Analysis of the gene-protein interaction network in glioma

LncRNA LINC01018/miR-942-5p/KNG1 axis regulates the malignant development of glioma in vitro and in vivo

AIMS

Since the inhibitory effect of KNG1 on glioma has been proved, this study further explores the regulation of the lncRNA/miRNA axis on KNG1 in glioma.

METHODS

The miRNAs that target KNG1 and the lncRNA that targets miR-942-5p were predicted by bioinformatics analysis and verified by experiments. The correlations between miR-942-5p and the survival of patients and between KNG1 and miR-942-5p were analyzed. After transfection, cell migration, invasion, proliferation, and cell cycle were detected through wound healing, Transwell, colony formation, and flow cytometry assays. A mouse subcutaneous xenotransplanted tumor model was established. The expressions of miR-942-5p, KNG1, LINC01018, and related genes were evaluated by quantitative real-time reverse transcription polymerase chain reaction (RT-qPCR), Western blot, or immunohistochemistry.

RESULTS

MiR-942-5p targeted KNG1, and LINC01018 sponged miR-942-5p. The high survival rate of patients was related to low miR-942-5p level. MiR-942-5p was highly expressed, whereas KNG1 was lowly expressed in glioma. MiR-942-5p was negatively correlated with KNG1. Silent LINC01018 or KNG1 and miR-942-5p mimic enhanced the migration, invasion, and proliferation of glioma cells, and regulated the expressions of metastasis-related and proliferation-related genes. LINC01018 knockdown and miR-942-5p mimic promoted glioma tumor growth in mice. The levels of miR-942-5p and KNG1 were decreased by LINC01018 knockdown, and LINC01018 expression was suppressed by miR-942-5p mimic. MiR-942-5p inhibitor, KNG1, and LINC01018 had the opposite effect to miR-942-5p mimic.

CONCLUSION

LINC01018/miR-942-5p/KNG1 pathway regulates the development of glioma cells in vitro and in vivo.

Gene biomarker prediction in glioma by integrating scRNA-seq data and gene regulatory network

Background

Although great efforts have been made to study the occurrence and development of glioma, the molecular mechanisms of glioma are still unclear. Single-cell sequencing technology provides a new perspective for researchers to explore the pathogens of tumors to further help make treatment and prognosis decisions for patients with tumors.

Methods

In this study, we proposed an algorithm framework to explore the molecular mechanisms of glioma by integrating single-cell gene expression profiles and gene regulatory relations. First, since there were great differences among malignant cells from different glioma samples, we analyzed the expression status of malignant cells for each sample, and then tumor consensus genes were identified by constructing and analyzing cell-specific networks. Second, to comprehensively analyze the characteristics of glioma, we integrated transcriptional regulatory relationships and consensus genes to construct a tumor-specific regulatory network. Third, we performed a hybrid clustering analysis to identify glioma cell types. Finally, candidate tumor gene biomarkers were identified based on cell types and known glioma-related genes.

Results

We got six identified cell types using the method we proposed and for these cell types, we performed functional and biological pathway enrichment analyses. The candidate tumor gene biomarkers were analyzed through survival analysis and verified using literature from PubMed.

Conclusions

The results showed that these candidate tumor gene biomarkers were closely related to glioma and could provide clues for the diagnosis and prognosis of patients with glioma. In addition, we found that four of the candidate tumor gene biomarkers (NDUFS5, NDUFA1, NDUFA13, and NDUFB8) belong to the NADH ubiquinone oxidoreductase subunit gene family, so we inferred that this gene family may be strongly related to glioma.

Long noncoding RNA (MEG3) in urinal exosomes functions as a biomarker for the diagnosis of Hunner-type interstitial cystitis (HIC)

BACKGROUND

Toll-like receptor-7 (TLR7) is functionally involved in the pathogenesis of Hunner-type interstitial cystitis (HIC). In addition, maternally expressed gene 3 (MEG3) is implicated in many urethral diseases. In this study, we aimed to verify the hypothesis that exosomal MEG3 in urine can be used as a novel diagnostic biomarker for HIC.

METHODS

Electron microscopy was utilized to observe the distribution of urinary exosomes between the case group and the control group. Receiver operating characteristic analysis was utilized to compare the diagnostic values of MEG3 and miR-19a-3p. Computational analysis and luciferase assay were conducted to identify the correlation between MEG3 and miR-19a-3p as well as between TLR7 and miR-19a-3p. In addition, real-time polymerase chain reaction and Western blot were performed to establish the signaling pathways implicated in the pathogenesis of HIC.

RESULTS

When age and gender distributions are excluded, urinary exosomes were equally distributed between case and control groups. The area under the curve of MEG3 was larger than that of miR-19a-3p, indicating that MEG3 has a better value in the diagnosis of HIC. In addition, patients with HIC showed elevated MEG3 expression and inhibited miR-19a-3p expression, thus establishing a negative correlation between MEG3 and miR-19a-3p. MEG3 and TLR7 were both identified as targets of miR-19a-3p, establishing a MEG3/miR-19a-3p/TLR7 signaling pathway, in which MEG3 enhances the expression of TLR7 via inhibiting the expression of miR-19a-3p.

CONCLUSION

MEG3 level was upregulated in patients with HIC. In addition, MEG3 downregulated miR-19a-3p expression while upregulating TLR7 expression. Furthermore, MEG3 contributes to the pathogenesis of HIC. Therefore, exosomal MEG3 in urine can be used as a biomarker for HIC diagnosis.

PASNet: pathway-associated sparse deep neural network for prognosis prediction from high-throughput data

Background

Predicting prognosis in patients from large-scale genomic data is a fundamentally challenging problem in genomic medicine. However, the prognosis still remains poor in many diseases. The poor prognosis may be caused by high complexity of biological systems, where multiple biological components and their hierarchical relationships are involved. Moreover, it is challenging to develop robust computational solutions with high-dimension, low-sample size data.

Results

In this study, we propose a Pathway-Associated Sparse Deep Neural Network (PASNet) that not only predicts patients’ prognoses but also describes complex biological processes regarding biological pathways for prognosis. PASNet models a multilayered, hierarchical biological system of genes and pathways to predict clinical outcomes by leveraging deep learning. The sparse solution of PASNet provides the capability of model interpretability that most conventional fully-connected neural networks lack. We applied PASNet for long-term survival prediction in Glioblastoma multiforme (GBM), which is a primary brain cancer that shows poor prognostic performance. The predictive performance of PASNet was evaluated with multiple cross-validation experiments. PASNet showed a higher Area Under the Curve (AUC) and F1-score than previous long-term survival prediction classifiers, and the significance of PASNet’s performance was assessed by Wilcoxon signed-rank test. Furthermore, the biological pathways, found in PASNet, were referred to as significant pathways in GBM in previous biology and medicine research.

Conclusions

PASNet can describe the different biological systems of clinical outcomes for prognostic prediction as well as predicting prognosis more accurately than the current state-of-the-art methods. PASNet is the first pathway-based deep neural network that represents hierarchical representations of genes and pathways and their nonlinear effects, to the best of our knowledge. Additionally, PASNet would be promising due to its flexible model representation and interpretability, embodying the strengths of deep learning. The open-source code of PASNet is available at https://github.com/DataX-JieHao/PASNet.

Identification of new progestogen-associated networks in mammalian ovulation using bioinformatics

Background

Progesterone plays an essential role in mammalian ovulation. Although much is known about this process, the gene networks involved in ovulation have yet to be established. When analyze the mechanisms of ovulation, we often need to determine key genes or pathways to investigate the reproduction features. However, traditional experimental methods have a number of limitations.

Results

Data, in this study, were acquired from GSE41836 and GSE54584 which provided different samples. They were analyzed with the GEO2R and 546 differentially expressed genes were obtained from two data sets using bioinformatics (absolute log₂ FC > 1, P < 0.05). This study identified four genes (PGR, RELN, PDE10A and PLA2G4A) by protein-protein interaction networks and pathway analysis, and their functional enrichments were associated with ovulation. Then, the top 25 statistical pathway enrichments related to hCG treatment were analyzed. Furthermore, gene network analysis identified certain interconnected genes and pathways involved in progestogenic mechanisms, including progesterone-mediated oocyte maturation, the MAPK signaling pathway, the GnRH signaling pathway and focal adhesion, etc. Moreover, we explored the four target gene pathways. q-PCR analysis following hCG and RU486 treatments confirmed the certain novel progestogenic-associated genes (GNAI1, PRKCA, CAV1, EGFR, RHOA, ZYX, VCL, GRB2 and RAP1A).

Conclusions

The results suggested four key genes, nine predicted genes and eight pathways to be involved in progestogenic networks. These networks provide important regulatory genes and signaling pathways which are involved in ovulation. This study provides a fundamental basis for subsequent functional studies to investigate the regulation of mammalian ovulation.

Electronic supplementary material

The online version of this article (10.1186/s12918-018-0577-7) contains supplementary material, which is available to authorized users.

Identification of Driver Genes and Key Pathways of Glioblastoma Shows JNJ-7706621 as a Novel Antiglioblastoma Drug

OBJECTIVE

The aim of this study is to identify novel targets of diagnosis, therapy, and prognosis for glioblastoma, as well as to verify the therapeutic effect of JNJ-7706621 regarding glioblastoma.

METHODS

The gene expression profiles of GSE42656, GSE50161, and GSE86574 were obtained respectively from the Gene Expression Omnibus database. The differentially expressed genes (DEGs) were identified with comparison between gene expression profiles of the glioblastoma tissues and normal tissues. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and protein-protein interaction (PPI) network analyses were performed. Quantitative reverse transcription polymerase chain reaction and survival curve analysis were also conducted to verify the correlation between expression of hub genes and prognosis. Moreover, in vitro, MTT assay, colony-forming assay, the scratch assay, and flow cytometry were performed to verify the therapeutic effect of JNJ-7706621.

RESULTS

AURKA, NDC80, KIF4A, and NUSAP1 were identified as hub genes after PPI network analysis. Differential expression of those genes was detected between human normal glial cells and glioblastoma cells by quantitative reverse transcription polymerase chain reaction (P < 0.05), and the survival curve analysis showed that the patients with low expression of gene AURKA, NDC80, KIF4A, and NUSAP1 had a significant favorable prognosis (P < 0.05). In vitro assays showed that JNJ-7706621 inhibited glioblastoma cellular viability, proliferation, and migration via inducing glioblastoma cells apoptosis.

CONCLUSIONS

AURKA, NDC80, KIF4A, and NUSAP1 were significantly more highly expressed in glioblastoma cells than in human normal glial cell. Patients with low expression of those 4 genes had a favorable prognosis. JNJ-7706621 was a potential drug in treatment of patients with glioblastoma.

Leucine-rich glioma inactivated 3: integrative analyses support its prognostic role in glioma

Background

Leucine-rich glioma inactivated 3 (LGI3) is a secreted protein member of LGI family. We previously reported that LGI3 was expressed in brain, adipose tissues and skin, where it played roles as a multifunctional cytokine. We postulated that LGI3 may be involved in cytokine network in cancers.

Aim

This study aimed to analyze differentially expressed genes in glioma tissues and glioma cohort data to investigate the prognostic role of LGI3 and its receptors.

Materials and methods

Expression microarray data from Gene Expression Omnibus and glioma cohort data were analyzed using bioinformatic tools for statistical analysis, protein–protein interactions, functional enrichment and pathway analyses and prognostic association analysis.

Results

We found that LGI3 and its receptors, ADAM22 and ADAM23, were significantly downregulated in glioma tissues. Eleven upregulated genes and two downregulated genes in glioma tissues were found to be the previously reported LGI3-regulated genes. Protein–protein interaction network analysis showed that 85% of the LGI3-regulated and glioma-altered genes formed a cluster of interaction network. Functional enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed the association of these genes with hypoxia responses, p53 and Akt signaling and various cancer-related pathways including glioma. Analysis of expression microarray data of glioma cohorts demonstrated that low expression levels of LGI3, ADAM22 and ADAM23 were significantly associated with poor prognosis of glioma.

Conclusion

These results propose that LGI3 and its receptors may play a prognostic role in glioma.

Long Noncoding RNA MEG3 Suppresses Glioma Cell Proliferation, Migration, and Invasion by Acting as a Competing Endogenous RNA of miR-19a

Glioma, with varying malignancy grades and histological subtypes, is the most common primary brain tumor in adults. Long noncoding RNAs (lncRNAs) are non-protein-coding transcripts and have been proven to play an important role in tumorigenesis. Our study aims to elucidate the combined effect of lncRNA maternally expressed gene 3 (MEG3) and microRNA-19a (miR-19a) in human glioma U87 and U251 cell lines. Real-time PCR revealed that MEG3 was downregulated and miR-19a was upregulated in malignant glioma tissues and cell lines. Bioinformatics analyses (TargetScan, miRanda, and starBase V2.0) showed that phosphatase and tensin homolog (PTEN) is a target of miR-19a with complementary binding sites in the 3'-UTR. As expected, luciferase results verified the putative target site and also revealed the complementary binding between miR-19a and MEG3. miR-19a represses the expression of PTEN and promotes glioma cell proliferation, migration, and invasion. However, MEG3 could directly bind to miR-19a and effectively act as a competing endogenous RNA (ceRNA) for miR-19a to suppress tumorigenesis. Our study is the first to demonstrate that lncRNA MEG3 suppresses glioma cell proliferation, migration, and invasion by acting as a ceRNA of miR-19a, which provides a novel insight about the pathogenesis of glioma.