Screening and verification of potential gene targets in the metastasis of breast cancer by bioinformatics analysis and immunohistochemistry

doi:10.21203/rs.3.rs-2761895/v1

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Screening and verification of potential gene targets in the metastasis of breast cancer by bioinformatics analysis and immunohistochemistry

https://doi.org/10.21203/rs.3.rs-2761895/v1

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BACKGROUND

Breast cancer (BC), one of the most common cancers among females worldwide, has a high mortality rate, especially for patients with BC metastasis. However, BC metastasis pathogenesis has not yet been completely elucidated.

METHODS

We integrated multiple databases to clarify and verify potential key candidate biomarkers and signal transduction pathways in BC metastasis. Differentially expressed genes (DEGs) of breast cancer metastasis and non-metastasis sites were screened using microarray data from three publicly available datasets (GSE14776, GSE103357 and GSE32489). GEO2R, DAVID 6.8, STRING, Cytoscape, GEPIA 2.0 and R 4.0.5 were utilized in this study. The correlations between hub genes and clinical value were validated through the GEPIA online tool, UALCAN and immunohistochemistry (IHC) stain.

RESULTS

A total of 295 DEGs were identified, which were significantly enriched in terms related to focal adhesion, and cell division. KEGG pathway analysis showed that significant pathways included the MAPK signaling pathway, the Rap1 signaling pathway, cell adhesion molecules. Eight hub genes (TYMS, SKA1, ADCY7, POLR3H, CDCA8, PRC1(ASE1), KIF14, and MX1 and two biomarkers (PRC1 and POLR3H) with significantly prognostic values were screened by multi-omics data analysis and verified by IHC stain.

CONCLUSIONS

In this study, we identified a robust set of potential candidate biomarkers in BC metastasis, which would provide potential value for its early diagnosis and prognosis, and would promote molecular targeting therapy for BC metastasis.

Breast cancer

metastasis

bioinformatics analysis

differently expressed genes

therapeutic agent

survival analysis

Breast cancer (BC) is the most common malignant tumor threatening the health of females worldwide, and is responsible for the second most frequent cancer-related deaths in the United States, with an estimated 271,270 new cases and 42,260 deaths in 2019[1]. Patients with in situ BC usually have a good prognosis; however, advanced BC with distant metastasis tends to lead to life-threatening outcomes for patients[2]. Survival of BC is strongly related to the stage of the disease at diagnosis. Overall, the average 5-year survival rate of BC is 90%, but in cases with distant metastasis, the 5-year survival rate decreases to 26%[3].

The molecular mechanisms involved in BC progression are not fully understood. Given its high mortality rate, it is imperative to clarify the potential molecular mechanisms involved in BC metastasis to identify new biomarkers and to discover potential targeted molecular agents.

Previous studies have investigated the roles of several genes and their involvement in pathways that contribute to BC metastasis, Chenet al.[4] identified molecular signaling pathways (i.e., ECM-receptor interaction, FAK-calcium signaling pathway, and the PI3K/AKT signaling pathway) that may contribute to the aggressive behavior of BC metastasis to bone. The GSE102484 dataset was analyzed by Cai et al.[5] and five hub genes TPX2, KIF2C, CDCA8, BUB1B, and CCNA2 were reported to associated with the risk of BC distant metastasis. Zheng et al.[6] identified several BC metastasis-related genes by analyzing the GSE8977 dataset, which pairs genomic information of normal breast tissue with invasive breast tumor samples. In the studies mentioned above, a single dataset was analyzed and some potential BC metastatic mechanism was revealed. Nonetheless, with increasing sequencing profiles made available by researchers worldwide, a study that combines genomic information of cases with similar breast metastatic background has yet to be conducted.

In this study, we search for BC metastasis-related differentially expressed genes in the GSE32489 (mainly lymphatic metastasis), GSE14776 (bone metastasis), and GSE103357 (bone metastasis) datasets. Further, GO function and KEGG pathways involvement of these genes were analyzed by DAVID, while a protein-protein interaction (PPI) network analysis revealed ten important modules. Hub genes were obtained through sub-module analysis. Subsequently, Cytoscape software (https://cytoscape.org/) was used to explore the GO function of hub genes, the Kaplan-Meier plotter online tool was applied to analyze the correlation between the hub genes and overall survival and distant metastasis-free survival, and the GEPIA web tool (http://gepia.cancer-pku.cn/), based on TCGA database, was used to validate the correlations between the hub genes and clinical stages. The aim of this study was to gain a better understanding of underlying mechanisms involved in BC, and to identify potential biomarkers and therapeutic targets treatment of metastatic BC.

Data collection

Three data sets GSE32489, GSE14776 and GSE103357 were chosen from GEO database[7] (http://www.ncbi.nlm.nih.gov/geo) based on the their target gene type and their publication time(we mainly focused on the profiles that targets total RNA and those being published recently). The probes were converted into the corresponding gene symbol according to the annotation information in the platform. GSE32489 includes 120 samples in total: 19 autopsy tissues of the chest wall, liver, lymph nodes, lung, spleen, liver, and breast, 5 negative controls, 6 positive controls, and 90 lymph node metastasis. GSE14776 consists of 8 disseminated tumor cells samples and 6 bone metastatic tumor samples. The gene expression profile of 2 primary tumor cell samples and 3 bone metastatic tumor samples (GSE103357) were also collected. Three datasets mentioned above were mainly focusing on population of western people, GSE14776 were from Canada[8], GSE103357 were from Australia[9] and GSE32489 were from America[10].

Identification of DEGs

GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/), is a data processing tool on GEO. Statistically significant differences were identified based on a classic t-test, considering p < 0.05 and |log FC| > 1 as the cut-off criteria. In this study, we used GEO2R to filter the original data to determine DEGs. The respective DEGs from separate data sets were overlapped with VENN (http://bioinformatics.psb.ugent.be/webtools/Venn/) to detect the co-differentially-expressed genes. Probe sets without corresponding gene symbols or genes with more than one probe set were removed or averaged.

GO term and KEGG pathway analysis

GO is used to perform functional studies on gene sets[11]. Kyoto Encyclopedia of Genes and Genomes (KEGG) is a collection of databases dealing with genomes and biological pathways[12]. The Database for Annotation Visualization and Integrated Discovery (DAVID; http://david.ncifcrf.gov) (version 6.8)[13] integrating the information of biological functions of genes and analysis tools was used to unravel the GO and KEGG pathway of identified co-DEGs.

PPI network analysis of DEGs and modules selection

Search Tool for the Retrieval of Interacting Genes (STRING), an online biological database and web resource, which elucidating protein–protein interaction[14]. The PPI network with significant gene pairs was visualized using Cytoscape[15]. The combined score of > 0.4 was selected as the threshold. PPI network can help us identity the key genes and important gene modules which are involved in BC metastasis from interaction level. In addition, the Molecular Complex Detection (MCODE) app plugin of Cytoscape was applied to verify important modules with the following standard: degree cutoff = 2, node score cutoff = 0.2, k- core = 2, and max. depth = 100 [16].

Heatmap drawing, GO and clinical analysis of hub genes

The cytoHubba plugin and the maximal clique centrality (MCC) method was used to identify the top 20 hub genes. Heatmaps of hub genes in GSE32488, GSE103357 and GSE14776 were drawn by the use of heatmap package in R. Survival analysis was achieved using Kaplan Meier-plotter (KM plotter, http://kmplot.com/analysis/). GO function of hub genes was assessed by BiNGO plugin of Cytoscape.

Survival analysis of DEGs

Kaplan-Meier plotter (KM plotter, www.kmplot. com), an online survival analysis software, which was capable of evaluating the effect of 22,277 genes on BC prognosis using microarray data of 1,809 patients [17]. The patients with BC were split into two groups according to the expression of a particular gene (high vs. low expression). The two patient cohorts were compared by a Kaplan-Meier survival plot, and the hazard ratio (HR) with 95% confidence intervals and log rank P value were calculated. The GEPIA[18] is an online Gene expression profiling analysis tool based on TCGA database, it was applied to validate the correlations between the hub genes and clinical stages. We compared the expression of each candidate hub gene in BC samples at different clinical stages.

Clinical Information & Immunohistochemistry verification

With the approval of our institutional ethics review board, we collected clinical information from 23 BC patients, the clinical data were collected between June 2021 and August 2022 from the Department of General Surgery and Pathology in Zhongshan Hospital (Xiamen), Fudan University, in China. Patient’s information were in the supplement material.

Biopsy or surgical specimen were fixed with 10% formalin, Subsequently, the samples were embedded in paraffin and sliced into 3-µm sections. Samples were deparaffinized in xylene and rehydrated via a graded series of ethanol treatments. Hematoxylin staining was performed for 5 min and samples were then rinsed with ddH₂O. Eosin staining was performed for 1 min, dehydrated in graded ethanol (75% ethanol, 95% ethanol and absolute ethanol), sealed with neutral resin.

The paraffin-embedded tissues were sliced into 3‐µm sections, dewaxed in xylene, rehydrated in graded ethanol and subjected to antigen retrieval. Endogenous peroxidase activity was then inactivated by adding 0.3% H₂O₂ and incubating at room temperature for 10 minutes. After washing with PBS buffer three times, the sections were incubated overnight at 4℃ with the primary antibodies: anti‐PRC1(1:150, Biorbyt), anti‐MX1 (1:300, Proteintech), anti‐POLR3H (1:100, Proteintech), anti‐KIF4 (1:150, Proteintech). The sections were then added with horseradish peroxidase (HRP) at room temperature for 15 minutes, washed by PBS 3 times, developed by freshly prepared 3,3‐diaminobenzidine (DAB), stained with hematoxylin. IHC results were observed and photographed under 200X microscope.

Identification of DEGs and co-DEGs in three GSEs

GSE32489 containing 120 samples in total: 19 autopsy tissues of the chest wall, liver, lymph nodes, lung, spleen, liver, and breast, 5 negative controls, 6 positive controls, and 90 lymph node metastases. GSE14776 containing 8 disseminated tumor cells samples and 6 bone metastatic tumor samples. GSE103357 containing 2 primary tumor cell samples and 3 bone metastatic tumor samples. A total of 1875, 7673 and 6580 DEGs (differentially expressed genes) between metastasis sites and non-metastasis site were detected in three datasets respectively and 295 genes are identified as co-DEGs. Among which 151 was up-regulated (Fig. 1.A) and 144 was down-regulated (Fig. 1.B). Adjust p value < 0.05 and |log2FC|>1 were set as the cut-off criterion.

GO term enrichment and KEGG pathway analysis of DEGs

DAVID online tool was used to elucidate GO and KEGG pathways in the enriched co-DEGs. The results demonstrated that for biological process (BP), the different expressed genes were mainly enriched in innate immune response, apoptotic process, cAMP biosynthetic process (Fig. 2A); for cellular component (CC), they were mainly enriched in cytosol and nucleoplasm (Fig. 2B); for molecular function (MF), the genes were dominantly enriched on protein binding, MHC class II protein complex binding (Fig. 2C).

KEGG pathways results showed that the DEGs were significantly enriched in signaling pathways about cancer development including MAPK signaling pathway and Rap1 signaling pathway (Fig. 2D). These significantly enriched terms and pathways will provide further insight toward future research directions about the role which DEGs play in BC metastasis.

PPI network analysis of DEGs and modules selection

The PPI network of DEGs was constructed with 280 nodes and 357 edges, based on STRING database. Using the maximal clique centrality (MCC) method. A total of 20 genes were selected as hub genes (Fig. 3A). such as TYMS, CDCA8, PRC1 which might play a critical role in BC progression. Ten important modules were obtained from PPI network of DEGs using MCODE (Fig. 3B). Functional and KEGG pathway enrichment analysis revealed that genes in these modules were mainly related with extracellular matrix structural constituent, protein binding, bridging, ECM-receptor interaction and Focal adhesion.

Survival analysis of hub genes

The prognostic value of 20 hub genes was analyzed in Kaplan-Meier plotter. DMFS and OS for BC patient was obtained according to the low and high expression of each gene. It showed that low expression of TYMS, SKA1, ADCY7 and high expression of MX1 were related with worse OS (Fig. 4A). Low expression of POLR3H and high expression of CDCA8, ASE1, KIF14, MX1 were associated with worse DMFS (Fig. 4B). The training dataset (TCGA-BRCA) was applied to validate the correlations between the 20 hub genes and clinical stages. We compared the expression of each hub gene in BC samples at different clinical stages and found that the candidate hub genes were closely related to clinical stages (Fig. 4C).

Expression analysis of hub genes

We used the UALCAN database to analyze the expression of 5 candidate genes in cancer tissues, adjacent normal tissues and nodal metastatic tissues. Results show that PRC1, CDCA8, MX1, KIF14 were all significant upregulated in cancer tissues (p < 0.001; Figs. 5A, C, D, E). Moreover, PRC1, CDCA8, MX1 and KIF14 were significantly differentially expressed during the pathological stage in BC (p < 0.05; Figs. 5A, C, D, E). Additionally, the expressions of CDCA8, PRC1 ,MX1 and KIF14 differed significantly between different nodal metastatic stages and normal (p < 0.05; Figs. 5F–I). (*P < 0.05; **P < 0.01; ***P < 0.001.)

Immunohistochemistry results

IHC results revealed that protein expression levels of PRC1 and CDCA8 were significantly overexpressed in breast cancer tissues with metastasis compared with primary foci, while KIF4 and POLR3H were significantly decreased in metastatic lesion compared with primary lesion. But no significant difference of MX1 was observed between metastatic lesion and primary lesion. (*P < 0.05; **P < 0.01; ***P < 0.001.)

BC is one of the most common malignancies among women and most BC patients with advanced stage disease have a poor prognosis. Thus, it is imperative to seek encouraging strategies against the disease. With the rapid progress of high-throughput techniques and the availability of public genetic databases, more disease-related gene-based analysis of data derived from microarray studies can be analyzed or screened, which may facilitate early diagnosis and treatment.

The NCBI GEO database features a striking volume of microarray profiling data and next-generation sequencing results for a variety of human cancers. In the present study, three GEO datasets which include cases of BC with metastasis in which gene profiles were obtained were downloaded for further analysis. A total of 295 genes were identified as metastasis-related genes, including 151 up-regulated and 144 down-regulated genes. For a more in-depth understanding of function regarding these genes, we performed a functional enrichment analysis, which indicated that these hub genes played a role in the progression of metastasis through certain pathways.

The results demonstrated that the up-regulated genes were mainly involved in the type I interferon signaling pathway (BP), apoptotic processed (BP), focal adhesion (CC), protein homodimerization activity (MF), the Rap1 signaling pathway,[19] the MAPK signaling pathway[20–22], antigen processing and presentation (KEGG pathway), cell adhesion molecules (CAMs) (KEGG pathway), While down-regulated genes were mainly enriched in the regulation of signal transduction by p53 class mediators (BP), cell division (BP), the innate immune response (BP), the estrogen signaling pathway (KEGG pathway), and the chemokine signaling[23] pathway (KEGG pathway). These findings are highly consistent with reported studies indicating the essential role of apoptosis, cell migration and adhesion during the progression of BC. Previous studies have shown the importance of protein homodimerization activity in predicting invasive potential and clinical outcome of BC, which is supportive of our current results[24]. Further PPI analysis was carried out to construct a protein interaction network to yield a holistic map of these important genes. From the PPI map, 20 genes with higher connectivity were selected as hub genes. GO analysis of hub genes using Cytoscape was consistent with the results from the differential gene expression analysis (Fig. 3B) indicating that hub genes mainly focused on regulation of cell division, cell death, cell apoptosis (BP), nucleotide binding, protein binding, catalytic activity and molecular transducer activity (MF), and with membrane-bounded organelles (CC).

Twenty genes were selected as hub genes using the maximal clique centrality (MCC) method. Kaplan-Meier plots stratified for differential expression of these genes suggested that low expression of TYMS, SKA1, or ADCY7 was associated with worse OS whereas MX1 manifested an opposite trend. Low expression of POLR3H and high expression of CDCA8, ASE1, KIF14, MX1 contributed to worse DMFS. Previous studies have provided considerable evidence about the function of these genes in the development of BC cancer or other cancers. For instance, TYMS has been reported to be a target for chemotherapeutic agents (i.e., 5-fluorouracil) and is relevant for tumor growth due to its function as thymidylate synthase [25, 26]. Its expression was associated with lymph node metastasis in low grade glioma[27] and serves as a prognostic indicator of lymph node metastasis in patients with colorectal cancer[28], some studies have shown that it is overexpressed only in tumors that are generally refractory to the drug pemetrexed[29] and was related with the resistance and sensitivity to chemotherapy in patients with advanced BC[30]. Studies of the relevance of TYMS with BC metastasis are rare and whether it could be a prognostic factor for BC still needs further investigation. CDCA8 encodes a component of the chromosomal passenger complex and is an essential regulator of mitosis and cell division, while SKA1 serves as a key factor for spindle and kinetochore associated complex subunit 1, which is also associated with the mitotic cell cycle, both have been identified as hub genes for BC distant metastasis[5, 31]. KIF14 has been reported to promote BC progression through the negative regulation of the Rap1a-Radil signaling pathway[32]. Interestingly, up-regulated MX1, which is involved in the cellular antiviral response mechanism, has been associated with BC aggressiveness and lymph node invasion[33, 34].

PRC1(ASE1) encodes a protein that is involved in cytokinesis and is upregulated during the S and G2/M phases of mitosis, Of note, although PRC1 has been reported to contribute to tumorigenesis in lung adenocarcinoma and early recurrence of hepatocellular carcinoma by the Wnt/beta-catenin signaling pathway[35, 36], and has a strong link with the metastasis of nasopharyngeal carcinoma[37]. The mechanism for its association with BC metastasis has rarely been reported. Brynychova et al.[38] studied the gene expression profile of BC patients and found poor DFS in the PRC1 overexpression group, yet could not explain paclitaxel-induced PRC1 expression (Paclitaxel toxicity) by functional analyses. Uribe et al.[39] successfully inhibits mammary tumors metastasis in mice by using TSHZ2, which binds to the cytokinesis regulator PRC1. A recent study has revealed the potential role of phosphorylating PRC1 in the progression of triple-negative breast cancer[40], Whether PRC1 plays a role as an indirect marker of BC metastasis still needs further validation.

POLR3H has been reported to be associated with pro-tumoral effects in various kinds of cancers[41, 42]. Although together with MX1, there is no evidence of differences associated with clinical stages, they were associated with poor DMFS and is worthy further in-depth study to determine the exact functions or mechanisms involved in BC tumor metastasis.

Our Study has several limitations. Firstly, the data included in our article has unavoidable heterogeneity. Though they were mainly focusing on western people (Canada, Australia and America) and targeting the same gene type (total RNA) and using the same gene chip platforms (illumina HumanHT). Several characteristics including origin, family history, age of onset, Genetics information were unavailable from each datasets. Secondly, in our study, the gene expression is only confirmed by protein expression at single cell level, several of the hub genes we found need further experimental validation, such as trans-well analysis or gene knock-out analysis, among which, we found two novel metastasis-related genes (PRC1 and POLR3H). One experimental study related to PRC1 illustrated its association of poor survival outcome of breast cancer patients, which might due to its role in tumor metastasis according to our study, Therefore, Further research will include experimental verification of these two genes to confirm the possible effects and mechnisms.

In summary, our present study analyzed multiple datasets from the GEO database and identified the dominant genes and the relative interaction network involved in BC metastasis. Some of these genes have not been previously reported to influence the progression of BC, and thus may serve as potential drug targets or diagnostic biomarkers of BC metastasis. However, the hub genes identified in our study require further experimental validation. Additional genes need to be detected to enrich the network to provide a more comprehensive understanding of BC development.

Differentially expressed genes (DEGs), Gene Expression Omnibus database (GEO), Gene ontology (GO), protein-protein interaction (PPI), Overall survival (OS), distant metastasis-free survival (DMFS), Breast cancer (BC)

Ethics approval and consent to participate
All datasets have their own individual Ethics approval, and for our study itself, no clinical data were collected thus ethics approval was not needed.
Consent for publication
All authors reviewed the manuscript and approved the final version for publication.
Availability of data and material
The datasets (GSE103357, GSE14776, GSE32489) used in our study were available on the GEO website (http://www.ncbi.nlm.nih.gov/geo)
Competing interests
There are no competing interests for this article.
Funding
No funding support was received for this study
Authors' contributions
QW, KL, WW contributed equally to this work and should be considered as co-first authors. QW conceived and designed the study. KL and WW analyzed the data. QW prepared the figures and wrote the text for the main manuscript. JL,XL,HC,YG provided technical guidance, QW, JX, JS revised the manuscript. All authors reviewed the manuscript and approved the final version for publication.

Acknowledgements

We express our great gratitude to all the people who contributed to this work, and declare no conflicts of interest regarding the publication of the study.

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Screening and verification of potential gene targets in the metastasis of breast cancer by bioinformatics analysis and immunohistochemistry

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Abstract

BACKGROUND

METHODS

RESULTS

CONCLUSIONS

Figures

Background

Materials And Methods

Data collection

Identification of DEGs

GO term and KEGG pathway analysis

PPI network analysis of DEGs and modules selection

Heatmap drawing, GO and clinical analysis of hub genes

Survival analysis of DEGs

Clinical Information & Immunohistochemistry verification

Results

Identification of DEGs and co-DEGs in three GSEs

GO term enrichment and KEGG pathway analysis of DEGs

PPI network analysis of DEGs and modules selection

Survival analysis of hub genes

Expression analysis of hub genes

Immunohistochemistry results

Discussion

Conclusions

Abbreviations

Declarations

References

Additional Declarations

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