ResearchPad - database-analysis https://www.researchpad.co Default RSS Feed en-us © 2020 Newgen KnowledgeWorks <![CDATA[Glutathione Peroxidase 8 as a Prognostic Biomarker of Gastric Cancer: An Analysis of The Cancer Genome Atlas (TCGA) Data]]> https://www.researchpad.co/article/elastic_article_15119 Glutathione peroxidase 8 (GPX8) has previously been shown to play a role in Keshan disease. In the present study, we explored the prognostic relevance of GPX8 expression in patients with gastric cancer (GC) based upon The Cancer Genome Atlas (TCGA) data.Material/MethodsWe assessed the relationship between the expression of GPX8 and clinicopathological findings in GC patients via logistic regression analyses, Kruskal-Wallis tests, and Wilcoxon signed-rank tests. We further assessed the prognostic relevance of specific variables using Kaplan-Meier and Cox regression analyses. We lastly conducted gene set enrichment analyses (GSEA).ResultsWe detected a significant association between elevated GPX8 levels and more advanced GC tumor stage (OR=5.92 for I vs. IV), as well as more advanced T (OR=22.91 for T1 vs. T4) and N classification (OR=1.82 for N0 vs. N3). We found worse prognosis in patients expressing high levels of GPX8 relative to those with lower expression of this gene (P=0.021). In a univariate analysis, we found high GPX8 expression was strongly correlated with worse OS (hazard ratio [HR]: 1.05; 95% confidence interval [CI]: 1.01–1.08; P=0.018), and multivariate analysis confirmed that GPX8 expression independently predicts GC patient OS (HR: 1.04; CI: 1.00–1.08, P=0.041). GSEA revealed that elevated GPX8 expression was associated with enrichment of pathways consistent with MAPK signaling, JAK/STAT signaling, TGF-β signaling, melanoma, and basal cell carcinoma.ConclusionsThe expression of GPX8 may have prognostic relevance, being positively associated with worse OS in GC patients. ]]> <![CDATA[Comprehensive Analysis of Fibroblast Growth Factor Receptor (FGFR) Family Genes in Breast Cancer by Integrating Online Databases and Bioinformatics]]> https://www.researchpad.co/article/elastic_article_11864 Fibroblast growth factor receptors (FGFRs) play vital roles in the development and progression of human cancers. This study aimed to comprehensively understand the prognostic performances of FGFR1–4 expression in breast cancer (BC) by mining databases.Material/MethodsThe levels of FGFR1–4 expression in BC were analyzed by online databases, GEPIA (Gene Expression Profiling Interactive Analysis) and UALCAN. Survival analysis of FGFR1–4 was carried out by Kaplan-Meier plotter. GSE74146 was downloaded from Gene Expression Omnibus (GEO) and analyzed by GEO2R to screen the differentially expressed genes (DEGs) between FGFR2-silenced BC cells and control. Over-presentation for DEGs were done by Enrichr tool. Networks of DEGs were obtained by using Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape software. Hub genes were identified by cytoHubba Cytoscape plugin.ResultsThe online databases showed that FGFR1 was significantly downregulated whereas FGFR3 was upregulated in BC. Kaplan-Meier plotter demonstrated the upregulation of both FGFR1 and FGFR3 indicated favorable relapse free survival (RFS) whereas FGFR4 overexpression predicted unfavorable overall survival (OS) in BC patients. Importantly, our results showed FGFR2 overexpression robustly predicted favorable OS and RFS in BC. Further bioinformatics analysis of GSE74146 suggested FGFR2 mainly participated in regulating degradation and organization of the extracellular matrix and signaling of retinoic acid. Moreover, CXCL8, CD44, MMP9, and BMP7 were identified as crucial FGFR2-related hub genes.ConclusionsOur study comprehensively analyzed the prognostic values of FGFR1–4 expression in BC and proposed FGFR2 might serve as a promising biomarker. However, the underlying mechanisms remain to be elucidated. ]]> <![CDATA[Epidemiological Analysis of the First 1389 Cases of COVID-19 in Poland: A Preliminary Report]]> https://www.researchpad.co/article/elastic_article_11695 The World Health Organization has declared COVID-19 a global pandemic. This paper presents an epidemiological analysis of the first phase of the COVID-19 epidemic in Poland.Material/MethodsThis cross-sectional study was carried out between 3 and 27 March 2020 on a sample of 1389 laboratory-confirmed COVID-19 cases in Poland. Data were obtained from epidemiological reports collected by the Chief Sanitary Inspectorate. Analysis includes the number of COVID-19 cases, number of deaths, number of hospitalizations, number of people quarantined, and number of laboratory tests performed.ResultsThe first case was confirmed on 4 March 2020. Over 24 days after the first case, the total number of confirmed infections rose to 1389 (34,000 laboratory tests were performed). The highest incidence rates (over 5 per 100,000) were observed in the 2 central administrative regions (Mazowieckie and Łódzkie) and in the south-western region of Dolnośląskie, which borders the Czech Republic and Germany. Based on available data about age and sex, a clearly higher incidence was observed in the 20–29 years (4.0 per 100,000), 40–49 years (4.1 per 100,000), and 50–59 years (4.3 per 100,000) age groups. In the period analyzed (24 days), there were 16 confirmed deaths (average age 65.5 years; 81.2% males).ConclusionsThe proportion of women and men with confirmed COVID-19 infection was similar to the sex ratio in the general population. Infections were relatively less common in those aged under 20 years. The largest numbers of confirmed cases were detected in 3 of the 4 largest cities, each of which has an international airport. ]]> <![CDATA[An Integrated Network Analysis of mRNA and Gene Expression Profiles in Parkinson’s Disease]]> https://www.researchpad.co/article/N5f81d296-0ab2-4cb3-b510-8679a1c9ab50

Background

Parkinson’s disease (PD) is a degenerative neurologic disease. This study aimed to undertake bioinformatics analysis using the publicly available Gene Expression Omnibus (GEO) database to integrate mRNA expression data from patients with PD and to compare differentially expressed genes (DEGs) in tissue from the substantia nigra and whole blood from patients with PD and normal controls.

Material/Methods

Integrated network analysis included GEO datasets to identify DEGs in the substantia nigra and whole blood of patients with PD. Bioinformatics analysis was used to identify the roles of the DEGs and included the development of protein–protein interaction (PPI) networks and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment. Expression levels of DEGs were validated using GSE100054.

Results

In patients with PD, there were 1,076 upregulated DEGs and 1,075 down-regulated DEGs in the substantia nigra tissue, and 699 upregulated and 930 down-regulated DEGs in whole blood samples. The apoptotic process, the mitogen-activated protein kinase (MAPK) signaling pathway, the Wnt signaling pathway, and the Notch signaling pathway were significantly enriched in DEGs in the substantia nigra in PD. In both the substantia nigra and whole blood, the most common DEGs were significantly enriched in lysosomes, PD, Alzheimer’s disease, Huntington’s disease. SORT1 and CRYAB were the hub proteins in the network of the substantia nigra; PSMA1 and SDHA were the hub proteins in the network of whole blood in PD.

Conclusions

DEGs, including SORT1, CRYAB, PSMA1, and SDHA may have roles in the pathogenesis of PD through the MAPK, Wnt, and Notch signaling pathways.

]]>
<![CDATA[Secreted Phosphoprotein 1 (SPP1) and Fibronectin 1 (FN1) Are Associated with Progression and Prognosis of Esophageal Cancer as Identified by Integrated Expression Profiles Analysis]]> https://www.researchpad.co/article/N205b830c-6b80-4119-97c4-b5b62808cd81

Background

Esophageal cancer is a malignant tumor with a complex pathogenesis and a poor 5-year survival rate, which encourages researchers to explore its molecular mechanisms deeper to improve the prognosis.

Material/Methods

DEGs were from 4 Gene Expression Omnibus (GEO) databases (GSE92396, GSE20347, GSE23400, and GSE45168) including 87 esophageal tumor samples and 84 normal samples. We performed Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, Protein-Protein interaction (PPI) analysis, and GeneMANIA to identify the DEGs. Gene set enrichment analysis (GSEA) and Kaplan-Meier survival analyses were performed.

Results

There was an overlapping subset consisting of 120 DEGs that was present in all esophageal tumor samples. The DEGs were enriched in extracellular matrix (ECM)-receptor interaction, as well as focal adhesion and transcriptional mis-regulation in cancer. The 2 most crucial regulatory pathways in esophageal cancer were the amebiasis pathway and the PI3K-Akt signaling pathway. Secreted phosphoprotein 1 (SPP1) and fibronectin 1 (FN1) were selected and verified in an independent cohort and samples using the TCGA and GTEx projects. Gene set enrichment analysis (GSEA) showed that proteasome and nucleotide excision repair were 2 most differentially enriched pathways in the SPP1 high-expression phenotype, and ECM-receptor interaction and focal adhesion in FN1 high-expression phenotype. Kaplan-Meier survival analysis showed that SPP1 and FN1 were significantly positively related to overall survival and had the potential to predict patient relapse.

Conclusions

Our analysis is the first to show that SPP1 and FN1 might work as biological markers of progression and prognosis in esophageal carcinoma (ESCA).

]]>
<![CDATA[Systemic Analysis of the Prognosis-Related RNA Alternative Splicing Signals in Melanoma]]> https://www.researchpad.co/article/N60724ea7-d26d-4486-958d-c8b483626ab2

Background

Alternative splicing (AS), the mechanism underlying the occurrence of protein diversity, may result in cancer genesis and development when it becomes out of control, as suggested by a growing number of studies. However, systemically analyze of AS events at the genome-wide level for skin cutaneous melanoma (SKCM) is still in a preliminary phase. This study aimed to systemically analyze the bioinformatics of the AS events at a genome-wide level using The Cancer Genome Atlas (TCGA) SKCM data.

Material/Methods

The SpliceSeq tool was used to analyze the AS profiles for SKCM clinical specimens from the TCGA database. The association between AS events and overall survival was analyzed by Cox regression analysis. AS event intersections and a gene interaction network were established by UpSet plot. A multivariate survival model was used to establish a feature genes prognosis model.

Results

A total of 103 SKCM patients with full clinical parameters available were included in this study. We established an AS network that investigated the relationship between AS events and clinical prognosis information. Furthermore, 4 underlying feature genes of SKCM (MCF2L, HARS, TFR2, and RALGPS1) were found in the AS network. We performed function analysis as well as correlation analysis of AS events with gene expression. Using the multivariate survival model, we further confirmed the 4 genes that impacted the classifying SKCM prognosis at the level of AS events as well as gene expression, especially in wild-type SKCM.

Conclusion

AS events could be ideal indicators for SKCM prognosis. The key feature gene MCF2L played an important role in wild-type SKCM.

]]>