Skip to main content
NCBI home page
Medicine logoLink to Medicine
. 2023 Sep 15;102(37):e35273. doi: 10.1097/MD.0000000000035273

Identification of early diagnostic biomarkers for breast cancer through bioinformatics analysis

Shaozhang Yan a,b, Shi Yue a,b,*
PMCID: PMC10508380  PMID: 37713876

Abstract

In the realm of clinical practice, there is currently an insufficiency of distinct biomarkers available for the detection of breast cancer. It is of utmost importance to promptly employ bioinformatics methodologies to investigate prospective biomarkers for breast cancer, with the ultimate goal of achieving early diagnosis of the disease. The initial phase of this investigation involved the identification of 2 breast cancer gene chips meeting the specified criteria within the gene expression omnibus database. Subsequently, paired data analysis was conducted on these datasets, leading to the identification of differentially expressed genes (DEGs). In addition, this study executed Gene Ontology enrichment analysis and Kyoto encyclopedia of genes and genomes pathway enrichment analysis. The subsequent stage involved the construction of a protein-protein interaction network graph using the STRING website and Cytoscape software, facilitating the calculation of Hub genes. Lastly, the UALCAN database and Kaplan–Meier survival plots were utilized to perform differential expression and survival analysis on the selected Hub genes. A total of 733 DEGs were identified from the combined analysis of 2 datasets. Among these DEGs, 441 genes were found to be downregulated, while 292 genes were upregulated. The selected DEGs underwent comprehensive analysis, including gene ontology enrichment analysis, Kyoto encyclopedia of genes and genomes pathway enrichment analysis, and establishing a protein-protein interaction network. As a result, 10 Hub genes closely associated with early diagnosis of breast cancer were identified: PDZ-binding kinase, cell cycle protein A2, cell division cycle-associated protein 8, maternal embryonic leucine zipper kinase, nucleolar and spindle-associated protein 1, BIRC5, cell cycle protein B2, hyaluronan-mediated motility receptor, mitotic arrest deficient 2-like 1, and protein regulator of cytokinesis 1. The findings of this study unveiled the significant involvement of the identified 10 Hub genes in facilitating the growth and proliferation of cancer cells, particularly cell cycle protein A2, cell division cycle-associated protein 8, maternal embryonic leucine zipper kinase, nucleolar and spindle-associated protein 1, hyaluronan-mediated motility receptor, and protein regulator of cytokinesis 1, which demonstrated a more pronounced connection with the onset and progression of breast cancer. Further analysis through differential expression and survival analysis reaffirmed their strong correlation with the incidence of breast cancer. Consequently, the investigation of these 10 pertinent Hub genes presents novel prospects for potential biomarkers and valuable insights into the early diagnosis of breast cancer.

Keywords: bioinformatics, breast cancer, diagnosis, hub genes

1. Introduction

Breast cancer, being among the most prevalent and high-incidence malignancies affecting women globally, poses a substantial risk to women’s health and life preservation.[1] Now recognized as a pressing global health crisis, breast cancer accounted for an estimated 2.3 million new cases in 2022, ranking it among the leading causes of cancer-related mortality in women. Although there are promising improvements in survival rates within developed nations, around 2-thirds of these new instances are emerging in underdeveloped areas, where healthcare resources are inadequate, impeding prompt and effective diagnosis.[2] Consequently, as the mortality rates associated with breast cancer continue to escalate, the importance of accurately diagnosing early-stage breast cancer cannot be overstated.

Breast cancer can be categorized into multiple subtypes, including Luminal A breast cancer, Luminal B (HER2-negative) breast cancer, Luminal B (HER2-positive) breast cancer, HER2-positive breast cancer, and triple-negative breast cancer.[3] The intricate nature of these subtypes can complicate early breast cancer diagnosis and potentially postpone the timely initiation of patient treatment.[4]

The early detection of breast cancer is medically significant as it has the potential to offer life-saving interventions to patients across various countries and regions. Nevertheless, the exact mechanisms that underlie the evolution of breast cancer remain elusive. Studies indicate that factors such as age, sex, lifestyle habits, and familial history of breast cancer may contribute to its development.[5] As medical detection technology advances, a multitude of diagnostic methods for breast cancer has emerged. According to the American Cancer Society, the lion’s share of breast cancers can be detected via routine self-examinations of the breast and mammography screenings.[6] Besides conventional diagnostic approaches, Wolfgang Buchberger et al,[7] have employed MRI to distinguish benign from malignant breast tissues. Moreover, the work by Saeed Roshani et al[8] implies that sensor technology could be a potent tool for diagnosing early-stage breast cancer. En Zhou Ye et al,[9] found that employing deep learning-generated semantic segmentation mapping analysis of multimodal medical imagery could aid in diagnosing malignant breast tissue. Although mammography is considered the most efficacious diagnostic tool for breast cancer, it is not devoid of risks such as false positives, radiation exposure, and discomfort associated with the procedure.[10] False positives are a particularly significant worry, with a 61% probability of false-positive outcomes among breast cancer patients aged 40 to 50, resulting in overdiagnosis.[11] Hence, the pursuit of improved diagnostic methods for breast cancer represents a growing trend.

In recent times, the field of bioinformatics has experienced rapid advancements, with microarray technology emerging as one of the most successful tools. This technology facilitates parallel analysis across a spectrum of areas, including combinatorial chemistry, genomics, and proteomics.[12] In this study, we leverage bioinformatics techniques to identify potential biomarkers that could aid in the early detection of breast cancer. By examining 2 breast cancer-related datasets from the gene expression omnibus (GEO) database, we identified differentially expressed genes and conducted enrichment analysis specific to these datasets. This was followed by a survival analysis and a sequence of functional analyses. The findings of this research introduce new biomarkers for early breast cancer detection, providing fresh perspectives and methodologies for the clinical diagnosis of this disease.

2. Methods

2.1. Collection and processing of breast cancer-related microarray datasets

We procured and processed breast cancer-associated microarray datasets from the GEO database using the keywords “Breast Cancer,” “Homo sapiens,” and “GPL570.” Specifically, the datasets GSE31192 and GSE5764 were downloaded, both of which were executed on the GPL570 platform (Affymetrix Human Genome U133 Plus 2.0 Array). The GSE31192 dataset encompasses 33 samples, of which 20 are breast tumor samples and the remaining 13 are normal breast tissue samples. Similarly, the GSE5764 dataset comprises 30 samples, with 10 breast tumor samples and 20 normal breast tissue samples (Table 1).

Table 1.

The specific information of the two microarray databases from the GEO database is as follows.

GEO ID Experiment type Platforms Number of normal samples vs tumor sample PubMed ID
GSE31192 Expression profiling by array GPL570 20 vs 13 23479404
GSE5764 Expression profiling by array GPL570 10 vs 20 17389037
Open in a new tab

GEO = gene expression omnibus.

2.2. Acquisition and analysis of differentially expressed genes (DEGs)

The datasets GSE31192 and GSE5764 were obtained by utilizing the “GEO query” and “Biobase” R packages, respectively. Subsequently, the “Limma” R package was employed to standardize, merge, and correct the batches of the 2 microarray datasets. As a result, gene expression profiles were generated for both datasets, as shown in Figure 1. The chip data were then divided into 2 groups, namely “Normal” and “Tumor,” and differential analysis was conducted using the “Limma” R package to identify genes with differential expression. The selection criteria included a significant P value and an absolute Log2Foldchange >1, with statistical significance indicated by P < .05, as depicted in Figure 1.

Figure 1.

Figure 1.

Open in a new tab

Pre- and post-batch effect correction plots. The gene expression profiles of the samples from both datasets were standardized, merged, and subjected to batch effect correction. “Original” represents the data before batch effect correction, while “Batch Corrected” represents the data after batch effect correction.

2.3. Enrichment analysis and signaling pathway analysis methods

The gene ontology (GO) database is employed to annotate and elucidate the functions of genes and proteins at the cellular level. It segments gene functions into 3 categories: Cellular component, molecular function, and biological process. The Kyoto encyclopedia of genes and genomes (KEGG) serves as a pathway database that facilitates molecular signaling enrichment predictions based on gene data and expression. In the present study, the “clusterProfiler” and “enrichplot” R packages were used to extract DEGs from the dataset and conduct enrichment analysis, with a significance level set at P < .05.

2.4. Construction and building of protein-protein interaction (PPI) network

Pertinent information was sieved using RStudio, and the STRING database (https://cn.string-db.org) was consulted to discern the interactions between proteins encoded by DEGs. Visualization and construction of the PPI network were performed using the Cytoscape software. Moreover, to compute the top 10 Hub genes, we employed the cytoHubba plugin.

2.5. Correlation analysis of hub genes in breast cancer

The UALCAN database (https://ualcan.path.uab.edu/cgi-bin/ualcan-res.pl) was employed to conduct expression and survival analysis of critical genes, relying on the TCGA database. Furthermore, the open-access Kaplan–Meier Plotter database (http://www.kmplot.com) was harnessed to evaluate the differential expression and survival of cancer transcriptomes. Data yielding a significance level of P < .05 were deemed statistically significant.

3. Result

3.1. Integration and Identification of DEGs in GSE31192 and GSE5764 datasets

Upon amalgamating the datasets, we included a total of 33 breast cancer tissue samples and 30 normal breast tissue samples, and acquired their gene expression profiles. The samples were batch-corrected in a consolidated manner, setting the following screening parameters: significance level (P < .05) and fold change (|Log2Foldchange|>1). Post-screening, we identified a total of 733 DEGs, comprising 441 downregulated genes and 292 upregulated genes. Given these results, we used the “ggplot2,” “dplyr,” and “pheatmap” R packages to create the gene heatmap, which exemplifies the similarity of DEGs amongst samples, and the volcano plot, which demonstrates the distribution of DEGs (Fig. 2A). Additionally, heatmaps displaying the top 10 upregulated and downregulated DEGs were constructed (Fig. 2B).

Figure 2.

Figure 2.

Open in a new tab

(A) Volcano plot and heatmap of DEGs. On the left is the volcano plot of DEGs, where blue dots represent downregulated genes, red dots represent upregulated genes, and gray dots represent genes with no significant difference. The selection criteria for differentially expressed genes were set as P < .05 and |Log2FC|>1. On the right is the heatmap of all DEGs, with red indicating upregulated genes, while blue indicates downregulated genes. (B) Heatmap of the Top 10 Upregulated and Top 10 Downregulated DEGs. The heatmap showcases the expression patterns of the top 10 upregulated and top 10 downregulated DEGs. The color red signifies upregulation, whereas the color blue denotes downregulation. DEGs = differentially expressed genes, Log2FC = Log2Foldchange.

3.2. GO enrichment analysis and KEGG pathway analysis of DEGs in GSE31192 and GSE5764

In the present investigation, we conducted GO enrichment analysis and KEGG pathway analysis on the DEGs obtained from the GSE31192 and GSE5764 datasets. These analyses were facilitated through the use of R packages such as “clusterProfiler” and “enrichplot.” The GO analysis indicated that DEGs were significantly represented in several biological process, including nuclear division, mitotic nuclear division, nuclear chromosome segregation, sister chromatid segregation, and mitotic sister chromatid segregation. Regarding cellular component, the DEGs were primarily associated with elements like the collagen-containing extracellular matrix, condensed chromosome, chromosome centromeric region, and the NMS complex. In the molecular function category, the DEGs were enriched in functions such as glycosaminoglycan binding, extracellular matrix structural constituent, heparin binding, Wnt-protein binding, and CXCR chemokine receptor binding. The results of the GO enrichment analysis were visually represented through bubble plots and bar charts, offering a holistic view of the enriched GO terms and their statistical significance. An in-depth view of the GO analysis bubble plot and bar chart is given in Figure 3.

Figure 3.

Figure 3.

Open in a new tab

GO enrichment analysis of DEGs in this study. The left image depicts the GO analysis bubble plot, with differentially expressed genes categorized into CC, MF, and BP. The right image exhibits the GO analysis bar chart, where larger dots indicate a higher number of genes enriched in a specific pathway. The color signifies the pathway’s significance, with darker shades indicating smaller P values and greater importance (−log10 (P value)). BP = biological process, CC = cellular component, DEGs = differentially expressed genes, GO = gene ontology, MF = molecular function.

Additional enrichment analysis of DEGs was performed utilizing the KEGG pathway. Figure 4 illustrates the findings, encompassing both the KEGG enrichment bubble plot and bar chart. Notably, enriched pathways include the PI3K-Akt signaling pathway, Cytokine-cytokine receptor interaction, Focal adhesion, Cell cycle, Viral protein interaction with cytokine and cytokine receptor, ECM-receptor interaction, Staphylococcus aureus infection, and PPAR signaling pathway.

Figure 4.

Figure 4.

Open in a new tab

KEGG pathway enrichment analysis of datasets DEGS in this study. The left image displays the KEGG pathway enrichment of DEGS analysis in a bar chart format, the greater the numerical value, the higher the degree of enrichment. While the right image represents the KEGG pathway enrichment analysis of DEGS in a bubble plot format. In the bubble plot, the size of the bubbles represents the number of genes, and the varying color intensities indicate the significance of the P-values. KEGG = Kyoto encyclopedia of genes and genomes.

3.3. PPI network and hub genes

In this investigation, we applied RStudio for data filtration to obtain the files identifiable on the STRING website. Using these downloaded files, we built a PPI network diagram (Fig. 5). Subsequently, we performed an in-depth analysis using the cytoHubba plugin within the Cytoscape software to calculate the top 10 Hub genes: PDZ-binding kinase (PBK), cell cycle protein A2 (CCNA2), cell division cycle-associated protein 8 (CDCA8), maternal embryonic leucine zipper kinase (MELK), nucleolar and spindle-associated protein 1 (NUSAP1), baculoviral IAP repeat containing 5 (BIRC5), cell cycle protein B2 (CCNB2), hyaluronan-mediated motility receptor (HMMR), mitotic arrest deficient 2-like 1 (MAD2L1), and protein regulator of cytokinesis 1 (PRC1). The proteins encoded by these genes may hold crucial implications for the early diagnosis of breast cancer (Fig. 6).

Figure 5.

Figure 5.

Open in a new tab

PPI network diagram of breast cancer. The PPI network diagram of breast cancer was obtained using the online tool, STRING. Dots of different colors represent proteins, and the lines connecting them indicate the interactions between proteins. PPI = protein-protein interaction.

Figure 6.

Figure 6.

Open in a new tab

Hub gene network diagram. The top 10 differentially expressed genes represent the Hub genes, which are closely related to breast cancer.

3.4. Expression analysis and survival analysis of hub genes

Expression analysis of the 10 identified Hub genes - PBK, CCNA2, CDCA8, MELK, NUSAP1, BIRC5, CCNB2, HMMR, MAD2L1, and PRC1 - was carried out using the UALCAN database. The analysis revealed a marked overexpression of these genes in breast tumor samples (Fig. 7). Survival outcomes linked to these Hub genes were further investigated by utilizing Kaplan–Meier survival plots from the freely accessible Kaplan–Meier Plotter database. Interestingly, an elevated expression of all Hub genes corresponded to a significant reduction in patient survival (Fig. 8). Moreover, leveraging the network analysis capability of UALCAN, we explored the correlations between the identified Hub genes and different subtypes of breast cancer. The Hub genes demonstrated an increased expression across all breast cancer subtypes, with the most notable expression observed in triple-negative breast cancer (Fig. 9).

Figure 7.

Figure 7.

Open in a new tab

Differential expression levels of Hub genes between breast cancer tissue and normal breast tissue. The data for this graph is based on the TCGA database, where red represents tumor tissue and blue represents normal tissue. The graph clearly shows that the 10 Hub genes exhibit significant expression in breast cancer samples.

Figure 8.

Figure 8.

Open in a new tab

Survival prognosis analysis of Hub genes is depicted in this figure. The Kaplan–Meier plot is used to identify the prognostic information of Hub genes, where the red line represents high expression and the black line represents low expression. (In the Kaplan–Meier plot, CT84 is an alias for PBK, and ASE1 is an alias for PRC1). PBK = PDZ-binding kinase, PRC1 = protein regulator of cytokinesis 1.

Figure 9.

Figure 9.

Open in a new tab

Expression profile of Hub genes in different subtypes of breast cancer.

4. Discussion

In the contemporary era, the occurrence rate of breast cancer is exhibiting a consistent upward trend across diverse countries and ethnic groups.[13] The existing limitations of medical technology hinder progress in identifying early-stage breast cancer biomarkers, thereby creating a deficit in trustworthy biomarkers for clinical use. This deficiency potentially leads to delayed detection and diagnosis of initial-stage breast cancer, consequently surpassing the prime treatment period and escalating risks to patient life and wellbeing. With the rapid progression of gene expression microarray technology, the avenues for potential cancer biomarker research have broadened.[14] This study intends to leverage bioinformatics analysis approaches to gather information from breast cancer microarray datasets and conduct differential evaluations to pinpoint DEGs. The 2 microarray datasets, GSE31192 and GSE5764, were chosen for differential assessment, with a principal concentration on GO enrichment analysis and KEGG signaling pathways. Via software analysis, the top 10 Hub genes - PBK, CCNA2, CDCA8, MELK, NUSAP1, BIRC5, CCNB2, HMMR, MAD2L1, and PRC1. - were ascertained. These DEGs are intimately linked to breast cancer, displaying elevated expression across diverse breast cancer subtypes and being notably correlated with diminished patient survival rates compared to standard levels.

PBK is a serine-threonine kinase implicated in the facilitation of cellular proliferation, growth, and apoptosis.[15,16] PBK has been associated with various malignant conditions, with research highlighting its significant role in the evolution, proliferation, and metastasis of leukemia and multiple myeloma.[17,18] Moreover, an elevated expression of PBK in tissues of gastric adenocarcinoma, esophageal cancer, and ovarian cancer has been observed, showing a direct correlation with the severity of these diseases. Another key player, the[19,20] CCNA2, acts as a cell cycle regulator by binding and activating cyclin-dependent kinase 2, thereby propelling the transition from G1/S to G2/M phase.[21] CCNA2 has been linked to the enhancement of cancer invasion, metastasis, recurrence, and drug resistance.[22] Studies have shown that CCNA2 expression is significantly higher in cervical cancer and breast cancer than in normal tissues,[23] impacting cell proliferation.[24] CDCA8, a component of the chromosomal passenger complex, is an important regulatory factor in mitosis and cell division. This protein is cell cycle-regulated and essential for chromatin-induced microtubule stabilization and spindle formation. Aberrant expression of CDCA8 has been observed in breast cancer,[25] bladder cancer,[26] and melanoma,[27] contributing to cancer growth and metastasis. CDCA8 promotes the overexpression of the positive regulatory factor NF-Y, enhancing CDCA8 promoter activity, transcriptionally activating cancer cells, and promoting cancer cell proliferation and growth.[28] MELK encodes a Ser/Thr protein kinase that is strongly expressed in oocytes and highly expressed in the human thymus. MELK participates in cell proliferation, metabolism, and apoptosis processes in cancer tissues.[29] Elevated MELK levels are associated with an increased risk of cervical cancer, breast cancer, colorectal cancer, and other cancers.[30] NUSAP1 is a nucleolar spindle-associated protein and has been found to play a coordinating role in spindle microtubule organization. Abnormal spindle structure can lead to chromosomal instability, resulting in the occurrence and progression of malignant tumors.[31] NUSAP1 is closely associated with the AMPK/PPARγ signaling pathway, and its excessive abnormal expression can promote the development of malignant breast tumors.[32] BIRC5 is a member of the inhibitor of apoptosis protein gene family and encodes a protein that negatively regulates apoptotic cell death. Its primary association lies in inhibiting cell apoptosis and promoting cell proliferation.[33] Studies have shown that BIRC5 is highly expressed in liver cancer, lung cancer, and breast cancer, indicating its close relationship with cancer cells.[34] CCNB2, a member of the cyclin family, specifically the B-type cyclins, is connected to p34cdc2, a vital component in regulating the cell cycle. Aberrations in CCNB2 can disrupt the G2/M checkpoint function, resulting in permanent alterations in gene function and the formation of malignant tumors.[35] Studies have shown that CCNB2 is highly expressed in malignant tumors such as gastric cancer,[36] breast cancer,[37] and colorectal cancer.[38] HMMR is involved in encoding proteins that regulate cell movement, cellular activity, and metastasis.[39] HMMR demonstrates noteworthy expression in breast cancer and plays a role in regulating cell division and the apical-basal polarization of mammary epithelial cells. It is recognized as a potential modifier influencing the risk of BRCA1-related breast cancer.[40] MAD2L1 is an integral part of the mitotic spindle assembly checkpoint, responsible for impeding the initiation of anaphase until proper alignment of chromosomes on the metaphase plate is achieved. Research has demonstrated the involvement of MAD2L1 in cell cycle progression, apoptosis, and its close association with the growth of colorectal cancer tissue.[41] PRC1 is a crucial epigenetic regulatory factor responsible for encoding a protein that participates in cytokinesis. When PRC1 is present in the nucleus, it dynamically interacts with the mitotic spindle and relocates to the cell body during cytokinesis. This protein actively facilitates the onset and progression of cancer.[42] PRC1 has been found to promote the development of triple-negative breast cancer.[43]

In conclusion, by utilizing bioinformatics analysis, this study identified a total of 10 Hub genes (PBK, CCNA2, CDCA8, MELK, NUSAP1, BIRC5, CCNB2, HMMR, MAD2L1, PRC1) associated with breast cancer. These Hub genes are directly implicated in the growth and proliferation of cancer cells, with CCNA2, CDCA8, MELK, NUSAP1, HMMR, and PRC1 demonstrating a stronger association with the evolution of breast cancer. This study offers novel perspectives and methodologies for early-stage breast cancer diagnosis.

However, this research has several constraints. Primarily, it only investigated nonredundant samples from 2 datasets (GSE31192 and GSE5764), and the sample size was not sufficiently expansive. Secondly, the data employed in this study were derived from online databases, necessitating further clinical experiments to validate the results and enhance the practical applicability of the study. Lastly, the study did not conduct any specific molecular experiments on the identified Hub genes, indicating a future research direction.

5. Conclusion

This study selected 10 Hub genes as potential biomarkers for the diagnosis of breast cancer. The research findings underscored that these key hub genes, namely PBK, CCNA2, CDCA8, MELK, NUSAP1, BIRC5, CCNB2, HMMR, MAD2L1, and PRC1, manifest heightened expression levels across diverse breast cancer subtypes, especially in triple-negative breast cancer. The investigation also revealed that these 10 hub genes contribute to the promotion of cancer cell growth and proliferation, with CCNA2, CDCA8, MELK, NUSAP1, HMMR, and PRC1 displaying a more pronounced link with the initiation and progression of breast cancer. The critical role of DEGs in breast cancer was underscored in this study, offering novel perspectives for early diagnosis. The elevated expression of these 10 hub genes in breast cancer provides fresh paths and tools for early detection. Further dedication and resources should be channeled towards validating the precision and applicability of these genes, as they offer promising prospects for future investigations.

Acknowledgments

We would like to express our gratitude to the databases such as GEO and the data contributors for providing meaningful datasets. We also appreciate the developers of R language, STRING website, and Cytoscape software for providing the analysis tools. We acknowledge all authors whose publications were included in our article.

Author contributions

Conceptualization: Shi Yue.

Data curation: Shaozhang Yan, Shi Yue.

Writing – original draft: Shaozhang Yan.

Writing – review & editing: Shaozhang Yan, Shi Yue.

Abbreviations:

BIRC5
baculoviral IAP repeat containing 5
CCNA2
cell cycle protein A2
CCNB2
cell cycle protein B2
CDCA8
cell division cycle-associated protein 8
DEGs
differentially expressed genes
GEO
gene expression omnibus
GO
gene ontology
HMMR
hyaluronan-mediated motility receptor
KEGG
Kyoto encyclopedia of genes and genomes
MAD2L1
mitotic arrest deficient 2-like 1
MELK
maternal embryonic leucine zipper kinase
NUSAP1
nucleolar and spindle-associated protein 1
PBK
PDZ-binding kinase
PPI
protein-protein interaction
PRC1
protein regulator of cytokinesis 1

This study was conducted in strict accordance with the ethical principles approved by the Ethics Committee of Shanxi Provincial Hospital of Traditional Chinese Medicine, with approval number: AWE201905231.

The authors have no conflicts of interest to disclose.

The datasets generated during and/or analyzed during the current study are publicly available.

How to cite this article: Yan S, Yue S. Identification of early diagnostic biomarkers for breast cancer through bioinformatics analysis. Medicine 2023;102:37(e35273).

References

  • [1].Siegel RL, Miller KD, Wagle NS, et al. Cancer statistics, 2023. CA Cancer J Clin. 2023;73:17–48. [DOI] [PubMed] [Google Scholar]
  • [2].Sung H, Ferlay J, Siegel RL, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71:209–49. [DOI] [PubMed] [Google Scholar]
  • [3].Parker JS, Mullins M, Cheang MCU, et al. Supervised risk predictor of breast cancer based on intrinsic subtypes. J Clin Oncol. 2009;27:1160–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [4].Hammerl D, Smid M, Timmermans AM, et al. Breast cancer genomics and immuno-oncological markers to guide immune therapies. Semin Cancer Biol. 2018;52:178–88. [DOI] [PubMed] [Google Scholar]
  • [5].Wilkinson L, Gathani T. Understanding breast cancer as a global health concern. Br J Radiol. 2022;95:20211033. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [6].Winters S, Martin C, Murphy D, et al. Breast cancer epidemiology, prevention, and screening. Prog Mol Biol Transl Sci. 2017;151:1–32. [DOI] [PubMed] [Google Scholar]
  • [7].Buchberger W, Oberaigner W, Kremser C, et al. Non-mass enhancement in breast MRI: characterization with BI-RADS descriptors and ADC values. SciMedicine J. 2021;3:77–87. [Google Scholar]
  • [8].Roshani S, Coccia M, Mosleh M. Sensor technology for opening new pathways in diagnosis and therapeutics of breast, lung, colorectal and prostate cancer. HighTech Innov J. 2022;3:356–75. [Google Scholar]
  • [9].Ye E, Ye E, Ye RZ. Deep Image Translator V2: Analysis of Multimodal Medical Images Using Semantic Segmentation Maps Generated Through Deep Learning; 2021. [Google Scholar]
  • [10].Khrouf S, Letaief Ksontini F, Ayadi M, et al. Breast cancer screening: a dividing controversy. Tunis Med. 2020;98:22–34. [PubMed] [Google Scholar]
  • [11].Barbieri I, Kouzarides T. Role of RNA modifications in cancer. Nat Rev Cancer. 2020;20:303–22. [DOI] [PubMed] [Google Scholar]
  • [12].Díaz-Mochón JJ, Tourniaire G, Bradley M. Microarray platforms for enzymatic and cell-based assays. Chem Soc Rev. 2007;36:449–57. [DOI] [PubMed] [Google Scholar]
  • [13].Shoemaker ML, White MC, Wu M, et al. Differences in breast cancer incidence among young women aged 20–49 years by stage and tumor characteristics, age, race, and ethnicity, 2004–2013. Breast Cancer Res Treat. 2018;169:595–606. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [14].Ji F, Sadreyev RI. RNA-seq: basic bioinformatics analysis. Curr Protoc Mol Biol. 2018;124:e68. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [15].Abe Y, Matsumoto S, Kito K, et al. Cloning and expression of a novel MAPKK-like protein kinase, lymphokine-activated killer T-cell-originated protein kinase, specifically expressed in the testis and activated lymphoid cells. J Biol Chem. 2000;275:21525–31. [DOI] [PubMed] [Google Scholar]
  • [16].Gaudet S, Branton D, Lue RA. Characterization of PDZ-binding kinase, a mitotic kinase. Proc Natl Acad Sci U S A. 2000;97:5167–72. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [17].Ishikawa C, Senba M, Mori N. Mitotic kinase PBK/TOPK as a therapeutic target for adult T-cell leukemia/lymphoma. Int J Oncol. 2018;53:801–14. [DOI] [PubMed] [Google Scholar]
  • [18].Ikeda Y, Park J-H, Miyamoto T, et al. T-LAK cell-originated protein kinase (TOPK) as a prognostic factor and a potential therapeutic target in ovarian cancer. Clin Cancer Res. 2016;22:6110–7. [DOI] [PubMed] [Google Scholar]
  • [19].Ohashi T, Komatsu S, Ichikawa D, et al. Overexpression of PBK/TOPK contributes to tumor development and poor outcome of esophageal squamous cell carcinoma. Anticancer Res. 2016;36:6457–66. [DOI] [PubMed] [Google Scholar]
  • [20].Ohashi T, Komatsu S, Ichikawa D, et al. Overexpression of PBK/TOPK relates to tumour malignant potential and poor outcome of gastric carcinoma. Br J Cancer. 2017;116:218–26. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [21].Zhang S, Tischer T, Barford D. Cyclin A2 degradation during the spindle assembly checkpoint requires multiple binding modes to the APC/C. Nat Commun. 2019;10:3863. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [22].Fischer M, Quaas M, Steiner L, et al. The p53-p21-DREAM-CDE/CHR pathway regulates G2/M cell cycle genes. Nucleic Acids Res. 2016;44:164–74. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [23].Chen Z-H, Jing Y-J, Yu J-B, et al. ESRP1 induces cervical cancer cell G1-phase arrest via regulating cyclin A2 mRNA stability. Int J Mol Sci . 2019;20:3705. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [24].Moore NL, Edwards DP, Weigel NL. Cyclin A2 and its associated kinase activity are required for optimal induction of progesterone receptor target genes in breast cancer cells. J Steroid Biochem Mol Biol. 2014;144:471–82. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [25].Bu Y, Shi L, Yu D, et al. CDCA8 is a key mediator of estrogen-stimulated cell proliferation in breast cancer cells. Gene. 2019;703:1–6. [DOI] [PubMed] [Google Scholar]
  • [26].Bi Y, Chen S, Jiang J, et al. CDCA8 expression and its clinical relevance in patients with bladder cancer. Medicine (Baltim). 2018;97:e11899. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [27].Ci C, Tang B, Lyu D, et al. Overexpression of CDCA8 promotes the malignant progression of cutaneous melanoma and leads to poor prognosis. Int J Mol Med. 2019;43:404–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [28].Dai C, Miao C-X, Xu X-M, et al. Transcriptional activation of human CDCA8 gene regulated by transcription factor NF-Y in embryonic stem cells and cancer cells. J Biol Chem. 2015;290:22423–34. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [29].Thangaraj K, Ponnusamy L, Natarajan SR, et al. MELK/MPK38 in cancer: from mechanistic aspects to therapeutic strategies. Drug Discov Today. 2020;25:2161–73. [DOI] [PubMed] [Google Scholar]
  • [30].Gray D, Jubb AM, Hogue D, et al. Maternal embryonic leucine zipper kinase/murine protein serine-threonine kinase 38 is a promising therapeutic target for multiple cancers. Cancer Res. 2005;65:9751–61. [DOI] [PubMed] [Google Scholar]
  • [31].Matsuda K, Miyoshi H, Hiraoka K, et al. Clinicopathological and prognostic value of transforming acidic coiled-coil-containing protein 3 (TACC3) expression in soft tissue sarcomas. PLoS One. 2017;12:e0188096. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [32].Gao S, Yin H, Tong H, et al. Nucleolar and spindle associated protein 1 (NUSAP1) promotes bladder cancer progression through the TGF-β signaling pathway. OncoTargets Ther. 2020;13:813–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [33].Khan S, Ferguson Bennit H, Asuncion Valenzuela MM, et al. Localization and upregulation of survivin in cancer health disparities: a clinical perspective. Biol Targets Ther. 2015;9:57–67. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [34].Ye H-B, Ma B-J, Meng G-Q, et al. Bioinformatics analysis of BIRC5 in human cancers. Ann Transl Med. 2022;10:888. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [35].Yoshitome S, Furuno N, Prigent C, et al. The subcellular localization of cyclin B2 is required for bipolar spindle formation during Xenopus oocyte maturation. Biochem Biophys Res Commun. 2012;422:770–5. [DOI] [PubMed] [Google Scholar]
  • [36].Shubbar E, Kovács A, Hajizadeh S, et al. Elevated cyclin B2 expression in invasive breast carcinoma is associated with unfavorable clinical outcome. BMC Cancer. 2013;13:1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [37].Shi Q, Wang W, Jia Z, et al. ISL1, a novel regulator of CCNB1, CCNB2 and c-MYC genes, promotes gastric cancer cell proliferation and tumor growth. Oncotarget. 2016;7:36489–500. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [38].Park S-H, Yu G-R, Kim W-H, et al. NF-Y-dependent cyclin B2 expression in colorectal adenocarcinoma. Clin Cancer Res. 2007;13:858–67. [DOI] [PubMed] [Google Scholar]
  • [39].Liu X-F, Bera TK, Kahue C, et al. ANKRD26 and its interacting partners TRIO, GPS2, HMMR and DIPA regulate adipogenesis in 3T3-L1 cells. PLoS One. 2012;7:e38130. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [40].Maxwell CA, Benítez J, Gómez-Baldó L, et al. Interplay between BRCA1 and RHAMM regulates epithelial apicobasal polarization and may influence risk of breast cancer. PLoS Biol. 2011;9:e1001199. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [41].Ding X, Duan H, Luo H. Identification of core gene expression signature and key pathways in colorectal cancer. Front Genet. 2020;11:45. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • [42].Li J, Dallmayer M, Kirchner T, et al. PRC1: linking cytokinesis, chromosomal instability, and cancer evolution. Trends Cancer. 2018;4:59–73. [DOI] [PubMed] [Google Scholar]
  • [43].Li X, Li J, Xu L, et al. CDK16 promotes the progression and metastasis of triple-negative breast cancer by phosphorylating PRC1. J Exp Clin Cancer Res. 2022;41:149. [DOI] [PMC free article] [PubMed] [Google Scholar]

Articles from Medicine are provided here courtesy of Wolters Kluwer Health

RESOURCES