Identification of genes and pathways potentially related to PHF20 by gene expression profile analysis of glioblastoma U87 cell line
Liu et al. Cancer Cell Int
Identification of genes and pathways potentially related to PHF20 by gene expression profile analysis of glioblastoma U87 cell line
Tianlong Liu 0 2
Jitao Wang 1
Xiaohu Zhai 0 2
Wenxing Liu 0 2
Peijin Shang 0 2
Yi Ding 0 2
Aidong Wen 0 2
Yuwen Li 0 1 2
0 Department of Pharmacy, Xijing Hospital, Fourth Military Medical University , Xi'an , China
1 Department of Pharmacy, The First Affiliated Hospital
2 Department of Pharmacy, Xijing Hospital, Fourth Military Medical University , Xi'an , China
Background: Glioblastoma is the most common and aggressive brain tumor associated with a poor prognosis. Plant homeodomain finger protein 20 (PHF20) is highly expressed in primary human gliomas and its expression is associated with tumor grade. However, the molecular mechanism by which PHF20 regulates glioblastoma remains poorly understood. Methods: Genome wide gene expression analysis was performed to identify differentially expressed genes (DEGs) in U87 cells with PHF20 gene knockdown. Gene ontology (GO) and pathway enrichment analyses were performed to investigate the functions and pathways of DEGs. Pathway-net and signal-net analyses were conducted to identify the key genes and pathways related to PHF20. Results: Expression of 540 genes, including FEN1 and CCL3, were significantly altered upon PHF20 gene silencing. GO analysis results showed that DEGs were significantly enriched in small molecule metabolic and apoptotic processes. Pathway analysis indicated that DEGs were mainly involved in cancer and metabolic pathways. The MAPK, apoptosis and p53 signaling pathways were identified as the hub pathways in the pathway network, while PLCB1, NRAS and PIK3 s were hub genes in the signaling network. Conclusions: Our findings indicated that PHF20 is a pivotal upstream regulator. It affects the occurrence and development of glioma by regulating a series of tumor-related genes, such as FEN1, CCL3, PLCB1, NRAS and PIK3s, and activation of apoptosis signaling pathways. Therefore, PHF20 might be a novel biomarker for early diagnosis, and a potential target for glioblastoma therapies.
PHF20; Glioblastoma; U87 cell; Gene expression profile; Bioinformatics
Glioblastoma is the most common and lethal tumor of
the central nervous system [
], owing to poor
prognosis and repercussions on cognitive function [
advances in knowledge and therapies over several
decades, survival has not significantly improved, only 5.1%
of patients with glioblastoma have a 5-year survival rate
]. Thus, understanding the mechanisms that regulate
glioblastoma progression is critical to developing novel
therapies to improve patient outcome.
One particular protein of interest in glioblastoma
regulation is plant homeodomain finger protein 20 (PHF20).
PHF20 is a potent transcriptional activator, which binds
to methylated lysine residues on the histone tail [
PHF20 is overexpressed in various cancer tissues
compared to adjunct normal tissues, including advanced
small-cell lung cancers and advanced adenocarcinomas
]. Besides, PHF20 is highly expressed in primary human
glioma specimens [
], and functions as an immunogenic
antigen in glioblastoma [
]. Auto-antibodies against
PHF-20 were also detected in hepatocellular carcinoma
 and meduloblastoma [
]. PHF20 expression levels
have also been associated with the pathological tumor
grade of gliomas [
To elucidate the mechanisms regulated by PHF20 in
glioma as well as identify potential prognostic
biomarkers and targets for drug discovery and immunotherapy,
a microarray analysis was conducted to harness the
systematic gene expression profile related to genomic and
phenotypic information on glioblastoma in U87 cells.
Human glioblastoma cell lines U87, U251 and A172
originated from the Type Culture Collection of the
Chinese Academy of Sciences (Shanghai, China). Cell lines
LN229, HS683 and HEB were kindly provided by the
department of neurosurgery at The First Affiliated
Hospital of SooChow University. The cells were cultured in
Dulbecco’s modified Eagle’s medium (DMEM)
(Corning, NY, USA) containing 10% fetal bovine serum (FBS),
50 U/mL penicillin and 50 μg/mL streptomycin at 37 °C
with 5% CO2 incubator. The cell lines tested negative for
any mycoplasma contamination.
1 × 106 cultured cells were lysed with lysis buffer as
previously described [
]. Protein concentration was measured
using the BCA protein assay kit (Beyotime, Shanghai,
China). The same amount of protein was separated by 10%
sodium dodecyl sulfate–polyacrylamide (SDS-PAGE). A
polyvinylidene difluoride (PVDF) membrane (Millipore,
Bedford, MA, USA) was then used for electro-transfer.
The membrane was blocked with 5% nonfat milk at room
temperature for 1 h and incubated in primary antibodies
against PHF20 (1:500, Cell Signaling Technology, USA),
overnight at 4 °C. Subsequently, the membrane was
incubated in the appropriate secondary antibody at room
temperature for 1 h. In addition, β-actin was used as the
loading control. Protein bands were visualized through
enhanced chemiluminescence (ECL) reagent and detected
using BioImaging Systems (UVP, Upland, CA, USA). The
relative protein levels were calculated with Image J
software (National Institutes of Health, USA). All
experiments were performed in triplicate.
Lentivirus‑based shRNA infection
GFP-Lentiviral particles with PHF20-specific shRNA
(shPHF20) were purchased from Genechem Co., Ltd.
(Shanghai, China). The target sequence was TGACT
TGGTTGTATCAGAT. Random sequence, TTCTCCG
AACGTGTCACGT, was used as a negative control
(shCON). U87 cells in 6-well plates were infected with
lentiviral particles containing either shCON or shPHF20
to generate negative control (NC) or PHF20 knockdown
(KD) U87 cells, respectively. 12 h after infection, the
virus containing culture medium was replaced with fresh
DMEM supplemented with 10% FBS for 72 h. The
lentiviral infection efficiency was demonstrated by
observing the presence of green fluorescent protein within the
U87 cells using Olympus-IX71 fluorescence microscope
(Tokyo, Japan) and RT-PCR assay.
RNA extraction and quantitation
Total RNA was isolated using Trizol Reagent (Pufei,
Shanghai, China) according to the manufacturer’s
protocol. The RNA content was examined by identifying A260
and A280 values by using the Nanodrop 2000 (Thremo
Scientific, Waltham, MA, USA). RNA integrity was
assessed using a 2100 Bioanalyzer (Agilent Technologies)
and an RNA 6000 Nano Kit (Agilent Technologies).
RNA with A260/A280 nm values over than 1.9,
concentrations over 300 ng/μL and 28S/18S ratios over than
1.4 were used.
Quantitative real time PCR analysis
Total RNA isolated was processed for cDNA synthesis
using M-MLV reverse transcriptase (Promega
Corporation, Madison, WI, USA). cDNA was amplified by PCR in
StratageneMX3000p (Agilent Technologies, Santa Clara,
CA, USA) using SYBR Master Mixture (TaKaRa, Tokyo,
Japan). The expression levels of target genes were
standardized against the GAPDH, an internal control, and
calculated using the 2−△△Ct method. The sequences of the
primers used in PCRs are listed in Additional file 1. All
the assays were performed in triplicate.
Total RNA was processed for double-strand cDNA
synthesis, IVT and amplified RNA fragmentation using the
GeneChip 3′IVT Express Kit (Affymetrix, Santa Clara,
CA, USA) according to the manufacturer’s instruction.
RNA was then processed for hybridization at 45 °C for
17 h using The PrimeView™ Human Gene Expression
Array (Affymetrix), which contains 49,395 probes
covering more than 36,000 transcripts and variants. The arrays
were washed in the GeneChip Fluidic Station 450
(Affymetrix), and scanned by the GeneChip Scanner 3000
(Affymetrix). These microarray data have been deposited
in NCBI Gene Expression Omnibus (GEO) under
accession number GSE93680.
The raw data, expressed as CEL files, were normalized by
the log scale robust multi-array analysis (RMA) method
with the Expression Console software version 1.1
(Affymetrix). The screening standard for a distinctly
significant gene was an absolute fold change (|FC|) > 2 and a
corrected p < 0.05.
Gene ontology and pathway analysis
Gene ontology (GO) analysis was applied to analyze the
main function of differentially expression genes (DEGs)
according to the gene ontology, the key functional
classification of National Center of Biotechnology
Information (NCBI) [
]. Two-side Fisher’s exact test and χ2 tests
were used to classify the GO category. The false
discovery rate (FDR) [
] was calculated to correct the p value.
The standard of difference screening was FDR < 0.05.
Pathway analysis was used to find out the significant
pathway of the DEGs according to Kyoto Encyclopedia
of Genes and Genomes (KEGG) [
]. The data analysis
method and filter criteria were similar to the GO analysis.
Pathway-net analysis was built according to the
interaction among pathways of the KEGG database to directly
and systemically determine the interaction among the
significant pathways [
Signaling processes analysis
Based on the KEGG pathway map (http://www.genome.
] ,DEGs involved in key
pathways were labeled to clearly visualize the position of
specific genes in the signaling processes and determine the
regulatory role of DEGs involved in key pathways.
A gene–gene interaction network was constructed using
the source of the interaction database from KEGG. For
instance, if there is confirmative evidence that two genes
interact with each other, an interaction edge is assigned
between the two genes. The networks are stored and
presented as graphs, where nodes represent main genes
(protein, compound, etc.) and edges represent the
relationship between the nodes, such as activation or
phosphorylation. The algorithms and construction of the
network were achieved using published methods [
PHF20 is highly expressed in glioma cell lines
We first examined the expression of PHF20 in
various glioma cell lines. Expression of PHF20 protein was
significantly higher in glioma cell lines (A172, LN229,
U251, HS683 and U87) than that in human astrocyte cell
line HEB (p < 0.05, Fig. 1a). The relative expression level
of PHF20 in U87 cells was higher than in other glioma
cell lines (p < 0.05, Fig. 1b). PHF20 mRNA level was
verified by qPCR (Fig. 1c). Thus, the U87 cell line was used
to establish PHF20 knockdown cells in following
studies. The U87 cells were successfully infected with shCON
and shPHF20 72 h after transfection, resulting in a 72%
decrease in PHF20 expression (Fig. 1d).
Identification of DEGs that regulated by PHF20
A genome wide gene expression analysis was carried out
to identify DEGs between NC and KD U87 cells. A total
of 540 DEGs were identified, including 175 up-regulated
genes and 365 down-regulated genes (Fig. 2a, Additional
file 2). A subset of DEGs was verified by qPCR.
Expression of BBOF1, FBXO36 and SPARC increased, while the
expression of TPM4, FEN1, AGPS, BCAT1 and CCL3
decreased in PHF20 knockdown cells (Fig. 2b).
GO analysis of PHF20 associated DEGs
To determine the primary functions regulated by PHF20
in glioma cells, a comprehensive gene ontology (GO)
analysis was performed. A total of 540 DEGs were
assigned to 236 GO terms (111 were up-regulated and
125 were down-regulated, Additional file 3). The
analysis revealed that the up-regulated genes were significantly
involved in homophilic cell adhesion, protein transport,
and ER to Golgi vesicle-mediated transport.
Down-regulated genes were involved in small molecule metabolic
process, transcription (DNA-dependent), signal and
transduction (Fig. 3). Additionally, both up and
downregulated genes were enriched in small molecule
metabolic, transcription and apoptotic processes.
KEGG pathway analysis of PHF20 associated DEGs
Pathway enrichment analysis of DEGs was conducted on
the basis of the KEGG pathway database. This analysis
yielded 147 significant pathways, including 41
up-regulated pathways and 106 down-regulated pathways (see
Additional file 4). The most significant up-regulated and
down-regulated pathways were shown in Fig. 4. In
addition, pathways in cancer were identified as up-regulated
pathways as well as down-regulated pathways.
Pathway‑net analysis of significant PHF20‑regulated pathways
In order to define functional relationships among
pathways, an interaction net of the significant pathways
associated with PHF20 was built (Fig. 5). 46 key pathways
Fig. 2 Differentially expressed genes (DEGs) between the U87 cells infected with the negative control (NC) and those infected with shPHF20 (KD).
a Hierarchical clustering for DEGs. Green represents down-regulated genes, red represent up-regulated genes (p<0.05). b A subset of genes
differentially expressed between NC and KD cells were validated by qPCR. White bars represent the fold change in expression level between KD and NC
as indicated by microarray analysis. Black bars represent the mean fold change of gene expression calculated by qPCR method. GAPDH was used for
normalization. Each grey bar is the mean ± SEM of three independent biological replicates
and the 147 connections between them were
represented by nodes and edges, respectively. Primary
interactions occurred between the MAPK, apoptosis, cancer,
p53, ErbB, cytokine–cytokine receptor interaction, focal
adhesion, and JAK-STAT signaling pathways (Table 1).
Signaling processes analysis of key pathways
Signaling processes analysis, with the KEGG pathway
map, was performed using DEG expression data to
determine the regulatory role of DEGs involving in key
pathways. As shown in Fig. 6, the MAPK signaling pathway
included 7 up-regulated and 8 down-regulated genes
while pathways in cancer contained 13 up-regulated and
20 down-regulated PHF20-regulated DEGs.
Signal‑net analysis of DEGs
Finally, we identified the key gene interactions between
PHF20-related DEGs to construct a regulatory
network map. 78 genes were included in the signaling
network, and 106 potential direct interactions were
identified (Fig. 7). PLCB1, NRAS, PIK3CD, PIK3CA, PIK3R1,
HDAC4, HDAC8, CRKL, RAB7A and ITGB3 were the
most significantly expressed genes according to the
degree size (Table 2).
PHF20 was originally identified in glioma patients [
is significantly associated with glioma pathological tumor
]. In recent years, a growing number of studies
have shown that PHF20 is closely related to the
development of various tumors [
] and plays important roles
in tumor suppression and progression. However, the
underlying molecular pathways regulated by PHF20 in
glioma remain largely undetermined. Therefore, further
in-depth investigations are essential for better
understanding of the biological roles of PHF20 in cancer.
In the present study, gene expression profile analysis
was performed to identify differentially expressed genes
(DEGs) between PHF20 knockdown U87 cells and
negative control cells. A total of 540 genes (175 up-regulated
genes and 365 down-regulated genes) were differentially
expressed following knockdown of PHF20, which
suggests that PHF20 may be a key regulator in glioblastoma.
Multiple DEGs, including FEN1, BCAT1, AGPS and
CCL3, have been implicated in the progression of
various cancers. For example, FEN1 is overexpressed in
]. FEN1 polymorphisms and variant
genotypes are associated with glioma susceptibility [
]. CCL3 is also highly expressed in glioma, and may
promote glioblastoma cell proliferation and migration
Gene ontology enrichment analysis revealed that highly
enriched biological functions were related to PHF20,
such as hemophilic cell adhesion, protein transport,
metabolic process, transcription and apoptotic process.
Thus, PHF20 may influence glioma progression by
altering these biological processes.
Several DEG enriched pathways associated with
tumorigensis were identified including protein processing in
endoplasmic reticulum, metabolic pathways, ubiquitin
mediated proteolysis, pathways in cancer, and thyroid
hormone signaling pathways. Furthermore,
pathwaynet analysis revealed that multiple pathways participate
in the occurrence and development of cancer including
the p53 signaling pathway, apoptosis, pathways in
cancer, and the TLR signaling pathway. The p53 signaling
pathway was also enriched as a significant pathway by an
array comparative genomic hybridization analysis in
pilocytic astrocytoma [
]. Furthermore, our findings are in
line with previous studies that found that PHF20 could
stabilize and activate p53 by promoting p53 methylation
], and that PHF20 inhibits p53 transcriptional activity
via PKB mediated PHF20 phosphorylation [
]. A recent
study also showed that PHF20 inhibits tumorigenicity by
inducing apoptosis mediated by p53 and Bax [
Moreover, accumulating evidences suggested that PHF20 was
expressed in a number of tumors, including glioma [
lung cancer [
] and myeloid malignancies [
addition, elevated expression of PHF20 could cause
constitutive NF-B activation [
], which is a key downstream gene
of TLR signaling pathway [
Finally, signal-net analysis revealed the interactions
between 78 PHF20-regulated genes. Core genes PLCB1,
PIK3CD/CA/R1, CRKL, RAB7A and ITGB3 were
downregulated while NRAS and HDAC4/8 were up-regulated.
PLCB1 plays critical roles in intracellular transduction
and regulating signal activation [
], which are important
to tumorigenesis. As one of the RAS oncogene family,
NRAS have been reported to be involved in
development of leukemia [
], melanoma [
] and glioma [
]. Members of the PIK3 family are frequently detected
in a wide range of cancers and have been proposed as
biomarkers for patient survival and drug response [
]. PHF20 has been suggested as a substrate of PKB
]. HDACs regulate various nuclear and cytoplasmic
], which are common in various human
]. In addition, synergistic anti-tumor
actions between HDAC and PIK3 inhibitors have been
Overall, this study indicated that PHF20 is a pivotal
upstream gene that influences the occurrence and
development of glioma by regulating a series of
tumorrelated genes, like FEN1, CCL3, PLCB1, NRAS and
PIK3s, and involved in apoptosis signaling pathways.
Thus, PHF20 might be a novel biomarker for early
diagnosis and therapeutic target for treatment of glioma.
Nevertheless, further studies in molecular pathogenesis
and large scale clinical tumor specimen validation are
Additional file 2. The differentially expressed genes (DEGs) between
common U87 cells and PHF20 knockdown U87 cells.
Additional file 3. Significant gene ontology (GO) analysis of differentially
expressed genes (DEGs) related to PHF20.
Additional file 4. Significant pathway analysis of differentially expressed
genes (DEGs) related to PHF20.
DEGs: differentially expressed genes; FDR: false discovery rate; GLEA2:
gliomaexpressed antigen 2; GO: gene ontology; HCA58: hepatocellular carcinoma
associated antigen 58; KEGG: Kyoto Encyclopedia of Genes and Genomes;
PHF20: plant homeodomain finger protein 20.
LYW and WAD designed and directed the study. DY performed the statistical
analysis. LTL collected background information, drafted the manuscript and
performed the microarray assay. ZTJ and ZF conducted the networks. ZXH
performed the cell culture. MN performed the western blotting. JP
participated in study design and helped to draft the manuscript. LMN finished the
experiments of RNA extraction. LWX performed the lentivirus infection. SPJ
and WJT finished the real-time PCR array. All authors read and approved the
of SooChow University, Suzhou, China. 3 State Key Laboratory of Cancer
Biology, Department of Biopharmaceutics, School of Pharmacy, Fourth Military
Medical University, Xi’an, China. 4 Department of Pharmacology, Chungnam
National University, Daejon, South Korea. 5 Department of Nephrology, Xijing
Hospital, Fourth Military Medical University, Xi’an, China. 6 Department of
Neurosurgery, The First Affiliated Hospital of SooChow University, Suzhou, China.
The authors declare that they have no competing interests.
Availability of data and materials
The datasets generated and analyzed during the current study are available in
the Gene Expression Omnibus repository (https://www.ncbi.nlm.nih.gov/geo/
Consent for publication
Ethics approval and consent to participate
The work has been supported by National Natural Science Foundation of
China (Nos. 81501003, 81673631, 81201985) and China Postdoctoral Science
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
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