Ke Gong1, Huiling Zhou1, Haidan Liu2, Ting Xie1, Yong Luo1, Hui Guo1, Jinlan Chen1, Zhiping Tan2, Yifeng Yang1, Li Xie1. 1. Department of Cardiovascular Surgery, The Second Xiangya Hospital of Central South University, Central South University, Changsha, PR China. 2. The Clinical Center for Gene Diagnosis and Therapy of The State Key Laboratory of Medical Genetics, The Second Xiangya Hospital of Central South University, Central South University, Changsha, Hunan, PR China.
Abstract
Background: Non-small cell lung cancer (NSCLC) is the most common type of lung cancer affecting humans. However, appropriate biomarkers for diagnosis and prognosis have not yet been established. Here, we evaluated the gene expression profiles of patients with NSCLC to identify novel biomarkers. Methods: Three datasets were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes were analyzed. Venn diagram software was applied to screen differentially expressed genes, and gene ontology functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed. Cytoscape was used to analyze protein-protein interactions (PPI) and Kaplan-Meier Plotter was used to evaluate the survival rates. Oncomine database, Gene Expression Profiling Interactive Analysis (GEPIA), and The Human Protein Atlas (THPA) were used to analyze protein expression. Quantitative real-time polymerase (qPCR) chain reaction was used to verify gene expression. Results: We identified 595 differentially expressed genes shared by the three datasets. The PPI network of these differentially expressed genes had 202 nodes and 743 edges. Survival analysis identified 10 hub genes with the highest connectivity, 9 of which (CDC20, CCNB2, BUB1, CCNB1, CCNA2, KIF11, TOP2A, NDC80, and ASPM) were related to poor overall survival in patients with NSCLC. In cell experiments, CCNB1, CCNB2, CCNA2, and TOP2A expression levels were upregulated, and among different types of NSCLC, these four genes showed highest expression in large cell lung cancer. The highest prognostic value was detected for patients who had successfully undergone surgery and for those who had not received chemotherapy. Notably, CCNB1 and CCNA2 showed good prognostic value for patients who had not received radiotherapy. Conclusion: CCNB1, CCNB2, CCNA2, and TOP2A expression levels were upregulated in patients with NSCLC. These genes may be meaningful diagnostic biomarkers and could facilitate the development of targeted therapies.
Background: Non-small cell lung cancer (NSCLC) is the most common type of lung cancer affecting humans. However, appropriate biomarkers for diagnosis and prognosis have not yet been established. Here, we evaluated the gene expression profiles of patients with NSCLC to identify novel biomarkers. Methods: Three datasets were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes were analyzed. Venn diagram software was applied to screen differentially expressed genes, and gene ontology functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed. Cytoscape was used to analyze protein-protein interactions (PPI) and Kaplan-Meier Plotter was used to evaluate the survival rates. Oncomine database, Gene Expression Profiling Interactive Analysis (GEPIA), and The Human Protein Atlas (THPA) were used to analyze protein expression. Quantitative real-time polymerase (qPCR) chain reaction was used to verify gene expression. Results: We identified 595 differentially expressed genes shared by the three datasets. The PPI network of these differentially expressed genes had 202 nodes and 743 edges. Survival analysis identified 10 hub genes with the highest connectivity, 9 of which (CDC20, CCNB2, BUB1, CCNB1, CCNA2, KIF11, TOP2A, NDC80, and ASPM) were related to poor overall survival in patients with NSCLC. In cell experiments, CCNB1, CCNB2, CCNA2, and TOP2A expression levels were upregulated, and among different types of NSCLC, these four genes showed highest expression in large cell lung cancer. The highest prognostic value was detected for patients who had successfully undergone surgery and for those who had not received chemotherapy. Notably, CCNB1 and CCNA2 showed good prognostic value for patients who had not received radiotherapy. Conclusion: CCNB1, CCNB2, CCNA2, and TOP2A expression levels were upregulated in patients with NSCLC. These genes may be meaningful diagnostic biomarkers and could facilitate the development of targeted therapies.
Lung cancer is highly invasive and metastatic and is a major cause of cancer-related
deaths. Non-small cell lung cancer (NSCLC), the most common of lung cancer, accounts
for 85% of all lung cancers.
Despite major advances in the diagnosis and treatment of NSCLC with the
development of medical technology in recent years, the 5-year survival rate of
patients with NSCLC is only 17%.
Changes in social lifestyles and environments have led to an increase in the
incidence of NSCLC, and approximately 234 000 new cases of NSCLC are reported in the
United States of America each year.[3,4] Regardless of which therapeutic
option is chosen, chemotherapy is still an essential adjuvant treatment for patients
with lung cancer. However, serious adverse reactions can occur following
administration of chemotherapy drugs, and the efficacy of these therapies is often
not satisfactory.[5,6]
Therefore, there is an urgent need for new treatment strategies to complement
traditional chemotherapy. Moreover, with commencement of the genomic era and
advancements in molecular biology research, molecular mechanisms of life phenomena
and disease have attracted much attention, and recent research on NSCLC has focused
on the identification of novel targets to facilitate the development of new
molecular targeted drugs.With the development of sequencing technology and large-scale sequencing research,
large amounts of sequencing data have been collected in many databases.
To discover biomarkers and potential targets related to cancer, researchers
can resequence the tumor and then re-analyze the tumor from gene to protein
expression.[8,9]
Furthermore, to avoid inaccurate experimental results owing to the use of multiple
platforms or small sample sizes, comprehensive bioinformatics methods can be used to
obtain valuable biological information in cancer research.[10,11]Many novel targets and biomarkers of NSCLC have been reported in recent studies,
providing substantial contributions to NSCLC research. For example, in a study by Hu
et al. miR-210 was found to serve as a potential biomarker for
NSCLC detection, and they found that the use of a set of multiple biomarkers may be
a more comprehensive indicator than the analysis of miR-210 alone.
Additionally, Wang provided a dataset of NSCLC biomarkers with potential
applications in prognosis and found that TOP2A may be a valuable
biomarker for survival and prognosis in patients with NSCLC.
Saigusa and colleagues also revealed that new metabolites related to
NRF2 activity may be diagnostic biomarkers for
NRF2 activation, providing important insights into the
selective toxicity of new metabolic nodes in NRF2-activated NSCLC.
Despite these findings, NSCLC remains a complex and diverse disease, and more
genetic data and biological information are needed to improve diagnosis and
treatment strategies.In the present study, we selected three datasets, ie, GSE18842,
GSE44077,
and GSE19804,
from the Gene Expression Omnibus (GEO) database and conducted various
bioinformatics analyses to evaluate gene expression and protein interactions in
NSCLC tumor tissues, with the aim of elucidating the underlying molecular mechanisms
in NSCLC and for establishing novel biomarkers for its diagnosis and treatment.
Materials and Methods
The gene expression profile data (GSE18842, GSE44077, and GSE19804) were downloaded
from GEO (Supplementary Tables S1-S3).
The inclusion criteria for gene expression data were as follows: (1) the
samples used for analysis were tissues, (2) all tissues were categorized as NSCLC or
normal tissues, (3) samples were collected from the same species group, (4) probes
could be converted, (5) complete information was available for analysis, and (6) the
sample size of each study was larger than 10 samples. The array data for GSE18842
included 46 NSCLC tumor and 45 normal tissues as the control group. GSE19804
included 60 NSCLC tumors and 60 adjacent normal tissues. GSE44077 was composed of 66
tumor tissues and 55 normal tissues.GEO2R was used to analyze differentially expressed genes (DEGs) between NSCLC samples
and normal samples (http://www.ncbi.nlm.nih.gov/geo/geo2r). GEO2R is a web tool that can
be used to compare and analyze DEGs in NSCLC samples and normal lung tissue samples
through the Limma and GEOquery R packages of the Bioconductor project. Adjusted
P values and |log2 fold change|
(|log2FC|) values were used to assess the significance of DEGs, and the
cut-off criteria were set as |log2FC| > 1 and adjusted
P value < .01.Venn diagram software in the Bioinformatics & Evolutionary Genomics platform
(http://bioinformatics.psb.ugent.be/webtools/Venn/) was used to
perform intersection analysis on the DEGs of our three independent samples. We
screened out genes that were differentially expressed in the three independent
samples and determined whether the selected genes were up- or downregulated based on
log2FC values of DEGs between NSCLC and normal tissues.Gene Ontology (GO), a commonly used bioinformatics tool for comprehensive evaluation
of gene function; Kyoto Encyclopedia of Genes and Genomes (KEGG), a database for
annotation of the advanced functions of biological systems at the molecular level;
and the Database for Annotation, Visualization, and Integrated Discovery (DAVID;
https://david.ncifcrf.gov/), an online tool for enriching and
analyzing bioinformatics resources.
, were used to clarify gene functions and functional enrichment. Upregulated
DEGs (uDEGs) and downregulated DEGs (dDEGs) were identified using DAVID for the
three gene expression profile datasets. The cut-off criterion was set as a
P value less than 0.05.The STRING (http://string-db.org/) database, a search tool for retrieving
interacting genes and providing important information regarding protein-protein
interactions (PPIs),
was used to construct a PPI network of DEGs, and Cytoscape (version 3.7.2)
was used to process and analyze the PPI network.
The cut-off criterion was a combined score greater than or equal to 0.9.
Subsequently, we used a Cytoscape plug-in Molecular Complex Detection (MCODE) to
detect important modules in the PPI network, with the following parameters: cut-off
degree = 2, cut-off node score = 0:2, K-core = 2, and maximum depth = 100.
Functional annotation of DEGs in the identified module was investigated with DAVID.
The cut-off criterion was set to a P value less than 0.05.The Kaplan–Meier Plotter database (http://kmplot.com/analysis/index.php?p=service&cancer=lung),
which provides information on the relationships of more than 54 000 genes (mRNAs,
microRNAs, and proteins) with survival rates in 21 cancer types,
was used to investigate whether the top 10 hub genes were related to overall
survival in patients with NSCLC. We also assessed relationships according to
histological type, including adenocarcinoma (LUAD) and squamous cell carcinoma
(LUSC). Subsequently, we analyzed the association of overall survival rate with hub
genes according to treatment strategies in order to compare the prognostic
significance of hub genes under different treatment regimens. The selection criteria
were as follows: hazard ratio (HR) within the 95% confidence interval (CI) and
log-rank P value less than .05.Next, we used the Cytoscape plug-in CytoHubba to identify hub genes, subnets of
complex networks, and central elements in the network. The Biological Networks Gene
Oncology Tool (BiNGO; version 3.0.3) plug-in in Cytoscape was used to analyze and
visualize the biological processes (BPs) associated with the hub genes. The Oncomine
database (http://www.oncomine.org) was used to compare the mRNA expression
levels of hub genes between lung cancer tissues and normal control tissues and
between different types of lung cancer. Data were collected from all related
datasets.Gene Expression Profiling Interactive Analysis (GEPIA; http://gepia.cancer-pku.cn/index.html) is a new web-based
interactive analysis and visualization tool based on The Cancer Genome Atlas (TCGA)
database and genotype tissue expression.
To further verify the 10 hub genes identified from the PPI network,
differences in gene expression between LUSC, LUAD, and adjacent lung tissues were
mapped in TCGA database and GTEx database using the GEPIA web tool Box Plots. The
patient data were grouped according to the results per million reads (TPM).
Log2(TPM + 1) was used as the logarithmic scale, and the criteria
were as follows: |log2FC| > 1 and p < .01.The Human Protein Atlas (THPA), which maps all human proteins in cells, tissues and
organs by integrating various omics technologies (including antibody-based imaging,
mass spectrometry-based proteomics, transcriptomics, and systems).
, was used to assess protein-based differences between NSCLC and normal
tissues. First, we downloaded the histological section images and corresponding
information on overexpressed hub genes from normal bronchial respiratory tract
epithelial tissues and lung cancer tissues obtained by immunohistochemistry from
THPA. Because some antibody staining may be inconsistent, the detection results were
reported as low, medium, or high according to the staining intensity and score of
the stained cells. We then performed Mann–Whitney U tests using SPSS 23.0 software
to compare the antibody staining levels of hub genes between normal lung tissues and
bronchial epithelial tissues. The cut-off P value was set to
.05.All cell lines were obtained from Professor Haidan Liu (Changsha). A549 human NSCLC
cells (epidermal growth factor receptor wild type) and HBE normal immortalized lung
epithelial cells were purchased from American Type Culture Collection (ATCC). The
cells were cultured in a humidified incubator at 37°C with 5% CO2
according to ATCC protocols. A549 cells were subjected to mycoplasma analysis and
were cytogenetically tested and authenticated before being frozen. HBE cells were
maintained in Dulbecco's modified Eagle's medium (Thermo Fisher Scientific)
supplemented with 10% fetal bovine serum (FBS; Biological Industries) and 1%
antibiotics. A549 cells were maintained in RPMI1640 medium (Thermo Fisher
Scientific) supplemented with 10% FBS (Biological Industries) and 1%
antibiotics.For quantitative real-time polymerase chain reaction (qPCR), HBE and A549 cells were
seeded in 100-mm Petri dishes and grown for 1 day at 37 °C in an atmosphere
containing 5% CO2. After 48 h, total RNA was extracted using a GeneJET
RNA Purification Kit (Thermo Fisher Scientific) according to the manufacturer's
instructions. Additionally, a RevertAid First Strand cDNA Synthesis Kit (Thermo
Fisher Scientific) was used to synthesize cDNA based on the manufacturer's
recommendations. qPCR was then performed in a Thermo 9700 Fast Real-time PCR system
using PowerUp SYBR Green Master Mix (Thermo Fisher Scientific). The qPCR reaction
conditions were: UDG enzyme activation, 50℃ for 2 min, a hold; pre-denaturation, 95℃
for 2 min, a hold; denaturation, 95℃ for 3 s and annealing and extension, 60℃ for 30
s, 40 cycles. The melting curve selects the default setting. The reaction system
used a 10ul reaction system, and the sample amount of cdna is 10 ng per reaction.
The primers used were as follows: ARF5-F,
5′-ATCTGTTTCACAGTCTGGGACG-3′; ARF5-R, 5′-CCTGCTTGTTGGCAAATACC-3′;
CDC20-F, 5′-AGACATTCACCCAGCATCAAG-3′; CDC20-R,
5′-GAGATGAGCTCCTTGTAATGGG-3′; CCNB1-F,
5′-GGCTTTCTCTGATGTAATTCTTGC-3′; CCNB1-R,
5′-GTATTTTGGTCTGACTGCTTGC-3′; TOP2A-F, 5′-ATCTAAACCTCTTGCAGCCC-3′;
TOP2A-R, 5′-GCACCATTTATCAGCACCATG-3′; CCNB2-F,
5′-ACCTACTGCTTCTGTCAAACC-3′; CCNB2-R, 5′-TGTCCTCGATTTTGCAGAGC-3′;
CCNA2-F, 5′-CCTTTCATTTAGCACTCTACACAG-3′;
CCNA2-R, 5′-CCAGGGTATATCCAGTCTTTCG-3′; KIF11-F,
5′-ACCTCATGTTCCTTATCGAGAATC-3′; KIF11-R,
5′-GCATATTCCAATGTACTCAGAGTTTC-3′. ARF5 was used as an internal control,
and fold changes in mRNA levels were determined using the 2−ΔΔCT
method.All data were presented as means ± standard deviations. GraphPad Prism 8.0
(GraphPad Software) and SPSS software were used for all statistical analyses.
Two-tailed Student's t-tests were used to assess the significance
of differences between the two groups. Unless otherwise stated, results with
P values less than .05 were considered statistically
significant.
Results
Identification of Differentially Expressed Cells in Non-Small Cell Lung
Cancer
According to GEO2R analysis, after standardizing the chip data, DEGs (4642 in
GSE18842, 1960 in GSE19804, and 1235 in GSE44077) were identified. Among these
DEGs, the three datasets together contained 594 genes, as demonstrated by Venn
diagram analysis in the Bioinformatics & Evolutionary Genomics platform
(Figure 1A and
Supplementary Table S4). We matched the log2FC values
of three independent datasets to DEGs and found that 177 uDEGs and 417 dDEGs
were identified between NSCLC and normal tissues in all three independent
datasets.
Figure 1.
Venn diagram, protein-protein interaction (PPI) network, the most
significant module, and the top 10 highest scoring nodes of
differentially expressed genes (DEGs). (A) DEG identification in three
gene expression profile datasets (GSE18842, GSE44077, and GSE19804). In
total, 594 DEGs were identified. (B) A PPI network was generated using
Cytoscape (combined score ≥ 0.9). (C) The significant module obtained
from the PPI network contained 24 nodes and 256 edges. (D) The
interaction network of the 10 nodes with the highest screening scores.
Upregulated genes are marked in dark red; downregulated genes are marked
in dark blue. The red, orange, and yellow nodes represented the top 10
hub genes in the network.
Venn diagram, protein-protein interaction (PPI) network, the most
significant module, and the top 10 highest scoring nodes of
differentially expressed genes (DEGs). (A) DEG identification in three
gene expression profile datasets (GSE18842, GSE44077, and GSE19804). In
total, 594 DEGs were identified. (B) A PPI network was generated using
Cytoscape (combined score ≥ 0.9). (C) The significant module obtained
from the PPI network contained 24 nodes and 256 edges. (D) The
interaction network of the 10 nodes with the highest screening scores.
Upregulated genes are marked in dark red; downregulated genes are marked
in dark blue. The red, orange, and yellow nodes represented the top 10
hub genes in the network.
Gene Ontology Functional Pathways and Analysis of Differentially Expressed
Genes
GO analyses showed that uDEGS were mainly enriched in mitotic nuclear division,
whereas dDEGs were mainly enriched in cell adhesion (Supplementary Table S5). Furthermore, cell component (CC)
analysis indicated that most of the uDEGs were located in the midbody, whereas
the dDEGs were mainly distributed in the proteinaceous extracellular matrix.
Additionally, according to molecular function (MF) analysis, uDEGs were
significantly associated with metalloendopeptidase activity, whereas dDEGs were
associated with heparin binding (Table 1). KEGG pathway analysis
suggested that most uDEGs were mainly involved in the BPs of the cell cycle,
whereas most dDEGs were involved in cell adhesion (Table 2).
Table 1.
GO Function Annotation of uDEGs and dDEGs Associated With NSCLC (top
five).
GO Function Annotation of uDEGs and dDEGs Associated With NSCLC (top
five).Abbreviations: dDEGS: downregulated differentially expressed gene;
GO: Gene Ontology; NSCLC: non-small cell lung cancer; uDEGs:
upregulated differentially expressed genes.KEGG Pathway Analysis of DEGs Associated With NSCLC.Abbreviations: DEGs: differentially expressed genes; dDEGs:
downregulated differentially expressed genes; KEGG: Kyoto
Encyclopedia of Genes and Genomes; NSCLC: non-small cell lung
cancer; uDEGs: upregulated differentially expressed genes.
Protein-Protein Interaction Network Construction and Analysis of
Modules
Next, we constructed a PPI network of DEGs. The PPI network contained 202 nodes
and 743 edges, including 75 uDEGs and 124 DEGs (Figure 1B). According to the selection
conditions, 19 sets of datasets were obtained, and the one with the highest
cluster score was selected for subsequent in-depth analyses. The PPI network
with the highest cluster score calculated using the MCODE plug-in generated 24
nodes and 256 edge-related modules, with a cluster score of 22.261 (Figure 1C and Supplementary Table S6). The top 10 highest scoring nodes,
including CDC20, CCNB2, BUB1,
CCNB1, CCNA2, KIF11,
TOP2A, NDC80, ASPM,
CDK1, were then evaluated using the Cytohubba plug-in
(Table 3) and
were used to build an interaction network in MCODE (Figure 1D). In addition to the 10 genes
described above, other nodes in the module included KIF4A,
PRC1, RRM2, PTTG1,
MELK, TPX2, AURKA,
CEP55, MAD2L1, NEK2,
NUF2, DLGAP5, KIF2C, and
PBK. The expression levels of all genes in the module were
upregulated.
Table 3.
Top 10 in Network Ranked by Degree Method.
Rank
Name
Type
Score
1
CDK1
up
42
2
CDC20
up
40
3
CCNB2
up
35
4
BUB1
up
34
4
CCNB1
up
34
6
CCNA2
up
33
7
KIF11
up
32
8
TOP2A
up
31
9
NDC80
up
30
10
ASPM
up
29
Top 10 in Network Ranked by Degree Method.
Hub Gene Selection and Survival Analysis
Using the Kaplan–Meier Plotter online database, we evaluated the relationships
between nine of the 10 hub genes (excluding CDK1) and overall
survival rates (Figure
2). The results indicated that the overexpressed hub genes were
related to unfavorable overall survival in patients with NSCLC, as follows:
CDC20 (HR = 1.82 [95% CI: 1.6-2.07], log-rank
P < 1e − 16), CCNB2 (HR = 1.99 [95%
CI: 1.74-2.26], log-rank P < 1e − 16), BUB1
(HR = 1.77 [95% CI: 1.55-2.01], log-rank P = 1e-16],
CCNB1 [HR = 1.62 [95% CI: 1.37-1.91], log-rank
P = 8.7e − 09], CCNA2 [HR = 1.76 [95%
CI: 1.55-2], log-rank P < 1e − 16], KIF11
[HR = 1.52 [95% CI: 1.34-1.73], log-rank P = 1.1e − 10],
TOP2A [HR = 1.65 [95% CI: 1.45-1.88], log-rank
P = 1.5e − 14], NDC80 [HR = 1.47 [95%
CI: 1.29-1.67], log-rank P = 2.8e − 09],
ASPM [HR = 1.77 [95% CI: 1.55-2.01], log-rank
P < 1e − 16]. Analysis of the BPs enriched for the nine
hub genes is shown in Figure
3. Furthermore, using the Oncomine database, we confirmed that the
nine hub genes were upregulated in NSCLC tissues compared with control tissues
and cells [all P < 1e-4; Figure 4].
Figure 2.
Association of hub genes with overall survival rates.
Kaplan–Meier Plotter was used to evaluate overall survival rates based
high or low expression of (A) CDC20, (B)
CCNB2, (C) BUB1, (D)
CCNB1, (E) CCNA2, (F)
KIF11, (G) TOP2A, (H)
NDC80, and (I) ASPM in patients
with non-small cell lung cancer (NSCLC).
Figure 3.
Analysis of biological processes (BPs) enriched in hub
genes. BiNGO was used to assess the BPs enriched in hub
genes. The color depth of the node reflects the corrected
P value.
Figure 4.
Evaluation of gene expression in cancer tissues and normal tissues
using Oncomine. Oncomine was used to evaluate (A)
CDC20, (B) CCNB2, (C)
BUB1, (D) CCNB1, (E)
CCNA2, (F) KIF11, (G)
TOP2A, (H) NDC80, and (I)
ASPM expression in cancer tissues versus normal
tissues.
Association of hub genes with overall survival rates.
Kaplan–Meier Plotter was used to evaluate overall survival rates based
high or low expression of (A) CDC20, (B)
CCNB2, (C) BUB1, (D)
CCNB1, (E) CCNA2, (F)
KIF11, (G) TOP2A, (H)
NDC80, and (I) ASPM in patients
with non-small cell lung cancer (NSCLC).Analysis of biological processes (BPs) enriched in hub
genes. BiNGO was used to assess the BPs enriched in hub
genes. The color depth of the node reflects the corrected
P value.Evaluation of gene expression in cancer tissues and normal tissues
using Oncomine. Oncomine was used to evaluate (A)
CDC20, (B) CCNB2, (C)
BUB1, (D) CCNB1, (E)
CCNA2, (F) KIF11, (G)
TOP2A, (H) NDC80, and (I)
ASPM expression in cancer tissues versus normal
tissues.
Verification of hub Genes Using Gene Expression Profiling Interactive
Analysis
To determine the reliability of DEGs identified from GSE18842, GSE19804, and
GSE44077, we used GEPIA for the assessment of hub gene expression levels in
LUAD, LUSC, and normal lung tissues reported in the TCGA database. Consistent
with the results of bioinformatics analysis of GEO data, the nine hub genes were
identified, and their expression levels were found to be significantly increased
(Figure 5).
Figure 5.
Expression levels of the nine hub genes. Heat map showing
the expression levels of nine hub genes (CDC20,
CCNB2, BUB1,
CCNB1, CCNA2,
KIF11, TOP2A,
NDC80, and ASPM) in LUAD, LUSC,
and normal lung tissues based on TCGA data analyzed using the GEPIA web
tool. T represents LUAD or LUSC tumor tissues, and N represents normal
lung tissues.
Expression levels of the nine hub genes. Heat map showing
the expression levels of nine hub genes (CDC20,
CCNB2, BUB1,
CCNB1, CCNA2,
KIF11, TOP2A,
NDC80, and ASPM) in LUAD, LUSC,
and normal lung tissues based on TCGA data analyzed using the GEPIA web
tool. T represents LUAD or LUSC tumor tissues, and N represents normal
lung tissues.
Verification of hub Genes Based on Proteomics Analysis
Next, we used immunohistochemical images of proteins from THPA database to
further verify the relevance of six hub genes in lung cancer tissues (Figure 6A). CDC20
(18/21), CCNB2 (17/26), CCNB1 (29/35), CCNA2 (10/11), KIF11 (31/33), and TOP2A
(34/34) had high positive staining rates; however, only CDC20
(P = .000014), CCNB1 (P = .000021),
and TOP2A (P = .000013) were compared by Mann–Whitney U test
using normal lung cells, bronchial epithelial cells, and lung cancer tissues.
The results showed statistically significant differences in the protein levels
of these hub genes (Figure
6B), suggesting potential applications as biomarkers in the diagnosis
of NSCLC.
Figure 6.
Verification of the protein expression levels of hub genes.
(A) Immunohistochemistry images of six hub genes were obtained from
https://www.proteinatlas.org/ENSG00000117399-CDC20/pathology/lung + cancer#ihc
(CDC20), https://www.proteinatlas.org/ENSG00000157456-CCNB2/pathology/lung + cancer#ihc
(CCNB2), https://www.proteinatlas.org/ENSG00000134057-CCNB1/pathology/lung + cancer#ihc
(CCNB1), https://www.proteinatlas.org/ENSG00000145386-CCNA2/pathology/lung + cancer#ihc
(CCNA2), https://www.proteinatlas.org/ENSG00000138160-KIF11/pathology/lung + cancer#ihc
(KIF11), and https://www.proteinatlas.org/ENSG00000131747-TOP2A/pathology/lung + cancer#ihc
(TOP2A) in THPA. (B) The antibodies used targeted CDC20 (HPA055288 and
CAB004525), CCNB2 (CAB009575 and HPA008873), CCNB1 (CAB000115,
CAB003804, and HPA061448), CCNA2 (CAB000114), KIF11 (HPA006916,
HPA010568, and CAB017617), and TOP2A (HPA006458, HPA026773, and
CAB002448). Mann–Whitney U tests were used for assessing significance,
with a cut-off P value of .05.
Verification of the protein expression levels of hub genes.
(A) Immunohistochemistry images of six hub genes were obtained from
https://www.proteinatlas.org/ENSG00000117399-CDC20/pathology/lung + cancer#ihc
(CDC20), https://www.proteinatlas.org/ENSG00000157456-CCNB2/pathology/lung + cancer#ihc
(CCNB2), https://www.proteinatlas.org/ENSG00000134057-CCNB1/pathology/lung + cancer#ihc
(CCNB1), https://www.proteinatlas.org/ENSG00000145386-CCNA2/pathology/lung + cancer#ihc
(CCNA2), https://www.proteinatlas.org/ENSG00000138160-KIF11/pathology/lung + cancer#ihc
(KIF11), and https://www.proteinatlas.org/ENSG00000131747-TOP2A/pathology/lung + cancer#ihc
(TOP2A) in THPA. (B) The antibodies used targeted CDC20 (HPA055288 and
CAB004525), CCNB2 (CAB009575 and HPA008873), CCNB1 (CAB000115,
CAB003804, and HPA061448), CCNA2 (CAB000114), KIF11 (HPA006916,
HPA010568, and CAB017617), and TOP2A (HPA006458, HPA026773, and
CAB002448). Mann–Whitney U tests were used for assessing significance,
with a cut-off P value of .05.
Verification of hub Gene Upregulation by Quantitative Polymerase Chain
Reaction
We then used qPCR to analyze the expression levels of the six hub genes
(including three screened out and three unverified) in A549 and HBE cells.
Consistent with bioinformatics analysis data, CCNB1,
CCNB2, CCNA2, and TOP2A
were significantly upregulated in A549 cells compared with that in HBE cells
(Figure 7 and
Supplementary Tables S7-S9).
Figure 7.
Expression levels of six hub genes in A549 and HBE cells.
Expression levels were verified using RT-qPCR. (A)
CDC20, (B) CCNB1, (C)
CCNB2, (D) CCNA2, (E)
KIF11, TOP2A. *P
< .05, **P < .01, ***P <
.001, ****P < .0001, ns: not significant.
Expression levels of six hub genes in A549 and HBE cells.
Expression levels were verified using RT-qPCR. (A)
CDC20, (B) CCNB1, (C)
CCNB2, (D) CCNA2, (E)
KIF11, TOP2A. *P
< .05, **P < .01, ***P <
.001, ****P < .0001, ns: not significant.
Analysis of hub Gene Expression Levels in Different Types of Non-Small Cell
Lung Cancer
The Oncomine database was used to analyze differences in the expression level of
four hub genes in different types of NSCLCs reported in Hou's dataset.
The analytical results showed that compared with normal control tissues,
gene expression levels were elevated in all types of NSCLCs, consistent with the
verification results. Moreover, in a comparison between large cell lung
carcinoma, LUAD, and LUSC, the expression levels of all four hub genes were
highest in large cell lung carcinoma, followed by LUSC and then LUAD (Figure 8).
Figure 8.
Predictive ability of hub genes in distinguishing among different
types of non-small cell lung cancer (NSCLC) tissues. Data
were evaluated using Oncomine. (A) CCNB1, (B)
CCNB2, (C) CCNA2, and (D)
TOP2A.
Predictive ability of hub genes in distinguishing among different
types of non-small cell lung cancer (NSCLC) tissues. Data
were evaluated using Oncomine. (A) CCNB1, (B)
CCNB2, (C) CCNA2, and (D)
TOP2A.
Analysis of the Relationships Between Overall Survival and hub Gene
Expression Based on Treatment Strategies in Patients With Non-Small Cell Lung
Cancer
Finally, we used Kaplan–Meier Plotter online database to assess the relationships
between overall survival rates and hub gene expression according to treatment
strategies. As shown in Figure
9A, F, K, and P, the four hub genes significantly predicted overall
survival for patients who underwent surgical resection only. Moreover, as shown
in Figure 9B, G, L, and
Q, among the four hub genes, only CCNB1 could predict overall
survival in patients who underwent chemotherapy; however, the median survival
time was lower for patients categorized as having high expression of the four
hub genes than for patients categorized as having low expression. We also found
that the four hub genes significantly predicted overall survival in patients who
were not treated with chemotherapy (Figure 9C, H, M, and R), whereas no
significant prediction ability was observed in patients who underwent
radiotherapy (Figure
9D, I, N, and S), although the median survival time in patients with high
expression was still shorter than that in patients with low hub gene expression.
Additionally, in patients who were not treated with radiotherapy,
CCNB1 and CCNA2 significantly predicted
overall survival, and patients with high expression of the other two hub genes
also had lower median survival times (Figure 9E, J, O, and T).
Figure 9.
Predictive ability of hub genes for overall survival in patients
treated using different approaches. Kaplan–Meier Plotter was
used to evaluate overall survival rates according to high and low
expression of (A–E) CCNB1, (F–J)
CCNB2, (K–O) CCNA2, and (P–T)
TOP2A in patients with NSCLC. (A, F, K, and P)
Surgical margin-negative patients; (B, G, L, and Q) chemotherapy; (C, H,
M, and R) no chemotherapy; (D, I, N, and S) radiotherapy; (E, J, O, and
T) no radiotherapy.
Predictive ability of hub genes for overall survival in patients
treated using different approaches. Kaplan–Meier Plotter was
used to evaluate overall survival rates according to high and low
expression of (A–E) CCNB1, (F–J)
CCNB2, (K–O) CCNA2, and (P–T)
TOP2A in patients with NSCLC. (A, F, K, and P)
Surgical margin-negative patients; (B, G, L, and Q) chemotherapy; (C, H,
M, and R) no chemotherapy; (D, I, N, and S) radiotherapy; (E, J, O, and
T) no radiotherapy.
Discussion
Lung cancer is a major threat to human health, and although targeted immunotherapies
have improved the quality of life of many patients, clinical outcomes in patients
with NSCLC remain poor. To date, the pathogenic mechanisms of NSCLC have not been
fully elucidated; however, multiple genes and pathways are known to be involved,
resulting in complex biological behaviors. Accordingly, improving our understanding
of the molecular mechanisms of NSCLC, particularly using genomics, transcriptomics,
proteomics, and metabolomics analyses, is expected to lead to the development of
better diagnostic and therapeutic strategies based on identification of novel biomarkers.In this study, we selected 594 DEGs, including 177 uDEGs and 417 dDEGs, and found
that the uDEGs were primarily involved in mitotic nuclear division, cell cycle, p53
signaling, oocyte meiosis, progesterone-mediated oocyte maturation, and
extracellular matrix-receptor interactions, whereas the dDEGs were mainly enriched
in cell adhesion, malaria, vascular smooth muscle contraction, peroxisome
proliferator-activated receptor signaling, complement molecules, and coagulation
cascade. Thus, this functional enrichment analysis provided insights into the
signaling pathways involved in the occurrence and development of NSCLC. In normal
and tumor cells, mitotic nuclear division, cell cycle, and meiotic division of
oocytes are known to be important cellular processes,
and key tumor-related genes usually modulate tumor progression by regulating
these cellular processes.
Furthermore, some studies have shown that cell adhesion molecules play
crucial roles in tumor development,
consistent with our current results. Thus, although the specific mechanisms
of disease progression are still not clearly defined, the signaling pathways
identified above were confirmed to be related to NSCLC.Next, we generated a PPI network of the DEGs. The top 10 genes and one relevant
module extracted from the PPI network were all upregulated and survival analysis
showed that nine of these genes (CDC20, CCNB2,
BUB1, CCNB1, CCNA2,
KIF11, TOP2A, NDC80, and
ASPM) were significantly correlated with overall survival in
patients with NSCLC. We further confirmed that CCNB1,
CCNB2, CCNA2, and TOP2A were
upregulated in NSCLC cells compared with normal lung cells and demonstrated that
these four hub genes showed higher prognostic value in patients who underwent
surgery and in patients who had not received chemotherapy. Furthermore,
CCNB1 and CCNA2 had good prognostic value in
patients who had not received radiotherapy. Overall, these findings suggested that
the four hub genes may have applications as promising biomarkers in NSCLC.CCNB1 encodes the regulatory protein cyclin B1, which is involved in
mitosis. The gene product can interact with p34 (CDC2) to form a maturation
promoting factor and has been shown to modulate the G2/M transition phase
of the cell cycle. Moreover, CCNB1 has been shown to act as a
potential diagnostic marker for rhabdomyosarcoma and estrogen receptor (ER)-positive
breast cancer.
CCNB1 exerts carcinogenic effects in colorectal cancer cells and may be an
effective target in the development of new treatments for colorectal cancer.
Researchers have also shown that overexpression of G2 and S
phase-expressed-1 promotes cell proliferation, migration, and invasion by regulating
CCNB1 and other pathways and predicts poor outcomes in patients with bladder cancer.
In a study of NSCLC, Arora et al. discovered that a network composed of
miR-20b-5p, CCNB1, HMGA2, and
E2F7 plays important roles in the development and progression
of NSCLC,
suggesting that these targets may be effective prognostic biomarkers and may
facilitate the development of novel treatments for NSCLC. Indeed, the cell cycle and
immune surveillance mechanisms can be targeted through HMGA2 and E2F7, and the
CCNB1 gene may be a crucial component in various cancers.Cyclin B2, encoded by the CCNB2 gene, is a type B cyclin that has
been shown to be upregulated in human cancer. CCNB2 is vital for controlling the
cell cycle during the G2/M transition (mitosis) via modulation of p34
(CDC2). The subcellular localization of cyclins B1 and B2 differs; CCNB2 is mainly
localized to the Golgi apparatus, whereas cyclin B1 is expressed near microtubules.
Because CCNB2 also binds to transforming growth factor (TGF) βRII, CCNB2/CDC2 may
have essential roles in TGF-β-mediated cell cycle transformation.
In several studies, the relative expression level of CCNB2
was found to be significantly higher in patients with cancer than in normal controls
and individuals with benign disease. Another study showed that overexpression of
CCNB2 protein was related to the clinical progression and poor prognosis in patients
with NSCLC, suggesting that CCNB2 may be a biomarker of NSCLC.
Finally, overexpression of CCNB2 protein has been shown to be related to
clinical progression and poor prognosis in hepatocellular carcinoma and
nasopharyngeal carcinoma.[37,38]Cyclin A2, encoded by CCNA2, is another cyclin family member and
cell cycle regulator. The protein binds to and activates cyclin-dependent kinase 2,
thereby facilitating the G1/S and G2/M transitions.
CCNA2 is also a prognostic biomarker for ER-positive breast cancer,
and overexpression of CCNA2 in tumor tissue predicts poor
survival in patients with pancreatic ductal adenocarcinoma.DNA topoisomerase II alpha, encoded by the TOP2A gene, modulates DNA
topology during transcription by catalyzing the breakage and recombination of
double-stranded and is also involved in DNA transcription and replication, chromatid
separation, and chromosome condensation.[42,43] The TOP2A
gene has been used as a target for several anticancer drugs; however, mutations in
TOP2A are associated with acquired resistance. Moreover,
decreased enzyme activity may be involved in ataxia-telangiectasia. In previous
studies, TOP2A was shown to be a therapeutic target for adrenocortical carcinoma and
neuroblastoma tumors,[44,45] and TOP2A gene amplification has been used to
predict the response to anthracycline therapy in breast cancer.There were some limitations to our study. First, although we repeated our experiments
independently three times, some target genes did not show significant differences,
likely owing to the influence of random errors. Additionally, we only compared the
expression levels of the target genes in one cancer cell line (A549 cells) and one
normal lung cell line (HBE cells); therefore, the results should be interpreted with
caution. In the future, additional experiments using more clinical specimens and
different cell lines are required to verify our findings.
Conclusion
In summary, in this study, we identified DEGs involved in the progression of NSCLC
using integration of microarray data from multiple GEO datasets comprising large
sample cohorts. We established a PPI network and determined the relationships of hub
genes with diagnosis and prognosis in NSCLC. The diagnostic value and robustness of
NSCLC predicted by the hub genes were evaluated using the GEPIA web tool based on
TCGA datasets. We finally verified the hub genes at the protein level using THPA.
The upregulation of CCNB1, CCNB2,
CCNA2, and TOP2A was verified in cell
experiments. However, the functions of these hub genes in NSCLC must be verified in
vitro and in vivo in future studies. In addition, we found that the predictive
ability of the identified hub genes differed according to the treatment strategy
used in the patients as well as the histological type of NSCLC. Overall, our
findings provide important insights into the treatment of NSCLC based on
genomics..Click here for additional data file.Supplemental material, sj-xls-1-tct-10.1177_15330338211060202 for Identification
and Integrate Analysis of Key Biomarkers for Diagnosis and Prognosis of
Non-Small Cell Lung Cancer Based on Bioinformatics Analysis by Ke Gong, Huiling
Zhou, Haidan Liu, Ting Xie, Yong Luo, Hui Guo, Jinlan Chen, Zhiping Tan, Yifeng
Yang and Li Xie in Technology in Cancer Research & TreatmentClick here for additional data file.Supplemental material, sj-xls-2-tct-10.1177_15330338211060202 for Identification
and Integrate Analysis of Key Biomarkers for Diagnosis and Prognosis of
Non-Small Cell Lung Cancer Based on Bioinformatics Analysis by Ke Gong, Huiling
Zhou, Haidan Liu, Ting Xie, Yong Luo, Hui Guo, Jinlan Chen, Zhiping Tan, Yifeng
Yang and Li Xie in Technology in Cancer Research & TreatmentClick here for additional data file.Supplemental material, sj-xls-3-tct-10.1177_15330338211060202 for Identification
and Integrate Analysis of Key Biomarkers for Diagnosis and Prognosis of
Non-Small Cell Lung Cancer Based on Bioinformatics Analysis by Ke Gong, Huiling
Zhou, Haidan Liu, Ting Xie, Yong Luo, Hui Guo, Jinlan Chen, Zhiping Tan, Yifeng
Yang and Li Xie in Technology in Cancer Research & TreatmentClick here for additional data file.Supplemental material, sj-xls-4-tct-10.1177_15330338211060202 for Identification
and Integrate Analysis of Key Biomarkers for Diagnosis and Prognosis of
Non-Small Cell Lung Cancer Based on Bioinformatics Analysis by Ke Gong, Huiling
Zhou, Haidan Liu, Ting Xie, Yong Luo, Hui Guo, Jinlan Chen, Zhiping Tan, Yifeng
Yang and Li Xie in Technology in Cancer Research & TreatmentClick here for additional data file.Supplemental material, sj-xls-5-tct-10.1177_15330338211060202 for Identification
and Integrate Analysis of Key Biomarkers for Diagnosis and Prognosis of
Non-Small Cell Lung Cancer Based on Bioinformatics Analysis by Ke Gong, Huiling
Zhou, Haidan Liu, Ting Xie, Yong Luo, Hui Guo, Jinlan Chen, Zhiping Tan, Yifeng
Yang and Li Xie in Technology in Cancer Research & TreatmentClick here for additional data file.Supplemental material, sj-xls-6-tct-10.1177_15330338211060202 for Identification
and Integrate Analysis of Key Biomarkers for Diagnosis and Prognosis of
Non-Small Cell Lung Cancer Based on Bioinformatics Analysis by Ke Gong, Huiling
Zhou, Haidan Liu, Ting Xie, Yong Luo, Hui Guo, Jinlan Chen, Zhiping Tan, Yifeng
Yang and Li Xie in Technology in Cancer Research & TreatmentClick here for additional data file.Supplemental material, sj-xls-7-tct-10.1177_15330338211060202 for Identification
and Integrate Analysis of Key Biomarkers for Diagnosis and Prognosis of
Non-Small Cell Lung Cancer Based on Bioinformatics Analysis by Ke Gong, Huiling
Zhou, Haidan Liu, Ting Xie, Yong Luo, Hui Guo, Jinlan Chen, Zhiping Tan, Yifeng
Yang and Li Xie in Technology in Cancer Research & TreatmentClick here for additional data file.Supplemental material, sj-xls-8-tct-10.1177_15330338211060202 for Identification
and Integrate Analysis of Key Biomarkers for Diagnosis and Prognosis of
Non-Small Cell Lung Cancer Based on Bioinformatics Analysis by Ke Gong, Huiling
Zhou, Haidan Liu, Ting Xie, Yong Luo, Hui Guo, Jinlan Chen, Zhiping Tan, Yifeng
Yang and Li Xie in Technology in Cancer Research & TreatmentClick here for additional data file.Supplemental material, sj-xls-9-tct-10.1177_15330338211060202 for Identification
and Integrate Analysis of Key Biomarkers for Diagnosis and Prognosis of
Non-Small Cell Lung Cancer Based on Bioinformatics Analysis by Ke Gong, Huiling
Zhou, Haidan Liu, Ting Xie, Yong Luo, Hui Guo, Jinlan Chen, Zhiping Tan, Yifeng
Yang and Li Xie in Technology in Cancer Research & Treatment
Authors: Abel Sanchez-Palencia; Mercedes Gomez-Morales; Jose Antonio Gomez-Capilla; Vicente Pedraza; Laura Boyero; Rafael Rosell; M Esther Fárez-Vidal Journal: Int J Cancer Date: 2010-11-28 Impact factor: 7.396
Authors: Tanya Barrett; Stephen E Wilhite; Pierre Ledoux; Carlos Evangelista; Irene F Kim; Maxim Tomashevsky; Kimberly A Marshall; Katherine H Phillippy; Patti M Sherman; Michelle Holko; Andrey Yefanov; Hyeseung Lee; Naigong Zhang; Cynthia L Robertson; Nadezhda Serova; Sean Davis; Alexandra Soboleva Journal: Nucleic Acids Res Date: 2012-11-27 Impact factor: 16.971
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