Diabetic peripheral neuropathy (DPN) is a common complication of diabetes mellitus (DM). It is not diagnosed or managed properly in the majority of patients because its pathogenesis remains controversial. In this study, human whole genome microarrays identified 2898 and 4493 differentially expressed genes (DEGs) in DM and DPN patients, respectively. A further KEGG pathway analysis indicated that DPN and DM share four pathways, including apoptosis, B cell receptor signaling pathway, endocytosis, and Toll-like receptor signaling pathway. The DEGs identified through comparison of DPN and DM were significantly enriched in MAPK signaling pathway, NOD-like receptor signaling pathway, and neurotrophin signaling pathway, while the “neurotrophin-MAPK signaling pathway” was notably downregulated. Seven DEGs from the neurotrophin-MAPK signaling pathway were validated in additional 78 samples, and the results confirmed the initial microarray findings. These findings demonstrated that downregulation of the neurotrophin-MAPK signaling pathway may be the major mechanism of DPN pathogenesis, thus providing a potential approach for DPN treatment.
Background/Aims: Diabetic peripheral neuropathy (DPN) is the most common complication of diabetes mellitus (DM). Because of its controversial pathogenesis, DPN is still not diagnosed or managed properly in most patients. Methods: In this study, human lncRNA microarrays were used to identify the differentially expressed lncRNAs in DM and DPN patients, and some of the discovered lncRNAs were further validated in additional 78 samples by quantitative realtime PCR (qRT-PCR). Results: The microarray analysis identified 446 and 1327 differentially expressed lncRNAs in DM and DPN, respectively. The KEGG pathway analysis further revealed that the differentially expressed lncRNA-coexpressed mRNAs between DPN and DM groups were significantly enriched in the MAPK signaling pathway. The lncRNA/mRNA coexpression network indicated that BDNF and TRAF2 correlated with 6 lncRNAs. The qRT-PCR confirmed the initial microarray results. Conclusion: These findings demonstrated that the interplay between lncRNAs and mRNA may be involved in the pathogenesis of DPN, especially the neurotrophin-MAPK signaling pathway, thus providing relevant information for future studies.
Studies have shown that microRNAs (miRNAs) play a vital role in tumor progression and patients’ prognosis. Therefore, we aimed to construct a miRNA model for forecasting the survival of hepatocellular carcinoma (HCC) patients. The gene expression data of 433 patients with HCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus public databases were remined by survival analysis and receptor manipulation characteristic curve (ROC). A prognostic model including six miRNAs (hsa‐mir‐26a‐1‐3p, hsa‐mir‐188‐5p, hsa‐mir‐212‐5p, hsa‐mir‐149‐5p, hsa‐mir‐105‐5p, and hsa‐mir‐132‐5p) were constructed in the training dataset (TCGA, n = 333). HCC patients were stratified into a high‐risk group and a low‐risk group with significantly different survival (median: 2.75 vs. 8.93 years, log‐rank test p < .001). Then we proved its performance of stratification in another independent dataset (GSE116182, median: 2.55 vs 6.96 years, log‐rank test p = .008). Cox regression analysis showed that the prognostic model was an independent prognostic indicator for HCC patients. Then time‐dependent ROC analyses were performed to test the prognostic ability of the model with that of TNM staging, we found the model had a better performance, especially at 5 years (AUC = 0.76). Functional prediction showed that the genes targeted by the six prognostic miRNAs in the prognostic model were highly expressed in the P53‐related pathway. In conclusion, we constructed a prognostic miRNA model that could indicate the survival of HCC patients.
Adult human mesenchymal stem cells have the potential to differentiate into osteoblast, which plays crucial roles in bone regeneration and repair. Some transcriptional factors (TFs), such as BMP-2 and RUNX2, have been demonstrated to control the differentiation processes. It is important to discover more key regulators in osteoblast differentiation. Recently, some studies found long noncoding RNAs (lncRNAs) participating in osteoblast differentiation, such as MALAT1, DANCR, and ANCR. In this study, we performed a network-based computational analysis to investigate the lncRNA-messenger RNA (mRNA) crosstalks via integrating microRNA (miRNA)-RNA interactions, gene coexpression, and protein-protein interactions.First, multiple topology analyses were performed to osteoblast-differentiationrelated lncRNA-mRNA network (ODLMN). Several lncRNAs with central topology structures were identified as key regulators. Results showed that these lncRNAs participated in osteoblast differentiation via phosphoinositide 3-kinase (PI3K), mitogen-activated protein kinase, and Ras signals. Previous studies have demonstrated that lncRNAs exert functions by involving in close modules. Second, after performing module searching in ODLMN, two functional modules were identified, which played crucial roles through involving in PI3K/protein kinase B, cyclic adenosine 3ʹ,5ʹ-monophosphate, and hypoxia-inducible factor 1 pathways. Third, a subset of core lncRNA-TF crosstalks that might form feedback loops to control the biological processes in osteoblast differentiation was identified. These core lncRNA-TF feedback loops showed more TF binding affinity than other lncRNAs.All these results can help us to uncover the molecular mechanism and provide new targets for bone regeneration and repair.
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