Identification of the targets of hematoporphyrin derivative in lung adenocarcinoma using integrated network analysis.
ATPases Associated with Diverse Cellular Activities
/ drug effects
Adenocarcinoma of Lung
/ drug therapy
Cell Line, Tumor
Cluster Analysis
DNA-Binding Proteins
/ drug effects
Flow Cytometry
Gene Expression Regulation, Neoplastic
Gene Regulatory Networks
/ genetics
HSP90 Heat-Shock Proteins
/ drug effects
Hematoporphyrin Derivative
/ pharmacology
Humans
Lung Neoplasms
/ drug therapy
MicroRNAs
/ metabolism
Protein Inhibitors of Activated STAT
/ drug effects
Ribosomal Protein L3
Ribosomal Proteins
/ drug effects
Sequence Analysis, RNA
Small Ubiquitin-Related Modifier Proteins
/ drug effects
Transcription Factors
Hematoporphyrin derivative
Integrated network
Lung adenocarcinoma
Protein–protein interaction network
X-ray
Journal
Biological research
ISSN: 0717-6287
Titre abrégé: Biol Res
Pays: England
ID NLM: 9308271
Informations de publication
Date de publication:
04 Feb 2019
04 Feb 2019
Historique:
received:
07
09
2018
accepted:
16
01
2019
entrez:
6
2
2019
pubmed:
6
2
2019
medline:
1
3
2019
Statut:
epublish
Résumé
Hematoporphyrin derivative (HPD) has a sensibilization effect in lung adenocarcinoma. This study was conducted to identify the target genes of HPD in lung adenocarcinoma. RNA sequencing was performed using the lung adenocarcinoma cell line A549 after no treatment or treatment with X-ray or X-ray + HPD. The differentially expressed genes (DEGs) were screened using Mfuzz package by noise-robust soft clustering analysis. Enrichment analysis was carried out using "BioCloud" online tool. Protein-protein interaction (PPI) network and module analyses were performed using Cytoscape software. Using WebGestalt tool and integrated transcription factor platform (ITFP), microRNA target and transcription factor (TF) target pairs were separately predicted. An integrated regulatory network was visualized with Cytoscape software. A total of 815 DEGs in the gene set G1 (continuously dysregulated genes along with changes in processing conditions [untreated-treated with X-ray-X-ray + treated with HPD]) and 464 DEGs in the gene set G2 (significantly dysregulated between X-ray + HPD-treated group and untreated/X-ray-treated group) were screened. The significant module identified from the PPI network for gene set G1 showed that ribosomal protein L3 (RPL3) gene could interact with heat shock protein 90 kDa alpha, class A member 1 (HSP90AA1). TFs AAA domain containing 2 (ATAD2) and protein inhibitor of activated STAT 1 (PIAS1) were separately predicted for the genes in gene set G1 and G2, respectively. In the integrated network for gene set G2, ubiquitin-specific peptidase 25 (USP25) was targeted by miR-200b, miR-200c, and miR-429. RPL3, HSP90AA1, ATAD2, and PIAS1 as well as USP25, which is targeted by miR-200b, miR-200c, and miR-429, may be the potential targets of HPD in lung adenocarcinoma.
Sections du résumé
BACKGROUND
BACKGROUND
Hematoporphyrin derivative (HPD) has a sensibilization effect in lung adenocarcinoma. This study was conducted to identify the target genes of HPD in lung adenocarcinoma.
METHODS
METHODS
RNA sequencing was performed using the lung adenocarcinoma cell line A549 after no treatment or treatment with X-ray or X-ray + HPD. The differentially expressed genes (DEGs) were screened using Mfuzz package by noise-robust soft clustering analysis. Enrichment analysis was carried out using "BioCloud" online tool. Protein-protein interaction (PPI) network and module analyses were performed using Cytoscape software. Using WebGestalt tool and integrated transcription factor platform (ITFP), microRNA target and transcription factor (TF) target pairs were separately predicted. An integrated regulatory network was visualized with Cytoscape software.
RESULTS
RESULTS
A total of 815 DEGs in the gene set G1 (continuously dysregulated genes along with changes in processing conditions [untreated-treated with X-ray-X-ray + treated with HPD]) and 464 DEGs in the gene set G2 (significantly dysregulated between X-ray + HPD-treated group and untreated/X-ray-treated group) were screened. The significant module identified from the PPI network for gene set G1 showed that ribosomal protein L3 (RPL3) gene could interact with heat shock protein 90 kDa alpha, class A member 1 (HSP90AA1). TFs AAA domain containing 2 (ATAD2) and protein inhibitor of activated STAT 1 (PIAS1) were separately predicted for the genes in gene set G1 and G2, respectively. In the integrated network for gene set G2, ubiquitin-specific peptidase 25 (USP25) was targeted by miR-200b, miR-200c, and miR-429.
CONCLUSION
CONCLUSIONS
RPL3, HSP90AA1, ATAD2, and PIAS1 as well as USP25, which is targeted by miR-200b, miR-200c, and miR-429, may be the potential targets of HPD in lung adenocarcinoma.
Identifiants
pubmed: 30717818
doi: 10.1186/s40659-019-0213-z
pii: 10.1186/s40659-019-0213-z
pmc: PMC6360726
doi:
Substances chimiques
DNA-Binding Proteins
0
HSP90 Heat-Shock Proteins
0
HSP90AA1 protein, human
0
MIRN200 microRNA, human
0
MIRN429 microRNA, human
0
MicroRNAs
0
PIAS1 protein, human
0
Protein Inhibitors of Activated STAT
0
RPL3 protein, human
0
Ribosomal Protein L3
0
Ribosomal Proteins
0
Small Ubiquitin-Related Modifier Proteins
0
Transcription Factors
0
Hematoporphyrin Derivative
68335-15-9
ATAD2 protein, human
EC 3.6.1.3
ATPases Associated with Diverse Cellular Activities
EC 3.6.4.-
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
4Commentaires et corrections
Type : ErratumIn
Références
Nucleic Acids Res. 2015 Jan;43(Database issue):D1049-56
pubmed: 25428369
Lung Cancer. 2014 Apr;84(1):13-22
pubmed: 24524818
Nat Biotechnol. 2015 Mar;33(3):290-5
pubmed: 25690850
Neoplasia. 2016 May;18(5):282-293
pubmed: 27237320
J Clin Oncol. 2010 Nov 20;28(33):4953-60
pubmed: 20940188
Anticancer Res. 2014 Feb;34(2):753-7
pubmed: 24511009
Oncotarget. 2014 Nov 30;5(22):11737-51
pubmed: 25473889
Proc Soc Exp Biol Med. 1948 Jul-Aug;68(3):640
pubmed: 18884315
Arch Med Res. 2016 Feb;47(2):89-95
pubmed: 27131099
Theranostics. 2016 Oct 1;6(13):2295-2305
pubmed: 27877235
Exp Oncol. 2008 Dec;30(4):315-8
pubmed: 19112430
Mol Cancer. 2014 Jul 06;13:166
pubmed: 24997798
Cancer. 2001 Sep 15;92(6):1525-30
pubmed: 11745231
J Bioinform Comput Biol. 2005 Aug;3(4):965-88
pubmed: 16078370
PLoS One. 2014 Feb 11;9(2):e87780
pubmed: 24523873
Nucleic Acids Res. 2013 Jul;41(Web Server issue):W77-83
pubmed: 23703215
Nucleic Acids Res. 2013 Jan;41(Database issue):D808-15
pubmed: 23203871
Biochem Biophys Res Commun. 2014 Jul 18;450(1):154-9
pubmed: 24866238
PLoS One. 2008 Mar 05;3(3):e0001722
pubmed: 18320023
Biochim Biophys Acta. 2004 Sep 20;1704(2):59-86
pubmed: 15363861
J Cancer Res Clin Oncol. 2016 Apr;142(4):813-21
pubmed: 26581214
J Clin Laser Med Surg. 2002 Feb;20(1):3-7
pubmed: 11902352
Br J Cancer. 2004 Feb 9;90(3):646-51
pubmed: 14760379
Adv Nutr. 2016 Mar 15;7(2):418-9
pubmed: 26980827
Nucleic Acids Res. 2016 Jan 4;44(D1):D457-62
pubmed: 26476454
Bioinformatics. 2008 Oct 15;24(20):2416-7
pubmed: 18713790
Cancer Sci. 2010 Jan;101(1):180-7
pubmed: 19860842
Adv Exp Med Biol. 2016;893:43-57
pubmed: 26667338
Biomed Res Int. 2013;2013:957913
pubmed: 23509821
Nat Methods. 2012 Jan 30;9(2):145-51
pubmed: 22290186
Cancer. 2012 Jul 1;118(13):3365-76
pubmed: 22139708
Genome Res. 2012 Sep;22(9):1760-74
pubmed: 22955987
Cell Cycle. 2016;15(1):41-51
pubmed: 26636733
Genome Biol. 2013 Apr 25;14(4):R36
pubmed: 23618408
Int J Cancer. 2001 Aug 15;93(4):475-80
pubmed: 11477550
Nat Methods. 2012 Nov;9(11):1069-76
pubmed: 23132118
Cancer Res. 2012 May 1;72(9):2275-84
pubmed: 22406621
BMC Bioinformatics. 2003 Jan 13;4:2
pubmed: 12525261
Oncogene. 2010 Sep 16;29(37):5171-81
pubmed: 20581866
Bioinformatics. 2011 Mar 15;27(6):863-4
pubmed: 21278185
PLoS One. 2014 Jul 08;9(7):e101899
pubmed: 25003366