A bioinformatics analysis: ZFHX4 promotes metastasis in ovarian cancer by modulating epithelial-mesenchymal transition and remodeling extracellular matrix

doi:10.21203/rs.3.rs-1704861/v1

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A bioinformatics analysis: ZFHX4 promotes metastasis in ovarian cancer by modulating epithelial-mesenchymal transition and remodeling extracellular matrix

https://doi.org/10.21203/rs.3.rs-1704861/v1

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Background

Ovarian cancer is a highly malignant gynecological tumor with high mortality worldwide. The poor prognosis mainly results from extensive metastasis at the time of presentation. Although some mechanisms of metastasis in ovarian cancer were proposed, few have been used in the clinical practice. In the study, we aimed to identify novel genes and pathways contributing to metastatic ovarian cancer from bioinformatics databases, thus providing more potential candidates for clinical applications.

Methods

Studies collecting matched primary tumors and metastatic lesions from ovarian cancer patients were searched in the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between groups were screened by software R language. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis for the DEGs were implemented by using online software Metascape. Venn diagram was plotted to present overlapping DEGs. The associations between the overlapping DEGs and prognosis were tested by Cox proportional hazard regression model using a cohort from the TCGA database. Genes affecting patients’ outcome significantly were served as hub genes. The mechanisms of the hub genes in promoting ovarian cancer metastasis were predicted using samples from TCGA database by R software.

Results

Two gene expression profiles (GSE30587 and GSE73168) met the inclusion criteria and were finally analyzed. A total of 362 genes were significantly differentially expressed in GSE30587, whereas 653 genes were in GSE73168. In GSE30587, DEGs were mainly involved in extracellular matrix (ECM) organization; For GSE73168, most of DEGs showed ion trans-membrane transport activity. Both of the DEG sets were related to system development. There were 8 overlapping genes between the two datasets (DCN, SVEP1, MS4A6A, FABP4, APBB1IP, VCAM1 and ZFHX4), and ZFHX4 was proved to be an independent adverse prognostic factor for ovarian cancer patients (HR = 1.40, 95%CI: 1.11–1.75, p = 0.004). Mechanistically, ZFHX4 was positively significantly correlated with epithelial-mesenchymal transition (EMT) markers (r = 0.54, p = 2.59×10^− 29) and ECM-related genes (r = 0.52, p = 2.86×10^− 27).

Conclusions

ZFHX4 could promote metastasis in ovarian cancer by regulating EMT and reprogramming matrisome, which rendered ZFHX4 as a potential therapeutic target and prognostic biomarker for ovarian cancer metastasis.

ovarian cancer

metastasis

ZFHX4

epithelial-mesenchymal transition

extracellular matrix

Ovarian cancer is the leading cause of gynecological cancer death in the developed countries and the second mortal gynecologic malignancy in China [1, 2]. Extensive metastasis due to late diagnosis and refractory relapse, which always presented as widespread intraperitoneal dissemination, is chiefly responsible for this discouraging statistic [3, 4]. Therefore, metastasis has been put under the spotlight in the investigations for ovarian cancer. Although various mechanisms were proposed to be associated with ovarian cancer metastasis based on in vitro and in vivo experiments [5, 6], few have been recognized and used in the clinical practice. Consequently, there is a critical need for finding out more novel genes and pathways, which can become potential candidates for clinical applications.

Currently, with the rapid development of molecular expression profiling technologies, such as gene microarray, next generation sequencing and protein chips, and bioinformatics tools, it is more convenient for us to identify cancer-related gene alterations and postulate signaling pathways involved in the tumor progression. In this study, we aimed to further probe into underlying mechanisms that promoting ovarian cancer metastasis, and screened out hub genes which were fit for drug targets and biomarkers for ovarian cancer metastasis by mining bioinformatics databases. For the purposes, we searched the studies including samples of primary tumors and metastatic lesion in the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) between groups were, then, screened by programming software R language. This was followed by Gene Ontology (GO) functional analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment of DEGs. Next, the hub genes were identified by using Venn diagram and Cox regression analysis, and the functions of the hub genes in ovarian cancer metastasis were explored using samples from TCGA database with R software.

2.1. Strategies of Search

We systematically searched for the relevant studies in the GEO database (https://www.ncbi.nlm.nih.gov/gds/). The search terms were shown as follow: [("Ovarian Neoplasms") OR ("Ovarian Neoplasms") OR ("Ovarian Neoplasm") OR ("Ovary Neoplasms") OR ("Ovary Neoplasm") OR ("Ovary Cancer") OR ("Ovary Cancers") OR ("Ovarian Cancer") OR ("Ovarian Cancers") OR ("Cancer of Ovary") OR ("Cancer of the Ovary")] AND [("Metastasis") OR ("Metastases") OR ("metastatic")]. The inclusion criteria for eligible studies were that (1) matched primary tumors and metastatic lesions from ovarian cancer patients were collected and analyzed; (2) platforms used for sequencing or gene chip were introduced; (3) five pairs of matched samples were at least included in the series. The exclusion criteria were that (1) The data in the downloaded files were incomplete or invalid; (2) gene probe ID in certain platforms could not be transformed into gene names.

2.2. Selection of DEGs

The raw data were downloaded as Series Matrix Files. The data values in the microarray were log₂-transformed, and normalized using function normalize.quantiles in R programming tools (version 4.2.0). Probes were converted to gene symbols according to the annotation information of the platform. PCA plots were visualized to access the qualities of data using “simpleaffy” package from R software (version 4.2.0). Then, “limma” package in the R language (version 4.2.0) was used to screen DEGs. The thresholds for DEGs identification were set as p value < 0.05 and |log₂ (Fold Change)|≧1. Volcano plots and heat maps were generated using TBtools (version 1.049), which is an integrative toolkit developed for interactive analyses of big biological data [7].

2.3. Analysis of functions and pathways

To predict potential biological functions of DEGs and pathways they might be involved in, we using Metascape (https://metascape.org/) to perform annotation analysis. Metascape was a web-based tool, and has been designed for experimentalists to apply powerful computational analysis pipelines to analyze and interpret large-scale datasets [8]. Using Metascape, we implemented the GO enrichment for the lists of DEGs. Deregulated pathways were also ranked on the basis of − log₁₀ (p value) from KEGG analysis by Metascape.

2.4. Identification of hub genes

Firstly, Venn diagram was plotted online to present DEGs which were common in the included gene expression profiles (http://jvenn.toulouse.inra.fr/app/example.html), and the overlapping genes were regarded as candidate genes for further analysis. Next, the associations between candidate genes and prognosis were tested by Cox proportional hazard regression model on a cohort from the TCGA database. Univariate and multivariate cox regression analysis were performed using “forestplot” package from R software (version 4.2.0). A nomogram was developed to predict the 1, 3, and 5-year overall survival using “rms” package in R programming language (version 4.2.0). The nomogram was validated by calibration curves using the same R package. We served genes affecting patients’ outcome significantly (p༜0.05) as hub genes. Finally, Kaplan-Meier analysis was used to verify the effect of hub genes on survival using Kaplan-Meier plotter (http://kmplot.com/analysis/).

2.5. Exploration of mechanisms of hub genes in ovarian cancer metastasis

The functions of the hub genes and the signaling pathways they might be involved in were explored using samples from TCGA database. “GSVA” package in R software (version 4.2.0) was used to analyze the correlation between hub genes and pathway scores by Spearman correlation [9], and p values less than 0.05 were considered statistically significant. The associations of hub genes and other gene sets were also performed using the same R package.

3.1. Data source

One hundred and ten studies were retrieved from the GEO database. After screening titles and abstracts, 101 records were excluded. In the remaining 9 studies, 5 did provide enough platform annotation information for us to convert EntrezID into gene names, so 4 records, which contains GSE30587, GSE73064, GSE73091 and GSE73168, respectively, met the inclusion criteria. Because GSE73064 and GSE73091 were subgroups of GSE73168, 2 gene expression profiles (GSE30587 and GSE73168) were finally analyzed.

GSE30587 consisted of 9 pairs of matched primary and metastatic tumors from ovarian cancer patients, while GSE73168 contained 8 pairs of matched lesions. The chip-based platform GPL6244 (HuGene-1_0-st; Affymetrix Human Gene 1.0 ST Array) was applied for the GSE30587, and the data from GSE73168 was generated from GPL570 platform (HG-U133_Plus_2; Affymetrix Human Genome U133 plus 2.0 Array).

3.2. Gene Expression Changes in GSE30587 and GSE73168

In GSE30587, 4 primary samples (GSM758800, GSM758804,GSM758810 and GSM758816) and 2 metastatic samples(GSM758803 and 758809)were ruled out since they were found to be outliers by principle component analysis (PCA). A total of 362 genes were significantly differentially expressed in GSE30587 with a significant level of p value < 0.05 and absolute fold change ≧ 2 (Fig. 1A Left), whereas 653 genes were differentially expressed in GSE73168 (Fig. 1A Right). The green dots represented down-regulated genes, while the up-regulated genes were red-color coded. The top 50 up-regulated and down-regulated genes were then determined and showed as heat maps (Fig. 1B).

GO analysis was done to annotate the biological functions DEGs occurring during metastasis. In GSE30587, DEGs were mainly located in the extracellular matrix (ECM) of cellular components (CC), exhibited the molecular functions (MF) of ECM structural constituent, glycosaminoglycan binding, integrin binding and so on, and were involved in the biological processes (BP) such as extracellular matrix organization, regulation of cell adhesion and cell-cell adhesion (Fig. 2A Left). For GSE73168, DEGs were primarily enriched in plasma membranes, showed functions of gated channel activity, anion trans-membrane transporter activity, neuro-transmitter trans-membrane transporter activity, and participated in the processes such as anion transport, anion trans-membrane transport and inorganic ion trans-membrane transport (Fig. 2A Right). Remarkably, terms related with development, for instance, vasculature development, heart development, cell fate commitment, skeletal system development, embryonic organ development, regulation of wound healing and tissue remodeling, were common in both of the two data sets (Fig. 2A), which indicated that the process of metastasis imitated the development procedure and underwent mesenchymal transition.

KEGG analysis was done to find out deregulated pathways involved in metastasis. Complement and coagulation cascades and ECM-receptor interaction were the most significant pathways in the GSE30587 (Fig. 2B Left), while the most significantly correlated pathways in GSE73168 were Pathways in Cancer and JAK-STAT signaling pathway (Fig. 2B Right). GO enrichment and KEGG pathway analysis offered guides for exploring the functions of key genes screened out for subsequent experiments.

3.3. Overlapping Genes in GSE30587 and GSE73168

It is expected that the expressions of various genes altered as the tumors progressed from primary sites to metastasis loci. As mentioned above, GSE30587 showed that the process of metastasis resulted in deregulation of 362 mRNAs, while the dissemination formation was accompanied by changes of 653 mRNAs in GSE73168. In the next step, we characterized the overlap in the two datasets. Of the 362 and 653 DEGs in GSE30587 and GSE73168, respectively, 8 overlapping genes were identified (Fig. 3A). Seven genes were commonly up-regulated in both of the datasets, however, one gene was up-regulated in GSE30587 but down-regulated in GSE73168 (Fig. 3B). The co-upregulated DEGs included DCN, SVEP1, MS4A6A, FABP4, APBB1IP, VCAM1 and ZFHX4, whereas PIEZO2 represented different directions of change in the two groups (Fig. 3C and D). Therefore, the 8 overlapping genes were considered as candidate genes for further analysis.

3.4. Univariate and multivariate analysis of ovarian cancer patients in the TCGA database

To validate the effects of candidate genes on the outcome of ovarian cancer, we conducted univariate and multivariate Cox proportional hazards analysis on 374 patients from the TCGA database. The characteristics of the patients in the cohort were included in Table 1. The median follow-up was 86.0 months. At the end of follow-up, a total of 230 (61.5%) deaths occurred. The median age at diagnosis was 59 years (range: 30–87). Patients with stage III/IV ovarian cancer accounted for 94.1%.

Table 1

Clinical information of ovarian cancer patients in the TCGA database
	Characteristics	N (%)
Status	Alive	144 (38.50)
Status	Dead	230 (61.50)
Age	Median (Min, Max )	59 (30, 87)
Gender	Female	374 (100.00)
Race	American Indian	8 (2.14)
	Asian	11 (2.94)
	Black	25 (6.68)
	Islander	6 (1.61)
	White	324 (86.63)
TNM stage	IC	2 (0.53)
	IIA	3 (0.81)
	IIB	5 (1.34)
	IIC	15 (4.01)
	IIIA	7 (1.87)
	IIIB	14 (3.74)
	IIIC	271 (72.46)
	IV	57 (15.24)
Grade	G1	6 (1.60)
	G2	42 (11.24)
	G3	320 (85.56)
	G4	6 (1.60)
Note: Age was presented using median and range.

Univariable analysis indicated that ZFHX4 and PIEZO2 were the prognostic predictors among the candidate genes (Fig. 4A). In multivariable analysis, ZFHX4 was proved to be an independent adverse prognostic factor for ovarian cancer patients (HR = 1.40, 95%CI: 1.11–1.75, p = 0.004) (Fig. 4B). A nomogram was plotted to predict 1-, 3-, and 5-year overall survival (OS) of ovarian cancer patients according to the ZFHX4 values (Fig. 4C). The calibration curve certified that nomogram-predicted curves were close to the observed curves, suggesting satisfactory agreement between the predicted results and the actual values (Fig. 4D).

We also checked the effect of ZFHX4 on ovarian cancer patient’s prognosis using Kaplan-Meier Plotter. Patients were spilt by median expression, and increased ZFHX4 caused significantly decreased OS (HR = 1.28, 95%CI: 1.11–1.47, p = 0.00066) and progress-free survival (PFS) (HR = 1.86, 95%CI: 1.53–2.25, p = 1.4×10^− 10) (Fig. 4E and F). ZFHX4, thus, was selected as the hub gene for the function exploration.

3.5. Mechanisms of ZFHX4 in ovarian cancer metastasis

To better understand the potential mechanisms of the hub gene in promoting metastasis in ovarian cancer, we explored the common pathways in which ZFHX4 might be involved in using samples from TCGA database by R software GSVA package via ssGSEA algorithm. The most significantly positively correlated functions included collagen formation (r = 0.67, p = 4.72×10^− 51) and degradation of ECM (r = 0.63, p = 2.84×10^− 43) (Fig. 5A and B). ECM-related genes (r = 0.52, p = 2.86×10^− 27) and EMT markers (r = 0.54, p = 2.59×10^− 29) were the most significantly positively correlated gene sets (Fig. 5C and D). There was a significant positive correlation between TGF-β signaling pathway and the expression of ZFHX4 (r = 0.60, p = 2.31×10^− 37) (Fig. 5E).

As mentioned above, GO and KEGG analysis indicated that EMT and matrisome alteration were key features during ovarian cancer progression, which were in line with the predicted functions of ZFHX4. As enrichment analysis also implied that ion channels might play an important role in ovarian cancer metastasis, we further analyzed the relationships between ZFHX4 and ion trans-membrane transporters using gene sets from GO datasets (GO: 0015318). We found that TRPM3, BEST1, TRPV4, SLC39A8, SLC10A7, ABCC8, CACNB4, SLC6A15, TSPO2, KCNK1 and PON3 were significantly positively correlated with ZFHX4 (p < 0.05), while COX4I1 was significantly negatively correlated with ZFHX4 (p < 0.05) (Fig. 6). The results suggested that ZFHX4 might be regulated by the activation of ion trans-membrane channels.

Notably, we also found that the expression of ZFHX4 was significantly negatively correlated with DNA replication (r = − 0.23, p = 8.5×10^− 06), MYC targets (r = − 0.27, p = 1.72×10^− 07), DNA repair (r = − 0.29, p = 6.44×10^− 09) and tumor proliferation signature (r = − 0.11, p = 0.032) (Fig. 7A, B, C and D), but significantly positively correlated with apoptosis (r = 0.41, p = 1.65×10^− 16) (Fig. 7E). These data suggested that ZFHX4 repressed the proliferative phenotype of ovarian cancer cells while it boosted their metastatic potentials.

In the present study, we did a bioinformatics analysis for primary tumors and metastatic lesions of ovarian cancer samples from GEO database to explore new genes and their mechanisms in ovarian cancer metastasis. While some information was available on the factors and pathways involved in the promoting invasiveness [10, 11], the progression of ovarian cancer was an intricate, multistage process along with numerous molecular changes, which further needed to be explored. Therefore, we systematically searched relevant studies in the GEO data library, and comprehensively analyzed the available datasets to find out common and unique signatures during the process of metastasis in ovarian cancer. Our results showed that the alterations of matrisome components and changes of gated channel activity were involved in this process. Moreover, we firstly identified ZFHX4 as the hub gene contributing to the ovarian cancer metastasis, and EMT regulation and ECM remodeling were considered as its potential mechanisms.

It has been well demonstrated that gene expression profiling of ECM components underwent deregulation during early colonization [12]. In turn, ECM remodeling could be conducive to cancer metastasis [13]. In the primary sites, basement membranes, which lined the basal surface of tumors and presented as dense collagen structures, were broken through proteolytic ECM degradation and non-proteolytic force by neoplastic cells and cancer-associated fibroblasts (CAFs) when tumor rapid growth led to diffusion-limited oxygen and nutrient supply [14–16]. Next, collagens in interstitial matrix were linearized by tumor-derived lysyl oxidase (LOX), which created migratory tracks used for the migration of the tumor cells [17, 18]. In the metastatic sites, however, CAFs deposited more collagens into matrix to support new lesions growth and help them evade immune surveillance [19, 20]. In accordance with the previous studies, we found that genes encoding alpha chains for fibrillar collagens, such as COL5A1, COL5A2, COL1A1 and COL1A2, were the top up-regulated DEGs in the metastatic samples compared to the primary ones. Besides collagens, altered glycoproteins, another kind of component in the ECM, might also contribute to the tumor metastasis. For instance, the expression of POSTN, which encoded a secreted extracellular matrix protein named Periostin, showed significant increase in the GSE30587. Yue.al reported that stromal Periostin could fuel the migration and invasion of ovarian cancer cells by binding to integrin αvβ3 and subsequently activating the PI3K/Akt pathway and inducing the EMT [21]. The regulation of TGFBI was disordered as well according to our results, and its encoding protein (transforming growth factor β induced protein, TGFBIp) was located in the extracellular matrix to bind to type I, II and IV collagens. As mentioned in the recent papers, macrophage-derived TGFBIp promoted ovarian cancer cell migration via inducing an immunosuppressive microenvironment [22, 23].

Ion channel disorder appeared to be responsible for the enhanced migration of cancer cells and ECM dysregulation [24, 25]. Aberrant activation of Ca²⁺-permeable channels on the endoplasmic reticulum membrane (STIM1) and plasma membrane (ORAI1) led to impulse Ca²⁺ influx [26]. The oscillatory Ca²⁺ signaling facilitated invadopodial precursor assembly via Src activation and increased proteolytic activity of invadopodia by recruitment of ECM-degrading membrane type 1 matrix metalloproteinase (MT1-MMP) [27]. Then, Ca²⁺-activated K⁺ channels, such as SK3, enhanced the driving force for Ca²⁺ entry through hyperpolarization of resting membrane potential [28, 29]. Moreover, abnormal opens of sodium ion channels also contributed to invadopodia formation via F-actin polymerization [30]. Na⁺/H⁺ exchangers on the membrane caveola resulted in a per-imembrane acidification, which rendered secreted cathepsins, pH-dependent ECM-digesting proteases, more active [31]. In our study, inorganic ion trans-membrane transports for calcium (TRPM3), sodium (SLC family) and potassium (KCNK1) were significantly up-regulated in the metastatic ovarian cancer samples compared to the primary ones, which were in accord with previous researches.

ZFHX4 is one of the five members of zinc finger homobox family with molecular weights of 397 kDa [32]. The protein comprises 22 typical C₂H₂ structures of zinc finger and 4 homeodomains, and its coding gene extends over 180 kb and is located on 8q13.3-q21.11[32]. ZFHX4 is predicted to be active in nucleus, and enable DNA-binding transcription factor activity. Fèvre-Montange et.al firstly reported that ZFHX4 was highly expressed in parapineal tumors, a rare but aggressive type of pineal region tumor, using oligonucleotide arrays in 2006 [33].Similarly, Wang et.al found that ZFHX4 amplifications were more common in the metastatic colorectal cancer patients than those without metastases [34]. In ovarian cancer, elevated expression of ZFHX4 was also demonstrated to be correlated with poor outcomes, which was mainly influenced by metastasis, in bioinformatics databases and real-world cohort samples [35, 36]. Our results were in accord with these studies, which indicated that ZFHX4 reduced the survival times of ovarian cancer patients through promoting metastasis.

Currently, researches for the functions of ZFHX4 largely focused on the therapy resistance and consequent recurrence. Chudnovsky et al. suggested that ZFHX4 interacted with the CHD4, an essential member of the NuRD (nucleosome remodeling and deacetylase) complex, to regulate the therapy-resistant tumor initiating cells in glioblastomas [37]. However, the detailed mechanism by which ZFHX4 regulated metastasis of ovarian cancer remained unknown. According to our bioinformatics analysis, we, for the first time, linked ZFHX4 with EMT-associated genes. In 2020, “the EMT International Association (TEMTIA)” issued a consensus statement on the famous academic journal “NATURE REVIEWS MOLECULAR CELL BIOLOGY” to provide guidelines and definitions for EMT [38]. In the statement, experts emphasized central roles of transcription factors belonging to the ZEB families (Zeb1 and Zeb2) in the execution of EMT in cancer metastasis [38]. Coincidentally, Zeb1 and Zeb2 were also two members of zinc finger homobox family, and they shared similar molecular structures with ZFHX4 [39], which rendered the function of ZFHX4 as an EMT inducer reasonable. Besides, ZFHX4 could be modulated by canonical pathways for EMT regulation, such as TGF-β signaling pathway [40, 41], which made further efforts to support the contributions of ZFHX4 to EMT. Simultaneously, we found the positive correlation between ZFHX4 and ECM-related genes. ZFHX4, on the one hand, was conducive to the collagen formation, but on the other hand, induced degradation of ECM. The results might be explained by the fact that individual cancer cell migrated faster in a mesenchymal phenotypes, while collective cell migration depended on the cell-matrix junctions [42]. Next, significant correlations were found between ZFHX4 and lots of ion channels. Eskandari et al. reported that Kv11.1 channel reprogrammed EMT in colon cancer via regulating zinc finger homobox proteins [43], suggesting that ZFHX4 might also act as a mediator converting the signals from ion transporter activation to EMT occurrence and ECM reprogramming in ovarian cancer. Although ZFHX4 took an active part in promoting cancer metastasis, it functioned as a tumor suppressor in tumor proliferation. This rather interesting finding might be explained by the opinion that expression changes of certain genes necessary for the initial tumorigenesis were not essential for subsequent metastasis, which was proposed by Mitra et al [12]. In their study, they observed that most of shared deregulated genes in the primary tumors versus normal tissues and metastasis versus primary tumors were altered in the different directions [12].

There were surely some limitations in the study. Firstly, DEGs were screened from primary tumors and metastatic lesions in GEO database, while the candidate genes were generated from ovarian cancer patients with various stages in TCGA database. Secondly, laboratory-based experiments have not been conducted to validate the mechanisms of ZFHX4 in metastasis in ovarian cancer. In the future studies, clinical samples from ovarian cancer patients would be collected to detect the expressions of ZFHX4. Then, the relationships between ZFHX4 and EMT- and ECM-related proteins would be further confirmed by in vitro or (and) in vivo experiments.

In summary, based on the bioinformatics analysis, we hypothesized that ZFHX4 could promote metastasis in ovarian cancer by regulating EMT and reprogramming matrisome. Therefore, ZFHX4 was expected to be a potential therapeutic target and prognostic biomarker for ovarian cancer metastasis.

GEO: Gene Expression Omnibus；DEGs: Differentially expressed genes；GO : Gene Ontology；KEGG: Kyoto Encyclopedia of Genes and Genomes；ECM: extracellular matrix；EMT: epithelial-mesenchymal transition；PCA: principle component analysis；CC : cellular components；MF molecular functions；BP : biological processes；CAFs : cancer-associated fibroblasts；LOX: lysyl oxidase；STIM1: endoplasmic reticulum membrane ()；plasma membrane (ORAI1)；MT1-MMP: matrix 1-metalloproteinase；

Ethics approval and consent to participate

Not applicable

Consent for publication

Not applicable

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

This work was supported by the Foundation Research Project (Natural Science Foundation) of Jiangsu Province in China (Grant No. BK20201013), Key Research and Development Program (Social Development) of Xuzhou City in China (Grant No. KC20158) and “Shuang-Chuang Doctor” Project of Jiangsu Province in China (Grant No. 2020204003).

Authors’ contributions

Shuai Zong and Ping-ping Xu performed the data mining and drafted the manuscript, and they were contributed equally to the whole study. Yin-hai Xu participated in the data analysis and interpretation. Yi Guo was involved in the study design and critical revision of the manuscript. All authors have read and approved the final manuscript. As the corresponding author, Yi Guo had the final responsibility for the decision to submit the article for publication.

Acknowledgments

We would like to extend our thanks to the generous researchers who have uploaded their experimental data onto online databases.

Authors' information

1. Department of Laboratory Medicine, Affiliated Hospital of Xuzhou Medical University, Jiangsu, 221006, China; 2. Department of Laboratory Medicine, Xuzhou Central Hospital, Jiangsu, 221006, China.

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No competing interests reported.

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Editorial decision: Major revision
30 Jun, 2022
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A bioinformatics analysis: ZFHX4 promotes metastasis in ovarian cancer by modulating epithelial-mesenchymal transition and remodeling extracellular matrix

Status:

Version 1

Abstract

Background

Methods

Results

Conclusions

Figures

1. Introduction

2. Materials And Results

2.1. Strategies of Search

2.2. Selection of DEGs

2.3. Analysis of functions and pathways

2.4. Identification of hub genes

2.5. Exploration of mechanisms of hub genes in ovarian cancer metastasis

3. Results

3.1. Data source

3.2. Gene Expression Changes in GSE30587 and GSE73168

3.3. Overlapping Genes in GSE30587 and GSE73168

3.4. Univariate and multivariate analysis of ovarian cancer patients in the TCGA database

3.5. Mechanisms of ZFHX4 in ovarian cancer metastasis

4. Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1