Cargando…
Identification of key genes and pathways at the downstream of S100PBP in pancreatic cancer cells by integrated bioinformatical analysis
BACKGROUND: The aim of the present study was to identify key genes and pathways downstream of S100PPBP in pancreatic cancer cells. METHODS: The microarray datasets GSE35196 (S100PBP knockdown) and GSE35198 (S100PBP overexpression) were downloaded from the Gene Expression Omnibus (GEO). Differentiall...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
AME Publishing Company
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8799081/ https://www.ncbi.nlm.nih.gov/pubmed/35116411 http://dx.doi.org/10.21037/tcr-20-2531 |
_version_ | 1784641982152310784 |
---|---|
author | Lu, Yu-Jie Yang, Yi Hu, Ting-Hui Duan, Wei-Ming |
author_facet | Lu, Yu-Jie Yang, Yi Hu, Ting-Hui Duan, Wei-Ming |
author_sort | Lu, Yu-Jie |
collection | PubMed |
description | BACKGROUND: The aim of the present study was to identify key genes and pathways downstream of S100PPBP in pancreatic cancer cells. METHODS: The microarray datasets GSE35196 (S100PBP knockdown) and GSE35198 (S100PBP overexpression) were downloaded from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were obtained separately from GEO2R, and heatmaps showing clustering analysis of DEGs were generated using R software. Gene Ontology and pathway enrichment analyses were performed for identified DEGs using the Database for Annotation, Visualization, and Integrated Discovery and Kyoto Encyclopedia of Genes and Genomes, respectively. A protein-protein interaction (PPI) network was created using the Search Tool for the Retrieval of Interacting Genes and Cytoscape software. Relevant expression datasets of key identified genes were downloaded from The Cancer Genome Atlas, and overall survival (OS) analysis was performed with R software. Finally, Gene Expression Profiling Interactive Analysis was used to evaluate the expression of key DEGs in pancreatic cancer tissues. RESULTS: A total of 34 DEGs (11 upregulated and 23 downregulated) were screened out from the two datasets. Gene Ontology enrichment analysis revealed that the identified DEGs were mainly functionally enriched in ATPase activity, production of siRNA involved in RNA interference, and production of miRNAs involved in gene silencing by miRNA. The pathway enrichment analysis of the identified DEGs showed enrichment mainly in apoptosis, non-homologous end-joining, and miRNA pathways in cancer. The protein–protein interaction network was composed of 21 nodes and 30 edges. After survival analysis and gene expression analysis, 4 genes associated with poor prognosis were selected, including LMNB1, PRKRA, SEPT2, and XRCC5. CONCLUSIONS: LMNB1, PRKRA, SEPT2, and XRCC5 could be key downstream genes of the S100PBP gene in the inhibition of pancreatic cancer cell adhesion. |
format | Online Article Text |
id | pubmed-8799081 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | AME Publishing Company |
record_format | MEDLINE/PubMed |
spelling | pubmed-87990812022-02-02 Identification of key genes and pathways at the downstream of S100PBP in pancreatic cancer cells by integrated bioinformatical analysis Lu, Yu-Jie Yang, Yi Hu, Ting-Hui Duan, Wei-Ming Transl Cancer Res Original Article BACKGROUND: The aim of the present study was to identify key genes and pathways downstream of S100PPBP in pancreatic cancer cells. METHODS: The microarray datasets GSE35196 (S100PBP knockdown) and GSE35198 (S100PBP overexpression) were downloaded from the Gene Expression Omnibus (GEO). Differentially expressed genes (DEGs) were obtained separately from GEO2R, and heatmaps showing clustering analysis of DEGs were generated using R software. Gene Ontology and pathway enrichment analyses were performed for identified DEGs using the Database for Annotation, Visualization, and Integrated Discovery and Kyoto Encyclopedia of Genes and Genomes, respectively. A protein-protein interaction (PPI) network was created using the Search Tool for the Retrieval of Interacting Genes and Cytoscape software. Relevant expression datasets of key identified genes were downloaded from The Cancer Genome Atlas, and overall survival (OS) analysis was performed with R software. Finally, Gene Expression Profiling Interactive Analysis was used to evaluate the expression of key DEGs in pancreatic cancer tissues. RESULTS: A total of 34 DEGs (11 upregulated and 23 downregulated) were screened out from the two datasets. Gene Ontology enrichment analysis revealed that the identified DEGs were mainly functionally enriched in ATPase activity, production of siRNA involved in RNA interference, and production of miRNAs involved in gene silencing by miRNA. The pathway enrichment analysis of the identified DEGs showed enrichment mainly in apoptosis, non-homologous end-joining, and miRNA pathways in cancer. The protein–protein interaction network was composed of 21 nodes and 30 edges. After survival analysis and gene expression analysis, 4 genes associated with poor prognosis were selected, including LMNB1, PRKRA, SEPT2, and XRCC5. CONCLUSIONS: LMNB1, PRKRA, SEPT2, and XRCC5 could be key downstream genes of the S100PBP gene in the inhibition of pancreatic cancer cell adhesion. AME Publishing Company 2021-02 /pmc/articles/PMC8799081/ /pubmed/35116411 http://dx.doi.org/10.21037/tcr-20-2531 Text en 2021 Translational Cancer Research. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/. |
spellingShingle | Original Article Lu, Yu-Jie Yang, Yi Hu, Ting-Hui Duan, Wei-Ming Identification of key genes and pathways at the downstream of S100PBP in pancreatic cancer cells by integrated bioinformatical analysis |
title | Identification of key genes and pathways at the downstream of S100PBP in pancreatic cancer cells by integrated bioinformatical analysis |
title_full | Identification of key genes and pathways at the downstream of S100PBP in pancreatic cancer cells by integrated bioinformatical analysis |
title_fullStr | Identification of key genes and pathways at the downstream of S100PBP in pancreatic cancer cells by integrated bioinformatical analysis |
title_full_unstemmed | Identification of key genes and pathways at the downstream of S100PBP in pancreatic cancer cells by integrated bioinformatical analysis |
title_short | Identification of key genes and pathways at the downstream of S100PBP in pancreatic cancer cells by integrated bioinformatical analysis |
title_sort | identification of key genes and pathways at the downstream of s100pbp in pancreatic cancer cells by integrated bioinformatical analysis |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8799081/ https://www.ncbi.nlm.nih.gov/pubmed/35116411 http://dx.doi.org/10.21037/tcr-20-2531 |
work_keys_str_mv | AT luyujie identificationofkeygenesandpathwaysatthedownstreamofs100pbpinpancreaticcancercellsbyintegratedbioinformaticalanalysis AT yangyi identificationofkeygenesandpathwaysatthedownstreamofs100pbpinpancreaticcancercellsbyintegratedbioinformaticalanalysis AT hutinghui identificationofkeygenesandpathwaysatthedownstreamofs100pbpinpancreaticcancercellsbyintegratedbioinformaticalanalysis AT duanweiming identificationofkeygenesandpathwaysatthedownstreamofs100pbpinpancreaticcancercellsbyintegratedbioinformaticalanalysis |