Mining the glioma susceptibility genes in children from gene expression profiles and a methylation database

Prognostic biomarker prediction for glioma induced by heavy metals and their mixtures: An in-silico study

Although there is an association between heavy metals and glioma, the molecular mechanisms involved in glioma development remain unclear. Therefore, this study aimed to assess the molecular mechanisms implicated in glioma development induced by heavy metals and their mixtures using various methodologies and databases (CTD, Google Scholar, PubMed, ScienceDirect, SpringerLink, miRNAsong, GeneMANIA, Metascape, MIENTURNET, UALCAN). I found that heavy metals, particularly arsenic, mercury, lead, and cadmium, as well as their mixtures, have substantial influences on the etiology of gliomas. "glioblastoma signaling pathways," "integrated cancer pathway," "central carbon metabolism in cancer," "microRNAs in cancer," "p53 signaling pathway," "chemical carcinogenesis-DNA adducts," "glioma," "TP53 network," and "MAPK signaling pathway" were the predominant molecular pathways implicated in the glioma development induced by the studied heavy metals and their mixtures. Five genes (SOD1, CAT, GSTP1, PTGS2, TNF), two miRNAs (hsa-miR-26b-5p and hsa-miR-143-3p), and transcription factors (DR1 and HNF4) were identified as key components related to combined heavy metal and glioma development. Physical interactions were found to be the most common among the heavy metals and their mixtures studied (ranging from 45.2% to 77.6%). The expression level of SOD1 was significantly lower in glioblastoma multiforma samples compared to normal samples, whereas GSTP1 and TP53 expression levels were significantly higher. Brain lower and grade glioma patients who had higher levels of TP53, hsa-miR-25, hsa-miR-34, hsa-miR-222, and hsa-miR-143 had a reduced likelihood of survival. Our findings suggest that further priority should be given to investigating the impact of specific heavy metals or their mixtures on these molecular processes.

Identification of Signature Genes in the PD-1 Relative Gastric Cancer Using a Combined Analysis of Gene Expression and Methylation Data

BACKGROUND

The morbidity and mortality rates for gastric cancer (GC) rank second among all cancers, indicating the serious threat it poses to human health, as well as human life. This study aims to identify the pathways and genes as well as investigate the molecular mechanisms of tumor-related genes in gastric cancer (GC).

METHOD

We compared differentially expressed genes (DEGs) and differentially methylated genes (DMGs) in gastric cancer and normal tissue samples using The Cancer Genome Atlas (TCGA) data. The Kyoto Encyclopedia of Gene and Genome (KEGG) and the Gene Ontology (GO) enrichment analysis' pathway annotations were conducted on DMGs and DEGs using a clusterProfiler R package to identify the important functions, as well as the biological processes and pathways involved. The intersection of the two was chosen and defined as differentially methylated and expressed genes (DMEGs). For DMEGs, we used the principal component analysis (PCA) to differentiate gastric cancer from adjacent samples. The linear discriminant analysis method was applied to categorize the samples using DMEGs methylation data and DMEGs expression profiles data and was validated using the leave-one-out cross-validation (LOOCV) method. We plotted the ROC curve for the classification and calculated the AUC (area under the ROC curve) value for a more intuitive view of the classification effect. We also used the NetworkAnalyst 3.0 tool to analyze DMEGs, using DrugBank to acquire information on protein-drug interactions and generate a network map of gene-drug interactions.

RESULTS

We identified a total of 971 DMGs in 188 PD-1 negative and 187 PD-1 positive gastric cancer samples obtained from TCGA. The KEGG and GO enrichment analysis showed the involvement of the regulation of ion transmembrane transport, collagen-containing extracellular matrix, cell-cell junction, and peptidase regulator activity. We simultaneously obtained 1,189 DEGs, out of which 986 were downregulated, while 203 were upregulated in tumors. The enriched analysis of the GO's and KEGG's pathways indicated that the most significant pathways included an intestinal immune network for IgA production, Staphylococcus aureus infection, cytokine-cytokine receptor interaction, and viral protein interaction with cytokine and cytokine receptor, which have previously been linked with gastric cancer. The compound DB01830 can bind well to the active site of the LCK protein and shows good stability, thus making it a potential inhibitor of the LCK protein. To observe the relationship between DMEGs' expression and prognosis, we observed 10 genes, among which were TRIM29, TSPAN8, EOMES, PPP1R16B, SELL, PCED1B, IYD, JPH1, CEACAM5, and RP11-44K6.2. Their high expressions were related to high risks. Besides, those genes were validated in different internal and external validation sets.

CONCLUSION

These results may provide potential molecular biological therapy for PD-1 negative gastric cancer.

ITPKA induces cell senescence, inhibits ovarian cancer tumorigenesis and can be downregulated by miR-203

Overcoming senescence is a feature of ovarian cancer cells; however, the mechanisms underlying senescence regulation in ovarian cancer cells remain largely unknown. In this study, we found that ITPKA was downregulated in ovarian cancer samples, and the lower expression correlated with poor survival. Overexpression of ITPKA inhibited the anchorage-independent growth of ovarian cancer cells and induced senescence. However, knockdown of ITPKA promoted the anchorage-independent growth of ovarian cancer cells and inhibited senescence. Mechanistically, ITPKA was found to interact with MDM2, which stabilized P53, an essential regulator of senescence. Moreover, ITPKA was negatively regulated by miR-203, a microRNA that has been previously reported to be upregulated in ovarian cancer. Taken together, the results of this study demonstrated the tumor suppressive roles of ITPKA in ovarian cancer and provided a good explanation for the oncogenic roles of miR-203.

Identification of hub genes and small-molecule compounds in medulloblastoma by integrated bioinformatic analyses

Background

Medulloblastoma (MB) is the most common intracranial malignant tumor in children. The genes and pathways involved in the pathogenesis of MB are relatively unknown. We aimed to identify potential biomarkers and small-molecule drugs for MB.

Methods

Gene expression profile data sets were obtained from the Gene Expression Omnibus (GEO) database and the differentially expressed genes (DEGs) were identified using the Limma package in R. Functional annotation, and cell signaling pathway analysis of DEGs was carried out using DAVID and Kobas. A protein-protein interaction network was generated using STRING. Potential small-molecule drugs were identified using CMap.

Result

We identified 104 DEGs (29 upregulated; 75 downregulated). Gene ontology analysis showed enrichment in the mitotic cell cycle, cell cycle, spindle, and DNA binding. Cell signaling pathway analysis identified cell cycle, HIF-1 signaling pathway, and phospholipase D signaling pathway as key pathways. SYN1, CNTN2, FAIM2, MT3, and SH3GL2 were the prominent hub genes and their expression level were verified by RT-qPCR. Vorinostat, resveratrol, trichostatin A, pyrvinium, and prochlorperazine were identified as potential drugs for MB. The five hub genes may be targets for diagnosis and treatment of MB, and the small-molecule compounds are promising drugs for effective treatment of MB.

Conclusion

In this study we obtained five hub genes of MB, SYN1, CNTN2, FAIM2, MT3, and SH3GL2 were confirmed as hub genes. Meanwhile, Vorinostat, resveratrol, trichostatin A, pyrvinium, and prochlorperazine were identified as potential drugs for MB.

Bioinformatics analysis of microarray data to reveal the pathogenesis of diffuse intrinsic pontine glioma

Background

Diffuse intrinsic pontine glioma (DIPG) is the main cause of pediatric brain tumor death. This study was designed to identify key genes associated with DIPG.

Methods

The gene expression profile GSE50021, which consisted of 35 pediatric DIPG samples and 10 normal brain samples, was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) were identified by limma package. Functional and pathway enrichment analyses were performed by the DAVID tool. Protein–protein interaction (PPI) network, and transcription factor (TF)–microRNA (miRNA)–target gene network were constructed using Cytoscape. Moreover, the expression levels of several genes were validated in human glioma cell line U251 and normal glia HEB cells through real-time polymerase chain reaction (PCR).

Results

A total of 378 DEGs were screened (74 up-regulated and 304 down-regulated genes). In the PPI network, GRM1, HTR2A, GRM7 and GRM2 had higher degrees. Besides, GRM1 and HTR2A were significantly enriched in the neuroactive ligand–receptor interaction pathway, and calcium signaling pathway. In addition, TFAP2C was a significant down-regulated functional gene and hsa-miR-26b-5p had a higher degree in the TF-miRNA-target gene network. PCR analysis revealed that GRM7 and HTR2A were significantly downregulated while TFAP2C was upregulated in U251 cells compared with that in HEB cells (p < 0.001). GRM2 was not detected in cells.

Conclusions

GRM1 and HTR2A might function in DIPG through the neuroactive ligand–receptor interaction pathway and the calcium signaling pathway. Furthermore, the TFAP2C and hsa-miR-26b-5p might play important roles in the development and progression mechanisms of DIPG.