Screening and predicted value of potential biomarkers for breast cancer using bioinformatics analysis

Abstract

Breast cancer is the most common cancer and the leading cause of cancer-related deaths in women. Increasing molecular targets have been discovered for breast cancer prognosis and therapy. However, there is still an urgent need to identify new biomarkers. Therefore, we evaluated biomarkers that may aid the diagnosis and treatment of breast cancer. We searched three mRNA microarray datasets (GSE134359, GSE31448 and GSE42568) and identified differentially expressed genes (DEGs) by comparing tumor and non-tumor tissues using GEO2R. Functional and pathway enrichment analyses of the DEGs were performed using the DAVID database. The protein–protein interaction (PPI) network was plotted with STRING and visualized using Cytoscape. Module analysis of the PPI network was done using MCODE. The associations between the identified genes and overall survival (OS) were analyzed using an online Kaplan–Meier tool. The redundancy analysis was conducted by DepMap. Finally, we verified the screened HUB gene at the protein level. A total of 268 DEGs were identified, which were mostly enriched in cell division, cell proliferation, and signal transduction. The PPI network comprised 236 nodes and 2132 edges. Two significant modules were identified in the PPI network. Elevated expression of the genes Discs large-associated protein 5 ( DLGAP5 ), aurora kinase A ( AURKA ), ubiquitin-conjugating enzyme E2 C ( UBE2C ), ribonucleotide reductase regulatory subunit M2( RRM2 ), kinesin family member 23( KIF23 ), kinesin family member 11( KIF11 ), non-structural maintenance of chromosome condensin 1 complex subunit G ( NCAPG ), ZW10 interactor ( ZWINT ), and denticleless E3 ubiquitin protein ligase homolog( DTL) are associated with poor OS of breast cancer patients. The enriched functions and pathways included cell cycle, oocyte meiosis and the p53 signaling pathway. The DEGs in breast cancer have the potential to become useful targets for the diagnosis and treatment of breast cancer.

Full text

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Screening and predicted value
of potential biomarkers for breast
cancer using bioinformatics
analysis
Xiaoyu Zeng, Gaoli Shi, Qiankun He* & Pingping Zhu*
Breast cancer is the most common cancer and the leading cause of cancer-related deaths in women.
Increasing molecular targets have been discovered for breast cancer prognosis and therapy. However,
there is still an urgent need to identify new biomarkers. Therefore, we evaluated biomarkers that
may aid the diagnosis and treatment of breast cancer. We searched three mRNA microarray datasets
(GSE134359, GSE31448 and GSE42568) and identied dierentially expressed genes (DEGs) by
comparing tumor and non-tumor tissues using GEO2R. Functional and pathway enrichment analyses
of the DEGs were performed using the DAVID database. The protein–protein interaction (PPI) network
was plotted with STRING and visualized using Cytoscape. Module analysis of the PPI network was
done using MCODE. The associations between the identied genes and overall survival (OS) were
analyzed using an online Kaplan–Meier tool. The redundancy analysis was conducted by DepMap.
Finally, we veried the screened HUB gene at the protein level. A total of 268 DEGs were identied,
which were mostly enriched in cell division, cell proliferation, and signal transduction. The PPI network
comprised 236 nodes and 2132 edges. Two signicant modules were identied in the PPI network.
Elevated expression of the genes Discs large-associated protein 5 (DLGAP5), aurora kinase A (AURKA),
ubiquitin-conjugating enzyme E2 C (UBE2C), ribonucleotide reductase regulatory subunit M2(RRM2),
kinesin family member 23(KIF23), kinesin family member 11(KIF11), non-structural maintenance of
chromosome condensin 1 complex subunit G (NCAPG), ZW10 interactor (ZWINT), and denticleless
E3 ubiquitin protein ligase homolog(DTL) are associated with poor OS of breast cancer patients. The
enriched functions and pathways included cell cycle, oocyte meiosis and the p53 signaling pathway.
The DEGs in breast cancer have the potential to become useful targets for the diagnosis and treatment
of breast cancer.
Breast cancer has now overtaken lung cancer as the leading cause of cancer incidence worldwide, with an esti-
mated 2.3 million new cases, accounting for 11.7% of all cancer cases1. In China, more than 300,000 women are
diagnosed with breast cancer each year. About 70–80% of breast cancer patients with early stage non-metastatic
disease can be cured, while advanced breast cancers with distant organ metastases are considered untreatable
with currently available therapies2.
e global death rate from breast cancer is declining because of new therapeutic strategies, especially the
targeted therapy. Increasing molecular targets have been discovered for breast cancer prognosis and therapy.
In 2000, Perou and Sorlie reported that breast cancer could be divided into three subtypes according to the
enrichment of three genes, luminal (estrogen receptor [ER]-positive), human epidermal growth factor receptor
2 (HER2, encoded byERBB2)-positive and ER-negative, and basal subtypes3. Since then, several genes have
been identied as predictive and prognostic biomarkers for breast cancer, which play important roles in targeted
therapy. e most commonly used molecular-targeted drugs for HER2-positive breast cancer include tucatinib4,
trastuzumab5, pertuzumab, lapatinib, neratinib and trastuzumab emtansine (T-DM1)6,7. Several drugs target the
phosphoinositide 3-kinase (PI3K)/serine/threonine kinase(AKT) /mammalian target of rapamycin (mTOR)
signaling pathway, including GDC-0068, Bez235, bupacoxib, abencoxib and alpelisib8–10. Vascular endothelial
growth factor (VEGF) has also been identied as a key target for anti-angiogenic therapy, and its inhibitors beva-
cizumab, sorafenib, and sunitinib are also used for breast cancer therapy11,12. Androgen receptor (AR)-targeted
OPEN
School of Life Sciences, Zhengzhou University, Zhengzhou, China. *email: qiankunhe@zzu.edu.cn; zhup@zzu.
edu.cn
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therapies, including AR agonists and AR antagonists, have shown promising results in clinical trials for breast
cancer patients, and combinations of AR-targeted therapies with other reagents (eg. PI3K inhibitor) have been
studied to overcome resistance to AR-targeted therapies13. In addition, targeted therapies have been developed
for epidermal growth factor receptor (EGFR), BRCA1/2-mutated polyadenosine diphosphate ribose polymerase
(PARP), cyclin-dependent kinase 4/6 (CDK4/6), BTB and CNC homology1 (BACH1), and so on14–18. However,
because of tumor heterogeneity, low ratios of responders, relapse and drug resistance, there is still an urgent need
to identify new biomarkers that may aid the diagnosis and treatment of breast cancer.
Bioinformatics analysis is a valuable strategy for the comprehensive analysis of large databases, including
complicated genetic information. In our study, we used sophisticated bioinformatics methods to screen poten-
tial biomarkers that may be useful for breast cancer. e Gene Expression Omnibus (GEO) [https:// www. ncbi.
nlm. nih. gov/ geo/] database is an open database that allows researchers to select appropriate mRNA expression
proles. Online analysis tools are available to detect DEGs between tumors and normal tissues. In our study, we
obtained three mRNA microarray datasets from the GEO (GSE134359, GSE31448, and GSE42568) and searched
for DEGs using GEO2R. We then performed functional and pathway enrichment analyses of the identied DEGs
using the DAVID database. PPI networks were constructed using STRING and visualized using Cytoscape. Con-
duct module analyses of the PPI network were performed using MCODE. e associations of these genes with
OS were determined using an online Kaplan–Meier analysis tool. Finally, several breast cancer-related molecules
were selected to investigate their potential role in a breast cancer diagnostic system.
Methods
Subjects and gene information. e GEO database is a national repository of genetic information
databases, including microarray and next-generation sequencing data19. GEO2R is an online tool that can be
used to detect DEGs from two or more GEO datasets. In this study, we retrieved three gene expression proles
(GSE134359, GSE31448 and GSE42568) from the GEO database. GSE134359 (Platform GPL17586) comprised
74 breast cancer samples and 12 noncancerous samples, GSE31448 (Platform GPL570) comprised 29 breast
cancer samples and 4 noncancerous samples, and GSE42568 (Platform GPL570) comprised 104 breast cancer
samples and 17 noncancerous samples. e characteristics of these datasets are shown in Table1. More detailed
patient information is provided in supplementary material 1. e “Reporting recommendations for tumor
marker prognostic studies (REMARK)” were followed20.
Data analysis. We used GEO2R with screening criteria of adj. P < 0.05, log2 FC (fold change) > 1.5, or log2
FC < − 1.5 to detect DEGs in breast cancer tissues compared with normal samples. We also used an online tool
(http:// bioin forma tics. psb. ugent. be/ webto ols/ Venn/) to plot Venn diagrams of the DEGs of three datasets.
Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway analy-
sis. DAVID (http:// david. ncifc rf. gov; version 6.8) is an open database that integrates biological data and ana-
lytical tools for functional annotation of genes and pathways21. GO is a bioinformatics tool for annotating genes
and analyzing the biological processes they are involved in. KEGG is a database for analyzing relevant signaling
pathways in largescale molecular datasets generated by high-throughput experimental techniques22. DAVID was
used for GO enrichment analysis of the DEGs in terms of the molecular function, cell composition and biologi-
cal process for each gene. KEGG pathway enrichment analysis was performed to clarify the function of the DEGs
and the cell signaling pathways.
PPI network visualization. We used the online analysis tool STRING (http:// www. string- db. org/), with a
condence of 0.4, to construct the PPI network diagram for the identied DEGs. Cytoscape soware23 was then
used to construct the interaction network map, and the MCODE plug-in was used to screen the key gene mod-
ules in the network map. Cytoscape (version 3.8.0) is an open-source bioinformatics tool used to generate visual
molecular interaction networks and the plug-in Molecular Complex Detection (MCODE) can develop key gene
modules in the network. For this, we set the following parameters in MCODE: Degree Cut-o = 2, Node Score
Cut-o = 0.2, K-Core = 2 and Max D epth = 10024.
Table 1. Summary of patient characteristics of three GEO datasets.
GEO accession GSE134359 GSE31448 GSE42568
Sample
Health 12 4 17
Tum or 74 29 104
Histological type
Luminal A 24 1 NA
Luminal B 23 0 NA
HER2+ 14 1 NA
Basal 13 25 NA
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Kaplan–Meier survival and redundancy analyses of DEGs. A Kaplan–Meier plotter has been devel-
oped to evaluate the eects of 54,000 transcripts (mRNA, miRNA, protein) on survival for 21 types of can-
cer, including breast cancer (n = 6234), ovarian cancer (n = 2190), lung cancer (n = 3452), and stomach cancer
(n = 1440)25. is database collates data from the GEO, European Genome-phenome Archive, and e Cancer
Genome Atlas (TCGA) databases. e website was used to plot the OS for breast cancer patients for each gene.
By selecting the best cuto, a survival analysis was performed and False-Discovery Rate (FDR) was computed
using the Benjamini–Hochberg method to correct for multiple hypothesis testing26. e hazard ratio (HR) and
log-rank P values with 95% condence intervals (CI) were calculated and displayed on the graph.
Cancer Dependency Map (Cancer DepMap, https:// depmap. org/ portal/), a RNAi and CRISPR-Cas9 knockout
database27,28, was used to identied the functional target genes those are essential for breast cancer survival. e
essential genes are potential therapeutic targets for breast cancer.
Protein level verication. We visualized the selected hub-gene through ualcan29, and the protein expres-
sion data with 18 normal and 125 breast cancer samples were from CPTAC (Oce of Cancer Clinical Proteom-
ics Research, https:// prote omics. cancer. gov/ progr ams/ cptac).
Results
Screening of DEGs. A total of 1529, 1550, and 2188 DEGs were identied from the GSE134359, GSE31448,
and GSE42568 datasets, respectively. Of these, 268 genes were present in all three datasets (Fig.1A). 89 genes
consistently showed high expression and 179 genes showed low expression in all three databases. e top 22
DEGs are shown on the heatmap, based on the criteria |log2 FC|> 3 and adj.P < 0.05 (Fig.1B).
GO and KEGG pathway enrichment analysis. GO enrichment and KEGG pathway analysis were per-
formed on the DEGs using the DAVID database. GO enrichment analysis covers three aspects: biological pro-
cesses, cell composition and molecular function (Fig.2A). e upregulated genes were mainly related to mitotic
cytokinesis, mitotic spindle assembly and microtubule-based movement; while the downregulated genes were
mainly involved in cell adhesion, the response to mechanical stimuli and the response to glucose. e KEGG
pathway analysis showed that the genes upregulated in tumors were enriched in cell cycle, oocyte meiosis and
the P53 signaling pathway, while the downregulated genes were enriched in PPAR signaling pathway, AMPK
signaling pathway, tyrosine metabolism, pathways in cancer and so on (Fig.2B).
PPI network construction and module selection. Considering the critical role of protein interactions
in protein function, we used the STRING database and Cytoscape soware to generate PPI network once we had
Figure1. Identication of DEGs in the indicated breast cancer datasets.(A) ree online-available expression
proling datasets (GSE134359, GSE31448, GSE42568) were analyzed using GEO2R, and genes dierentially
expressed in breast tumor and peri-tumor samples (adj. P < 0.05 and |log2 FC |> 1.5) were dened as DEGs,
followed by Venn diagram of DEGs. (B) Heatmap of top DEGs (adj.P < 0.05 and |log2 FC|> 3) in datasets
GSE134359, GSE31448 and GSE42568.
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identied the 268 DEGs. e results showed that there were dense regions in PPI, that is, genes closely related to
breast cancer (HUB genes) modules.
A total of 236 nodes and 2132 edges were selected to plot the PPI network, which consisted of 87 up-regulated
genes and 149 down-regulated genes (Fig.3A). Subsequently, a pivotal module of 53 genes (CDK1, KIF11,
DLGAP5, KIF4A and so on) was identied with the degree ≥ 10 as the cut-o value by using MCODE (Fig.3B).
Another important module of 8 genes including both up-regulated and down-regulated genes was also identi-
ed (Fig.3C). e top 10 HUB genes were identied by cytoHubba (Top 10 genes ranked in MCC). GO and
KEGG analysis of these ten genes were conducted. HUB genes are related with cell division, mitotic cytokinesis
in Biologycal Process; spindle, nucleus, spindle microtubule in Cellular Component; protein kinase binding,
ATP binding in Molecular Function (Fig.3D). ey are also enriched in cell cycle, oocyte meiosis, p53 signaling
pathway and so on (Fig.3E).
Survival and redundancy analyses. Ten HUB genes in PPI network were evaluated for their prognostic
value on the Kaplan–Meier plotter. All 10 genes exhibited their potential in the prediction of survival based on
their expression. e OS for breast cancer patients was determined based on the expression level of each gene
(low vs. high). As shown in Fig.4, high mRNA expression of ZWINT (HR 1.6, 95% CI: 1.31–1.94, P < 2.9E−6,
FDR 1%) was associated with a poorer OS for breast cancer patients, and this association also works for DLGAP5
(HR 2.25, 95% CI: 1.74–2.92, P = 2.8e−10, FDR 1%), DTL (HR 1.61, 95% CI: 1.32–1.96, P < 1.5E−6, FDR 1%),
NCAPG(HR 1.6, 95% CI: 1.48–2.19, P < 1.9E−9, FDR 1%), CCNB1 (HR 1.66, 95% CI: 1.27–2.17, P < 0.00019,
FDR 10%), AURKA (HR 1.73, 95% CI: 1.42–2.11, P < 2.9E−8, FDR 1%), KIF23 (HR 1.59, 95% CI: 1.3–1.93,
P < 2.9E−6, FDR 1%), KIF11 (HR 1.64, 95% CI: 1.33–2.03, P < 3.2E−6, FDR 1%), RRM2 (HR 2.09, 95% CI:
1.63–2.68, P < 2.3E−9, FDR 1%) and UBE2C (HR 1.74, 95% CI: 1.43–2.12, P < 2.3E−8, FDR 1%). Among them,
FDR of CCNB1 was 10%. Maybe the relationship between CCNB1 and survival is not obvious.
It is of great signicance to analyze the role of HUB genes in breast cancer cell survival, and the essential genes
are potential therapeutic targets. Here we analyzed the function of HUB genes using online-available DepMap
tool, which was established based on CRISPR screening and siRNA screening data. ere are 2 genes (KIF11,
RRM2) that are common essential in both CRIAPR knockout and RNAi; 6 genes (AURKA, CCNB1, DTL, KIF23,
NCAPG, ZWINT) that are common essential only in CRISPR knockout, indicating that these genes are not only
diagnosis markers but also potential therapeutic targets (Fig.5).
Figure2. GO and KEGG analyses of DEGs. (A) GO analysis with up-regulated (red) and down-regulated
(green) DEGs. Enriched GO items with P < 0.01 are shown, including biological process, cellular component,
and molecular function. (B) KEGG analysis with up-regulated (red) and down-regulated (green) DEGs.
Enriched KEGG pathways (P < 0.01) are shown.
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Figure3. PPI and MCODE analyses of DEGs. (A) Protein–protein interaction network of 268 DEGs. (B) A
signicant module, containing 53 up-regulated proteins, was selected from protein–protein interaction network.
(C) Another module selected from protein–protein interaction network. (D) GO analysis of MCODE genes.
Enriched GO items with P < 0.01 are shown. (E) KEGG pathway analysis of MCODE genes. Enriched pathways
with P < 0.05 are shown. For (A–C), red nodes are up-regulated proteins, and green nodes are down-regulated
proteins. e lines represent the interaction relationship between nodes.
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Protein level verication. Finally, we veried the screened HUB genes at the protein level (Fig.6). e sta-
tistical signicance of ZWINT (< 1E-12), DLGAP5 (< 1E−12), DTL (1.335246E-04), NCAPG (2.391440E−04),
CCNB1 (3.434030E−03), AURKA (< 1E−12), KIF11 (5.4090702551948E−13), RRM2 (4.27904E−03) and
UBE2C (1.99866742542919E−06) was less than 0.05, except KIF23 (9.61308686E−01).
Discussion
Regardless of recent progress in the treatment of breast cancer, it has remained the most common cause of
cancer-related deaths in the past few years. e high mortality rate of breast cancer is partly due to the lack of
adequate screening methods with high sensitivity and specicity. erefore, it is necessary to identify potential
biomarkers for screening and early diagnosis of breast cancer. Microarray technologies and next-generation
sequencing have become key tools for providing comprehensive genetic information on breast cancer samples
and revealing the changes in disease progression. In this study, we used proven online bioinformatics tools to
investigate possible biomarkers for diagnosis of breast cancer. We identied a total of 268 DEGs common to all
three GEO datasets, which included 89 upregulated genes and 179 downregulated genes.
e upregulated genes were mainly involved in the three pathways, namely cell cycle, oocyte meiosis and the
P53 signaling pathway, which are closely associated with cancer. e downregulated genes were mainly enriched
in three other pathways: cell adhesion, the response to mechanical stimuli and the response to hormonal hypoxia.
Among the identied DEGs, 87 showed high degrees in the PPI network. Further analysis revealed that the
following 10 DEGs within these modules were closely associated with a shorter survival time of breast cancer
patients: DLGAP5, AURKA, UBE2C, CCNB1, RRM2, KIF23, KIF11, NCAPG, ZWINT and DTL.
DLGAP5 is involved in Aurora A signaling and its neurogenic locus notch homolog protein 3 (NOTCH3)
intracellular domain regulates transcription. DLGAP5 overexpression is associated with poor prognosis of breast
Figure4. Prognostic estimation of the top 10 HUB genes. e top 10 HUB genes including ZWINT, DLGAP5,
DTL, NCAPG, CCNB1, AURKA, KIF23, KIF11, RRM2 and UBE2C, were identied by cytoHubba, followed by
survival analysis. Breast cancer patients were divided into two groups according to auto select best cuto. Low,
patients with gene expression lower than best cuto; high, patients with gene expression higher than best cuto.
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cancer30. DLGAP5 is also associated with the prognosis of colorectal cancer, prostate cancer, and non-small cell
lung cancer (NSCLC)31–35. A study identied a critical target of NOTCH3 signaling was the mitotic apparatus
organizing protein DLGAP5 (HURP/DLG7)36. DLGAP5, which is regulated by nucleolar and spindle associated
protein 1 (NUSAP1), is associated with the proliferation, migration and invasion of invasive breast cancer37.
DLGAP5, required for AURKA-dependent, centrosome-independent mitotic spindle assembly, is essential for
the survival and proliferation ofSMARCA4/BRG1 mutant38.One subpopulation of prostate cancerwas associated
with enhanced expression ofDLGAP5 and decreased dependence upon androgen receptor signaling39.
AURKA plays an important role in cell cycle progression by promoting cell entry into mitosis, and is associ-
ated with increased risk of developing breast cancer. AURKA can translocate to the nucleus and enhance the
phenotype of breast cancer stem cells, promoting unique oncogenic properties in malignant cells40. It has been
reported that AURKA regulates the phenotype of breast cancer tumor stem cells by modifying and stabilizing
Drosha mRNA with M6A41. In addition, AURKA plays an important role in the treatment of drug-resistant breast
cancer42, and Aurora kinase A inhibitor has been in a ve-arm phase 2 study for safety and activity43.
Figure5. Redundancy analysis of the top 10 HUB genes. e essential role of indicated HUB genes in breast
cancer cell survival was analyzed via DepMap, (https:// depmap. org/ portal/), which was established from
CRISPR and RNAi screening data. (A) Redundancy analysis of ten genes in total cells lines. (B) e CERES
dependency score of ten genes in breast cancer cells. A lower CERES score indicates a higher likelihood that the
gene of interest is essential in a given cell line. A score of 0 indicates a gene is not essential (dotted line); − 1 is
comparable to the median of all pan-essential genes (red line).
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UBE2C can ubiquitinate Anaphase-Promoting Complex/Cyclosome (APC/C) (Ub)44. e high expression
of UBE2C in breast cancer was reported to be an independent prognostic factor associated with increased risk
of disease recurrence and death. us, it is considered as a potential therapeutic target for breast cancer45–47.
Cyclin B1, the protein encoded by CCNB1, is a regulatory protein involved in mitosis. It is necessary for
proper control of the G2/M transition phase of the cell cycle. A study showed cyclin B1 and B2 transgenic mice
are highly prone to tumors, including tumor types where B-type cyclins serve as prognosticators48. CCNB1 is
associated with radiosensitivity in colorectal cancer49. CCNB1 can also aect cavernous sinus invasion in pituitary
adenomas through the epithelial-mesenchymal transition50.
e gene RRM2 encodes ribonucleotide reductase regulatory subunit M2, one of two non-identical subunits
of ribonucleotide reductase. In a study that reported RRM2 acetylation at K95 suppresses tumor cell growth
invitro and invivo, and is therefore a potentially attractive strategy for cancer therapy51. In a study that searched
Figure6. Protein expression of the top 10 HUB genes. e top 10 HUB genes, including ZWINT, DLGAP5,
DTL, NCAPG, CCNB1, AURKA, KIF23, KIF11, RRM2 and UBE2C, were identied by cytoHubba, veried at
the protein level by Ualcan. Z-values represent standard deviations from the median across samples for the given
cancer type. Log2 Spectral count ratio values from CPTAC were rst normalized within each sample prole,
then normalized across samples.
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the GEO database for miRNA-mRNA or lncRNA-mRNA as novel biomarkers for breast cancer, the miR-21/
RRM2 axis was identied as a candidate biomarker for the diagnosis and treatment of breast cancer52. In another
study that showed a lincRNA, lincNMR, regulates tumor cell proliferation through a YBX1-RRM2-TYMS-TK1
axis governing nucleotide metabolism53. In addition, RRM2 was reported to be associated with the prognosis
of prostate cancer54.
Kinesin family member 23, the protein encoded by KIF23 is a member of the kinesin-like protein family, also
known as MKLP1. MKLP1/KIF23 is the kinesin component of the centralspindlin complex55. It was reported
that KIF23 expression is high in the majority of primary and metastatic lung cancer tissues or cell lines, and it is
associated with poor survival56. In a study that examined the association between members of the kinesin fam-
ily and breast cancer, KIF23 and KIF11 were found to be associated with poor prognosis57. KIF23 is regulated
through wnt signaling pathway and associated with recurrence of hepatocellular carcinoma58.
Kinesin family member 11, the protein encoded by KIF11, is another member of the kinesin-like protein
family. According to an Oncomine analysis of GEO and TCGA databases, KIF11 is a proto-oncogene associated
with breast cancer and is signicantly associated with poor prognosis59. KIF11 is also regulated through wnt
signaling pathway and associated with recurrence of hepatocellular carcinoma.
NCAPG is a potential prognostic marker in HER2+ breast cancer, anda therapeutic target to eectively over-
come trastuzumab resistance as well60. NCAPG has also been identied as a key gene in triple-negative breast
cancer61 as well as hepatocellular carcinoma62. Furthermore, it was reported that high expression of NCAPG is
associated with poor prognosis of various tumor types, and its overexpression may play an important role in the
regulation of tumor-related pathways in tumor growth63.
Currently, little is known about the role of ZW10 interactor (ZWINT) in breast cancer. Denticleless E3 ubiq-
uitin protein ligase homolog (DTL) is associated with proliferation and appears to be a promising molecular
therapeutic target in breast cancer64. DTL may also be associated with poor prognosis of acral melanoma and
gastric carcinoma65,66.
Based on redundancy analysis, two genes, KIF11 and RRM2, may serve as therapeutic targets or prognostic
indicators. e two genes are also dierentially expressed by protein level verication. ere are many dierences
between the predicted data and the clinical data, and the survival data derived from the Kaplan–Meier tool need
to be validated. In future studies, more attention should be paid to breast cancer patients. ere are many tumor
subtypes for breast cancer, and it is necessary to dene the biomarker characteristics of each subtypes. In our
future study, we intend to recruit a cohort of breast cancer patients to investigate the sensitivity and specicity
of these biomarkers for early screening of breast cancer; the results should facilitate the clinical application of
these biomarkers for the diagnosis of breast cancer.
Data availability
e datasets are available from the GEO database.
Received: 9 June 2021; Accepted: 8 October 2021
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Acknowledgements
e authors thank all individuals who contributed sample information and research personnel who have been
responsible for data acquisition. Data are available from GEO database. is work was supported by Ministry of
Science and Technology of the People’s Republic of China (2020YFA0803500), National Natural Science Foun-
dation of China (31922024). We thank the supporting grants from Zhengzhou University to Pingping Zhu, and
the technical support from Modern Analysis and Computer Center of Zhengzhou University.
Author contributions
X.Z. wrote the main manuscript text and prepared Figs.1, 2, 3, 4, and 5. G.S. prepared Fig.1. All authors reviewed
the manuscript.
Funding
This work was supported by Ministry of Science and Technology of the People’s Republic of China
(2020YFA0803500), National Natural Science Foundation of China (31922024).
Competing interests
e authors declare no competing interests.
Additional information
Supplementary Information e online version contains supplementary material available at https:// doi. org/
10. 1038/ s41598- 021- 00268-9.
Correspondence and requests for materials should be addressed to Q.H.orP.Z.
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