
==== Front
J Genet Eng Biotechnol
J Genet Eng Biotechnol
Journal of Genetic Engineering & Biotechnology
1687-157X
2090-5920
Academy of Scientific Research and Technology, Egypt

S1687-157X(24)00111-2
10.1016/j.jgeb.2024.100408
100408
Review Article
Integrated analysis of hub genes and intrinsically disordered regions in triple-negative breast cancer
Iqbal Azhar a
Ali Faisal a
Ali Alharbi Sulaiman sharbi@ksu.edu.sa
b
Sajid Muhammad sajid@uo.edu.pk
a⁎
Alfarraj Saleh salfarraj@hotmail.com
c⁎
Hussain Momina a
Siddique Tehmina a
Mustaq Rakhshanda a
Shafique Fakhra d
Iqbal Muhammad Sarfaraz Sarfaraz2250@gmail.com
e
a Department of Biotechnology, Faculty of Life Sciences, University of Okara, Okara 56300, Pakistan
b Department of Botany and Microbiology, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
c Zoology Department, College of Science, King Saud University, Riyadh 11451, Saudi Arabia
d District Headquarters Hospital (DHQ), Rawalpindi, Pakistan
e Department of Urology, Minimally Invasive Surgery Center, Guangdong Key Laboratory of Urology, Guangzhou Urology Research Institute, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China
⁎ Corresponding authors. sajid@uo.edu.pksalfarraj@hotmail.com
16 8 2024
12 2024
16 8 2024
22 4 10040813 5 2024
16 7 2024
1 8 2024
© 2024 The Authors
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Triple-negative breast cancer (TNBC) is the most prevalent breast cancer subtype. Its prognosis is poor because there are no effective treatment targets. Despite several attempts, the molecular pathways of TNBC remain unknown, posing a significant clinical barrier in the search for viable targets. Two microarray datasets were used to identify possible targets for TNBC, GSE38959 and GSE45827, retrieved from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) in TNBC samples compared with normal samples were identified using the GEO2R program. KEGG pathway enrichment and Gene Ontology functions were assessed for DEG pathways and functional annotation using ShinyGO 0.77. The STRING database and Cytoscape program were used for protein-protein interaction (PPI) analysis. Furthermore, we evaluated the predictive significance of hub gene expression in TNBC patients using the GEPIA2 online tool. We developed a comprehensive technique to assess whether intrinsically disordered regions (IDRs) are present in the TNBC hub genes. There were 48 DEGs were identified, all of which were upregulated. A putative protein complex containing these four core genes was selected for further analysis. Breast cancer patients with TTK, TOP2A, CENPF, and CCNA2 upregulation had a poor prognosis; TTK and CCNA2 were partially disordered, whereas TOP2A and CENPF were primarily disordered, according to IDR analysis. According to our study, TOP2A and CENPF may be useful therapeutic targets for disruption of the TNBC PPI network.

Keywords

Triple-negative breast cancer
ShinyGO
Protein-protein interactions
Differently expressed genes
Gene Expression Omnibus
And survival analysis
==== Body
pmc1 Introduction

Breast cancer (BC) is the most prevalent form of cancer and accounts for the majority of cancer-related deaths worldwide.1 The only effective treatments available for TNBC are radiation therapy and chemotherapy. It represents 10–15 % of all cancers.2 It mostly appears as high-grade ductal carcinoma that is invasive and frequently recurs early, usually in the first to third years of treatment. Most deaths involve children under the age of five.3 Breast cancer has a worse prognosis and is more likely to have distant metastases than the other subtypes.4, 5

TNBCs have a poorer prognosis, high rates of metastasis and proliferation, and unfavorable clinical features such as larger tumors, higher histological grades, and lymphatic node involvement. They are more prevalent among younger people.6, 7, 8 The principal systemic treatment techniques are chemotherapy and radiation because no molecular indicators exist.6 Several molecular traits associated with TNBC have been discovered using high-throughput technologies such as RNA sequencing and microarray analysis, include high incidence of TP53 mutations, abnormal PI3K pathway activation, BRCA1 and BRCA2 inactivation, RB1 loss, and cyclin E1 amplification.9 However, little is known about the etiological factors that contribute to the progression of TNBC, and the molecular processes underlying the TNBC disease are still unknown.

The identification of novel biomarkers is essential to understand the molecular mechanisms in TNBC that serve as the prognostic markers. Bioinformatics has aided in identifying biomarkers for the diagnosis, prognosis, and survival of illnesses, owing to rapid advancements in genomic and proteomic technologies.10 The objective of this study was to identify novel prognostic markers in TNBC patients by exploiting the IDR regions of hub genes. The present study utilized bioinformatic methods to examine gene expression profiles in order to find DEGs between TNBC and normal tissues. The ShinyGO server was used to perform pathway enrichment analysis, and the DEGs were screened and functionally characterized using GO analysis. A PPI network was constructed in order to identify the highly connected genes in the network which were called as hub genes. Furthermore, we developed an integrated technique to assess whether fundamentally IDRs regions are present in the TNBC hub genes.

2 Methods

2.1 Dataset selection

The gene expression profiles of GSE45827 and GSE38959 in tissues classified as TNBC and non-TNBC were retrieved from the NCBI-GEO11; which is a publicly accessible database containing a collection of microarray data. GSE38959 dataset contained thirty TNBC and 13 standard samples. And GSE45827 dataset contained 41 TNBC and 11 standard samples.12

2.2 Data processing and DEG identification

Using the GEO2R database, DEGs between TNBC samples and normal samples with an adjusted P-value < 0.05 and log2FC>2 cut-off values were determined. To determine the common DEGs between the two datasets, the cross-section of the unresolved data in text(.txt) files were retrieved and graphed online using Venn software.13

2.3 DEGs' GO and KEGG pathway analysis

Gene functions were categorized into three groups using the popular GO analysis method: cellular component (CC), molecular function (MF), and biological process (BP). This information will be useful for extensive functional enrichment studies. Genes, diseases, chemical compounds, pharmaceuticals, and biological pathways information is available in the KEGG database. ShinyGO 0.7714 was used to analyze DEG and KEGG pathway enrichment data. FDR<0.05 were regarded as to be significant.15

2.4 Construction of PPI network

The STRING database (https://string-db.org/)16 was used to construct the PPI network and further visualized using Cytoscape v3.9.1.17

2.5 Identification of hub genes

Using cytoHubba software18; the hub genes in the PPI network were identified. We employed a four-pronged strategy to collectively identify hub genes as a single technique to reduce the chances of false positives results. Four different algorithms, Maximum Neighborhood Component (MNC), Degree, Maximal Clique Centrality (MCC), and Edge Percolated Component (EPC), were used to identify hub genes. FunRich v3.1.3_2 (http://www.funrich.org/) was used to examine common hub genes.

2.6 Hub genes screening and analysis

The STRING database was used to construct a network of crucial hub genes and ShinyGO was used to perform KEGG analysis. The relevant hub genes were examined for relationships using Pearson's correlation test.

2.7 Expression and Kaplan-Meier analysis

GEPIA2 (http://gepia.cancer-pku.cn/index.html) is a repository that examines RNA-seq expression data from 9736 cancer tissues and 8587 non-tumor tissues from TCGA and GTEx projects.19 The GEPIA2 database was used to analyze the hub gene expression. Furthermore, GEPIA2 was used to predict overall survival in TNBC patients based on the expression of hub genes.

2.8 Identification of IDRs

Each protein's FASTA sequence was submitted to PONDR VSL2 and PONDR VLXT at http://www.pondr.com and IUPred at https://iupred.elte.hu to find IDRs in hub genes. The criterion for identifying areas of disorder was a propensity score of at least 0.5. Furthermore, molecular recognition feature (MoRF) regions were predicted using MoRFpred20; available at http://biomine.cs.vcu.edu/. Regions with MoRF predicted scores of ≥0.5 were considered as MoRF.

2.9 3D structural visualization of IDRs

The 3D structures of TTK (3CEK), TOP2A (1ZXM), and CCNA2 (1H1P) were downloaded from the PDB database to detect the IDRs. The Swiss Model predicts the CEPFN structure using homology modeling.21 With the help of the BIOVIA Discovery Studio Visualizer, the hub genes' 3-D structure was visualized.

3 Results

3.1 Identification of DEGs

Two TNBC-related gene expression profiles (GSE45827 and GSE38959) were used in this study. The GSE38959 dataset included 13 standard and 30 TNBC samples. The GSE45827 dataset included 11 standard samples and 41 TNBC samples. We identified the top 250 DEGs in each dataset by using GEO2R. GSE38959 contains 134 upregulated and 16 downregulated DEGs (Fig. 1A). GSE45827 contains 158 upregulated and 92 downregulated genes (Fig. 1B). The overlapping 48 common DEGs were identified using Venn diagram analysis (Fig. 1C), all of which were upregulated (Table 1).Fig. 1 Scanning of differentially expressed genes. The GSE45827 and GSE38959 datasets' A and B volcano plots show the distribution of DEGs. C Intersecting genes between both datasets.

Table 1 Shows the common upregulated DEGs in the two datasets compared with normal breast tissues.

Regulation	DEGs (Gene Symbol)	
Up-regulated	RRM2, TOP2A, TPX2, PRC1, CENPF, ASPM, KIF2C, SMC4, CCNA2, NUSAP1, UBE2T, CCNB1, CEP55, EZH2, ECT2, BUB1, CDK1, MELK, TTK, FANCI, ANLN, BIRC5, DTL, CKS2, ALYREF, RACGAP1, FAM83D, KPNA2, NDC80, CDKN3, TK1, GINS1, KIF23, NUF2, KIF18B, PTTG1, NCAPG, PBK, KIF20A, KIAA0101, UHRF1, CDCA5, HJURP, HN1, H2AFZ, CENPU, HMMR, MRPL13	

3.2 Functional enrichment analysis of DEGs in TNBC

The DEGs were predominantly enriched BPs terms associated with the cell cycle, specifically mitotic cell cycle and cell cycle regulation (Fig. 2A), CC terms associated with chromosomes, microtubule cytoskeleton, and spindle (Fig. 2B), and MF terms associated with adenyl nucleotide and ATP binding (Fig. 2C), according to the GO analysis. KEGG pathway analysis revealed that DEGs were most prevalent in the cell cycle (Fig. 2D).Fig. 2 Common DEGs' pathway enrichment and GO analysis. A–C analyses of enrichment for BP, CC, and MF. D Examining the KEGG Pathway.

3.3 PPI network analysis of DEGs

The PPI network consisted of 46 nodes and 1836 edges (Fig. 3). The top 10 genes were identified based on their degree of connectivity. Cyclin B1 and Kinesin Family Member 23 (CCNB1 and KIF23) Cyclin A2 (CCNA2) and Ribonucleotide Reductase 2 (RRM2) DNA Topoisomerase II Alpha, Cyclin-dependent kinase 1 (CDK1), KIF20A (Kinesin Family Member 20A), and TOP2A Anillin actin-binding protein, or ANLN Non-SMC condensin I complex subunit G (NCAPG) All of these hub genes, including PTTG1 (Regulator of Sister Chromatid Separation), showed higher expression.Fig. 3 The PPI network analysis. Nodes represent proteins and the edges show how they interact. The node color represents the degree of connectivity. High connectivity is denoted by a dark red color and low connectivity is indicated by a light color. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

3.4 Identification of hub genes

Hub genes were identified using the Degree, EPC, MCC, and MNC algorithms (Fig. 4A, B, C, and D). Using a Venn diagram, common hub genes (CENPF, CCNA2, TOP2A, and TTK) were identified (Fig. 4E). The degree of connectivity between hub genes (Table 2).Fig. 4 Hub gene PPI networks by applying the A Degree, B EPC, C MCC, and D MNC algorithms. E) Common hub genes (CTK, CCNA2, TOP2A, and ENPF).

Table 2 Hub Genes with varying levels of connectivity and types of regulations.

Gene	Degree	Type	
CCNA2	88	Up-regulated	
TOP2A	88	Up-regulated	
TTK	86	Up-regulated	
CENPF	86	Up-regulated	

3.4.1 Identifying and evaluating the significant hub genes

Four essential hub genes (CENPF, CCNA2, TOP2A, and TTK) were identified using Venn diagram. According to the ShinyGo analysis of the KEGG Pathway, these hub genes were primarily enrich in the cell cycle (Fig. 5A). The hub gene network had four nodes and 12 edges indicated the significant interactions between the hub genes (Fig. 5B). Notably, TNBC cells showed higher expression levels for all the hub genes.. TOP2A expression was positively correlated with all three hub genes. CENPF negatively correlated with TTK and CCNA2 levels. TTK was negatively correlated with CENPF, but not with TOP2A and CCNA2. CCNA2 was negatively correlated with CENPF but not with TOP2A or TTK. A strong positive correlation between CENPF and TOP2A, CCNA2 and TTK was observed which indicated that these genes are tend to be co-expressed (Fig. 5C).Fig. 5 Identification of the important hub genes. A KEGG enrichment analysis. B The hub gene network. C The heatmap displays the correlations between each hub gene. Red color indicates the positive correlation and blue color indicates the negative correlation. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

3.5 High gene expression revealed lower overall survival

The prognostic significance of the four putative hub genes was examined using the bioinformatics analysis tool, GEPIA2. Using the GEPIA2 server, we selected 135 TNBC samples for survival analysis.22 We discovered that poor overall survival in TNBC patients was related to the increased expression of these hub genes. Individuals with TNBC who had high hub gene expression had worse overall survival rates according to the Kaplan-Meier study (Fig. 6).23 These detailed data were as follows: the hazard ratio (HR) of CCNA2 was 0.79, and log-rank p = 0.61, The HR of CENPF was 0.57, log-rank p = 0.25, the HR of TOP2A was 1.9, and log-rank p = 0.2; the HR of TTK was 0.96, and log-rank p = 0.96.Fig. 6 Survival analysis of hub genes. Poor OS in patients with TNBC was correlated with substantial amounts of CCNA2, CENPF, TOP2A, and TTK expression; results with an asterisk (*) are significant.

3.6 Identification of IDRs

The intrinsically disordered regions (IDRs) of hub genes were predicted through the IUPred, PONDR VLXT, and PONDR VSL2 servers (Fig. 7). A region was considered disordered if it had a propensity ≥0.5. The MoRF region results for the prediction are shown in Fig. 8. MoRF was defined as an area with a predicted score of ≥0.5. Table 3 lists the locations of the MoRF regions. The 3D structure of each protein illustrates its predicted IDRs (Fig. 9).Fig. 7 Intrinsically disordered regions of (A) TOP2A, (B) TTK, (C) CCNA2, and (D) CENPF. VLXT (light blue), VSL2 (green), IUPred long (orange), and IUPred short (gray) were used for the disorder analysis. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Fig. 8 MoRF regions of TTK, CENPF, TOP2A, CCNA2, and B. The MoRF area was predicted using MoRFpred. The MoRF regions had predicted MoRF scores of at least 0.5.

Table 3 MoRF regions of Hub Genes.

Hub Gene	Region	From	To	Length	
CCNA2	1	47	48	2	
2	103	105	3	
3	158	162	5	
4	212	219	8	
5	298	300	3	
6	424	432	9	
TOP2A	1	8	12	5	
2	359	367	9	
3	811	812	2	
4	1128	1133	6	
5	1171	1173	3	
6	1221	1225	5	
7	1524	1531	8	
TTK	1	11	22	12	
2	460	482	23	
3	502	504	3	
4	514	515	2	
5	522	527	6	
6	573	574	2	
7	721	724	3	
8	809	857	47	
CENPF	1	2	4	3	
2	25	26	2	
3	101	104	4	
4	213	215	3	
5	240	246	7	
6	274	285	12	
7	428	433	6	
8	503	507	6	
9	722	724	3	
10	759	763	5	
11	830	834	5	
12	1133	1134	2	
13	1284	1295	12	
14	1552	1563	12	
16	2157	2162	6	
17	2231	2237	7	
18	2302	2362	61	
19	2841	2847	8	
20	2979	2988	10	
21	3111	3114	4	

Fig. 9 Visualization of chosen genes' three-dimensional structures' intrinsically disordered regions (IDRs). The areas in cyan are orderly portions, whereas the areas in red are predicted as the IDRS. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

TOP2A contained seven IDR regions ranging in length from two to 9 amino acids, whereas CCNA2 contained six IDR regions, indicating the possibility of functional flexibility in specific locations. The TTK IDR region ranges from 2 to 47 amino acids, whereas the CENPF IDR region is expected to range from 2 to 61 amino acids. As TTK and CENPF contain many IDR domains, these proteins may be functionally adaptable.

4 Discussion

This study examined two gene expression datasets (GSE45827 and GSE45827) using bioinformatic techniques to identify significant predictive indicators of TNBC. This study included 24 non-TNBC and 71 TNBC patients. The 48 common DEGs were identified using GEO2R and Venn diagrams. KEGG pathway analysis revealed that the DEGs were enriched in pathways related to oocyte meiosis and cell cycle. Cytoscape and STRING were used to create a DEG PPI network complex with 46 nodes and 1836 edges. Cytoscape CytoHubba analysis was used to remove four crucial genes from the PPI network complex.

Upon weighing the outcomes of both PPI analysis and KEGG pathway enrichment, it was determined that four selected genes, CCNA2, TOP2A, TTK, and CENPF, were essential components of numerous highly enriched pathways. In TNBC, these genes exhibit increased expression. As the Kaplan-Meier plot illustrates, patients with breast cancer had a poor prognosis when all the above-mentioned genes were overexpressed. Furthermore, intrinsically disordered analysis revealed that CENPF and TOP2A were predominantly disordered. IDR-projected mutation data in the anticipated MoRF regions in the breast samples were retrieved using the COSMIC database (Table 4). Further investigations are needed to examine the role of these mutations in cancer development.Table 4 Hub Genes with somatic mutation at predicted MoRF regions.

Hub Gene	MoRF Region (AA)	CDS Mutation	
CENPF	277	0c.830C>T	
1553	0c.4658A>C	
TOP2A	1222	0c.3664C>T	
CCNA2	219	0c.655G>T	
TTK	14	0c.40G>C	

The protein CENPF interacts with microtubules and promotes cell cycle progression.24, 25 Numerous malignancies, particularly breast and prostate cancer, have been reported to have high levels of CENPF expression. Furthermore, increased CENPF expression may cause breast cancer cells to spread to bones.26 Several studies have found that Increased CENPF has been linked to a bad prognosis for hepatocellular carcinoma, prostate, and breast cancer, according to several studies.26, 27, 28 However, our findings suggest that the poor OS prognosis in TNBC may be caused by high CENPF expression. Furthermore, intrinsically disordered analysis revealed that the CENPF was mostly disordered. When N-terminally shortened CENP-F mutants, which retain the ability to localize within the nucleus, are overexpressed, cell cycle arrest in the G2/M phase is observed.29 Mutations identified in CENPF in MoRF regions, particularly c.830C>T and c.4658A>C, have not yet been studied in cancer development. A comprehensive study to identify the role of these mutations in TNBC is required to identify the potential therapeutic targets.

The cyclin family, which is remarkably conserved, includes cell cycle regulator cyclin A2 (CCNA2). It has been shown to contribute to the growth of several cancers.30, 31, 32 It has been determined that CCNA2 is a potential treatment option for TNBC because previous research has shown that increased CCNA2 expression can form TNBC cells. 33, 34 According to our findings, lower CCNA2 expression leads to better survival rates in TNBC patients, which warrants further investigation. According to the IDR analysis, CCNA2 is partially disordered, and the MoRF study revealed that CCNA2 contains six MoRF regions, indicating the possibility of functional flexibility in specific locations. Mutations identified in CCNA2 in the MoRF region, particularly c.655G>T, have not yet been studied for their functional implications in cancer development.

TTK has been studied for its role in mitotic control.35, 36, 37, 38 TTK overexpression has been shown to promote genomic instability in cancer. TTK promotes cancer cell proliferation and invasion and increases genomic instability and aneuploidy. TTK is a desirable therapeutic target because of its role in tumor promotion. However, the biological mechanisms through which TTK promotes these processes remain unknown. TGF regulates miR-21 and AKT signaling, which in turn promotes cell invasion and survival through TTK. TOP2A, also known as DNA topoisomerase II alpha, is an enzyme that is required for cell division and DNA replication. It controls the topological state of a molecule by inducing brief double-strand breaks in the DNA molecule. These breaks allow DNA to be untangled, unwound, or supercoiled during replication and transcription.39, 40 Certain chemotherapeutic medications, including anthracyclines (such as doxorubicin) and etoposide target TOP2A. These medications prevent TOP2A from sealing the DNA, which causes DNA breaks to build up and ultimately results in cell death. TNBC patients with high TOP2A expression may react better to various treatment regimens.41 TOP2A copy number variation and mRNA expression have been linked to cancer development and resistance to chemotherapeutic treatments.42, 43 TOP2A mutations may alter the junction peptides between functional domains, affecting how well the TOP2A functional domains bind to DNA.

5 Conclusion

The study revealed a significant association between a poor prognosis and the levels of expression of TOP2A, TTK, CCNA2, and CENPF. Furthermore, the levels of TTK, CCNA2, and CENPF expression were higher in TNBC tissues compared to normal breast tissues. Moreover, the analysis of intrinsically disordered proteins showed that CENPF and TOP2A, key genes in the top module of the PPI network of DEGs, were primarily disordered. Our results suggest that CENPF and TOP2A may be a viable therapeutic target to interfere with the PPI network in TNBC.

Funding

This project was supported by Researchers Supporting Project Number (RSP2025R7) King Saud University, Riyadh, Saudi Arabia.

CRediT authorship contribution statement

Azhar Iqbal: Writing – original draft, Formal analysis, Data curation, Conceptualization. Faisal Ali: Writing – original draft, Formal analysis, Conceptualization. Sulaiman Ali Alharbi: Validation, Resources, Funding acquisition. Muhammad Sajid: Writing – original draft, Supervision, Formal analysis, Conceptualization. Saleh Alfarraj: Resources, Funding acquisition. Momina Hussain: Writing – original draft, Data curation, Conceptualization. Tehmina Siddique: Methodology, Data curation. Rakhshanda Mustaq: Writing – original draft, Formal analysis, Conceptualization. Fakhra Shafique: Validation, Resources, Methodology. Muhammad Sarfaraz Iqbal: Writing – original draft, Supervision, Formal analysis, Conceptualization.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Appendix A List of acronyms

Acronyms	Full Form	
TNBC	Triple Negative Breast Cancer	
GEO	Gene Expression Omnibus	
DEGs	Differentially expressed genes	
PPI	Protein-Protein Interaction	
IDRs	Intrinsically Disordered Regions	
BC	Breast Cancer	
BP	Biological Process	
MF	Molecular Function	
CC	Cellular Component	
EPC	Edge Percolated Component	
MCC	Maximal Clique Centrality	
MNC	Maximum Neighborhood Component	
MoRF	Molecular Recognition Feature	
GO	Gene Ontology	
KEGG	Kyoto Encyclopedia of Genes and Genomes	
CCNA2	Cyclin B1 and Kinesin Family Member 23 (CCNB1 and KIF23) Cyclin A2	
CENPF	Centromere Protein F	
TOP2A	DNA Topoisomerase II Alpha	
TTK	Threonine Tyrosine Kinase	
HR	Hazard Ratio	
CDS	Coding Sequence	

Acknowledgments

This project was supported by Researchers Supporting Project Number (RSP2025R7) 10.13039/501100002383 King Saud University , Riyadh, Saudi Arabia
==== Refs
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