Identification of Serum Circulating MicroRNAs as Novel Diagnostic Biomarkers of Gastric Cancer

Gastric cancer (GC) is one of the leading causes of cancer-associated deaths worldwide. Due to the lack of typical symptoms and effective biomarkers for non-invasive screening, most patients develop advanced-stage GC by the time of diagnosis. Circulating microRNA (miRNA)-based panels have been reported as a promising tool for the screening of certain types of cancers. In this study, we performed differential expression analysis of miRNA profiles of plasma samples obtained from gastric cancer and non-cancer patients using two independent Gene Expression Omnibus (GEO) datasets: GSE113486 and GSE124158. We identified three miRNAs, hsa-miR-320a, hsa-miR-1260b, and hsa-miR-6515-5p, to distinguish gastric cancer cases from non-cancer controls. The three miRNAs showed an area under the curve (AUC) over 0.95 with high specificity (>93.0%) and sensitivity (>85.0%) in both the discovery datasets. In addition, we further validated these three miRNAs in two external datasets: GSE106817 [sensitivity: hsa-miR-320a (99.1%), hsa-miR-1260b (97.4%), and hsa-miR-6515-5p (92.2%); specificity: hsa-miR-320a (88.8%), hsa-miR-1260b (89.6%), and hsa-miR-6515-5p (88.7%); and AUC: hsa-miR-320a (96.3%), hsa-miR-1260b (97.4%), and hsa-miR-6515-5p (94.6%)] and GSE112264 [sensitivity: hsa-miR-320a (100.0%), hsa-miR-1260b (98.0%), and hsa-miR-6515.5p (98.0%); specificity: hsa-miR-320a (100.0%), hsa-miR-1260b (100.0%), and hsa-miR-6515.5p (92.7%); and AUC: hsa-miR-320a (1.000), hsa-miR-1260b (1.000), and hsa-miR-6515-5p (0.988)]. On the basis of these findings, the three miRNAs can be used as potential biomarkers for gastric cancer screening, which can provide patients with a higher chance of curative resection and longer survival.

Identification of brain endothelial cell-specific genes and pathways in ischemic stroke by integrated bioinformatical analysis

BACKGROUND

Ischemic stroke (IS) is a life-threatening condition with limited treatment options; thus, finding the potential key genes for novel therapeutic targets is urgently needed. This study aimed to explore novel candidate genes and pathways of brain microvessel endothelial cells (ECs) in IS by bioinformatics analysis.

MATERIALS AND METHODS

The gene expression profiles of brain tissues or brain ECs in IS mice were downloaded from the online gene expression omnibus (GEO) to obtain the differentially expressed genes (DEGs) by R software. Functional enrichment analyses were used to cluster the functions and signaling pathways of the DEGs, while DEG-associated protein-protein interaction network was performed to identify hub genes. The target microRNAs and competitive endogenous RNA networks of key hub genes were constructed by Cytoscape.

RESULTS

Totally 84 DEGs were obtained from 6 brain tissue samples and 4 brain vascular EC samples both from IS mice in the datasets GSE74052 and GSE137482, with significant enrichment in immune responses, such as immune system processes and T-cell activation. Eight hub genes filtered by Cytoscape were validated by two other GEO datasets, wherein key genes of interest were verified by reverse transcription-polymerase chain reaction using an in vitro ischemic model of EC cultures. Our data indicated that AURKA and CENPF might be potential therapeutic target genes for IS, and Malat1/Snhg12/Xist-miR-297b-3p-CENPF, as well as Mir17 hg-miR-34b-3p-CENPF, might be RNA regulatory pathways to control IS progression.

CONCLUSIONS

Our work identified two brain EC-specific expressed genes in IS, namely, AURKA and CENPF, as potential gene targets for IS treatment. In addition, we presented miR-297b-3p/miR-34b-3p-CENPF as the potential RNA regulatory axes to prevent pathogenesis of IS.

Development and Validation of Hub Genes for Adrenal Aldosterone-Producing Adenoma by Integrated Bioinformatics Analysis

OBJECTIVE

To develop and validate hub genes involving in the development and progression of primary aldosteronism (PA) and adrenal aldosterone-producing adenoma (APA).

MATERIALS AND METHODS

A total of four datasets of gene expression profiles related to APA were downloaded from GEO datasets. GSE60042 and GSE8514 were used to identify DEGs. Weighted gene co-expression network analysis (WGCNA) and protein-protein interaction (PPI) network module analysis were conducted. GO and KEGG enrichment analysis was performed. GSE10927 and GSE33371 were used for further external validation.

RESULTS

We identified a total of 892 DEGs from GSE60042 and 1167 DEGs from GSE8514. WGCNA analysis demonstrated that the blue module (255 genes) and turquoise module (303 genes) were significantly correlated with APA. PPI networks were then constructed. GO term enrichment analysis suggested that cellular divalent inorganic cation homeostasis, calcium ion homeostasis, collagen-containing extracellular matrix, transport vesicle and metal ion transmembrane transporter activity were the vital annotations. KEGG pathway analysis found that these genes were significantly enriched in neuroactive ligand-receptor interaction, calcium signaling pathway. Finally, we identified a total of 11 candidate genes involving in the development and progression of APA and PA. Besides, two independent datasets (GSE10927 and GSE33371) were used for external validation, and there were seven hub genes successfully verified, including C3, GRM3, AVPR1A, WFS1, PTGFR, NTSR2, and JUN.

CONCLUSION

These newly identified genes could contribute to the understanding of potential mechanism in APA and PA and might be promising targets for the treatment of APA and PA.

Key genes involved in nonalcoholic steatohepatitis improvement after bariatric surgery

Background

Nonalcoholic steatohepatitis (NASH) is the advanced stage of nonalcoholic fatty liver disease (NAFLD), one of the most prevalent chronic liver diseases. The effectiveness of bariatric surgery in treating NASH and preventing or even reversing liver fibrosis has been demonstrated in numerous clinical studies, but the underlying mechanisms and crucial variables remain unknown.

Methods

Using the GSE135251 dataset, we examined the gene expression levels of NASH and healthy livers. Then, the differentially expressed genes (DEGs) of patients with NASH, at baseline and one year after bariatric surgery, were identified in GSE83452. We overlapped the hub genes performed by protein-protein interaction (PPI) networks and DEGs with different expression trends in both datasets to obtain key genes. Genomic enrichment analysis (GSEA) and genomic variation analysis (GSVA) were performed to search for signaling pathways of key genes. Meanwhile, key molecules that regulate the key genes are found through the construction of the ceRNA network. NASH mice were induced by a high-fat diet (HFD) and underwent sleeve gastrectomy (SG). We then cross-linked the DEGs in clinical and animal samples using quantitative polymerase chain reaction (qPCR) and validated the key genes.

Results

Seven key genes (FASN, SCD, CD68, HMGCS1, SQLE, CXCL10, IGF1) with different expression trends in GSE135251 and GSE83452 were obtained with the top 30 hub genes selected by PPI. The expression of seven key genes in mice after SG was validated by qPCR. Combined with the qPCR results from NASH mice, the four genes FASN, SCD, HMGCS1, and CXCL10 are consistent with the biological analysis. The GSEA results showed that the 'cholesterol homeostasis' pathway was enriched in the FASN, SCD, HMGCS1, and SQLE high-expression groups. The high-expression groups of CD68 and CXCL10 were extremely enriched in inflammation-related pathways. The construction of the ceRNA network obtained microRNAs and ceRNAs that can regulate seven key genes expression.

Conclusion

In summary, this study contributes to our understanding of the mechanisms by which bariatric surgery improves NASH, and to the development of potential biomarkers for the treatment of NASH.

Multiple-microarray analysis for identification of key genes involved in diabetic nephropathy

The purpose of our study was to discover genes with significantly aberrant expression in diabetic nephropathy (DN) and to determine their potential mechanism. We acquired renal tubules, glomerulus and blood samples data from DN patients and controls from the GEO database. The differentially expressed genes (DEGs) in renal tubules, glomerulus and blood samples between DN patients and controls were studied. Based on these DEGs, we carried out the functional annotation and constructed protein-protein interaction (PPI) network. By comparing DN patients and controls of DEGs, we acquired the shared DGEs in renal tubules, glomerulus and blood samples of DN patients and controls. DN patients compared to controls, we obtained 3000 DEGs, 3064 DEGs, and 2296 DEGs in renal tubules, glomerulus and blood samples, respectively. The PPI networks of top 40 DEGs in renal tubules, glomerulus and blood samples was consisted of 229 nodes and 229 edges, 540 nodes and 606 edges, and 132 nodes and 124 edges, respectively. In total, 21 shared genes were finally found, including CASP3, DHCR24, CXCL1, GYPC, INHBA, LTF, MT1G, MUC1, NINJ1, PFKFB3, PPP1R3C, CCL5, SRSF7, PHLDA2, RBM39, WTAP, BASP1, PLK2, PDK2, PNPLA4, and SNED1. These genes may be associated with the DN process. Our study provides a basis to explore the potential mechanism and identify novel therapeutic targets for DN.