interestingly, the majority of the miRNA network consisted of the miR-17/92 family of miRNAs, which are known for their oncogenic role in various cancers. To determine the transcriptional regulatory mechanism of miR-17/92 miRNAs, we first looked at sample-matched RNA-sequencing data. The gene encoding for the miR-17/92 miRNAs, MIR17HG, was significantly overexpressed (p<0.001). Further upstream, we observed that E2F1/2/3, known transcriptional regulators of MIR17HG, gene and protein expressions were significantly overexpressed (p<0.01). Sample-matched phospho-proteomics data indicated the E2F regulator, RB1, being hyperphosphorylated–additionally supporting the E2F activity in these samples. CONCLUSION These findings reveal a network of oncogenic miRNAs consisting of the miR-17/92 family that are distinctly overexpressed in aggressive pediatric brain tumors. Moreover, we suggest that the E2F-RB1 axis is the upstream regulator of the miR-17/92 family of miRNAs.

Regulatory network analysis based on integrated miRNA-TF reveals key genes in heart failure

The etiology and pathophysiology of heart failure are still unknown. Increasing evidence suggests that abnormal microRNAs (miRNAs) and transcription factors (TFs) expression may be associated with the development of heart failure. Therefore, this study aims to explore key miRNAs, TFs, and related genes in heart failure to gain a greater understanding of the pathogenesis of heart failure. To search and download the dataset of mRNA chips related to heart failure from the GEO database (GSE59867, GSE9128, and GSE134766), we analyzed differential genes and screened the common differentially expressed genes on two chips using R language software. The binary interactions and circuits among miRNAs, TFs, and corresponding genes were determined by Pearson correlation coefficient. A regulatory network of miRNAs, TFs, and target genes was constructed based on bioinformatics. By comparing the sequences of patients with and without heart failure, five downregulated genes with hypermethylated mRNA and three upregulated genes with hypomethylated mRNA were identified. The miRNA-TF gene regulatory network consisted of 26 miRNAs, 22 TFs and six genes. GO and KEGG analysis results revealed that BP terms like cellular response to organic substance, cellular response to cytokine stimulus, and KEGG pathways like osteoclast differentiation, MAPK signaling pathway, and legionellosis were enriched of the DEGs. TMEM87A, PPP2R2A, DUSP1, and miR-92a have great potential as biomarkers for heart failure. The integrated analysis of the mRNA expression spectrum and microRNA-transcription factor-gene revealed the regulatory network of heart failure, which may provide clues to its alternative treatment.

Establishing a Ten Disulfidptosis-related Gene Signature for Prognostic Prediction in Skin Cutaneous Melanoma.

Disulfidptosis is a new metabolic-related regulated cell death associated with cancer growth. This study aimed to investigate the molecular mechanisms associated with disulfidptosis in skin cutaneous melanoma (SKCM) and establish a disulfidptosis-related gene signature for prognostic prediction in SKCM. Disulfidptosis-associated genes were identified from RNA-seq data of SKCM. A risk score signature was developed and validated through univariate Cox and LASSO analyses. Additionally, the immune microenvironment related to the risk score signature was investigated. Finally, a disulfidptosis-related genes-transcription factor -miRNA network was developed, and the expression levels of five disulfidptosis-related genes were initially verified in SKCM cell lines. A total of 107 disulfidptosis-related differentially expressed genes in SKCM samples were identified. A ten-disulfidptosis-gene signature was established, including BIN2, CCL3L3, CCL8, CD79A, CIITA, CXCR3, DEFB1, GPR171, IL2RB, and SOCS1. The SKCM samples were divided into high- and low-risk groups, of which samples in the low-risk group showed better survival performance. The receiver operating characteristic curve analysis confirmed the good potency of the disulfidptosis-related gene prognostic model. Except for DEFB1, the other nine genes were positively related with T cell CD8+, T cell CD4+ memory activated, T cell gamma delta, NK cell activated, and macrophage M1, and they were all negatively related with NK cell resting, macrophage M0, macrophage M2, and mast cell activated. Finally, we verified downregulated levels of SOCS1 and DEFB1 and upregulated CXCR3, BIN2, and CCL3L3 in A875 and A375. We successfully established ten disulfidptosis-related genes' prediction prognostic signatures for SKCM patients.

Application of angiogenesis-related genes associated with immune infiltration in the molecular typing and diagnosis of acute myocardial infarction.

Angiogenesis has been discovered to be a critical factor in developing tumors and ischemic diseases. However, the role of angiogenesis-related genes (ARGs) in acute myocardial infarction (AMI) remains unclear. The GSE66360 dataset was used as the training cohort, and the GSE48060 dataset was used as the external validation cohort. The random forest (RF) algorithm was used to identify the signature genes. Consensus clustering analysis was used to identify robust molecular clusters associated with angiogenesis. The ssGSEA was used to analyze the correlation between ARGs and immune cell infiltration. In addition, we constructed miRNA-gene, transcription factor network, and targeted drug network of signature genes. RT-qPCR was used to verify the expression levels of signature genes. Seven signature ARGs were identified based on the RF algorithm. Receiver operating characteristic curves confirmed the classification accuracy of the risk predictive model based on signature ARGs (area under the curve [AUC] = 0.9596 in the training cohort and AUC = 0.7773 in the external validation cohort). Subsequently, the ARG clusters were identified by consensus clustering. Cluster B had a more generalized high expression of ARGs and was significantly associated with immune infiltration. The miRNA and transcription factor network provided new ideas for finding potential upstream targets and biomarkers. Finally, the results of RT-qPCR were consistent with the bioinformatics analysis, further validating our results. Angiogenesis is closely related to AMI, and characterizing the angiogenic features of patients with AMI can help to risk-stratify patients and provide personalized treatment.

Identification of the miRNA-mRNA regulatory network in a mouse model of early fracture.

Fracture healing is a complex process that involves multiple molecular events, and the regulation mechanism is not fully understood. We acquired miRNA and mRNA transcriptomes of mouse fractures from the Gene Expression Omnibus database (GSE76197 and GSE192542) and integrated the miRNAs and genes that were differentially expressed in the control and fracture groups to construct regulatory networks. There were 130 differentially expressed miRNAs and 4,819 differentially expressed genes, including 72 upregulated and 58 downregulated miRNAs, along with 2,855 upregulated and 1964 downregulated genes during early fracture healing. Gene ontology analysis revealed that most of the differentially expressed genes were enriched in the extracellular matrix (ECM) structure and the ECM organization. The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment suggested cell cycle, DNA replication, and mismatch repair were involved in the progression of fracture healing. Furthermore, we constructed a molecular network of miRNAs and mRNAs with inverse expression patterns to elucidate the molecular basis of miRNA-mRNA regulation in fractures. The regulatory network highlighted the potential targets, which may help to provide a mechanistic basis for therapies to improve fracture patient outcomes.

Exosome-Mediated Communication in Thyroid Cancer: Implications for Prognosis and Therapeutic Targets.

Thyroid cancer (THCA) is one of the most common malignancies of the endocrine system. Exosomes have significant value in performing molecular treatments, evaluating the diagnosis and determining tumor prognosis. Thus, the identification of exosome-related genes could be valuable for the diagnosis and potential treatment of THCA. In this study, we examined a set of exosome-related differentially expressed genes (DEGs) (BIRC5, POSTN, TGFBR1, DUSP1, BID, and FGFR2) by taking the intersection between the DEGs of the TCGA-THCA and GeneCards datasets. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses of the exosome-related DEGs indicated that these genes were involved in certain biological functions and pathways. Protein‒protein interaction (PPI), mRNA‒miRNA, and mRNA-TF interaction networks were constructed using the 6 exosome-related DEGs as hub genes. Furthermore, we analyzed the correlation between the 6 exosome-related DEGs and immune infiltration. The Genomics of Drug Sensitivity in Cancer (GDSC), the Cancer Cell Line Encyclopedia (CCLE), and the CellMiner database were used to elucidate the relationship between the exosome-related DEGs and drug sensitivity. In addition, we verified that both POSTN and BID were upregulated in papillary thyroid cancer (PTC) patients and that their expression was correlated with cancer progression. The POSTN and BID protein expression levels were further examined in THCA cell lines. These findings provide insights into exosome-related clinical trials and drug development.

Identification and validation of oxidative stress-related genes in sepsis-induced myopathy.

Sepsis-induced myopathy (SIM) a complication of sepsis that results in prolonged mechanical ventilation, long-term functional disability, and increased patient mortality. This study was performed to identify potential key oxidative stress-related genes (OS-genes) as biomarkers for the diagnosis of SIM using bioinformatics. The GSE13205 was obtained from the Gene Expression Omnibus (GEO) database, including 13 SIM samples and 8 healthy samples, and the differentially expressed genes (DEGs) were identified by limma package in R language. Simultaneously, we searched for the genes related to oxidative stress in the Gene Ontology (GO) database. The intersection of the genes selected from the GO database and the genes from the GSE13205 was considered as OS-genes of SIM, where the differential genes were regarded as OS-DEGs. OS-DEGs were analyzed using GO enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein-protein interaction (PPI) networks. Hub genes in OS-DEGs were selected based on degree, and diagnostic genes were further screened by gene expression and receiver operating characteristic (ROC) curve. Finally, a miRNA-gene network of diagnostic genes was constructed. A total of 1089 DEGs were screened from the GSE13205, and 453 OS-genes were identified from the GO database. The overlapping DEGs and OS-genes constituted 25 OS-DEGs, including 15 significantly upregulated and 10 significantly downregulated genes. The top 10 hub genes, including CD36, GPX3, NQO1, GSR, TP53, IDH1, BCL2, HMOX1, JAK2, and FOXO1, were screened. Furthermore, 5 diagnostic genes were identified: CD36, GPX3, NQO1, GSR, and TP53. The ROC analysis showed that the respective area under the curves (AUCs) of CD36, GPX3, NQO1, GSR, and TP53 were 0.990, 0.981, 0.971, 0.971, and 0.971, which meant these genes had very high diagnostic values of SIM. Finally, based on these 5 diagnostic genes, we found that miR-124-3p and miR-16-5p may be potential targets for the treatment of SIM. The results of this study suggest that OS-genes might play an important role in SIM. CD36, GPX3, NQO1, GSR, and TP53 have potential as specific biomarkers for the diagnosis of SIM.

LIMD2 is the Signature of Cell Aging-immune/Inflammation in Acute Myocardial Infarction.

Acute myocardial infarction (AMI) is an age-dependent cardiovascular disease in which cell aging, immunity, and inflammatory factors alter the course; however, cell aging-immune/inflammation signatures in AMI have not been investigated. Based on the GEO database to obtain microRNA (miRNA) sequencing, mRNA sequencing and single-cell sequencing data, and utilizing the Seurat package to identify AMI-associated cellular subpopulations. Subsequently, differentially expressed miRNAs and mRNAs were screened to establish a network of competing endogenous RNAs (ceRNAs). Senescence and immunity scores were calculated by single sample gene set enrichment analysis (ssGSEA), ESTIMATE and CIBERSORT algorithms, and the Hmisc package was used to screen for genes with the highest correlation with senescence and immunity scores. Finally, protein-protein interaction (PPI) and molecular docking analyses were performed to predict potential therapeutic agents for the treatment of AMI. Four cell types (Macrophage, Fibroblast, Endothelial cells, CD8 T cells) were identified in AMI, and CD8 T cells exhibited the lowest cell aging activity. A ceRNA network of miRNAs- mNRA interactions was established based on the overlapping genes in differentially expressed miRNAs (DEmiRNAs) target genes and differentially expressed mRNAs (DEmRNAs). Twenty-four marker genes of CD8 T cells were observed. LIMD2 was identified as cell aging- immune/inflammation-related hub gene in AMI. This study also identified a potential therapeutic network of DB03276-LIMD2-AMI, which showed excellent and stable binding status between DB03276-LIMD2. This study identified LIMD2 as a cell aging-immune/inflammation-related hub gene. The understanding of the pathogenesis and therapeutic mechanisms of AMI was enriched by the ceRNA network and DB03276-LIMD2-LAMI therapeutic network.

Identifying the miRNA-gene networks contributes to exploring paravertebral muscle degeneration's underlying pathogenesis and therapy strategy

Low back pain (LBP) is a worldwide problem with public health. Paravertebral muscle degeneration (PMD) is believed to be associated with LBP. Increasing evidence has demonstrated that microRNA (miRNA)-mRNA signaling networks have been implicated in the pathophysiology of diseases. Research suggests that cell death, oxidative stress, inflammatory and immune response, and extracellular matrix (ECM) metabolism are the pathogenesis of PMD; however, the miRNA-mRNA mediated the pathological process of PMD remains elusive. RNA sequencing (RNA-seq) and single cell RNA-seq (scRNA-seq) are invaluable tools for uncovering the functional biology underlying these miRNA and gene expression changes. Using scRNA-seq, we show that multiple immunocytes are presented during PMD, revealing that they may have been implicated with PMD. Additionally, using RNA-seq, we identified 76 differentially expressed genes (DEGs) and 106 differentially expressed miRNAs (DEMs), among which IL-24 and CCDC63 were the top upregulated and downregulated genes in PMD. Comprehensive bioinformatics analyses, including Venn diagrams, differential expression, functional enrichment, and protein-protein interaction analysis, were then conducted to identify six ferroptosis-related DEGs, two oxidative stress-related DEGs, eleven immunity-related DEGs, five ECM-related DEGs, among which AKR1C2/AKR1C3/SIRT1/ALB/IL-24 belong to inflammatory genes. Furthermore, 67 DEMs were predicted to be upstream miRNAs of 25 key DEGs by merging RNA-seq, TargetScan, and mirDIP databases. Finally, a miRNA-gene network was constructed using Cytoscape software and an alluvial plot. ROC curve analysis unveiled multiple key DEGs with the high clinical diagnostic value, providing novel approaches for diagnosing and treating PMD diseases.

Identification of common mechanisms and biomarkers of atrial fibrillation and heart failure based on machine learning.

Atrial fibrillation (AF) is the most common arrhythmia. Heart failure (HF) is a disease caused by heart dysfunction. The prevalence of AF and HF were progressively increasing over time. The co-existence of AF and HF presents a significant therapeutic challenge. In order to provide new ideas for the diagnosis of AF and HF, it is necessary to carry out biomarker related studies. The training set and validation set data of AF and HF patient samples were downloaded from the GEO database, 'limma' was used to compare the differences in gene expression levels between the disease group and the normal group to screen for differentially expressed genes (DEGs). Weighted correlation network analysis (WGCNA) identified the modules with the highest positive correlation with AF and HF. Functional enrichment and PPI network construction of key genes were carried out. Biomarkers were screened by machine learning. The infiltration of immune cells in AF and HF groups was evaluated by R-packet 'CIBERSORT'. The miRNA network was constructed and potential therapeutic agents for biomarker genes were predicted through the drugbank database. Through WGCNA analysis, it was found that the modules most positively correlated with AF and HF were MEturquoise (r=0.21, P value=0.09) and MEbrown (r=0.62, P value=8e-12), respectively. We screened 25 genes that were highly correlated with both AF and HF. Lasso regression analysis results showed 7 and 20 core genes in AF and HF groups, respectively. The top 20 important genes in AF and HF groups were obtained as core genes by RF model analysis. Four biomarkers were obtained after the intersection of core genes in four groups, namely, GLUL, NCF2, S100A12, and SRGN. The diagnostic efficacy of four genes in AF validation sets was good (AUC: GLUL 0.76, NCF2 0.64, S100A12 0.68, and SRGN 0.76), as well as in the HF validation set (AUC: GLUL 0.76, NCF2 0.84, S100A12 0.92, and SRGN 0.68). The highest correlation with neutrophils was observed for GLUL, NCF2, and S100A12, while SRGN exhibited the strongest correlation with T cells CD4 memory resting in the AF group. GLUL, NCF2, S100A12, and SRGN were most associated with neutrophils in the HF group. A total of 101 miRNAs were predicted by four genes, and GLUL, NCF2, and S100A12 predicted a total of 10 potential therapeutic agents. We identified four biological markers that are highly correlated with AF and HF, namely, GLUL, NCF2, S100A12, and SRGN. Our findings provide theoretical basis for the clinical diagnosis and treatment of AF and HF.

Heterogeneous graph inference with range constrainted [formula omitted]-collaborative matrix factorization for small molecule-miRNA association prediction

MicroRNAs (miRNAs) play a vital role in regulating gene expression and various biological processes. As a result, they have been identified as effective targets for small molecule (SM) drugs in disease treatment. Heterogeneous graph inference stands as a classical approach for predicting SM-miRNA associations, showcasing commendable convergence accuracy and speed. However, most existing methods do not adequately address the inherent sparsity in SM-miRNA association networks, and imprecise SM/miRNA similarity metrics reduce the accuracy of predicting SM-miRNA associations. In this research, we proposed a heterogeneous graph inference with range constrained L2,1-collaborative matrix factorization (HGIRCLMF) method to predict potential SM-miRNA associations. First, we computed the multi-source similarities of SM/miRNA and integrated these similarity information into a comprehensive SM/miRNA similarity. This step improved the accuracy of SM and miRNA similarity, ensuring reliability for the subsequent inference of the heterogeneity map. Second, we used a range constrained L2,1-collaborative matrix factorization (RCLMF) model to pre-populate the SM-miRNA association matrix with missing values. In this step, we developed a novel matrix decomposition method that enhances the robustness and formative nature of SM-miRNA edges between SM networks and miRNA networks. Next, we built a well-established SM-miRNA heterogeneous network utilizing the processed biological information. Finally, HGIRCLMF used this network data to infer unknown association pair scores. We implemented four cross-validation experiments on two distinct datasets, and HGIRCLMF acquired the highest areas under the curve, surpassing six state-of-the-art computational approaches. Furthermore, we performed three case studies to validate the predictive power of our method in practical application.

Profiling of Groundnut bud necrosis orthotospovirus–responsive microRNA and their targets in tomato based on deep sequencing

Tomato, an extensively cultivated vegetable crop produces miRNAs in response to infection with Groundnut bud necrosis orthotospovirus, a viral pathogen causing significant economic losses. High-throughput miRNA sequencing was performed on tomato leaves inoculated with GBNV and mock-inoculated leaves as controls. Analysis revealed 73 known miRNAs belonging to 24 miRNA families, with variable expression levels. Interestingly, 39 miRNAs were upregulated, and 34 were downregulated in response to GBNV infection. Stem-loop quantitative reverse transcription PCR validated the differential expression of selected miRNAs. Additionally, 30 miRNA encoded proteins were identified to be involved in disease resistance and susceptibility. The miRNA-target interactions were found to play significant roles in cellular and metabolic activities, as well as modulating signaling pathways during the plant-virus interaction. The findings shed light on the intricate regulatory network of miRNAs in tomato response to viral infection and may contribute to developing strategies for improving crop protection against viral diseases.

MGCNSS: miRNA-disease association prediction with multi-layer graph convolution and distance-based negative sample selection strategy.

Identifying disease-associated microRNAs (miRNAs) could help understand the deep mechanism of diseases, which promotes the development of new medicine. Recently, network-based approaches have been widely proposed for inferring the potential associations between miRNAs and diseases. However, these approaches ignore the importance of different relations in meta-paths when learning the embeddings of miRNAs and diseases. Besides, they pay little attention to screening out reliable negative samples which is crucial for improving the prediction accuracy. In this study, we propose a novel approach named MGCNSS with the multi-layer graph convolution and high-quality negative sample selection strategy. Specifically, MGCNSS first constructs a comprehensive heterogeneous network by integrating miRNA and disease similarity networks coupled with their known association relationships. Then, we employ the multi-layer graph convolution to automatically capture the meta-path relations with different lengths in the heterogeneous network and learn the discriminative representations of miRNAs and diseases. After that, MGCNSS establishes a highly reliable negative sample set from the unlabeled sample set with the negative distance-based sample selection strategy. Finally, we train MGCNSS under an unsupervised learning manner and predict the potential associations between miRNAs and diseases. The experimental results fully demonstrate that MGCNSS outperforms all baseline methods on both balanced and imbalanced datasets. More importantly, we conduct case studies on colon neoplasms and esophageal neoplasms, further confirming the ability of MGCNSS to detect potential candidate miRNAs. The source code is publicly available on GitHub https://github.com/15136943622/MGCNSS/tree/master.

Identification of survival related key genes and long-term survival specific differentially expressed genes related key miRNA network of primary glioblastoma

Primary glioblastoma(pGBM) is the most malignant tumor of the central nervous system. Radiotherapy, chemotherapy and surgical treatment have little effect on the survival of pGBM patients. The prognosis is often poorly once the tumor recurs. It is urgent to develop new therapies for patients. In recent years, studies have been clarified that miRNA have a powerful regulating effect on the genes. However, the main group of miRNAs in regulating long-term survival specific related genes of pGBM is still unclear. Given that the survival period of most glioma patients is relatively short, studying long-term survival patients with pGBM is of great value for this disease. Our study aim to identify key miRNAs with long-term survival related genes present in pGBM and uncover their potential mechanisms. The gene expression profiles of GSE53733, GSE15824, GSE30563, GSE50161 were obtained from the Gene Expression Omnibus database. Firstly, samples were divided into 3 groups according to its survival time and each group compare to the normal control group. Then we obtained differential expression genes (DEGs) with a long-term survival specific (LTSDEGs) and a short-term survival specific DEGs (STSDEGs). Next, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were conducted with LTSDEGs and STSDEGs together. Moreover, we used the UALCAN database to verify LTSDEGs and STSDEGs, and obtained long-term verified survival specific DEGs(LTVSDEGs) and short-term verified survival specific DEGs(STVSDEGs). Finally, we established the predicted key miRNAs-LTVSDEGs interaction network. The protein expressions of the top 4 LTVSDEGs were verified in the HPA database with immunohistochemical staining. In total, we found 260 genes changed in LTSDEGs and 822 genes changed in STSDEGs. GO and KEGG results shown that the major changes are focused on tumor metabolism. 9 LTVSDEGs and 18 STVSDEGs were verified in UALCAN database. As for protein expression verification in top 4 LTVSDEGs, ZNF630, BLVRB and RPA3 were verified, while TPBG was not detected. We obtained 59 key miRNA from the predicted key miRNAs-LTVSDEGs interaction network. 25 key miRNAs were verified using GSE90603. Finally, we constructed the key miRNAs-LTVSDEGs network using a Sankey diagram, including 25 miRNAs and 7 LTVSDEGs. In conclusion, our study shows that there is a close relationship between metabolic changes and survival in pGBM. Besides, we established a key miRNAs-LTVSDEGs network for pGBM, which could be the key path in prolonging the life of pGBM patients.

Identification of Anoikis-Related Genes in Spinal Cord Injury: Bioinformatics and Experimental Validation.

Spinal cord injury (SCI) is a serious disease without effective therapeutic strategies. To identify the potential treatments for SCI, it is extremely important to explore the underlying mechanism. Current studies demonstrate that anoikis might play an important role in SCI. In this study, we aimed to identify the key anoikis-related genes (ARGs) providing therapeutic targets for SCI. The mRNA expression matrix of GSE45006 was downloaded from the Gene Expression Omnibus (GEO) database, and the ARGs were downloaded from the Molecular Signatures Database (MSigDB database). Then, the potential differentially expressed ARGs were identified. Next, correlation analysis, gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) analysis were employed for the differentially expressed ARGs. Moreover, miRNA-gene networks were constructed by the hub ARGs. Finally, RNA expression of the top ten hub ARGs was validated in the SCI cell model and rat SCI model. A total of 27 common differentially expressed ARGs were identified at different time points (1, 3, 7, and 14days) following SCI. The GO and KEGG enrichment analysis of these ARGs indicated several enriched terms related to proliferation, cell cycle, and apoptotic process. The PPI results revealed that most of the ARGs interacted with each other. Ten hub ARGs were further screened, and all the 10 genes were validated in the SCI cell model. In the rat model, only seven genes were validated eventually. We identified 27 differentially expressed ARGs of the SCI through bioinformatic analysis. Seven real hub ARGs (CCND1, FN1, IGF1, MYC, STAT3, TGFB1, and TP53) were identified eventually. These results may expand our understanding of SCI and contribute to the exploration of potential SCI targets.

Tenascin-X is increased with decreased expression of miR-378a-5p and miR-486-5p in mice fed a methionine-choline-deficient diet that induces hepatic fibrosis.

We previously reported that tenascin-X (Tnxb) aggravates hepatic fibrosis in mice fed a high-fat and high-cholesterol diet with high levels of phosphorus and calcium (HFCD). In this study, we investigated Tnxb expression in livers with fibrosis caused by administration of a methionine-chorine-deficient (MCD) diet in mice. Whole transcriptome analysis showed that Tnxb was one of the genes with increased expression in livers of MCD diet-fed mice compared with that in livers of normal diet (ND)-fed mice. In microarray and subsequent microRNA (miRNA) network analyses, miR-378a-5p and miR-486-5p were identified in livers of MCD diet-fed mice as downregulated miRNAs, which have their predicted target sites in the 3' untranslated region of Tnxb mRNA and might suppress the translation of Tnxb mRNA. RT-qPCR analyses of livers of MCD diet-fed mice compared with livers of ND-fed mice verified the upregulation of Tnxb and fibrosis-triggering genes and conversely the downregulation of miR-378a-5p and miR-486-5p. Overexpression of miR-378a-5p and miR-486-5p resulted in decreased level not only of the FLAG-tagged fibrinogen-like domain of Tnxb protein (FLAG-mTNX-FG) but also of endogenous Tnxb protein in murine cultured cells. These results indicate that expression of Tnxb is regulated by miR-378a-5p and miR-486-5p in hepatic fibrosis following MCD diet feeding.

Switching action modes of miR408-5p mediates auxin signaling in rice

MicroRNAs (miRNAs) play fundamental roles in many developmental and physiological processes in eukaryotes. MiRNAs in plants generally regulate their targets via either mRNA cleavage or translation repression; however, which approach plays a major role and whether these two function modes can shift remains elusive. Here, we identify a miRNA, miR408-5p that regulates AUXIN/INDOLE ACETIC ACID 30 (IAA30), a critical repressor in the auxin pathway via switching action modes in rice. We find that miR408-5p usually inhibits IAA30 protein translation, but in a high auxin environment, it promotes the decay of IAA30 mRNA when it is overproduced. We further demonstrate that IDEAL PLANT ARCHITECTURE1 (IPA1), an SPL transcription factor regulated by miR156, mediates leaf inclination through association with miR408-5p precursor promoter. We finally show that the miR156-IPA1-miR408-5p-IAA30 module could be controlled by miR393, which silences auxin receptors. Together, our results define an alternative auxin transduction signaling pathway in rice that involves the switching of function modes by miR408-5p, which contributes to a better understanding of the action machinery as well as the cooperative network of miRNAs in plants.

Integrative Transcriptome Analysis of mRNA and miRNA in Pepper's Response to Phytophthora capsici Infection.

Phytophthora blight of pepper is a notorious disease caused by the oomycete pathogen Phytophthora capsici, which poses a great threat to global pepper production. MicroRNA (miRNA) is a class of non-coding small RNAs that regulate gene expressions by altering the translation efficiency or stability of targeted mRNAs, which play important roles in the regulation of a plant's response to pathogens. Herein, time-series mRNA-seq libraries and small RNA-seq libraries were constructed using pepper roots from the resistant line CM334 and the susceptible line EC01 inoculated with P. capsici at 0, 6, 24, and 48 h post-inoculation, respectively. For mRNA-seq analysis, a total of 2159 and 2971 differentially expressed genes (DEGs) were identified in CM334 and EC01, respectively. For miRNA-seq analysis, 491 pepper miRNAs were identified, including 330 known miRNAs and 161 novel miRNAs. Among them, 69 and 88 differentially expressed miRNAs (DEMs) were identified in CM334 and EC01, respectively. Examination of DEMs and their targets revealed 22 regulatory networks, predominantly featuring up-regulated miRNAs corresponding to down-regulated target genes. Notably, these DEM-DEG regulatory networks exhibited significant overlap between CM334 and EC01, suggesting that they might contribute to pepper's basal defense against P. capsici. Furthermore, five selected DEMs (miR166, miR1171, miR395, miR530 and miRN2) and their target genes underwent qRT-PCR validation, confirming a consistent negative correlation in the expression patterns of miRNAs and their targets. This comprehensive analysis provides novel insights into the regulatory networks of miRNAs and their targets, offering valuable contributions to our understanding of pepper's defense mechanisms against P. capsici.

Transgenerational toxicity induced by maternal AFB1 exposure in Caenorhabditis elegans associated with underlying epigenetic regulations

Aflatoxin B1 (AFB1), usually seriously contaminates in grain and oil foods or feed, displayed significant acute and chronic toxic effects in human and animal populations. However, little is known about the transgenerational toxic effects induced by a maternal AFB1 intake at a lower dose on offspring. In our study, only parental wild-type Caenorhabditis elegans was exposed to AFB1 (0–8 μg/ml) and the following three filial generations were grown on AFB1-free NGM. Results showed that the toxic effects of AFB1 on the growth (body length) and reproduction (brood size, generation time and morphology of gonad arm) can be transmitted through generations. Moreover, the levels of MMP and ATP were irreversibly inhibited in the filial generations. By using RNomics and molecular biology techniques, we found that steroid biosynthesis, phagosome, valine/leucine/isoleucine biosynthesis and oxidative phosphorylation (p < 0.05) were the core signaling pathways to exert the transgenerational toxic effects on nematodes. Also, notably increased histone methylation level at H3K36me3 was observed in the first generation. Taken together, our study demonstrated that AFB1 has notable transgenerational toxic effects, which were resulted from the complex regulatory network of various miRNAs, mRNAs and epigenetic modification in C. elegans.