Bioinformatics approaches for improving seminal plasma proteome analysis

Noor, Zainab; Ranganathan, Shoba

doi:10.1016/j.theriogenology.2019.05.036

Cited by 2 publications

(2 citation statements)

References 117 publications

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“…It now stores inventory files from original research deposited by the scientific community, and data on more than 1 million samples are currently available in the public domain. GEO currently accepts data from a variety of assays, including serial analysis of gene expression (SAGE) and real-time polymerase chain reaction (RT-PCR) data in addition to some gene-expression matrix data, and data from various omics in the current field of disease research ( 18 ).…”

Section: Introductionmentioning

confidence: 99%

Screening and identification of susceptibility genes for osteosarcoma based on bioinformatics analysis

Liu¹,

Xu²,

Cao³

et al. 2023

Ann Transl Med

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Background Gene play an important role in malignant tumors. However, there is still insufficient research on genetic variations in osteosarcoma (OS) patients. Therefore, we aimed to analyze the gene expression profile of OS using bioinformatics and to explore the pathogenesis of OS at the molecular level. Methods The gene chip dataset of OS samples was downloaded from the Gene Expression Omnibus (GEO) database for screening differentially expressed genes (DEGs). The R language clusterProfiler software package was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for DEGs. The central node proteins of the protein interaction network were analyzed by the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, Cytoscape, and its plug-ins cytoHubba and NetworkAnalyzer to find the key genes. Results A total of 631 DEGs were obtained, including 362 upregulated genes and 269 downregulated genes. DEGs were mainly involved in the regulation of leukocyte chemotaxis and migration, vascular development, and other biological processes (BPs); mediation of receptor ligand activity, growth factor binding, growth factor activity, integrin binding, and other molecular functions (MFs); and were enriched in the extracellular matrix (ECM). Conclusions DEGs in the ECM and growth factors play a key role in the development of OS. The leukocyte transendothelial migration pathway and the PI3K-AKT pathway are closely related to OS, and the related molecular mechanism is worthy of further study.

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Section: Introductionmentioning

confidence: 99%

Screening and identification of susceptibility genes for osteosarcoma based on bioinformatics analysis

Liu¹,

Xu²,

Cao³

et al. 2023

Ann Transl Med

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“…There were differences between the positive and negative expression of P53 in Karnofsky performance scale (KPS) score, WHO grade, and age of patients. This indicated that the positive expression of P53 was higher than the negative expression in KPS, WHO grade, and age (P<0.05) (31)(32)(33). In addition, we analyzed whether there was a correlation between the expression of P53 and the prognostic molecule Ki-67.…”

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confidence: 99%

Expression analysis of human glioma susceptibility gene and P53 in human glioma and its clinical significance based on bioinformatics

Li¹,

Li²,

Zhang³

et al. 2023

Ann Transl Med

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Background The exact mechanism of glioblastoma multiforme (GBM) remains unclear. This study was to clarify the expression of P53 in glioma and its molecular mechanism, and to explore the possibility of P53 as a potential therapeutic target of glioma and its clinical application value, so as to provide a new theoretical basis for the treatment of glioma. Methods Firstly, a dataset was established to analyze the expression of P53 in different stages of glioma and its relationship with prognosis by using The Cancer Genome Atlas (TCGA) database, RNA-seq data, and survival data of glioma and normal control samples in gene expression profiling and interactive analysis (GEPIA). The genes co-expressed with P53 were screened out, their differential expression between glioma and normal control group was analyzed, and their functions were analyzed by enrichment analysis. The TGGA database was used for data verification and analysis. The correlation between P53 expression and clinicopathological parameters was analyzed. Kaplan-Meier survival analysis was used to analyze the relationship between P53 expression and overall survival (OS) and progression-free survival (PFS) of glioma patients, and Cox regression analysis was used to analyze the independent factors affecting OS and PFS of glioma patients. Results The results of TCGA data analysis were as follows: The expression level of P53 was different from that of different stages of glioma, namely, the expression level of P53 between grade II and grade III, grade III and grade IV, and grade II and grade IV were significantly different (P<0.05). The results of P53 gene-related survival analysis showed that KNL1 high expression and low expression were significantly different in OS, and the high expression group was associated with poor prognosis (P<0.05). Conclusions The P53 expression can be an effective biological indicator of poor prognosis of glioma.

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Bioinformatics approaches for improving seminal plasma proteome analysis

Cited by 2 publications

References 117 publications

Screening and identification of susceptibility genes for osteosarcoma based on bioinformatics analysis

Screening and identification of susceptibility genes for osteosarcoma based on bioinformatics analysis

Expression analysis of human glioma susceptibility gene and P53 in human glioma and its clinical significance based on bioinformatics

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