Prediction of Blood miRNA-mRNA Regulatory Network in Gastric Cancer

Bioinformatics analysis of effective biomarkers and immune infiltration in type 2 diabetes with cognitive impairment and aging

With the increasing prevalence of diabetes mellitus worldwide, type 2 diabetes mellitus (T2D) combined with cognitive impairment and aging has become one of the common and important complications of diabetes mellitus, which seriously affects the quality of life of the patients, and imposes a heavy burden on the patients' families and the society. Currently, there are no special measures for the treatment of cognitive impairment and aging in type 2 diabetes mellitus. Therefore, the search for potential biological markers of type 2 diabetes mellitus combined with cognitive impairment and aging is of great significance for future precisive treatment. We downloaded three gene expression datasets from the GEO database: GSE161355 (related to T2D with cognitive impairment and aging), GSE122063, and GSE5281 (related to Alzheimer's disease). Differentially expressed genes (DEGs) were identified, followed by gene set enrichment analysis (GSEA). A protein-protein interaction (PPI) network was constructed using the STRING database, and the top 15 hub genes were identified using the CytoHubba plugin in Cytoscape. Core genes were ultimately determined using three machine learning methods: LASSO regression, Support Vector Machine Recursive Feature Elimination (SVM-RFE), and Linear Discriminant Analysis (LDA). The diagnostic performance of these genes was assessed using ROC curve analysis and validated in an independent dataset (GSE5281). Regulatory genes related to ferroptosis were screened from the FerrDb database, and their biological functions were further explored through GO and KEGG enrichment analyses. Finally, the CIBERSORT algorithm was used to analyze immune cell infiltration, and the correlation between core genes and immune cell infiltration levels was calculated, leading to the construction of an mRNA-miRNA regulatory network. In the GSE161355 and GSE122063 datasets, 217 common DEGs were identified. GSEA analysis revealed their enrichment in the PI3K-PLC-TRK signaling pathway, TP53 regulation of metabolic genes pathway, Notch signaling pathway, among others. PPI network analysis identified 15 candidate core genes, and further selection using LASSO, LDA, and SVM-RFE machine learning algorithms resulted in 6 core genes: BCL6, TP53, HSP90AA1, CRYAB, IL1B, and DNAJB1. ROC curve analysis indicated that these genes had good diagnostic performance in the GSE161355 dataset, with TP53 and IL1B achieving an AUC of 0.9, indicating the highest predictive accuracy. BCL6, HSP90AA1, CRYAB, and DNAJB1 also had AUCs greater than 0.8, demonstrating moderate predictive accuracy. Validation in the independent dataset GSE5281 showed that these core genes also had good diagnostic performance in Alzheimer's disease samples (AUC > 0.6). Ferroptosis-related analysis revealed that IL1B and TP53 play significant roles in apoptosis and immune response. Immune cell infiltration analysis showed that IL1B is significantly positively correlated with infiltration levels of monocytes and NK cells, while TP53 is significantly negatively correlated with infiltration levels of follicular helper T cells. The construction of the miRNA-mRNA regulatory network suggested that miR-150a-5p might play a key role in the regulation of T2D-associated cognitive impairment and aging by TP53. This study, by integrating bioinformatics and machine learning methods, identified BCL6, TP53, HSP90AA1, CRYAB, IL1B, and DNAJB1 as potential diagnostic biomarkers for T2D with cognitive impairment and aging, with a particular emphasis on the significance of TP53 and IL1B in immune cell infiltration. These findings not only enhance our understanding of the molecular mechanisms linking type 2 diabetes to cognitive impairment and aging, providing new targets for early diagnosis and treatment, but also offer new directions and targets for basic research.

The clinical diagnostic value of plasma miR-592 and miR-217-3p levels in retinoblastoma

BACKGROUND

This study was designed to investigate the abnormal expression of plasma miR-592 and miR-217-3p in retinoblastoma (Rb) and explore the clinical diagnostic value of their expression levels for Rb.

METHODS

The 100 Rb patients who came to Nanchang Hongdu Hospital of Traditional Chinese Medicine from January 2018 to January 2019 were selected as the Rb group, and 100 healthy patients who came to the physical examination centre during the same period were selected as the control group. Real-time fluorescence quantitative PCR (qRT-PCR) was used to detect the expression levels of plasma miR-592 and miR-217-3p in all subjects; analyse the relationship between plasma miR-592 and miR-217-3p levels and the clinicopathological characteristics of Rb. Pearson correlation analysis evaluated the relationship between plasma miR-592 and miR-217-3p levels and overall survival.

RESULTS

Plasma levels of miR-592 and miR-217-3p in the Rb group were significantly higher than those in the control group (p<0.0001), and the expression of miR-592 was significantly correlated with family genetic history (p 0.0001), tumour bias (p=0.0081), lymph node metastasis (p=0.0048) and pathological grade (p=0.0025), and the expression of miR-217-3p was significantly related to family genetic history (p 0.0001), optic nerve infiltration (p 0.0001), lymph node metastasis (p=0.0090), and pathological grade (p 0.0001). The high expression of miR-592 and miR-217-3p presents a more serious pathological manifestation of Rb, and the overall survival of patients is significantly shortened with the increase of miR-592 (r=-0.2276, p=0.0052) and miR-217-3p levels (r=-0.6461, p 0.0001).

CONCLUSIONS

and miR-217-3p are highly expressed in the plasma of Rb patients, and their elevated levels present severe pathological manifestations of Rb and shortened overall survival, which is expected to become biomarkers for clinical diagnosis of Rb.

Possible Role of lncRNA MEG3-microRNA-21 and Endoplasmic Reticulum (ER) Stress Proteins in the Pathogenesis of Psoriasis Vulgaris

BACKGROUND

Psoriasis is a chronic inflammatory immune mediated disease arising from interaction between genetic risk variants and the environment. Maternally expressed gene3 (MEG3) is a long noncoding RNA (lncRNA) known for gene transcription regulation and inhibiting proliferation. MEG3 competes with microRNA (miRNA-21) influencing cell proliferation and apoptosis balance. Endoplasmic reticulum (ER) stress proteins promote cell survival via unfolded protein response (UPR) influenced by MEG3. We aimed to detect the possible role of MEG3, miRNA-21 and ER stress proteins in pathogenesis of psoriasis vulgaris.

METHODS

Human GRP78, ATF6, caspase3 tissue levels were assayed by Enzyme Linked Immunosorbent Assay (ELISA). Assessment of long non-coding MEG3 and miRNA 21 expressions was done by quantitative real time polymerase chain reaction (qRT-PCR).

RESULTS

Expression of MEG3 was significantly downregulated, while miRNA-21 was remarkably upregulated, ER stress proteins GRP78, ATF6, and caspase 3 all showed low levels in homogenized psoriatic lesions when compared to normal skin. miRNA 21 and MEG3 were identified as possible diagnostic markers for psoriasis vulgaris.

DISCUSSION

MEG3 is barely expressed in psoriatic lesions while miRNA-21 expression is remarkably elevated but when correlated to each other there was unexpected positive correlation. MEG3 and miRNA-21 were identified as possible diagnostic markers for psoriasis. Undifferentiated psoriatic lesions have very weak UPR.

Bioinformatics Analysis of the Mechanisms of Diabetic Nephropathy via Novel Biomarkers and Competing Endogenous RNA Network

Diabetic nephropathy (DN) is one of the common chronic complications of diabetes with unclear molecular mechanisms, which is associated with end-stage renal disease (ESRD) and chronic kidney disease (CKD). Our study intended to construct a competing endogenous RNA (ceRNA) network via bioinformatics analysis to determine the potential molecular mechanisms of DN pathogenesis. The microarray datasets (GSE30122 and GSE30529) were downloaded from the Gene Expression Omnibus database to find differentially expressed genes (DEGs). GSE51674 and GSE155188 datasets were used to identified the differentially expressed microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), respectively. The DEGs between normal and DN renal tissues were performed using the Linear Models for Microarray (limma) package. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to reveal the mechanisms of DEGs in the progression of DN. The protein-protein interactions (PPI) of DEGs were carried out by STRING database. The lncRNA-miRNA-messenger RNA (mRNA) ceRNA network was constructed and visualized via Cytoscape on the basis of the interaction generated through the miRDB and TargetScan databases. A total of 94 significantly upregulated and 14 downregulated mRNAs, 31 upregulated and 121 downregulated miRNAs, and nine upregulated and 81 downregulated lncRNAs were identified. GO and KEGG pathways enriched in several functions and expression pathways, such as inflammatory response, immune response, identical protein binding, nuclear factor kappa b (NF-κB) signaling pathway, and PI3K-Akt signaling pathway. Based on the analysis of the ceRNA network, five differentially expressed lncRNAs (DElncRNAs) (SNHG6, KCNMB2-AS1, LINC00520, DANCR, and PCAT6), five DEmiRNAs (miR-130b-5p, miR-326, miR-374a-3p, miR-577, and miR-944), and five DEmRNAs (PTPRC, CD53, IRF8, IL10RA, and LAPTM5) were demonstrated to be related to the pathogenesis of DN. The hub genes were validated by using receiver operating characteristic curve (ROC) and real-time PCR (RT-PCR). Our research identified hub genes related to the potential mechanism of DN and provided new lncRNA-miRNA-mRNA ceRNA network that contributed to diagnostic and potential therapeutic targets for DN.